CN113408817B - Traffic distribution method, device, equipment and storage medium - Google Patents

Traffic distribution method, device, equipment and storage medium Download PDF

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CN113408817B
CN113408817B CN202110768120.6A CN202110768120A CN113408817B CN 113408817 B CN113408817 B CN 113408817B CN 202110768120 A CN202110768120 A CN 202110768120A CN 113408817 B CN113408817 B CN 113408817B
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刘宗节
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Beijing Jingdong Tuoxian Technology Co Ltd
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Abstract

The embodiment of the application provides a flow distribution method, a flow distribution device, flow distribution equipment and a storage medium, wherein user flow in a preset time period is obtained; analyzing and processing the user traffic based on the historical service data of each service mechanism, and determining the user traffic distribution weight of the service mechanism; and according to the user flow distribution weight and the user information of the current user, determining a target server in the service mechanism for the user. In the embodiment of the application, when the server is determined for the user, the historical service data and the user information of the service mechanism are fully considered, and reasonable servers can be distributed for the user, so that the user experience is improved, and meanwhile, the flow utilization rate and the conversion rate are ensured.

Description

Traffic distribution method, device, equipment and storage medium
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a method, an apparatus, a device, and a storage medium for distributing traffic.
Background
With the rapid development of internet technology, in order to provide more convenient medical services for users, more and more medical service institutions are continuously accessing each large network platform, for example, internet hospitals, private hospitals and the like, so that the sources of user traffic of the network platforms are also more and more. Therefore, how to reasonably distribute servers for users so as to achieve the best user experience, the maximization of flow utilization and the maximization of flow conversion, which are balanced and sustainable, is an important problem to be solved by the network platform at present.
The current flow distribution method is basically business dominant intervention, which results in unreasonable user flow distribution.
Disclosure of Invention
The application provides a traffic distribution method, a traffic distribution device, traffic distribution equipment and a storage medium, which can improve the rationality of a network platform when distributing user traffic.
In a first aspect, an embodiment of the present application provides a traffic distribution method, including: acquiring user flow in a preset time period; analyzing and processing the user traffic based on the historical service data of each service mechanism, and determining the user traffic distribution weight of the service mechanism; and according to the user flow distribution weight and the user information of the current user, determining a target server in the service mechanism for the user.
In a possible implementation manner, obtaining the user traffic in a preset time period includes: based on a flow estimation model and current user flow, obtaining user flow in a preset time period, wherein the flow estimation model is obtained by training according to historical user flow of each service mechanism, and is used for estimating the user flow in the preset time period according to the current user flow.
In one possible implementation, the analyzing and processing the user traffic based on the historical service data of each service organization, and determining the user traffic allocation weight of the service organization includes:
The user flow is used as input of a profit estimation model, the user flow distribution weight of each service mechanism is obtained, the profit estimation model is obtained by training according to historical service data of each service mechanism, and the profit estimation model is used for estimating the user flow distribution weight corresponding to each service mechanism when the profit is maximum; the historical service data includes at least one of: service scope, traffic quota, service benefit, rating data, service duration, and quality of service.
In one possible implementation, each service mechanism includes a plurality of sub-service mechanisms, and the method includes the steps of allocating weights according to user traffic and user information of current users, determining target servers in the service mechanisms for the users, and including: determining target distribution weights of all sub-service mechanisms in the service mechanism according to the user flow distribution weights; and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the user information.
In one possible implementation, determining a target allocation weight for each sub-service in the service according to the user traffic allocation weights includes: determining target distribution weights of all sub-service mechanisms in the service mechanism according to user flow distribution weights and flow distribution scenes, wherein the flow distribution scenes comprise at least one of the following: the traffic distribution scenario includes at least one of: search scenes, dispatch scenes, and personalized recommendation scenes. In one possible implementation form of the present invention,
In one possible implementation, determining a target server in a sub-service for a user based on a target assigned weight and user information includes: the method comprises the steps that user information is used as input of a flow distribution model, characteristic weights of all servers are obtained, the flow distribution model is obtained through training according to historical behavior data of users and historical behavior data of the servers, and the flow distribution model is used for determining the characteristic weights of the servers; and according to the target assigned weight and the characteristic weight, determining a target server in the sub-service mechanism for the user.
