Online advertisement API optimization method and device based on flow statistics

Abstract

The invention relates to the field of online advertisement API optimization, and discloses an online advertisement API optimization method and device based on traffic statistics, and the method comprises the steps: obtaining the traffic of a first online advertisement API in a first time period and a second time period, and obtaining the traffic of a second online advertisement API in the first time period and the second time period; determining a first flow difference value of the flow of the first online advertisement API and the second online advertisement API in the first time period, determining a second flow difference value of the flow of the first online advertisement API and the second online advertisement API in the second time period, and determining a difference value between the first flow difference value and the second flow difference value as a flow difference value fluctuation value; and when the first flow difference value and the second flow difference value are both greater than or equal to a preset flow difference value and a flow difference value fluctuation value is smaller than a preset flow difference value fluctuation value, optimizing the first online advertisement API. According to the method, the optimization direction of the online advertisement API can be accurately positioned, and the optimization effect of the online advertisement API is improved.

Description

Technical Field

[0001] The present application relates to the technical field of online advertising API optimization, and more specifically, to an online advertising API optimization method and device 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 ad server and requests appropriate advertising content. This process involves web page analysis, user behavior analysis, and content matching technology to determine the most appropriate online advertisement. In addition, Cookies and pixel tracking technologies are widely used to record user behavior in order to accurately target advertising. For example, information such as user clicks, browsing time, and historical searches are collected and analyzed to predict user interests and needs. In addition, the accuracy of online advertising benefits from the widespread 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 delivery strategies. Algorithms play an important role in market segmentation, dynamic pricing, and real-time bidding (RTB). By continuously optimizing algorithms, the effectiveness of advertising can be improved.

[0003] As a bridge between advertisers and advertising platforms, the online advertising API (Application Programming Interface) uses a series of advanced background 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 a standardized interface that allows developers to interact with the advertising platform through HTTP requests. These interfaces support a variety of operations, including creating and updating campaigns, obtaining advertising performance data, managing user audiences, etc. The API must be designed with high availability and scalability to cope with the huge traffic and database load of the advertising system. However, the current optimization methods of online advertising APIs mainly rely on the subjective system design of technicians, and it is difficult to accurately optimize the online advertising API according to the best optimization direction, resulting in poor optimization results of the online advertising API. Summary of the invention

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

[0005] An embodiment of the present application provides an online advertising API optimization method based on traffic statistics, the method comprising: obtaining the traffic of a first online advertising API in a first time period and a second time period respectively, and obtaining the traffic of a second online advertising API in the first time period and the second time period, 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; 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 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.

[0006] In a possible implementation, the method further includes: obtaining categories of all online advertising APIs of the same online advertisement, using an online advertising API of a preset category as a first online advertising API, and using an online advertising API that depends on the first online advertising API as a second online advertising API. The preset categories include an advertisement display category, an advertisement click category, and an advertisement conversion category.

[0007] In another possible implementation manner, the method further includes: obtaining a category of the second online advertising API, and determining a preset traffic difference and a 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, the method also includes: obtaining the error rate and dependency quantity 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 dependency quantity of the first online advertising API is greater than or equal to the preset quantity, 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 also includes: 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, 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 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, splitting the first online advertising API into multiple sub-online advertising APIs.

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

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

[0012] An embodiment of the present application also provides an online advertising API optimization device based on traffic statistics, including a unit for executing any of the methods described 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, wherein the processor implements any of the methods described above when executing the computer program.

[0014] An embodiment of the present application further provides a computer-readable storage medium, wherein 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 items is implemented.

[0015] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of any of the methods described above when executed by a processor.

