Online Advertising API Optimization Method and Device Based on Traffic Statistics

Abstract

This application relates to the field of online advertising API optimization, and discloses an online advertising API optimization method and device based on traffic statistics. The method includes: obtaining the traffic of the first online advertising API in the first time period and in the second time period respectively, and obtaining the traffic of the second online advertising API in the first time period and in the second time period; determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in the first time period, determining a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period, and determining the difference between the first traffic difference and the second traffic difference as the traffic difference fluctuation value; when both the first traffic difference and the second traffic difference are greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than a preset traffic difference fluctuation value, optimizing the first online advertising API. This application can accurately locate the optimization direction of the online advertising API and improve the optimization effect of the online advertising API.

Description

Technical Field

[0001] This application relates to the technical field of online advertising API optimization. More specifically, it relates to a method and device for optimizing an online advertising API based on traffic statistics. Background Art

[0002] As an important part of digital marketing, online advertising has complex and diverse technologies behind it. When users browse websites or use applications, the browser interacts with the advertising server to request appropriate advertising content. This process involves web page analysis, user behavior analysis, and content matching technologies to determine the most suitable online advertising. In addition, Cookies and pixel tracking technologies are widely used to record user behavior for precise targeted advertising. For example, information such as user clicks, browsing duration, and historical searches are collected and analyzed to predict user interests and needs. In addition, the accuracy of online advertising benefits from the wide application of big data and machine learning. Through in-depth mining and analysis of massive user data, machine learning models can predict users' potential needs and interests, thereby optimizing advertising content and placement strategies. Algorithms play an important role in market segmentation, dynamic pricing, Real-Time Bidding (RTB), etc. By continuously optimizing algorithms, the effectiveness of advertising placement can be improved.

[0003] As a bridge connecting advertisers and advertising platforms, the online advertising API (Application Programming Interface) uses a series of advanced backend technologies to achieve efficient data communication and advertising management. The online advertising API is based on the RESTful architecture or SOAP protocol. The advertising API provides standardized interfaces that allow developers to interact with the advertising platform through HTTP requests. These interfaces support various operations, including creating and updating advertising campaigns, obtaining advertising performance data, managing user audiences, etc. The design of the API must have high availability and scalability to handle the huge traffic and database load of the advertising system. However, currently, the optimization methods of online advertising APIs mainly rely on the subjective system design of technical personnel, making it difficult to accurately optimize the online advertising API in the best optimization direction, resulting in poor optimization effects of the online advertising API. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for optimizing an online advertising API based on traffic statistics, which solves the technical problem of poor optimization effects of the online advertising API and achieves the technical effects of accurately positioning the optimization direction of the online advertising API and improving the optimization effect of the online advertising API.

[0005] An online advertising API optimization method based on traffic statistics provided by an embodiment of the present application, the method includes: obtaining the traffic of a first online advertising API in a first time period and in a second time period respectively, and obtaining the traffic of a second online advertising API in the first time period and in the second time period, where the first online advertising API and the second online advertising API belong to the same online advertising and the second online advertising API depends on the execution of the first online advertising API; determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in the first time period, determining a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period, and determining the difference between the first traffic difference and the second traffic difference as the traffic difference fluctuation value; when both the first traffic difference and the second traffic difference are greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than a preset traffic difference fluctuation value, optimizing the first online advertising API.

[0006] In a possible implementation manner, the method further includes: obtaining the categories of all online advertising APIs of the same online advertising, taking the online advertising APIs with the category of the preset category as the first online advertising API, and taking the online advertising APIs that depend on the first online advertising API as the second online advertising API. Among them, the preset categories include the advertisement display category, the advertisement click category, and the advertisement conversion category.

[0007] In another possible implementation manner, the method further includes: obtaining the category of the second online advertising API, and determining the preset traffic difference and the preset traffic difference fluctuation value according to the category of the first online advertising API and the category of the second online advertising API.

[0008] In another possible implementation manner, the method further includes: obtaining the error rate and the number of dependencies of the first online advertising API; when the traffic of the first online advertising API is greater than or equal to a preset traffic, the error rate of the first online advertising API is greater than or equal to a preset error rate, and the number of dependencies of the first online advertising API is greater than or equal to a preset number, splitting the first online advertising API into multiple sub-online advertising APIs, and the multiple sub-online advertising APIs correspond to processing requests of different sub-online advertising APIs that depend on the first online advertising API.