In one possible implementation, determining a target server in a sub-service for a user based on a target assigned weight and a feature weight includes: obtaining a target service weight value of each server according to the target distribution weight and the characteristic weight; and determining a target server in the sub-service mechanism for the user according to the current flow distribution scene and the target service weight value.
In a possible implementation manner, the traffic distribution method further includes: adjusting a target service weight value of the server according to the current service characteristics of the server, wherein the current service characteristics comprise at least one of the following: good score and quality of service.
In a second aspect, an embodiment of the present application provides a flow distribution device, including: the acquisition module is used for acquiring the user flow in a preset time period;
the determining module is used for analyzing and processing the user flow based on the historical service data of each service mechanism, determining the user flow distribution weight of the service mechanism, and determining a target server in the service mechanism for the user according to the user flow distribution weight and the user information of the current user.
In one possible implementation, the obtaining module is specifically configured to: based on a flow estimation model and current user flow, obtaining user flow in a preset time period, wherein the flow estimation model is obtained by training according to historical user flow of each service mechanism, and is used for estimating the user flow in the preset time period according to the current user flow.
In a possible implementation manner, the determining module is specifically configured to: the user flow is used as input of a profit estimation model, the user flow distribution weight of each service mechanism is obtained, the profit estimation model is obtained by training according to historical service data of each service mechanism, and the profit estimation model is used for estimating the user flow distribution weight corresponding to each service mechanism when the profit is maximum; the historical service data includes at least one of: service scope, traffic quota, service benefit, rating data, service duration, and quality of service.
In a possible implementation manner, each service mechanism comprises a plurality of sub-service mechanisms, and the determining module is specifically configured to: determining target distribution weights of all sub-service mechanisms in the service mechanism according to the user flow distribution weights; and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the user information.
In a possible implementation manner, the determining module is specifically configured to: determining target distribution weights of all sub-service mechanisms in the service mechanism according to user flow distribution weights and flow distribution scenes, wherein the flow distribution scenes comprise at least one of the following: the traffic distribution scenario includes at least one of: search scenes, dispatch scenes, and personalized recommendation scenes.
In a possible implementation manner, the determining module is specifically configured to: the method comprises the steps that user information is used as input of a flow distribution model, characteristic weights of all servers are obtained, the flow distribution model is obtained through training according to historical behavior data of users and historical behavior data of the servers, and the flow distribution model is used for determining the characteristic weights of the servers; and according to the target assigned weight and the characteristic weight, determining a target server in the sub-service mechanism for the user.
In a possible implementation manner, the determining module is specifically configured to: obtaining a target service weight value of each server according to the target distribution weight and the characteristic weight; and determining a target server in the sub-service mechanism for the user according to the current flow distribution scene and the target service weight value.
In one possible embodiment, the flow distribution device further includes: the adjusting module is used for adjusting the target service weight value of the server according to the current service characteristics of the server, wherein the current service characteristics comprise at least one of the following: good score and quality of service.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the processor implements a traffic distribution method as in the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having a computer program stored therein, which when run on an electronic device causes the electronic device to perform the method of flow distribution as in the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when run on an electronic device, causes the electronic device to perform the data processing method of any of the first and/or second aspects.
The method, the device, the equipment and the storage medium for distributing the traffic acquire the user traffic in a preset time period; analyzing and processing the user traffic based on the historical service data of each service mechanism, and determining the user traffic distribution weight of the service mechanism; and according to the user flow distribution weight and the user information of the current user, determining a target server in the service mechanism for the user. In the embodiment of the application, when the server is determined for the user, the historical service data and the user information of the service mechanism are fully considered, and reasonable servers can be distributed for the user, so that the user experience is improved, and meanwhile, the flow utilization rate and the conversion rate are ensured.
