CN113408817A - Traffic distribution method, device, equipment and storage medium - Google Patents

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

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CN113408817A
CN113408817A CN202110768120.6A CN202110768120A CN113408817A CN 113408817 A CN113408817 A CN 113408817A CN 202110768120 A CN202110768120 A CN 202110768120A CN 113408817 A CN113408817 A CN 113408817A
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service
weight
traffic
traffic distribution
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CN113408817B (en
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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 traffic distribution method, a traffic distribution device, traffic distribution equipment and a storage medium, and user traffic in a preset time period is acquired; analyzing and processing the user flow based on the historical service data of each service mechanism, and 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 traffic distribution weight and the user information of the current 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 guaranteed.

Description

Traffic distribution method, device, equipment and storage medium
Technical Field
The present application relates to the field of internet technologies, and in particular, to a traffic distribution method, apparatus, device, and storage medium.
Background
With the rapid development of internet technology, in order to provide more convenient medical services to users, more and more medical service organizations are continuously resident on various large network platforms, such as internet hospitals and private hospitals, which makes the user traffic sources of the network platforms more and more. Therefore, how to reasonably allocate servers to users to achieve balance and sustainability among the optimal user experience, the maximized traffic utilization and the maximized traffic conversion is an important problem to be solved by the network platform at present.
The current traffic distribution method is basically service leading intervention, which causes unreasonable user traffic 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 in 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 flow based on the historical service data of each service mechanism, and 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 traffic distribution weight and the user information of the current user.
In a possible implementation manner, acquiring the user traffic within the preset time period includes: the method comprises the steps of obtaining user flow in a preset time period based on a flow pre-estimation model and current user flow, wherein the flow pre-estimation model is obtained by training according to historical user flow of each service mechanism, and the flow pre-estimation model is used for pre-estimating the user flow in the preset time period according to the current user flow.
In one possible embodiment, the analyzing and processing the user traffic based on the historical service data of each service organization to determine the user traffic distribution weight of the service organization includes:
the method comprises the steps that user traffic is used as input of a profit prediction model, user traffic distribution weight of each service mechanism is obtained, the profit prediction model is obtained by training according to historical service data of each service mechanism, and the profit prediction model is used for predicting user traffic distribution weight corresponding to each service mechanism when profit is the maximum; the historical service data includes at least one of: service scope, traffic quota, service revenue, evaluation data, service duration, and service quality.
In one possible embodiment, each service organization includes a plurality of sub-service organizations, and the target server in the service organization is determined for the user according to the user traffic distribution weight and the user information of the current user, including: determining target distribution weights of all sub-service mechanisms in the service mechanisms according to the user traffic 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, determining a target distribution weight of each sub-service in the service according to the user traffic distribution weight includes: determining the target distribution weight of each sub-service mechanism in the service mechanism according to the user traffic distribution weight and a traffic distribution scene, wherein the traffic distribution scene comprises at least one of the following: the traffic distribution scenario includes at least one of: a search scenario, a dispatch scenario, and a personalized recommendation scenario. In one possible embodiment of the method according to the invention,
in one possible embodiment, determining a target server in the sub-service organization for the user according to the target distribution weight and the user information includes: the method comprises the steps that 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 a user and historical behavior data of the server, and the flow distribution model is used for determining the characteristic weight of the server; and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the characteristic weight.
In one possible embodiment, determining a target server in the sub-service organization for the user according to the target assignment weight and the feature weight includes: obtaining a target service weight value of each service provider 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, 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: favorable rating and quality of service.
In a second aspect, an embodiment of the present application provides a traffic distribution apparatus, including: the acquisition module is used for acquiring user traffic within 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 the 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 a possible implementation manner, the obtaining module is specifically configured to: the method comprises the steps of obtaining user flow in a preset time period based on a flow pre-estimation model and current user flow, wherein the flow pre-estimation model is obtained by training according to historical user flow of each service mechanism, and the flow pre-estimation model is used for pre-estimating the user flow in the preset time period according to the current user flow.
In a possible implementation, the determining module is specifically configured to: the method comprises the steps that user traffic is used as input of a profit prediction model, user traffic distribution weight of each service mechanism is obtained, the profit prediction model is obtained by training according to historical service data of each service mechanism, and the profit prediction model is used for predicting user traffic distribution weight corresponding to each service mechanism when profit is the maximum; the historical service data includes at least one of: service scope, traffic quota, service revenue, evaluation data, service duration, and service quality.