[0009] In another possible implementation, the method further includes: when the traffic of the first online advertising API is greater than a preset traffic, the error rate of the first online advertising API is less than a preset error rate, and the horizontal expansion index of the first online advertising API is greater than or equal to a preset horizontal expansion index, optimizing the performance of the first online advertising API; wherein, the horizontal expansion index is used to characterize the horizontal scalability of the online advertising API; when the traffic of the first online advertising API is greater than a preset traffic, the error rate of the first online advertising API is less than a preset error rate, and the horizontal expansion index of the first online advertising API is less than a preset horizontal expansion index, splitting the first online advertising API into multiple sub-online advertising APIs.

[0010] In another possible implementation, the method further includes: obtaining the traffic of multiple second online advertising APIs that depend on the first online advertising API, and determining the average traffic of the multiple second online advertising APIs as the second average traffic. When the traffic of the target online advertising API among the multiple second online advertising APIs is lower than the second average traffic, optimizing the target online advertising API.

[0011] In another possible implementation, the method further includes: when the traffic of the target online advertising API among the multiple second online advertising APIs is lower than the second average traffic, optimizing the online advertising API other than the first online advertising API on which the target online advertising API depends.

[0012] An embodiment of the present application also provides an online advertising API optimization device based on traffic statistics, including units for executing the method described in any one of the above.

[0013] An embodiment of the present application also provides an online advertising API optimization device based on traffic statistics, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above is implemented.

[0014] An embodiment of the present application also provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above is implemented.

[0015] An embodiment of the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0016] In another possible implementation,

[0017] The beneficial effects of the embodiments of the present application compared with the prior art are:

[0018] An embodiment of the present application provides an optimization method for an online advertising API based on traffic statistics. The method includes: obtaining the traffic of a first online advertising API in a first time period and in a second time period, and obtaining the traffic of a second online advertising API in the first time period and in the second time period. The first online advertising API and the second online advertising API belong to the same online advertising, and the second online advertising API depends on the execution of the first online advertising API; determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in the first time period, determining a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period, and determining the difference between the first traffic difference and the second traffic difference as a traffic difference fluctuation value; when both the first traffic difference and the second traffic difference are greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than a preset traffic difference fluctuation value, optimizing the first online advertising API. The optimization method for the online advertising API based on traffic statistics in the embodiment of the present application can analyze the traffic of mutually dependent online advertising APIs of the same online advertising, screen the online advertising APIs to be optimized according to the traffic difference and the fluctuation value of the traffic difference of the mutually dependent online advertising APIs, and can improve the positioning accuracy and scientificity of the optimization requirements of the online advertising API, and improve the efficiency of optimizing the online advertising API. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of an optimization method for an online advertising API based on traffic statistics provided by an embodiment of the present application;

[0021] Figure 2 It is a schematic diagram of the working process of an optimization method for an online advertising API based on traffic statistics provided by an embodiment of the present application;

[0022] Figure 3 It is a flowchart of another optimization method for an online advertising API based on traffic statistics provided by an embodiment of the present application;

[0023] Figure 4 It is a schematic diagram of the logical structure of an optimization device for an online advertising API based on traffic statistics provided by an embodiment of the present application;

[0024] Figure 5Schematic diagram of the entity structure of an online advertising API optimization device based on traffic statistics provided by an embodiment of the present application. Detailed implementation manners

[0025] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0026] It should also be understood that the term "and / or" as used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0027] As used in the specification and appended claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0028] In addition, in the description of the specification and appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0029] The reference to "an embodiment" or "some embodiments" or the like described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, the statements "in an embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0030] Currently, the optimization method of online advertising APIs mainly relies on the subjective system design of technical personnel, making it difficult to accurately optimize online advertising APIs in accordance with the best optimization direction, resulting in poor optimization effects of online advertising APIs.