[0016] In another possible implementation,

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0018] An embodiment of the present application provides an online advertising API optimization method based on traffic statistics, the method comprising: 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, 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; 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 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. The online advertising API optimization method based on traffic statistics in the embodiment of the present application can analyze the traffic of interdependent online advertising APIs of the same online advertisement, and screen the online advertising APIs that need to be optimized according to the traffic difference of the interdependent online advertising APIs and the fluctuation value of the traffic difference, which can improve the positioning accuracy and scientificity of the online advertising API optimization needs 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 A flow chart of an online advertising API optimization method based on traffic statistics provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the working process of an online advertising API optimization method based on traffic statistics provided in an embodiment of the present application;

[0022] Figure 3 A flowchart of another method for optimizing an online advertising API based on traffic statistics provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the logical structure of an online advertising API optimization device based on traffic statistics provided in an embodiment of the present application;

[0024] Figure 5A schematic diagram of the physical structure of an online advertising API optimization device based on traffic statistics provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of 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 combinations thereof.

[0026] It should also be understood that the term “and / or” used in the specification and appended claims refers to any 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 this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

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

[0029] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some 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 other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0030] Currently, the optimization method of online advertising API mainly relies on the subjective system design of technical personnel. It is difficult to accurately optimize the online advertising API according to the best optimization direction, resulting in poor optimization effect of the online advertising API.

[0031] Based on the above reasons, an embodiment of the present application provides an online advertising API optimization method based on traffic statistics, the method comprising: obtaining the traffic of a first online advertising API in a first time period and a second time period respectively, and obtaining the traffic of a second online advertising API in the first time period and the second time period, 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; 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 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. The online advertising API optimization method based on traffic statistics in the embodiment of the present application can analyze the traffic of interdependent online advertising APIs of the same online advertisement, and screen the online advertising APIs that need to be optimized according to the traffic difference of the interdependent online advertising APIs and the fluctuation value of the traffic difference, which can improve the positioning accuracy and scientificity of the online advertising API optimization needs and improve the efficiency of optimizing the online advertising API.

[0032] In some scenarios, an online advertising API optimization method based on traffic statistics in an embodiment of the present application can be applied to the optimization of online advertising APIs, which may involve the optimization of online advertising APIs such as advertising display, advertising clicks, and advertising conversions, thereby improving the optimization efficiency and scientificity of online advertising APIs.

[0033] The following is a detailed description of an online advertising API optimization method based on traffic statistics provided by an embodiment of the present application with reference to specific examples.

[0034] Figure 1 A flow chart of an online advertising API optimization method based on traffic statistics provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes S110 to S130, and S110 to S130 are described in detail below.

[0035] S110. 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 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.

[0036] Figure 2A schematic diagram of the working process of an online advertising API optimization method based on traffic statistics provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, when the method in the embodiment of the present application is working, it can first obtain the traffic of the first online advertising API in the first time period and the second time period respectively, and obtain the traffic of the second online advertising API in the first time period and the second time period respectively. The traffic of the first online advertising API in the first time period and the second time period respectively represents the number of successful visits to the first online advertising API in the first time period and the second time period, and the traffic of the second online advertising API in the first time period and the second time period represents the number of successful visits to the second online advertising API in the first time period and the second time period.

[0037] During operation, the first online advertising API and the second online advertising API may be analyzed according to the number of successful visits to the first online advertising API and the second online advertising API in the first time period and the second time period, so as to achieve an optimization decision for 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 may belong to the same online advertisement, and the second online advertising API is an online advertising API that depends on the first online advertising API, and then the status of the first online advertising API and the second online advertising API may 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 may be adjacent in execution order, or the first online advertising API and the second online advertising API may not be adjacent in execution order. It only needs to be an online advertising API that is 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 may be the ad display API and ad click API of the same online advertisement. The ad display API is used to display advertisements, and the ad click API is used to send data and respond to user clicks based on the advertisement content displayed by the ad display API. The traffic of the ad click API reflects the effectiveness of the ad display API to a certain extent, and thus guidance can be provided for the optimization decision of the ad display API based on the traffic of the ad click API.