In a possible implementation manner, each service organization includes 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 mechanisms according to the user traffic 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, the determining module is specifically configured to: determining the target distribution weight of each sub-service mechanism in the service mechanism according to the user traffic distribution weight and a traffic distribution scene, wherein the traffic distribution scene comprises at least one of the following: the traffic distribution scenario includes at least one of: a search scenario, a dispatch scenario, and a personalized recommendation scenario.
In a possible implementation, the determining module is specifically configured to: the method comprises the steps that 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 a user and historical behavior data of the server, and the flow distribution model is used for determining the characteristic weight of the server; and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the characteristic weight.
In a possible implementation, the determining module is specifically configured to: obtaining a target service weight value of each service provider 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 traffic distribution apparatus further includes: the adjusting module is used for 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: favorable rating and quality of service.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the traffic distribution method according to the first aspect is implemented.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which a computer program is stored, and when the computer program runs on an electronic device, the electronic device is caused to execute the traffic distribution method according to the first aspect.
In a fifth aspect, the present application provides a computer program product, which includes a computer program, when the computer program runs on an electronic device, causes the electronic device to execute the data processing method according to any one of the first aspect and/or the second aspect.
The traffic distribution method, the traffic distribution device, the traffic distribution equipment and the storage medium provided by the embodiment of the application acquire user traffic within a preset time period; analyzing and processing the user flow based on the historical service data of each service mechanism, and 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 traffic distribution weight and the user information of the current 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 guaranteed.
These and other aspects of the present application will be more readily apparent from the following description of the embodiment(s).
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario of a traffic distribution method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a traffic distribution method according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a traffic distribution method according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of a traffic distribution apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
With the rapid development of the internet technology, in order to provide more convenient medical services for users, more and more medical service organizations are continuously resident on each large network platform, and considering the situation that the user traffic distribution in the current network platform is unreasonable, the embodiment of the application provides a traffic distribution method. As the historical service data and the user information of the service mechanism are fully considered, reasonable service providers can be distributed to the users, and the flow utilization rate and the conversion rate are guaranteed while the user experience is improved.
First, an application scenario of the traffic distribution method provided in the embodiment of the present application is explained:
fig. 1 is a schematic view of an application scenario 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 the embodiment of the present application, the client 101 may be any type of client, for example, a user equipment for machine type communication. In some embodiments, the client 101 may also be referred to as a User Equipment (UE), a Mobile Station (MS), a mobile terminal (mobile terminal), a terminal (terminal), and the like, for example, the client 101 may be a desktop computer, a notebook, a Personal Digital Assistant (PDA), a smart phone, a tablet computer, an automobile product, a wearable device, and the like, and this scenario is illustrated by taking the desktop computer as an example.
The server 101 is a service point for providing processes, databases, and communication facilities. The server 101 may be a unitary 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, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a 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 or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, 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 communication network such as 2G, 3G, 4G, or 5G, or may also be a wireless local area network, which is not limited herein.
Specifically, a plurality of service mechanisms reside on the network platform, a user can perform an 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 correspondingly, the server 102 determines a target server recommended by 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 the positional relationship between the devices in fig. 1, for example, in the application scenario illustrated in fig. 1, a data storage device may also be included, and the data storage device may be an external memory with respect to the server 102, or an internal memory integrated in the server 102.
Next, how to solve the above technical problems in the technical solutions of the present application will be described in detail through specific embodiments. It should be noted that the following specific 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 schematic flow chart of a traffic distribution method according to an embodiment of the present application. The traffic distribution method provided by the embodiment of the application is applied to a traffic 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, a description will be given by taking a server as an execution subject.
As shown in fig. 2, the traffic distribution method according to the embodiment of the present application includes the following steps:
s201, obtaining user flow in a preset time period.
For example, if a target server is to be determined for a user on a certain day, the preset time period is the day, and if a 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 the 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 one hand, the user traffic of the preset time period may be estimated according to the user traffic of the historical time period corresponding to the preset time period, for example, taking the user traffic of the current tuesday as an example, the user traffic of the current tuesday may be estimated according to the historical user traffic of all tuesdays on the platform, for example, the average value of the historical user traffic of all tuesdays may be determined as the user traffic of the current tuesday, it should be understood that the determination manners of other preset time periods are similar thereto, and are not described in detail herein.