[0031] For the above reasons, the embodiments of the present application provide an optimization method for online advertising APIs based on traffic statistics. The method includes: obtaining the traffic of the first online advertising API in the first time period and in the second time period respectively, and obtaining the traffic of the second online advertising API in the first time period and in the second time period. The first online advertising API and the second online advertising API belong to the same online advertising, and the second online advertising API depends on the execution of the first online advertising API; determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in the first time period, determining a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period, and determining the difference between the first traffic difference and the second traffic difference as the traffic difference fluctuation value; when both the first traffic difference and the second traffic difference are greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than a preset traffic difference fluctuation value, optimizing the first online advertising API. The optimization method for online advertising APIs based on traffic statistics in the embodiments of the present application can analyze the traffic of mutually dependent online advertising APIs of the same online advertising, screen the online advertising APIs to be optimized according to the traffic differences and the fluctuation value of the traffic differences of the mutually dependent online advertising APIs, and can improve the positioning accuracy and scientificity of the optimization requirements of the online advertising APIs, and improve the efficiency of optimizing the online advertising APIs.

[0032] In some scenarios, an optimization method for online advertising APIs based on traffic statistics in the embodiments of the present application can be applied to the optimization of online advertising APIs, and can involve the optimization of online advertising APIs such as advertisement display, advertisement click, and advertisement conversion, improving the optimization efficiency and scientificity of online advertising APIs.

[0033] The following specifically describes an optimization method for online advertising APIs based on traffic statistics provided by the embodiments of the present application with specific examples.

[0034] Figure 1 It is a schematic flow chart of an optimization method for online advertising APIs based on traffic statistics provided by the embodiments of the present application. As Figure 1 shown, this method includes S110 to S130, and the following specifically describes S110 to S130.

[0035] S110. Obtain the traffic of the first online advertising API in the first time period and in the second time period respectively, and obtain the traffic of the second online advertising API in the first time period and in the second time period. The first online advertising API and the second online advertising API belong to the same online advertising, and the second online advertising API depends on the execution of the first online advertising API.

[0036] Figure 2Schematic diagram of the working process of an online advertising API optimization method based on traffic statistics provided by an embodiment of the present application, as Figure 2 shown, when the method in the embodiment of the present application works, it can first obtain the traffic of the first online advertising API in the first time period and in the second time period, and obtain the traffic of the second online advertising API in the first time period and in the second time period. The traffic of the first online advertising API in the first time period and in the second time period represents the number of successful accesses of the first online advertising API in the first time period and in the second time period, and the traffic of the second online advertising API in the first time period and in the second time period represents the number of successful accesses of the second online advertising API in the first time period and in the second time period.

[0037] When working, it can analyze the first online advertising API and the second online advertising API according to the number of successful accesses of the first online advertising API and the second online advertising API in the first time period and in the second time period, so as to realize the optimization decision of the first online advertising API.

[0038] When the first online advertising API and the second online advertising API are applied, the first online advertising API and the second online advertising API can belong to the same online advertising, and the second online advertising API is an online advertising API that depends on the first online advertising API. Furthermore, the status of the first online advertising API and the second online advertising API can be analyzed according to the dependency relationship between the first online advertising API and the second online advertising API.

[0039] It should be noted that the second online advertising API is an online advertising API that depends on the first online advertising API. The first online advertising API and the second online advertising API can be adjacent in the execution order, or the first online advertising API and the second online advertising API can be non - adjacent in the execution order. As long as the second online advertising API is the online advertising API requested and called after the first online advertising API in the business process, the embodiment of the present application does not limit the specific execution time of the first online advertising API and the second online advertising API.

[0040] Exemplarily, the first online advertising API and the second online advertising API can be the advertising display API and the advertising click API of the same online advertising. The advertising display API is used to display the advertisement, and the advertising click API is used to send data and respond to the user's click according to the advertisement content displayed by the advertising display API. The traffic of the advertising click API reflects the effect of the advertising display API to a certain extent. Furthermore, it can provide guidance for the optimization decision of the advertising display API according to the traffic of the advertising click API.

[0041] Exemplarily, the first time period and the second time period can be two consecutive time periods in terms of time, or the first time period and the second time period can be two non - consecutive time periods in terms of time.

