CN119444319A - Online advertising API optimization method and device based on traffic statistics - Google Patents
Online advertising API optimization method and device based on traffic statistics Download PDFInfo
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Abstract
本申请涉及线上广告API优化领域，公开了一种基于流量统计的线上广告API优化方法和装置，方法包括：获取第一线上广告API分别在第一时间段内、在第二时间段内的流量，并获取第二线上广告API在第一时间段内、在第二时间段内的流量；确定第一线上广告API和第二线上广告API在第一时间段内的流量的第一流量差值，确定第一线上广告API和第二线上广告API在第二时间段内的流量的第二流量差值，并确定第一流量差值和第二流量差值的差值作为流量差值波动值；当第一流量差值和第二流量差值均大于或等于预设流量差值，并且流量差值波动值小于预设流量差值波动值时，对第一线上广告API进行优化。本申请能够精准定位线上广告API的优化方向，提高线上广告API的优化效果。
The present 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 comprising: obtaining the traffic of the first online advertising API in the first time period and the second time period respectively, and obtaining the traffic of the second online advertising API in the first time period and the second time period; determining the first traffic difference of the traffic of the first online advertising API and the second online advertising API in the first time period, determining the second traffic difference of 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 the first traffic difference and the second traffic difference are both greater than or equal to the 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 present 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
The application relates to the technical field of online advertisement API optimization, in particular to an online advertisement API optimization method and device based on flow statistics.
Background
On-line advertising, an important component of digital marketing, has a complex and diverse technology behind it. When a user browses a website or uses an application, the browser interacts with an advertisement server to request appropriate advertisement content, and the process involves web page analysis, user behavior analysis and content matching techniques to determine the most appropriate online advertisement. In addition, cookies and pixel tracking techniques are widely used to record user behavior for accurate targeted advertising. For example, information such as clicks, browsing durations, historical searches, etc. of the user are collected and analyzed to predict the interests and needs of the user. Furthermore, the accuracy of online advertising benefits from the wide range of applications of big data and machine learning. Through deep mining and analysis of massive user data, the machine learning model can predict potential needs and interests of users, so that advertisement content and delivery strategies are optimized. The algorithm plays an important role in market subdivision, dynamic pricing, real-time bidding (RealTime Bidding, RTB) and the like, and the benefit of advertisement delivery can be improved by continuously optimizing the algorithm.
The online advertisement API (Application Programming Interface) is used as a bridge for connecting an advertiser and an advertisement platform, and a series of advanced background technologies are adopted to realize efficient data communication and advertisement management. The online advertising API provides a standardized interface based on RESTful architecture or SOAP protocol, allowing developers to interact with the advertising platform through HTTP requests. These interfaces support a variety of operations including creating and updating ad series, retrieving ad performance data, managing the user audience, and the like. The design of the API must have high availability and extensibility to cope with the huge traffic and database load of the advertising system. However, the current optimization method of the online advertisement API mainly depends on subjective system design of technicians, and is difficult to accurately optimize the online advertisement API according to the optimal optimization direction, so that the optimization effect of the online advertisement API is poor.
Disclosure of Invention
The application aims to provide an online advertisement API optimization method and device based on flow statistics, which solve the technical problem of poor optimization effect of online advertisement APIs, and achieve the technical effects of accurately positioning the optimization direction of online advertisement APIs and improving the optimization effect of online advertisement APIs.
The online advertisement API optimization method based on the traffic statistics comprises the steps of obtaining traffic of a first online advertisement API in a first time period and traffic of a second online advertisement API in a second time period respectively, obtaining traffic of the second online advertisement API in the first time period and traffic of the second time period respectively, determining a first traffic difference value of traffic of the first online advertisement API and the second online advertisement API in the first time period, determining a second traffic difference value of traffic of the first online advertisement API and the second online advertisement API in the second time period, determining the difference value of the first traffic difference value and the second traffic difference value as traffic fluctuation values, and optimizing the first online advertisement API when the first traffic difference value and the second traffic difference value are larger than or equal to preset traffic difference values and the traffic fluctuation value is smaller than the preset traffic fluctuation value.
In one possible implementation, the method further includes obtaining categories of all online advertisement APIs of the same online advertisement, taking an online advertisement API of a category of a preset category as a first online advertisement API, and taking an online advertisement API dependent on the first online advertisement API as a second online advertisement API. The preset categories comprise an advertisement display category, an advertisement click category and an advertisement conversion category.
In another possible implementation, the method further includes obtaining a category of the second online advertising API, and determining the preset traffic difference value 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.
In another possible implementation, the method further includes obtaining an error rate and a number of dependencies of the first online advertising API, splitting the first online advertising API into a plurality of sub-online advertising APIs corresponding to processing requests of different sub-online advertising APIs that depend on 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.
In another possible implementation, the method further comprises optimizing performance of the first 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 lateral expansion index of the first online advertising API is greater than or equal to a preset lateral expansion index, wherein the lateral expansion index is used for characterizing the lateral scalability of the online advertising API, and splitting the first online advertising API into a plurality of sub-online advertising APIs 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 lateral expansion index of the first online advertising API is less than the preset lateral expansion index.