In the second aspect, the user traffic in the preset time period may also be estimated according to the user traffic trend in the current time period, where the current time is the starting time period of the preset time period. Illustratively, with a preset time period of 8: 00-18: 00 as an example, 8: user traffic of 00-9: 00, and then according to the ratio of 8: predicting the user traffic of 00-9: 00 by using the method that: the user traffic in 00-18: 00, as for the specific prediction method, is shown in the following embodiments.
In the scheme, the user traffic on the platform can be managed in a centralized manner, and the user traffic in the preset time period can be accurately estimated, so that the traffic trend of any time period can be accurately depicted, and the accuracy of subsequent traffic distribution is further improved.
S202, analyzing and processing the user flow based on the historical service data of each service mechanism, and determining the user flow distribution weight of the service mechanism.
Wherein the historical service data comprises: service scope of the service organization (e.g., TCM, Western medicine, orthopedics, internal medicine, etc.), traffic quota (e.g., historical traffic distribution weight), customer unit price, goodness rate, refusal rate, length of visit, income and quality of service scores, etc.
In some embodiments, the user traffic distribution weight corresponding to the current user traffic may be determined according to historical service data under the same user traffic condition. For example, taking 500 user flows in a preset time period as an example, in the historical service data, when the user flows are 500 (a quantity interval may be set according to a requirement, for example, the size of the interval is ± 50, the user flows are 450 to 550), and the comprehensive good evaluation rate of all the service mechanisms is the highest, the historical flow distribution weight corresponding to each service mechanism allocates the weight to the user flow corresponding to each current service mechanism, or when the comprehensive income of all the service mechanisms is the highest, the historical flow distribution weight corresponding to each service mechanism allocates the weight to the user flow corresponding to each current service mechanism, so that the method for determining the user flow distribution weight according to other historical service data is not described in detail here.
In other embodiments, the user traffic distribution weight of each service mechanism may also be determined according to a weight determination model, where the weight determination model is trained according to historical service data, and as for a specific determination manner, the following embodiments show it.
S203, 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.
Wherein the user information may include at least one of: the re-diagnosis rate of the user on the platform, the types of diagnosed diseases, the types of purchased or consulted medicines, regional information, character information, age information, sex and the like.
Specifically, the step is that firstly, the user quantity of each service mechanism is determined according to the user flow distribution weight of each service mechanism and the user flow of a preset time period, and then the service mechanism is determined for the current user according to the matching degree of the user information of each service mechanism and the current user.
Furthermore, the server serving the current user is determined according to the matching degree of the server and the user in the service mechanism.
The traffic distribution method provided by the embodiment of the application can fully consider the historical service data and the user information of the service mechanism, thereby distributing reasonable service providers for users, and ensuring the traffic utilization rate and the conversion rate while improving the user experience.
In some embodiments, in order to improve the accuracy of the user traffic, the user traffic within a preset time period may be estimated through a traffic estimation model, where the traffic estimation model is obtained by training according to the historical user traffic of each service organization, and the traffic estimation model is used to estimate the user traffic within the preset time period according to the current user traffic.
That is, in the step S201: and acquiring the user flow in a preset time period based on the flow pre-estimation model and the current user flow. The flow prediction model is trained according to the historical user flow in a certain time period and the user flow in the initial time period in the time period.
Specifically, the current user traffic is used as the input of the traffic prediction model, for example, the user traffic between 8:00 and 9:00 is input into the traffic prediction model, and the user traffic between 8:00 and 18:00 can be determined through the traffic prediction model.
In other embodiments, in step S201, the user traffic in the preset time period may also be obtained based on a 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 characteristic in a certain time period.
Specifically, in this step, the time characteristic of the estimated time period is used as the input of the traffic estimation model, for example, if the input data is tuesday, the traffic estimation model may output the user traffic of this tuesday according to the user traffic of tuesday in the historical user traffic.
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 assignment weight of each service mechanism can also be determined through a weight determination model. The weight determination model is obtained by training according to historical service data of each service mechanism, and is used for determining user traffic distribution weight of each service mechanism according to user traffic in a preset time period.