[0042] Exemplarily, the first time period can be a time period with a large website traffic and a large online advertisement display volume, and the second time period can be a time period with a small website traffic and a small online advertisement display volume.

[0043] Exemplarily, the first time period can be from 9:00 to 12:00 within the morning time period.

[0044] Exemplarily, the second time period can be from 14:00 to 17:00 within the afternoon time period.

[0045] Exemplarily, the traffic can be measured by the number of requests. The traffic of the first online advertisement API in the first time period and in the second time period can be the number of requests of the first online advertisement API in the first time period and in the second time period respectively. Similarly, the traffic of the second online advertisement API in the first time period and in the second time period can be the number of requests of the second online advertisement API in the first time period and in the second time period respectively.

[0046] S120. Determine the first traffic difference of the traffic of the first online advertisement API and the second online advertisement API in the first time period, determine the second traffic difference of the traffic of the first online advertisement API and the second online advertisement API in the second time period, and determine the difference between the first traffic difference and the second traffic difference as the traffic difference fluctuation value.

[0047] When evaluating the optimization requirements of the first online advertisement API and the second online advertisement API, the first traffic difference of the traffic of the first online advertisement API and the second online advertisement API in the first time period can be calculated and determined first. The first traffic difference characterizes the difference in the traffic of the successful accesses of the first online advertisement API and the second online advertisement API in the first time period.

[0048] When evaluating the optimization requirements of the first online advertisement API and the second online advertisement API, the second traffic difference of the traffic of the first online advertisement API and the second online advertisement API in the second time period can also be calculated and determined. The second traffic difference characterizes the difference in the traffic of the successful accesses of the first online advertisement API and the second online advertisement API in the second time period.

[0049] When evaluating the optimization requirements for the first online advertising API and the second online advertising API, the difference between the first traffic difference and the second traffic difference can also be calculated and determined as the traffic difference fluctuation value. The traffic difference fluctuation value characterizes the fluctuation amount of the traffic of the first online advertising API and the second online advertising API from the first time period to the second time period. Furthermore, the optimization requirements for the first online advertising API and the second online advertising API can be evaluated based on the traffic fluctuations of the first online advertising API and the second online advertising API in different time periods.

[0050] S130. When both the first traffic difference and the second traffic difference are greater than or equal to the preset traffic difference, and the traffic difference fluctuation value is less than the preset traffic difference fluctuation value, optimize the first online advertising API.

[0051] When evaluating the optimization requirements for the online advertising API, when both the first traffic difference and the second traffic difference are greater than the preset traffic difference, it indicates that the traffic differences between the first online advertising API and the second online advertising API in different time periods are both large. It may be that the first online advertising API causes too few request times for the second online advertising API.

[0052] When evaluating the optimization requirements for the online advertising API, when the traffic difference fluctuation value is less than the preset traffic difference fluctuation value, it indicates that the traffic difference fluctuations between the first online advertising API and the second online advertising API in different time periods are small, indicating that time has little impact on the traffic differences between the first online advertising API and the second online advertising API. The traffic differences between the first online advertising API and the second online advertising API show regular differences in different time periods. At this time, it can be judged that it may be the first online advertising API that causes too few request times for the second online advertising API.

[0053] After it is judged that it may be the first online advertising API that causes too few request times for the second online advertising API, the first online advertising API can be optimized.

[0054] The beneficial effects brought by the above implementation method are as follows: when the second online advertising API depends on the first online advertising API, when the traffic differences between the first online advertising API and the second online advertising API in different time periods are both large, and time has little impact on the traffic differences between the first online advertising API and the second online advertising API, it is judged that the first online advertising API causes too few request times for the second online advertising API, and then the first online advertising API is optimized, realizing the precise positioning of the optimization requirements of the online advertising API and improving the monitoring effect of the online advertising API.

[0055] In some implementations, the above method further includes: obtaining the categories of all online advertising APIs for the same online advertisement, using the online advertising APIs with categories being preset categories as the first online advertising APIs, and using the online advertising APIs that depend on the first online advertising APIs as the second online advertising APIs. Among them, the preset categories include an advertisement display category, an advertisement click category, and an advertisement conversion category.