These and other aspects of the application will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is an application scenario schematic diagram of a traffic distribution method according to an embodiment of the present application;
fig. 2 is a flow chart of a flow distribution method according to an embodiment of the present application;
Fig. 3 is a flow chart of a flow distribution method according to another embodiment of the present application;
FIG. 4 is a schematic structural diagram of a flow distribution device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Along with the rapid development of internet technology, in order to provide more convenient medical services for users, more and more medical service institutions are successively connected to each large network platform, and in consideration of the situation that user traffic distribution in the current network platform is unreasonable, the embodiment of the application provides a traffic distribution method, by means of historical service data of each service institution, the user traffic distribution weight of each service institution is determined, and then a target server is determined for the user according to the user traffic distribution weight and user information. Because the historical service data and the user information of the service mechanism are fully considered, reasonable servers can be distributed for the users, so that the flow utilization rate and the conversion rate are ensured while the user experience is improved.
First, an application scenario of the traffic distribution method provided in the embodiment of the present application is described:
fig. 1 is an application scenario schematic diagram of a traffic distribution method according to an embodiment of the present application. As shown in fig. 1, the application scenario may include: a client 101 and a server 102.
In embodiments of the present application, the client 101 may be any type of client, for example, a user device that may be a machine type communication. In some embodiments, the client 101 may also be called a User Equipment (UE), a Mobile Station (MS), a mobile terminal (mobile terminal), a terminal (terminal), etc., and for example, the client 101 may be a desktop computer, a notebook computer, a personal digital assistant (Personal Digital Assistant, abbreviated as PDA), a smart phone, a tablet computer, an automobile product, a wearable device, etc., where the scenario is illustrated by the desktop computer.
The server 101 is a service point providing processing, database, and communication facilities. The server 101 may be a monolithic server or a distributed server across multiple computers or computer data centers. The server 101 may be of various types such as, but not limited to, a web server, news server, mail server, message server, advertisement server, file server, application server, interaction server, database server, or proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported by or implemented by the server. For example, a server, such as a blade server, cloud server, etc., or may be a server group consisting of multiple servers, may include one or more of the types of servers described above, etc.
In some embodiments, the client 101 may communicate with the server 102 where the network platform is located through a wireless or wired network, so as to send data to the server 102, where the wireless network may be a 2G or 3G or 4G or 5G communication network, or may be a wireless lan, which is not limited herein.
Specifically, a plurality of service institutions reside on the network platform, a user can perform inquiry on the network platform through the client 101, in the inquiry process, the client 101 sends user information and inquiry data to the server 102 where the network platform is located, and accordingly, the server 102 determines a target server recommended for the user according to the user information and the inquiry data, and displays the target server on an interface of the client 101.
It should be noted that fig. 1 is only a schematic diagram of an application scenario provided in the embodiment of the present application, and the embodiment of the present application does not limit the devices and the number of devices included in fig. 1, nor limit the positional relationship between the devices in fig. 1, for example, in the application scenario shown in fig. 1, the application scenario may further include a data storage device, where the data storage device may be an external memory with respect to the server 102 or may be an internal memory integrated in the server 102.
The following describes how the technical solution of the present application solves the above technical problems in detail through specific embodiments. It should be noted that the following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a flow chart of a flow distribution method according to an embodiment of the present application. The flow distribution method provided by the embodiment of the application is applied to the flow distribution device, and the device can be realized in a software and/or hardware mode. Alternatively, in the scenario shown in fig. 1, the information processing apparatus may be integrated in a server as shown in fig. 1, for example, the information processing apparatus is a chip or a circuit in the server; alternatively, the information processing apparatus is a server as shown in fig. 1. Next, an example will be described in which a server is used as an execution subject.
As shown in fig. 2, the traffic distribution method in the embodiment of the present application includes the following steps:
s201, obtaining the user flow in a preset time period.
The preset time period may be determined according to the traffic distribution requirement, for example, if the target server is to be determined for a user on a certain day, the preset time period is the day, and if the target server is to be determined for a user on a certain month, the preset time period is one month.
Correspondingly, the user traffic is the number of users in a preset time period, and in practical application, the user traffic in the preset time period can be estimated according to the historical user traffic of the platform.
In practical application, on the one hand, the user flow in the preset time period can be estimated according to the user flow in the historical time period corresponding to the preset time period, and by taking the user flow in the present Tuesday as an example, the user flow in the present Tuesday can be estimated according to the historical user flows in all Tuesdays on the platform, for example, the average value of the historical user flows in all Tuesdays can be determined to be the user flow in the present Tuesday, and it is understood that the determination manners of other preset time periods are similar, and will not be repeated here.