In another possible implementation, the method further includes obtaining traffic of a plurality of second online advertising APIs that are dependent on the first online advertising APIs, and determining a traffic average of the plurality of second online advertising APIs as a second traffic average, and optimizing a target online advertising API of the plurality of second online advertising APIs when the traffic of the target online advertising API is below the second traffic average.
In another possible implementation, the method further includes optimizing an online advertising API other than the first online advertising API on which the target online advertising API depends when traffic of the target online advertising API of the plurality of second online advertising APIs is below a second traffic average.
The embodiment of the application also provides an online advertisement API optimizing device based on the traffic statistics, which comprises a unit for executing the method of any one of the above.
The embodiment of the application also provides an online advertisement API optimizing device based on flow statistics, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the method according to any one of the above when executing the computer program.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a method as claimed in any one of the above.
Embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method as claimed in any one of the preceding claims.
In another possible implementation form of the present invention,
Compared with the prior art, the embodiment of the application has the beneficial effects that:
The embodiment of the application provides an online advertisement API optimization method based on traffic statistics, which comprises the steps of obtaining traffic of a first online advertisement API in a first time period and a second time period respectively, obtaining traffic of a second online advertisement API in the first time period and the second time period respectively, determining a first traffic difference value of traffic of the first online advertisement API and the second online advertisement API in the first time period, determining a second traffic difference value of traffic of the first online advertisement API and the second online advertisement API in the second time period, determining the difference value of the first traffic difference value and the second traffic difference value as traffic fluctuation values, and optimizing the first online advertisement API when the first traffic difference value and the second traffic difference value are larger than or equal to preset traffic difference values and the traffic fluctuation values are smaller than the preset traffic fluctuation values. According to the online advertisement API optimization method based on the flow statistics, the flow of the interdependent online advertisement APIs of the same online advertisement can be analyzed, online advertisement APIs needing to be optimized are screened according to the flow difference value and the fluctuation value of the flow difference value of the interdependent online advertisement APIs, positioning accuracy and scientificity of online advertisement API optimization requirements can be improved, and efficiency of online advertisement API optimization is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of an online advertisement API optimization method based on flow statistics according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the working process of an online advertisement API optimization method based on traffic statistics according to an embodiment of the present application;
FIG. 3 is a flowchart of another online advertisement API optimization method based on traffic statistics according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a logic structure of an online advertisement API optimizing device based on traffic statistics according to an embodiment of the present application;
fig. 5 is a schematic entity structure diagram of an online advertisement API optimizing device based on traffic statistics according to an embodiment of the present application.
Detailed Description
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The current optimization method of the online advertisement API mainly depends on subjective system design of technicians, and is difficult to accurately optimize the online advertisement API according to the optimal optimization direction, so that the optimization effect of the online advertisement API is poor.
Based on the above reasons, the embodiment of the application provides an online advertisement API optimization method based on traffic statistics, which comprises the steps of obtaining traffic of a first online advertisement API in a first time period and a second time period respectively, obtaining traffic of the second online advertisement API in the first time period and the second time period respectively, determining a first traffic difference value of traffic of the first online advertisement API and the second online advertisement API in the first time period, determining a second traffic difference value of traffic of the first online advertisement API and the second online advertisement API in the second time period, determining the difference value of the first traffic difference value and the second traffic difference value as a traffic difference fluctuation value, and optimizing the first online advertisement API when the first traffic difference value and the second traffic difference value are both larger than or equal to a preset traffic difference value and the traffic fluctuation value is smaller than the preset traffic fluctuation value. According to the online advertisement API optimization method based on the flow statistics, the flow of the interdependent online advertisement APIs of the same online advertisement can be analyzed, online advertisement APIs needing to be optimized are screened according to the flow difference value and the fluctuation value of the flow difference value of the interdependent online advertisement APIs, positioning accuracy and scientificity of online advertisement API optimization requirements can be improved, and efficiency of online advertisement API optimization is improved.
In some scenes, the online advertisement API optimization method based on the traffic statistics can be applied to optimization of online advertisement APIs, and can relate to optimization of online advertisement APIs of advertisement display, advertisement clicking, advertisement conversion and the like, so that the optimization efficiency and scientificity of the online advertisement APIs are improved.
The following describes an online advertisement API optimization method based on traffic statistics in detail with reference to a specific example.
Fig. 1 is a flow chart of an online advertisement API optimization method based on traffic statistics according to an embodiment of the present application, and as shown in fig. 1, the method includes S110 to S130, and S110 to S130 are specifically described below.
S110, acquiring the flow of the first online advertisement API in the first time period and the second time period respectively, and acquiring the flow of the second online advertisement API in the first time period and the second time period, wherein the first online advertisement API and the second online advertisement API belong to the same online advertisement, and the second online advertisement API depends on the execution of the first online advertisement API.