[0056] When identifying the online advertising APIs to be optimized, the categories of all online advertising APIs for the same online advertisement can be obtained first, and the online advertising APIs to be optimized can be identified by category according to the categories of the online advertising APIs.

[0057] When identifying the online advertising APIs to be optimized, the online advertising APIs with categories being preset categories can be used as the first online advertising APIs. The preset categories include an advertisement display category, an advertisement click category, and an advertisement conversion category. Since the online advertising APIs for processing advertisement display requirements, advertisement click requirements, and advertisement display requirements generally serve as basic APIs and affect the execution of other online advertising APIs, when the online advertising APIs for processing advertisement display requirements, advertisement click requirements, and advertisement display requirements are not optimized enough, it may directly affect the execution of the subsequent online advertising APIs that depend on this online advertising API. Therefore, the online advertising APIs that depend on the first online advertising APIs can be used as the second online advertising APIs, and the optimization requirements of the first online advertising APIs can be evaluated through the traffic difference between the first online advertising APIs and the second online advertising APIs.

[0058] Exemplarily, when the first online advertising API is an online advertising API for processing advertisement display requirements, the second online advertising API can be a Targeting API for user positioning.

[0059] The beneficial effects brought by the above implementations are that the online advertising APIs for processing advertisement display requirements, advertisement click requirements, and advertisement display requirements generally serve as basic APIs and affect the execution process of other online advertising APIs that depend on the basic APIs. By using the online advertising APIs in the advertisement display category, advertisement click category, and advertisement conversion category as the first online advertising APIs, and making an optimization decision on the first online advertising APIs through the traffic difference between the first online advertising APIs and the second online advertising APIs, the accuracy and scientificity of evaluating the optimization requirements of the first online advertising APIs are improved.

[0060] In some implementations, the above method further includes: obtaining the categories of the second online advertising APIs, and determining a preset traffic difference and a preset traffic difference fluctuation value according to the categories of the first online advertising APIs and the second online advertising APIs.

[0061] When determining the preset traffic difference and the preset traffic difference fluctuation value, the category of the second online advertising API can be obtained. The category of the first online advertising API is related to the traffic of the first online advertising API, and the category of the second online advertising API is related to the traffic of the second online advertising API. Furthermore, the preset traffic difference and the preset traffic difference fluctuation value can be determined according to the category of the first online advertising API and the category of the second online advertising API.

[0062] Exemplarily, when the category of the first online advertising API is the advertisement display category and the category of the second online advertising API is the advertisement click category, when measuring the traffic of the online advertising API by the number of requests, the number of requests of the first online advertising API and the number of requests of the second online advertising API may not differ much, such that the traffic of the first online advertising API and the traffic of the second online advertising API are in the same order of magnitude. Furthermore, the preset traffic difference can be determined to be 50% of the traffic of the first online advertising API according to the category of the first online advertising API and the category of the second online advertising API, and the preset traffic difference fluctuation value can be determined to be 30% of the traffic of the first online advertising API according to the category of the first online advertising API and the category of the second online advertising API.

[0063] Exemplarily, when the category of the first online advertising API is the video advertisement playing category and the category of the second online advertising API is the effect measurement category, the first online advertising API requests an advertisement when the video advertisement starts playing, such that the number of requests of the first online advertising API is small; the second online advertising API is responsible for measuring the advertisement effect, such as the time and clicks of the user watching the advertisement, and the number of requests of the second online advertising API is high. When measuring the traffic of the online advertising API by the number of requests, there may be an order-of-magnitude difference between the number of requests of the first online advertising API and the number of requests of the second online advertising API, such that the traffic of the first online advertising API and the traffic of the second online advertising API are not in the same order of magnitude. Furthermore, the preset traffic difference can be determined to be 300% of the traffic of the first online advertising API according to the category of the first online advertising API and the category of the second online advertising API, and the preset traffic difference fluctuation value can be determined to be 200% of the traffic of the first online advertising API according to the category of the first online advertising API and the category of the second online advertising API.

[0064] The beneficial effects brought by the above implementation method are that, by determining the preset traffic difference and the preset traffic difference fluctuation value according to the category of the first online advertising API and the category of the second online advertising API, it is possible to scientifically determine the evaluation criteria for the optimization requirements of the online advertising API based on the influence of the category of the first online advertising API and the category of the second online advertising API on the traffic of the online advertising API, improving the accuracy and scientificity of the evaluation of the optimization requirements of the online advertising API.