In the second aspect, the user traffic of the preset time period may be estimated according to a user traffic trend of the current time period, where the current time is a starting time period of the preset time period. Illustratively, the preset time period is 8: 00-18:00, 8: 00-9:00 user traffic, according to 8: user traffic of 00-9:00 to predict 8: user traffic in 00-18:00, as to the specific prediction method, is shown in the following examples.
In the scheme, the user flow on the platform can be centrally managed, and the user flow in a preset time period can be accurately estimated, so that the flow trend of any time period can be accurately described, and the accuracy of subsequent flow distribution can be further improved.
S202, analyzing and processing the user traffic based on the historical service data of each service organization, and determining the user traffic allocation weight of the service organization.
Wherein the history service data includes: service institutions' service scope (e.g., traditional Chinese medicine, western medicine, orthopedics, internal medicine, etc.), flow quota (e.g., historical flow allocation weights), guest price, qualification rate, rejection rate, time of visit, benefits, and quality of service scores, etc.
In some embodiments, the user traffic allocation weight corresponding to the current user traffic may be determined according to the historical service data under the condition of the same user traffic. In the historical service data, when the user traffic is 500 (the number of intervals can be set according to the requirement, for example, the interval size is ±50, and the user traffic is 450-550), when the overall qualification rate of all service mechanisms is highest, the historical traffic distribution weight corresponding to each service mechanism is the current user traffic distribution weight corresponding to each service mechanism, or when the overall income of all service mechanisms is highest, the historical traffic distribution weight corresponding to each service mechanism is the current user traffic distribution weight corresponding to each service mechanism, and the method for determining the user traffic distribution weight according to other historical service data is not repeated herein.
In other embodiments, the user traffic allocation weights for each service may also be determined based on a weight determination model that is trained from historical service data, as detailed in the embodiments that follow.
S203, a target server in the service mechanism is determined for the user according to the user flow distribution weight and the user information of the current user.
Wherein the user information may include at least one of: the re-diagnosis rate of the user on the platform, the type of the diagnosed disease, the type of the purchased or consulted medicine, regional information, character information, age information, gender and the like.
Specifically, the method includes the steps of firstly, determining the number of users of each service mechanism according to the user flow distribution weight of each service mechanism and the user flow of a preset time period, and then determining the service mechanism for the current user according to the matching degree of the user information of each service mechanism and the current user.
Further, the server serving the current user is determined according to the matching degree of the server and the user in the service mechanism.
According to the traffic distribution method, the historical service data and the user information of the service mechanism can be fully considered, so that reasonable servers are distributed for users, the user experience is improved, and meanwhile, the traffic utilization rate and the conversion rate are guaranteed.
In some embodiments, in order to improve accuracy of the user traffic, the user traffic in the preset time period may be estimated by a traffic estimation model, where the traffic estimation model is obtained by training according to historical user traffic of each service mechanism, and the traffic estimation model is used to estimate the user traffic in the preset time period according to the current user traffic.
That is, in the step S201, it is: and acquiring the user flow in a preset time period based on the flow estimation model and the current user flow. Wherein the flow estimation model is trained based on historical user flow for a period of time and user flow for a starting period of time in the period of time.
Specifically, the current user flow is used as the input of the flow estimation model, for example, the user flow between 8:00 and 9:00 is input into the flow estimation model, and the user flow between 8:00 and 18:00 can be determined through the flow estimation model.
In other embodiments, in the step S201, the user traffic in the preset time period may be obtained based on the traffic estimation model and the current time period, where the traffic estimation model is obtained by training according to the historical user traffic in a certain time period and the time characteristics of the certain time period.
Specifically, in this step, the time feature of the estimated time period is used as input of a flow estimation model, for example, the input data is Tuesday, and the flow estimation model may output the user flow of Tuesday according to the user flow of Tuesday in the historical user flows.
In the embodiment of the application, the user flow in any time period can be estimated more accurately through the flow prediction model, so that the rationality of subsequent flow distribution can be further improved.