The type of the weight determination model is not specifically limited in the embodiments of the present application, and may be, for example, a profit estimation model, a goodness estimation model, a deal amount estimation model, a rejection rate estimation model, a quality of service estimation model, and the like.
Taking the profit estimation model as an example, the profit estimation model is obtained by training according to profits of each service mechanism under the condition that different users distribute weights, and is used for estimating the user traffic distribution weight 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 revenue estimation model to obtain the user flow distribution weight of each service mechanism.
In the scheme, the user traffic distribution weight of each service mechanism is estimated through the preset weight determination model, more reasonable user traffic distribution weight can be obtained, and the traffic utilization rate and the conversion rate are guaranteed while the user experience is improved. In addition, the embodiment of the application provides various types of weight determination models, and in practical application, different weight determination 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 organization also has a plurality of sub-service organizations, for example, the traditional Chinese medicine, western medicine, medical department, surgery department, etc. of each hospital organization, and it is necessary to ensure that the flow distribution of the sub-service organizations of each service organization is reasonable while ensuring that the flow distribution of each service organization is reasonable. The flow distribution of the sub-service is described in detail below with reference to fig. 3.
Fig. 3 is a schematic flow chart of a traffic distribution method according to another embodiment of the present application. As shown in fig. 3, the traffic distribution method provided by the embodiment includes the following steps:
s301, obtaining user flow in a preset time period.
S302, analyzing and processing the user flow based on the historical service data of each service mechanism, and determining the user flow distribution weight of the service mechanism.
The principle and the beneficial effects of steps S301 to S302 are similar to those in the embodiment shown in fig. 2, which can be referred to above specifically, and are not described herein again.
And S303, determining the target distribution weight of each sub-service mechanism in the service mechanism according to the user traffic distribution weight.
In some embodiments, the target distribution weight of each sub-service in the service organization may be determined according to the user traffic distribution weight and the traffic distribution scenario. Wherein the traffic distribution scenario includes at least one of: the method comprises the steps of searching scenes, dispatching scenes, personalized recommendation scenes and the like, wherein target distribution weights are flow distribution weights of sub-service mechanisms in each flow distribution scene.
Specifically, the target distribution weight of each sub-service mechanism may be determined according to the profit, the goodness of appreciation, the service quality, and the like corresponding to different distribution weights of each sub-service mechanism in each traffic distribution scenario.
Taking the profit as an example, the distribution weight corresponding to each sub-service mechanism when the profits of all traffic distribution scenarios are relatively equal may be determined as the target distribution weight, or the distribution weight corresponding to each sub-service mechanism when the total profit of each traffic distribution scenario is maximum may be determined as the target distribution weight.
For example, taking the sub-service organization a and the sub-service organization B as an example, the distribution weights of the sub-service organization a in the search scenario, the dispatch scenario and the personalized recommendation scenario are a1, a2 and a3 respectively, the distribution weights of the sub-service organization B in the search scenario, the dispatch scenario and the personalized recommendation scenario are B1, B2 and B3 respectively, and the profit of the sub-service organization a and the sub-service organization B in the distribution weights is calculated respectively.
Further, the distribution weight when the profits of the sub-service organization A and the sub-service organization B are equal or the total profits of the sub-service organization A and the sub-service organization B are maximum is determined as the target distribution weight.
In other embodiments, the target distribution weight of each sub-service mechanism may be determined according to a corresponding relationship between the user traffic distribution weight and the target distribution weight of the sub-service mechanism. The corresponding relationship is obtained according to the historical service data of each sub-service organization, and the specific content of the corresponding relationship is not described herein again.
By 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 according to the matching degree of the user information and the target server. Wherein the user information includes: the re-diagnosis rate of the user on the platform, the types of diagnosed diseases, the types of purchased or consulted medicines, regional information, character information, age information, sex and the like.
It should be noted that, in different traffic distribution scenarios, the number of target servers is also different, and for example, for a search scenario, a first preset number of target servers with the highest matching degree may be recommended to a user on a user search interface; for the order dispatching scene, after the order is placed by the user, a target server with the highest matching degree can be recommended for the user; for the personalized recommendation scene, a second preset number of target service providers with the highest matching degree can be recommended for 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 quantity and the second preset quantity may be freely set according to recommended requirements of the platform, and none of the embodiments of the present application is specifically limited.