[0065] Figure 3 It is a schematic flowchart of another online advertising API optimization method provided by an embodiment of the present application. As Figure 3 shown, the above method further includes S210 to S220, and the following is a specific description of S210 to S220.

[0066] S210. Obtain the error rate and the number of dependencies of the first online advertising API.

[0067] When planning an optimization strategy for the first online advertising API, the error rate and the number of dependencies of the first online advertising API can be obtained, and the optimization strategy for the first online advertising API can be planned according to the error rate and the number of dependencies of the first online advertising API.

[0068] Exemplarily, the error rate of the first online advertising API can be calculated by the total number of API requests sent within the first time period and the number of API requests that return errors within the first time period, and the error rate = total request times / error request times.

[0069] Exemplarily, the number of dependencies of the first online advertising API can be the number of online advertising APIs that depend on the first online advertising API. The number of online advertising APIs that depend on the first online advertising API can be more than 1. Furthermore, the optimization strategy for the first online advertising API can be adjusted according to the number of online advertising APIs that depend on the first online advertising API.

[0070] S220. When the traffic of the first online advertising API is greater than or equal to the preset traffic, the error rate of the first online advertising API is greater than or equal to the preset error rate, and the number of dependencies of the first online advertising API is greater than or equal to the preset number, split the first online advertising API into multiple sub-online advertising APIs, and the multiple sub-online advertising APIs correspond to processing requests for different sub-online advertising APIs that depend on the first online advertising API.

[0071] When planning an optimization strategy for the first online advertising API, when the traffic of the first online advertising API is greater than or equal to the preset traffic and the error rate of the first online advertising API is greater than or equal to the preset error rate, it indicates that the traffic of the first online advertising API is large and the error rate of the first online advertising API is high. Therefore, there is a need to optimize the first online advertising API.

[0072] When there is a need to optimize the first online advertising API and the number of dependencies of the first online advertising API is greater than or equal to a preset number, it indicates that the number of online advertising APIs dependent on the first online advertising API is large. Furthermore, the first online advertising API can be split into multiple sub-online advertising APIs, and the multiple sub-online advertising APIs correspond to processing requests for different sub-online advertising APIs that depend on the first online advertising API, so that the optimization of the first online advertising API can meet the requests of different sub-online advertising APIs that previously depended on the first online advertising API, and can reduce the error rate of the online advertising API and meet the demand for large traffic of the first online advertising API by splitting the first online advertising API into multiple sub-online advertising APIs.

[0073] The beneficial effects brought by the above implementation method are that when the traffic of the first online advertising API is large, the error rate is large, and the number of dependencies is large, the first online advertising API is split, so that the optimization of the first online advertising API can adapt to the characteristics of large traffic of the first online advertising API, and can solve the problems of large error rate and large number of dependencies, and improve the actual working effect of the first online advertising API.

[0074] In some implementation methods, the above method further includes: when the traffic of the first online advertising API is greater than a preset traffic, the error rate of the first online advertising API is less than a preset error rate, and the horizontal expansion index of the first online advertising API is greater than or equal to a preset horizontal expansion index, optimize the performance of the first online advertising API. Among them, the horizontal expansion index is used to characterize the horizontal scalability of the online advertising API. When the traffic of the first online advertising API is greater than a preset traffic, the error rate of the first online advertising API is less than a preset error rate, and the horizontal expansion index of the first online advertising API is less than a preset horizontal expansion index, split the first online advertising API into multiple sub-online advertising APIs.

[0075] When planning the optimization strategy of the first online advertising API, when the traffic of the first online advertising API is greater than a preset traffic and the error rate of the first online advertising API is less than a preset error rate, it indicates that the traffic of the first online advertising API is large and the error rate is small, and the first online advertising API is in a better working state.