[0041] Exemplarily, the first time period and the second time period may be two time periods that are continuous in time, or the first time period and the second time period may be two time periods that are discontinuous in time.

[0042] Exemplarily, the first time period may be a time period with a large number of website visits and a large number of online advertisement displays, and the second time period may be a time period with a small number of website visits and a small number of online advertisement displays.

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

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

[0045] For example, the traffic may be measured by the number of requests, and the traffic of the first online advertising API in the first time period and the second time period may be the number of requests of the first online advertising API in the first time period and the second time period. Similarly, the traffic of the second online advertising API in the first time period and the second time period may be the number of requests of the second online advertising API in the first time period and the second time period.

[0046] S120, determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in a first time period, determining 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 determining the difference between the first traffic difference and the second traffic difference as a traffic difference fluctuation value.

[0047] When evaluating the optimization requirements of the first online advertising API and the second online advertising API, a first traffic difference between the traffic of the first online advertising API and the second online advertising API in a first time period can be calculated and determined first. The first traffic difference represents the difference in traffic of successful visits to the first online advertising API and the second online advertising API in the first time period.

[0048] When evaluating the optimization requirements of the first online advertising API and the second online advertising API, a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period can also be calculated and determined, and the second traffic difference represents the difference in traffic of successful visits to the first online advertising API and the second online advertising API in the second time period.

[0049] When evaluating the optimization requirements of 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 represents 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. Then, the optimization requirements of the first online advertising API and the second online advertising API can be evaluated through the fluctuation of the traffic of the first online advertising API and the second online advertising API in different time periods.

[0050] S130: 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, optimize the first online advertising API.

[0051] When evaluating the optimization requirements of the online advertising API, when the first traffic difference and the second traffic difference are both greater than the preset traffic difference, it means that the traffic differences between the first online advertising API and the second online advertising API in different time periods are large, which may be because the first online advertising API caused the number of requests to the second online advertising API to be too small.

[0052] When evaluating the optimization needs of the online advertising API, when the traffic difference fluctuation value is less than the preset traffic difference fluctuation value, it means that the traffic difference fluctuation of the first online advertising API and the second online advertising API in different time periods is small, which means that the influence of time on the traffic difference between the first online advertising API and the second online advertising API is small, and the traffic difference between the first online advertising API and the second online advertising API shows a regular difference in different time periods. At this time, it can be judged that the number of requests to the second online advertising API may be too small due to the first online advertising API.

[0053] After determining that the number of requests to the second online advertising API may be too small due to the first online advertising API, the first online advertising API may be optimized.

[0054] The beneficial effect brought about by the above-mentioned implementation method is that when the second online advertising API depends on the first online advertising API, the traffic difference between the first online advertising API and the second online advertising API in different time periods is large, and the influence of time on the traffic difference between the first online advertising API and the second online advertising API is small, it is judged that the first online advertising API causes the number of requests to the second online advertising API to be too small, and then the first online advertising API is optimized, thereby achieving precise positioning of the optimization needs of the online advertising API and improving the monitoring effect of the online advertising API.

[0055] In some implementations, the method further includes: obtaining categories of all online advertising APIs of the same online advertisement, using an online advertising API of a preset category as a first online advertising API, and using an online advertising API that depends on the first online advertising API as a second online advertising API. The preset categories include an advertisement display category, an advertisement click category, and an advertisement conversion category.

[0056] When identifying the online advertising API that needs to be optimized, the categories of all online advertising APIs of the same online advertisement may be first obtained, and the online advertising API that needs to be optimized may be identified according to the categories of the online advertising APIs.