In other embodiments, a target server in the sub-service organization may be determined according to the traffic distribution model, and a detailed description is provided below by steps S3041 to S3042 on how to determine the target server through the sub-traffic distribution model:
s3041, the user information is used as an input of the traffic distribution model to obtain a feature weight of each server.
The traffic distribution model is obtained by training according to historical behavior data of the user and historical behavior data of the server, and is used for determining the characteristic weight of the server.
Specifically, the historical behavior data of the user may include at least one of: the type of diagnosed disease, the type of drug purchased or consulted, regional information, character information, age information, sex, etc. The historical behavioral data of the server may include at least one of: the time length of the call receiving, the communication turn of each communication, the time length of the first reply information of each communication, the good rating rate, the quality inspection qualification rate, the activity rate, the comprehensive rating, the service quality, the violation rate and the like.
S3042, determining a target server in the sub-service mechanism for the user according to the target distribution weight and the characteristic weight.
Specifically, a target service weight value of each service provider is obtained according to a target distribution weight and a feature weight, wherein the target service weight value is a product of the target distribution weight and the feature weight.
Further, a target server in the sub-service mechanism is determined for the user according to the current flow distribution scene and the target service weight value.
Correspondingly, the number of target service providers is different in different flow distribution scenes, for example, for a search scene, a first preset number of target service providers with the highest matching degree can be recommended to a user on a user search interface; for the order dispatching scene, after the order is placed by the user, a target server with the highest matching degree can be recommended for the user; for the personalized recommendation scene, a second preset number of target service providers with the highest matching degree can be recommended for the user in the recommendation page of the client.
In some embodiments, since the traffic distribution model is obtained by training 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 constantly changing, 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 characteristics of the server.
Wherein the current service features include at least one of: favorable rating and quality of service.
Fig. 4 is a schematic structural diagram of a traffic distribution apparatus according to an embodiment of the present application. The traffic 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, a traffic distribution apparatus provided in an embodiment of the present application includes:
an obtaining module 401, configured to obtain user traffic within a preset time period;
the determining module 402 is configured to analyze and process user traffic based on historical service data of each service mechanism, determine a user traffic distribution weight of the service mechanism, and determine a target server in the service mechanism for the user according to the user traffic distribution weight and user information of a current user.
The traffic distribution device provided in the embodiment of the present application may be used to execute the traffic distribution method in the embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
In a possible implementation manner, the obtaining module 401 is specifically configured to: the method comprises the steps of obtaining user flow in a preset time period based on a flow pre-estimation model and current user flow, wherein the flow pre-estimation model is obtained by training according to historical user flow of each service mechanism, and the flow pre-estimation model is used for pre-estimating the user flow in the preset time period according to the current user flow.
In a possible implementation, the determining module 402 is specifically configured to: the method comprises the steps that user traffic is used as input of a profit prediction model, user traffic distribution weight of each service mechanism is obtained, the profit prediction model is obtained by training according to historical service data of each service mechanism, and the profit prediction model is used for predicting user traffic distribution weight corresponding to each service mechanism when profit is the maximum; the historical service data includes at least one of: service scope, traffic quota, service revenue, evaluation data, service duration, and service quality.
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 mechanisms according to the user traffic 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, the determining module 402 is specifically configured to: determining the target distribution weight of each sub-service mechanism in the service mechanism according to the user traffic distribution weight and a traffic distribution scene, wherein the traffic distribution scene comprises at least one of the following: the traffic distribution scenario includes at least one of: a search scenario, a dispatch scenario, and a personalized recommendation scenario.
In a possible implementation, 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, the characteristic weight of each server is obtained, the flow distribution model is obtained by training according to historical behavior data of a user and historical behavior data of the server, and the flow distribution model is used for determining the characteristic weight of the server; and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the characteristic weight.
In a possible implementation, the determining module 402 is specifically configured to: obtaining a target service weight value of each service provider 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 traffic distribution apparatus further includes: an adjusting 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: favorable rating and quality of service.