Similarly, in practical applications, the user traffic allocation weight of each service may also be determined by a weight determination model. The weight determining model is trained according to historical service data of each service mechanism, and is used for determining the user flow distribution weight of each service mechanism according to the user flow in a preset time period.
The type of the weight determination model is not particularly limited, and may be, for example, a profit estimation model, a good evaluation rate estimation model, a success amount estimation model, a rejection rate estimation model, a quality of service estimation model, and the like.
Taking a profit estimation model as an example, the profit estimation model is obtained by training according to the profits of each service mechanism under the condition that different users are assigned weights, and is used for estimating the user flow assignment weights corresponding to each service mechanism when the profits are maximum. That is, step S202 in the above embodiment may be: and taking the user flow as the input of the profit estimation model to obtain the user flow distribution weight of each service mechanism.
In the scheme, the user flow distribution weight of each service mechanism is estimated through the preset weight determination model, so that more reasonable user flow distribution weight can be obtained, and the flow utilization rate and the conversion rate are ensured while the user experience is improved. In addition, the embodiment of the application provides a plurality of types of weight determining models, and in practical application, different weight determining models can be selected based on different scenes, so that the flexibility of the scheme is improved, and the application requirements of different scenes are met.
In some embodiments, each service facility has a plurality of sub-service facilities, for example, the service facilities of traditional Chinese medicine, western medicine, internal medicine, surgery and the like of each hospital facility, and the reasonable flow distribution of each service facility is ensured, and meanwhile, the reasonable flow distribution of the sub-service facilities of each service facility is ensured. The flow distribution method of the sub-service mechanism will be described in detail with reference to fig. 3.
Fig. 3 is a flow chart of a flow distribution method according to another embodiment of the present application. As shown in fig. 3, the flow distribution method provided for the embodiment includes the following steps:
s301, obtaining user flow in a preset time period.
S302, analyzing and processing the user traffic based on the historical service data of each service mechanism, and determining the user traffic distribution weight of the service mechanism.
The principles and advantages of steps S301 to S302 are similar to those of the embodiment shown in fig. 2, and reference may be made to the above description, and details are not repeated here.
S303, determining the target distribution weight of each sub-service mechanism in the service mechanism according to the user flow distribution weight.
In some embodiments, the target allocation weights of the sub-service mechanisms in the service mechanism may be determined according to the user traffic allocation weights and the traffic distribution scenario. Wherein the traffic distribution scenario comprises at least one of: searching scenes, dispatch scenes, personalized recommendation scenes and the like, wherein the target allocation weight is the flow allocation weight of the sub-service mechanism in each flow distribution scene.
Specifically, the target allocation weight of each sub-service mechanism can be determined according to the benefits, the good score and the service quality corresponding to different allocation weights of each sub-service mechanism in each flow distribution scene.
Taking benefits as an example, the allocation weight corresponding to each sub-service mechanism when the benefits of all the traffic distribution scenes are relatively equal can be determined to be the target allocation weight, or the allocation weight corresponding to each sub-service mechanism when the total benefits of all the traffic distribution scenes are maximum can be determined to be the target allocation weight.
Taking a sub-service mechanism a and a sub-service mechanism B as an example, the distribution weights of the sub-service mechanism a in the search scene, the dispatch scene and the personalized recommendation scene are respectively a1, a2 and a3, the distribution weights of the sub-service mechanism B in the search scene, the dispatch scene and the personalized recommendation scene are respectively B1, B2 and B3, and the benefits of the sub-service mechanism a and the sub-service mechanism B in the distribution weights are respectively calculated.
Further, it is determined that the allocation weight when the profit of the sub-service organization a and the sub-service organization B is leveled or the total profit of the sub-service organization a and the sub-service organization B is maximized is determined as the target allocation weight.
In other embodiments, the target allocation weights of the sub-service mechanisms may be determined according to the correspondence between the user traffic allocation weights and the target allocation weights of the sub-service mechanisms. The corresponding relationship is obtained according to the historical service data of each sub-service mechanism, and specific content of the corresponding relationship is not described herein.