[0057] When identifying an online advertising API that needs to be optimized, an online advertising API categorized as a preset category may be used as the first online advertising API. The preset categories include an advertising display category, an advertising click category, and an advertising conversion category. Since the online advertising API that handles advertising display needs, advertising click needs, and advertising display needs generally serves as a basic API and affects the execution of other online advertising APIs, when the online advertising API that handles advertising display needs, advertising click needs, and advertising display needs is insufficiently optimized, it may directly affect the execution of subsequent online advertising APIs that rely on this online advertising API. Therefore, the online advertising API that relies on the first online advertising API may be used as the second online advertising API, and the optimization needs of the first online advertising API may be evaluated through the traffic difference between the first online advertising API and the second online advertising API.

[0058] Exemplarily, when the first online advertising API is an online advertising API for processing advertising display needs, the second online advertising API may be a Targeting API for performing user positioning.

[0059] The beneficial effect brought about by the above-mentioned implementation method is that the online advertising API that processes advertising display needs, advertising click needs and advertising display needs generally serves as the basic API, which affects the execution process of other online advertising APIs that depend on the basic API. By taking the online advertising API of the advertising display category, advertising click category and advertising conversion category as the first online advertising API, and optimizing the first online advertising API through the traffic difference between the first online advertising API and the second online advertising API, the accuracy and scientificity of the evaluation of the optimization needs of the first online advertising API are improved.

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

[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. Then, the preset traffic difference and the preset traffic difference fluctuation value can be determined based on 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 advertising display category and the category of the second online advertising API is the advertising click category, when the traffic of the online advertising API is measured 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 be almost the same, so that the traffic of the first online advertising API and the traffic of the second online advertising API are on the same order of magnitude, and then the preset traffic difference can be determined to be 50% of the traffic of the first online advertising API based on 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 based on the category of the first online advertising API and the category of the second online advertising API.

[0063] For example, when the category of the first online advertising API is the video advertising playback 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 to play, so that the number of requests to the first online advertising API is relatively small; the second online advertising API is responsible for measuring the effect of the advertisement, such as the time and clicks of the user watching the advertisement, and the number of requests to the second online advertising API is relatively high. When the traffic of the online advertising API is measured 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, so 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, and then the preset traffic difference value 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 effect brought about by the above-mentioned implementation method is that the preset traffic difference and the preset traffic difference fluctuation value are determined according to the category of the first online advertising API and the category of the second online advertising API, and the evaluation criteria for the online advertising API optimization needs can be scientifically determined based on the impact 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, thereby improving the accuracy and scientificity of the evaluation of the online advertising API optimization needs.

[0065] Figure 3 A flow chart of another method for optimizing online advertising API based on traffic statistics provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the above method further includes S210 to S220, and S210 to S220 are described in detail below.

[0066] S210: Obtain the error rate and dependency quantity of the first online advertising API.

[0067] When planning the optimization strategy for the first online advertising API, the error rate and the number of dependencies of the first online advertising API may be obtained, and the optimization strategy for the first online advertising API may 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 issued in the first time period and the number of API requests that return errors in the first time period, error rate=total number of requests / number of error requests.

[0069] Exemplarily, the number of dependencies of the first online advertising API may be the number of online advertising APIs that depend on the first online advertising API, and the number of online advertising APIs that depend on the first online advertising API may be more than one, and thus the optimization strategy of the first online advertising API may 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, 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.

[0071] When planning the 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 means that the traffic of the first online advertising API is large and the error rate of the first online advertising API is high, so 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 the preset number, it means that the number of online advertising APIs that depend on the first online advertising API is large, and the first online advertising API can be split into multiple sub-online advertising APIs. The multiple sub-online advertising APIs correspond to processing requests of 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 adapt to the large traffic requirements of the first online advertising API by splitting the first online advertising API into multiple sub-online advertising APIs.

[0073] The beneficial effect brought about by the above-mentioned implementation method is 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 large traffic characteristics of the first online advertising API, and can solve the problems of high error rate and large number of dependencies, thereby improving the actual working effect of the first online advertising API.

[0074] In some implementations, the above method further includes: 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, optimizing the performance of the first online advertising API. 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, splitting the first online advertising API into multiple sub-online advertising APIs.