It should be noted that the traffic distribution device provided in the embodiment of the present application may be used to execute the traffic distribution method in the embodiments shown in fig. 2 to fig. 3, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the processing module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a function of the processing module may be called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. 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 the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules 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 (CPU) or other processor capable of calling program code. For another example, these 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 traffic distribution method embodiment.
The system bus 504 mentioned in fig. 5 may be a Peripheral Component Interconnect (PCI) bus, an 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, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface 503 is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries).
The Memory 502 may include a Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor 501 may be a general-purpose Processor, and includes a central processing unit, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program runs on an electronic device, the electronic device is caused to execute the traffic distribution method according to any one of the above method embodiments.
The embodiment of the present application further provides a chip for executing the instruction, where the chip is used to execute the traffic distribution method in any of the above method embodiments.
Embodiments of the present application further 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 can read the computer program from the computer-readable storage medium, and when the computer program is executed by the at least one processor, the at least one processor can implement the traffic distribution method according to any one of the above method embodiments.
In the above embodiments, the implementation may be wholly or partially realized 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. The processes or functions according to the embodiments of the present application are generated in whole or in part when the computer instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

Claims (12)

1. A traffic distribution method, comprising:
acquiring user flow in a preset time period;
analyzing and processing the user flow based on historical service data of each service mechanism, and 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 traffic distribution weight and the user information of the current user.
2. The traffic distribution method according to claim 1, wherein the obtaining of the user traffic within the preset time period includes:
the method comprises the steps of obtaining user flow in a preset time period based on a flow pre-estimation model and current user flow, wherein the flow pre-estimation model is obtained by training according to historical user flow of each service mechanism, and the flow pre-estimation model is used for pre-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 analyzing and processing the user traffic based on historical service data of each service organization to determine the user traffic distribution weight of the service organization comprises:
the user traffic is used as the input of a profit prediction model, the user traffic distribution weight of each service mechanism is obtained, the profit prediction model is obtained by training according to historical service data of each service mechanism, the profit prediction model is used for predicting the user traffic distribution weight corresponding to each service mechanism when the profit is the maximum, and the historical service data comprises at least one of the following: service scope, traffic quota, service revenue, evaluation data, service duration, and service quality.
4. The traffic distribution method according to any one of claims 1 to 3, wherein each service organization comprises a plurality of sub-service organizations, and the determining a target server in the service organization for the user according to the user traffic distribution weight and the user information of the current user comprises:
determining target distribution weights of all sub-service mechanisms in the service mechanisms according to the user traffic 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.
5. The traffic distribution method according to claim 4, wherein the determining the target distribution weight of each sub-service in the service according to the user traffic distribution weight comprises:
determining the target distribution weight of each sub-service mechanism in the service mechanism according to the user traffic distribution weight and a traffic distribution scene, wherein the traffic distribution scene comprises at least one of the following: the traffic distribution scenario includes at least one of: a search scenario, a dispatch scenario, and a personalized recommendation scenario.
6. The traffic distribution method according to claim 5, wherein the determining a target server in the sub-service for the user according to the target distribution weight and the user information comprises:
the user information is used as the input of a traffic distribution model, the characteristic weight of each server is obtained, the traffic distribution model is obtained by training according to the historical behavior data of the user and the historical behavior data of the server, and the traffic distribution model is used for determining the characteristic weight of the server;
and determining a target server in the sub-service mechanism for the user according to the target distribution weight and the characteristic weight.
7. The traffic distribution method according to claim 6, wherein the determining a target server in the sub-service for the user according to the target distribution weight and the feature weight comprises:
obtaining a target service weight value of each service provider 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.
8. The traffic distribution method according to claim 7, further comprising:
adjusting a target service weight value of a server according to a current service characteristic of the server, wherein the current service characteristic comprises at least one of the following: favorable rating and quality of service.
9. A flow distribution apparatus, comprising:
the acquisition module is used for acquiring user traffic within a preset time period;
and 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.
10. An electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the traffic distribution method according to any one of claims 1 to 9.
11. A computer-readable storage medium, in which a computer program is stored which, when run on an electronic device, causes the electronic device to perform the traffic distribution method according to any one of claims 1 to 9.
12. A computer program product comprising a computer program, characterized in that the computer program, when run on an electronic device, causes the electronic device to perform the traffic distribution method according to any of claims 1 to 9.
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