[0076] When the first online advertising API is in a better working state and it is determined by the methods in S110 to S130 above that the first online advertising API needs to be optimized, when the horizontal expansion index of the first online advertising API is greater than or equal to the preset horizontal expansion index, where the horizontal expansion index is used to characterize the horizontal scalability of the online advertising API, indicating that the horizontal scalability of the first online advertising API is better. At this time, only the performance of the first online advertising API can be optimized to meet the requirement of optimizing the first online advertising API determined by the methods in S110 to S130.

[0077] It should be noted that the horizontal scalability of the first online advertising API refers to the ability of the server system to handle a larger volume of first online advertising API requests by adding more machines or nodes when the business scale of online advertising grows, without significantly affecting the performance of the first online advertising API in the server or causing service interruption. The horizontal scalability can characterize the ability of the first online advertising API to add more instances (such as servers) in a distributed system to share the load.

[0078] When the first online advertising API is in a better working state and it is determined by the methods in S110 to S130 above that the first online advertising API needs to be optimized, when the horizontal expansion index of the first online advertising API is less than the preset horizontal expansion index, it indicates that the horizontal scalability of the first online advertising API is not good. At this time, the first online advertising API can be split into multiple sub-online advertising APIs to reduce the impact of the first online advertising API on the second online advertising API and improve the performance of the first online advertising API and the second online advertising API.

[0079] The beneficial effects brought by the above implementation methods are that when the traffic of the first online advertising API is large, the error rate is small, and the horizontal scalability is large, only optimizing the performance of the first online advertising API can meet the optimization requirements of the first online advertising API, reduce the impact of the first online advertising API on the second online advertising API, and improve the performance of the entire online advertising API.

[0080] The beneficial effects brought by the above implementation methods also lie in that when the traffic of the first online advertising API is large, the error rate is small, and the horizontal scalability is small, the first online advertising API can be split into multiple sub-online advertising APIs to reduce the impact of the first online advertising API on the second online advertising API and improve the performance of the first online advertising API and the second online advertising API.

[0081] In some implementations, the above method further includes: obtaining the traffic of multiple second online advertising APIs that depend on the first online advertising API, determining the average traffic of the multiple second online advertising APIs as the second average traffic, and when the traffic of the target online advertising API among the multiple second online advertising APIs is lower than the second average traffic, optimizing the target online advertising API.

[0082] When there are multiple second online advertising APIs that depend on the first online advertising API, when planning the optimization strategy for the first online advertising API, the optimization strategy for the second online advertising APIs can be planned simultaneously.

[0083] During operation, the traffic of multiple second online advertising APIs that depend on the first online advertising API can be obtained, and the average traffic of the multiple second online advertising APIs can be determined as the second average traffic, and then the second online advertising APIs that need to be optimized among the multiple second online advertising APIs can be identified according to the traffic of the multiple second online advertising APIs.

[0084] When the traffic of the target online advertising API among the multiple second online advertising APIs is lower than the second average traffic, it indicates that the traffic of the target online advertising API is low, and the performance of the target online advertising API may be poor due to the insufficient performance of the target online advertising API itself. Therefore, the target online advertising API can be optimized.

[0085] The beneficial effect brought by the above implementation is that when there are multiple second online advertising APIs that depend on the first online advertising API, by evaluating the performance of the multiple second online advertising APIs, the target online advertising API that needs to be optimized among the multiple second online advertising APIs can be identified, and the optimization of the second online advertising APIs associated with the first online advertising API can be achieved.

[0086] In some implementations, the above method further includes: when the traffic of the target online advertising API among the multiple second online advertising APIs is lower than the second average traffic, optimizing the online advertising APIs other than the first online advertising API on which the target online advertising API depends.

[0087] During operation, when the traffic of the target online advertising API among the multiple second online advertising APIs is lower than the second average traffic, it indicates that the performance of the target online advertising API may be affected by the online advertising APIs other than the first online advertising API on which the target online advertising API depends. Therefore, the online advertising APIs other than the first online advertising API on which the target online advertising API depends can be optimized.

[0088] The beneficial effects brought by the above implementation method also lie in that when the traffic of the target online advertising API is lower than the average traffic of multiple second online advertising APIs that depend on the first online advertising API, optimizing the online advertising APIs other than the first online advertising API on which the target online advertising API depends can accurately locate the online advertising APIs that need to be optimized, improving the optimization effect of the online advertising APIs.