Through the scheme, the reasonable flow distribution of the sub-service mechanisms of each service mechanism can be ensured while the reasonable flow distribution of each service mechanism is ensured.
S304, determining a target server in the sub-service mechanism for the user according to the target distribution weight and the user information.
In some embodiments, the target server in the sub-service organization may be determined based on the degree to which the user information matches the target server. Wherein, the user information includes: the re-diagnosis rate of the user on the platform, the type of the diagnosed disease, the type of the purchased or consulted medicine, regional information, character information, age information, gender and the like.
It should be noted that, in different flow distribution scenarios, the number of target servers is also different, and for an exemplary search scenario, a first preset number of target servers with highest matching degree may be recommended to a user in a user search interface; for the dispatch scene, after the user orders, a target server with highest matching degree can be recommended to the user; for the personalized recommendation scene, a second preset number of target servers with highest matching degree can be recommended to the user in the recommendation page of the client.
It should be understood that, in the embodiment of the present application, specific values of the first preset number and the second preset number may be freely set according to recommended requirements of the platform, and neither embodiment of the present application is specifically limited.
In other embodiments, the determination of the target server in the sub-service mechanism may also be performed according to the traffic distribution model, and the manner of determining the target server by the sub-traffic distribution model will be described in detail below through steps S3041 to S3042:
S3041, taking the user information as input of a flow distribution model, and obtaining the characteristic weight of each server.
The flow distribution model is obtained by training according to historical behavior data of a user and historical behavior data of a server, and is used for determining characteristic weights of the server.
Specifically, the historical behavior data of the user may include at least one of: the type of disease diagnosed, the type of medicine purchased or consulted, regional information, character information, age information, gender, etc. The historical behavioral data of the server may include at least one of: the method comprises the steps of diagnosis receiving time, communication rounds of each communication, time for first replying information of each communication, good evaluation rate, quality inspection qualification rate, activity rate, comprehensive score, service quality, violation rate and the like.
S3042, distributing weights and characteristic weights according to the targets, and determining target servers in the sub-service mechanism for the users.
Specifically, a target service weight value of each server is obtained according to the target distribution weight and the characteristic weight, wherein the target service weight value is the product of the target distribution weight and the characteristic weight.
Further, a target server in the sub-service mechanism is determined for the user according to the current traffic distribution scene and the target service weight value.
Correspondingly, the number of target servers is different in different flow distribution scenes, and for an exemplary search scene, a first preset number of target servers with highest matching degree can be recommended to a user in a user search interface; for the dispatch scene, after the user orders, a target server with highest matching degree can be recommended to the user; for the personalized recommendation scene, a second preset number of target servers with highest matching degree can be recommended to the user in the recommendation page of the client.
In some embodiments, since the traffic distribution model is trained according to the historical behavior data of the user and the historical behavior data of the server, and the service data of the server is continuously changed, in order to improve the accuracy of the target service weight value, the target service weight value may be adjusted according to the current service feature of the server.
Wherein the current service characteristics include at least one of: good score and quality of service.
Fig. 4 is a schematic structural diagram of a flow distribution device according to an embodiment of the present application. The flow distribution means may be implemented in software and/or hardware. Alternatively, in the scenario shown in fig. 1, the information processing apparatus may be integrated in a server as shown in fig. 1, for example, the information processing apparatus is a chip or a circuit in the server; alternatively, the information processing apparatus is a server as shown in fig. 1.
As shown in fig. 4, the flow distribution device provided in the embodiment of the present application includes:
an obtaining module 401, configured to obtain a user flow in a preset time period;
the determining module 402 is configured to analyze and process the user traffic based on the historical service data of each service organization, determine a user traffic allocation weight of the service organization, and determine a target server in the service organization for the user according to the user traffic allocation weight and the user information of the current user.
The flow distribution device provided in the embodiment of the present application may be used to execute the flow distribution method in the embodiment shown in fig. 2, and its implementation principle and technical effects are similar, and are not described herein again.
In a possible implementation manner, the obtaining module 401 is specifically configured to: based on a flow estimation model and current user flow, obtaining user flow in a preset time period, wherein the flow estimation model is obtained by training according to historical user flow of each service mechanism, and is used for estimating the user flow in the preset time period according to the current user flow.