[0075] When planning the optimization strategy for the first online advertising API, when the traffic of the first online advertising API is greater than the preset traffic and the error rate of the first online advertising API is less than the preset error rate, it means 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, when it is judged through the above-mentioned methods in S110 to S130 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, 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, which can solve the need to optimize the first online advertising API as judged 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 service interruption. 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, when it is determined through 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 means 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 after the first online advertising API is split, thereby improving the performance of the first online advertising API and the second online advertising API.

[0079] The beneficial effect brought about by the above-mentioned implementation method is that when the traffic of the first online advertising API is large, the error rate is small, and the horizontal scalability is large, only the performance of the first online advertising API can be optimized to meet the optimization needs 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 effect brought about by the above-mentioned implementation method is 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 also includes: obtaining the traffic of multiple second online advertising APIs that depend on the first online advertising API, and determining the traffic average of the multiple second online advertising APIs as the second traffic average, and when the traffic of a target online advertising API among the multiple second online advertising APIs is lower than the second traffic average, optimizing the target online advertising API.

[0082] When there are multiple second online advertising APIs that depend on the first online advertising API, the optimization strategy of the second online advertising API may be planned while the optimization strategy of the first online advertising API is being planned.

[0083] During operation, the traffic of multiple second online advertising APIs that depend on the first online advertising API can be obtained, and the traffic average of the multiple second online advertising APIs can be determined as the second traffic average, and then the second online advertising API that needs 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 a target online advertising API among multiple second online advertising APIs is lower than the second traffic average, it means that the traffic of the target online advertising API is low. The performance of the target online advertising API may be poor due to insufficient performance of the target online advertising API itself. Therefore, the target online advertising API can be optimized.

[0085] The beneficial effect brought about by the above-mentioned implementation method 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, thereby optimizing the second online advertising API associated with the first online advertising API.

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

[0087] During operation, when the traffic of a target online advertising API among multiple second online advertising APIs is lower than the second traffic average, 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, and thus 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 effect brought about by the above-mentioned implementation method is that when the traffic of the target online advertising API is lower than the average traffic of multiple second online advertising APIs that rely on the first online advertising API, the online advertising APIs other than the first online advertising API that the target online advertising API relies on are optimized, so that the online advertising API that needs to be optimized can be accurately located, thereby improving the optimization effect of the online advertising API.

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

[0090] Figure 4 A logical structure diagram of an online advertising API optimization device based on traffic statistics provided by an embodiment of the present application is shown in FIG. Figure 4 As 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 about by the embodiment of the present application have been described in the above method and will not be repeated here.

[0091] 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, wherein the processor implements any of the methods described above when executing the computer program.

[0092] Figure 5 A schematic diagram of the physical structure of an online advertising API optimization device based on traffic statistics provided by an embodiment of the present application is shown in FIG. Figure 5 As shown, the device 2 of this embodiment includes: at least one processing 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, the steps in any of the above-mentioned method embodiments are implemented. The beneficial effects brought about by the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0093] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0094] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0095] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0096] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0098] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0100] In the embodiments provided in the present 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[0102] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present 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, the first online advertising API is optimized.

2. The method according to claim 1, characterized in that The method further comprises: The categories of all online advertising APIs of the same online advertisement are obtained, and the online advertising API of the preset category is used as the first online advertising API, and the online advertising API that depends on the first online advertising API is used as the second online advertising API. The preset category includes the advertisement display category, the advertisement click category, and the advertisement conversion category.

3. The method according to claim 2, characterized in that The method further comprises: 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.

4. The method according to claim 3, 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.

5. The method according to claim 4, 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.

6. The method according to claim 5, 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.

7. An online advertising API optimization device based on traffic statistics, characterized in that: Comprising means for performing the method according to any one of claims 1 to 6.

8. 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 6 is implemented.

9. 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 6 is implemented.

10. 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 6 are implemented.

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