[0089] The embodiment of the present application also provides an online advertising API optimization device based on traffic statistics, including a unit for executing the method described in any one of the above.

[0090] Figure 4 FIG. is a schematic logical structure diagram of an online advertising API optimization device based on traffic statistics provided by an embodiment of the present application. As Figure 4 shown, the device 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects brought by the embodiment of the present application have been described in the above method and will not be elaborated here.

[0091] The embodiment of the present application also provides an online advertising API optimization device based on traffic statistics, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any one of the above.

[0092] Figure 5 FIG. is a schematic physical structure diagram of an online advertising API optimization device based on traffic statistics provided by an embodiment of the present application. As Figure 5 shown, the device 2 of this embodiment includes: at least one processor 20 ( Figure 5 only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, it implements the steps in any of the above method embodiments. The beneficial effects brought by the embodiment of the present application have been described in the above method and will not be elaborated here.

[0093] It should be noted that the information interaction, execution process, etc. between the above devices / units, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part for details and will not be elaborated here.

[0094] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0095] An embodiment of this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

[0096] An embodiment of this application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal is enabled to execute the steps in the foregoing method embodiments.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code to the photographing device / terminal device. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0098] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0099] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0100] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in an electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] The above-described embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. An online advertising API optimization method based on traffic statistics, characterized in that: The method comprises: Obtaining the traffic of a first online advertising API in a first time period and in a second time period, respectively, and obtaining the traffic of a second online advertising API in the first time period and in the second time period, wherein the first online advertising API and the second online advertising API belong to the same online advertisement and the second online advertising API depends on the execution of the first online advertising API; Determine a first traffic difference between the traffic of the first online advertising API and the second online advertising API in a first time period, determine a second traffic difference between the traffic of the first online advertising API and the second online advertising API in a second time period, and determine a difference between the first traffic difference and the second traffic difference as a traffic difference fluctuation value; When the first traffic difference and the second traffic difference are both greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than the preset traffic difference fluctuation value, optimizing the first online advertising API; Obtaining categories of all online advertising APIs for the same online advertisement, taking an online advertising API of a preset category as a first online advertising API, and taking an online advertising API that depends on the first online advertising API as a second online advertising API; wherein the preset categories include an advertisement display category, an advertisement click category, and an advertisement conversion category; The category of the second online advertising API is obtained, and a preset traffic difference value and a preset traffic difference value fluctuation value are determined according to the category of the first online advertising API and the category of the second online advertising API.

2. The method according to claim 1, characterized in that The method further comprises: Get the error rate and dependency count of the First Online Advertising API; When the traffic of the first online advertising API is greater than or equal to the preset traffic, the error rate of the first online advertising API is greater than or equal to the preset error rate, and the number of dependencies of the first online advertising API is greater than or equal to the preset number, the first online advertising API is split into multiple sub-online advertising APIs, and the multiple sub-online advertising APIs correspond to processing requests of different sub-online advertising APIs that depend on the first online advertising API.

3. The method according to claim 2, characterized in that The method further comprises: When the traffic of the first online advertising API is greater than the preset traffic, the error rate of the first online advertising API is less than the preset error rate, and the horizontal expansion index of the first online advertising API is greater than or equal to the preset horizontal expansion index, the performance of the first online advertising API is optimized; wherein the horizontal expansion index is used to characterize the horizontal scalability of the online advertising API; when the traffic of the first online advertising API is greater than the preset traffic, the error rate of the first online advertising API is less than the preset error rate, and the horizontal expansion index of the first online advertising API is less than the preset horizontal expansion index, the first online advertising API is split into multiple sub-online advertising APIs.

4. The method according to claim 3, characterized in that The method further comprises: The traffic of multiple second online advertising APIs that depend on the first online advertising API is obtained, and the traffic average of the multiple second online advertising APIs is determined as the second traffic average. When the traffic of a target online advertising API among the multiple second online advertising APIs is lower than the second traffic average, the target online advertising API is optimized.

5. An online advertising API optimization device based on traffic statistics, characterized in that: The method comprises means for performing the method according to any one of claims 1 to 4.

6. An online advertising API optimization device based on traffic statistics, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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