In a possible implementation manner, the determining module 402 is specifically configured to: the user flow is used as input of a profit estimation model, the user flow distribution weight of each service mechanism is obtained, the profit estimation model is obtained by training according to historical service data of each service mechanism, and the profit estimation model is used for estimating the user flow distribution weight corresponding to each service mechanism when the profit is maximum; the historical service data includes at least one of: service scope, traffic quota, service benefit, rating data, service duration, and quality of service.
In a possible implementation manner, each service organization includes a plurality of sub-service organizations, and the determining module 402 is specifically configured to: determining target distribution weights of all sub-service mechanisms in the service mechanism according to the user flow distribution weights; and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the user information.
In a possible implementation manner, the determining module 402 is specifically configured to: determining target distribution weights of all sub-service mechanisms in the service mechanism according to user flow distribution weights and flow distribution scenes, wherein the flow distribution scenes comprise at least one of the following: the traffic distribution scenario includes at least one of: search scenes, dispatch scenes, and personalized recommendation scenes.
In a possible implementation manner, the determining module 402 is specifically configured to: the method comprises the steps that user information is used as input of a flow distribution model, characteristic weights of all servers are obtained, the flow distribution model is obtained through training according to historical behavior data of users and historical behavior data of the servers, and the flow distribution model is used for determining the characteristic weights of the servers; and according to the target assigned weight and the characteristic weight, determining a target server in the sub-service mechanism for the user.
In a possible implementation manner, the determining module 402 is specifically configured to: obtaining a target service weight value of each server according to the target distribution weight and the characteristic weight; and determining a target server in the sub-service mechanism for the user according to the current flow distribution scene and the target service weight value.
In one possible embodiment, the flow distribution device further includes: an adjustment module 403, configured to adjust a target service weight value of the server according to a current service characteristic of the server, where the current service characteristic includes at least one of the following: good score and quality of service.
It should be noted that, the flow distribution device provided in the embodiment of the present application may be used to execute the flow distribution method in the embodiment shown in fig. 2 to 3, and its implementation principle and technical effects are similar, and are not described herein again.
It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the processing module may be a processing element that is set up separately, may be implemented in a chip of the above-mentioned apparatus, or may be stored in a memory of the above-mentioned apparatus in the form of program codes, and the functions of the above-mentioned processing module may be called and executed by a processing element of the above-mentioned apparatus. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element here may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more microprocessors (Digital Signal Processor, abbreviated as DSP), or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a System-On-a-Chip (SOC).
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 5, the electronic device 500 may include: a processor 501, a memory 502, a communication interface 503, and a system bus 504. The memory 502 and the communication interface 503 are connected to the processor 501 through the system bus 504 and complete communication with each other, the memory 502 is used for storing instructions, the communication interface 503 is used for communicating with other devices, and the processor 501 is used for calling the instructions in the memory to execute the scheme of the flow distribution method embodiment.
The system bus 504 mentioned in fig. 5 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The system bus 504 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface 503 is used to enable communication between the database access apparatus and other devices (e.g., clients, read-write libraries, and read-only libraries).
The memory 502 may include random access memory (Random Access Memory, simply RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 501 may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP) and the like; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.
The present application also provides a computer readable storage medium, in which a computer program is stored, which when executed on an electronic device, causes the electronic device to perform the traffic distribution method according to any of the method embodiments above.
The embodiment of the application also provides a chip for running the instruction, and the chip is used for executing the flow distribution method of any method embodiment.
The embodiments of the present application also provide a computer program product, which includes a computer program, where the computer program is stored in a computer readable storage medium, and at least one processor may read the computer program from the computer readable storage medium, where the at least one processor may implement the flow distribution method according to any of the method embodiments above when executing the computer program.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.) means from one website, computer, server, or data center. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tape), optical media (e.g., DVD), or semiconductor media (e.g., solid State Disk (SSD)), among others.

Claims (9)

1. A method of traffic distribution, comprising:
acquiring user flow in a preset time period;
analyzing and processing the user traffic based on the historical service data of each service mechanism, and determining the user traffic distribution weight of the service mechanism, wherein the user traffic distribution weight is the weight corresponding to each service mechanism when the total qualification rate of all the service mechanisms is highest or the total income is highest;
according to the user flow distribution weight and the user information of the current user, determining a target server in the service mechanism for the user;
each service mechanism comprises a plurality of sub-service mechanisms, the weight is allocated according to the user flow and the user information of the current user, a target server in the service mechanism is determined for the user, and the method comprises the following steps:
determining target distribution weights of all sub-service mechanisms in the service mechanism according to the user flow distribution weights and the flow distribution scene, wherein the flow distribution scene comprises at least one of the following components: searching scenes, dispatch scenes and personalized recommendation scenes, wherein the target allocation weights are the flow allocation weights of the sub-service mechanisms in each flow distribution scene, and the number of target servers is different in different flow distribution scenes;
The user information is used as input of a flow distribution model, the characteristic weight of each server is obtained, the flow distribution model is obtained by training according to historical behavior data of the user and historical behavior data of the server, and the flow distribution model is used for determining the characteristic weight of the server;
and according to the target distribution weight and the characteristic weight, determining a target server in the sub-service mechanism for the user.
2. The method for distributing traffic according to claim 1, wherein the obtaining the user traffic within the preset period of time includes:
based on a flow estimation model and current user flow, obtaining user flow in a preset time period, wherein the flow estimation model is obtained by training according to historical user flow of each service mechanism, and is used for estimating the user flow in the preset time period according to the current user flow.
3. The traffic distribution method according to claim 1, wherein the analyzing the user traffic based on the history service data of each service organization, determining the user traffic distribution weight of the service organization, comprises:
the user flow is used as input of a profit estimation model, the user flow allocation weight of each service mechanism is obtained, the profit estimation model is obtained by training according to historical service data of each service mechanism, the profit estimation model is used for estimating the user flow allocation weight corresponding to each service mechanism when the profit is maximum, and the historical service data comprises at least one of the following components: service scope, traffic quota, service benefit, rating data, service duration, and quality of service.
4. The traffic distribution method according to claim 1, wherein said determining a target server in the sub-service for the user based on the target allocation weight and the feature weight comprises:
obtaining a target service weight value of each server according to the target distribution weight and the characteristic weight;
and determining a target server in the sub-service mechanism for the user according to the current flow distribution scene and the target service weight value.
5. The traffic distribution method according to claim 4, further comprising:
adjusting a target service weight value of a server according to the current service characteristics of the server, wherein the current service characteristics comprise at least one of the following: good score and quality of service.
6. A flow distribution device, comprising:
the acquisition module is used for acquiring the user flow in a preset time period;
the determining module is used for analyzing and processing the user traffic based on the historical service data of each service mechanism, determining the user traffic distribution weight of the service mechanism, wherein the user traffic distribution weight is the weight corresponding to each service mechanism when the total qualification rate of all the service mechanisms is highest or the total income is highest, and determining a target server in the service mechanism for the user according to the user traffic distribution weight and the user information of the current user;
Each service organization comprises a plurality of sub-service organizations, and the determining module is specifically configured to:
determining target distribution weights of all sub-service mechanisms in the service mechanism according to the user flow distribution weights and the flow distribution scene, wherein the flow distribution scene comprises at least one of the following components: searching scenes, dispatch scenes and personalized recommendation scenes, wherein the target allocation weights are the flow allocation weights of the sub-service mechanisms in each flow distribution scene, and the number of target servers is different in different flow distribution scenes;
the user information is used as input of a flow distribution model, the characteristic weight of each server is obtained, the flow distribution model is obtained by training according to historical behavior data of the user and historical behavior data of the server, and the flow distribution model is used for determining the characteristic weight of the server;
and according to the target distribution weight and the characteristic weight, determining a target server in the sub-service mechanism for the user.
7. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor implements the flow distribution method of any of claims 1 to 5 when the computer program is executed by the processor.
8. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when run on an electronic device, causes the electronic device to perform the flow distribution method according to any of claims 1 to 5.
9. A computer program product comprising a computer program which, when run on an electronic device, causes the electronic device to perform the flow distribution method of any of claims 1 to 5.
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