Fig. 2 is a schematic diagram of an operation process of an online advertisement API optimization method based on traffic statistics, where, as shown in fig. 2, the method in the embodiment of the present application may first obtain traffic of a first online advertisement API in a first period and a second period, respectively, and obtain traffic of a second online advertisement API in the first period and the second period, where the traffic of the first online advertisement API in the first period and the second period represents access amounts of successful accesses of the first online advertisement API in the first period and the second period, and the traffic of the second online advertisement API in the first period and the second period represents access amounts of successful accesses of the second online advertisement API in the first period and the second period.
In operation, the first online advertisement API and the second online advertisement API can be analyzed according to the access amount of successful access of the first online advertisement API and the second online advertisement API in the first time period and the second time period, so that the optimization decision of the first online advertisement API is realized.
When the first online advertisement API and the second online advertisement API are applied, the first online advertisement API and the second online advertisement API can belong to the same online advertisement, and the second online advertisement API is an online advertisement API which depends on the first online advertisement API, so that the states of the first online advertisement API and the second online advertisement API can be analyzed according to the dependence relationship of the first online advertisement API and the second online advertisement API.
It should be noted that, the second online advertisement API is an online advertisement API that depends on the first online advertisement API, the first online advertisement API and the second online advertisement API may be adjacent in execution sequence, and the first online advertisement API and the second online advertisement API may not be adjacent in execution sequence, and only the second online advertisement API is required to be an online advertisement API that is requested and called after the first online advertisement API in the service flow.
The first online advertisement API and the second online advertisement API may be an advertisement showing API and an advertisement clicking API of the same online advertisement, where the advertisement showing API is used to show the advertisement, and the advertisement clicking API is used to respond to the click sending data of the user according to the advertisement content displayed by the advertisement showing API, and the traffic of the advertisement clicking API reflects the effect of the advertisement showing API to a certain extent, so that guidance may be provided to the optimization decision of the advertisement showing API according to the traffic of the advertisement clicking API.
Illustratively, the first and second time periods may be 2 time periods that are continuous in time, and the first and second time periods may also be 2 time periods that are discontinuous in time.
Illustratively, the first period may be a period of greater website access and greater online advertisement presentation, and the second period may be a period of lesser website access and lesser online advertisement presentation.
For example, the first time period may be 9:00 to 12:00 in the afternoon time period.
For example, the second time period may be 14:00 to 17:00 in the afternoon time period.
For example, the traffic may be measured by the number of requests, and the traffic of the first online advertising API during the first time period and the second time period, respectively, may be the number of requests of the first online advertising API during the first time period and the second time period, respectively. Similarly, the traffic of the second online advertising API over the first period of time may be the number of requests of the second online advertising API over the first period of time, respectively, over the second period of time.
S120, determining a first flow difference value of the flows 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 flows of the first online advertisement API and the second online advertisement API in the second time period, and determining the difference value of the first flow difference value and the second flow difference value as a flow difference fluctuation value.
In evaluating the optimization requirements of the first online advertising API and the second online advertising API, a first traffic difference value may be first calculated that determines traffic of the first online advertising API and the second online advertising API over a first period of time, the first traffic difference value characterizing a difference in traffic of successful accesses of the first online advertising API and the second online advertising API over the first period of time.
In evaluating the optimization requirements of the first online advertising API and the second online advertising API, a second traffic difference value that determines traffic of the first online advertising API and the second online advertising API over a second time period, the second traffic difference value characterizing a difference in traffic of successful accesses of the first online advertising API and the second online advertising API over the second time period, may also be calculated.
When the optimization requirements of the first online advertisement API and the second online advertisement API are evaluated, the difference value of the first flow difference value and the second flow difference value can be calculated and determined to be a flow difference fluctuation value, the flow difference fluctuation value characterizes the fluctuation amount of the flow of the first online advertisement API and the second online advertisement API from the first time period to the second time period, and further the optimization requirements of the first online advertisement API and the second online advertisement API can be evaluated through the fluctuation of the flow of the first online advertisement API and the second online advertisement API in different time periods.
And S130, optimizing the first online advertisement API when the first flow difference value and the second flow difference value are both larger than or equal to the preset flow difference value and the flow difference value fluctuation value is smaller than the preset flow difference value fluctuation value.
When the optimization requirement of the online advertisement API is evaluated, when the first flow difference value and the second flow difference value are both larger than the preset flow difference value, the flow difference values of the first online advertisement API and the second online advertisement API in different time periods are larger, and the number of times of requesting the second online advertisement API is possibly too small due to the fact that the first online advertisement API.
When the optimization requirement of the online advertisement API is evaluated, when the fluctuation value of the flow difference value is smaller than the preset fluctuation value of the flow difference value, the fact that the fluctuation of the flow difference value of the first online advertisement API and the second online advertisement API in different time periods is smaller is indicated, the influence of the description time on the flow difference value of the first online advertisement API and the second online advertisement API is smaller, the flow difference values of the first online advertisement API and the second online advertisement API all show the difference value of regularity in different time periods, and at the moment, the fact that the request times of the second online advertisement API possibly caused by the first online advertisement API are too small can be judged.
The first online advertising API may be optimized after determining that the number of requests of the second online advertising API is too small, which may be due to the first online advertising API.
The implementation method has the beneficial effects that when the second online advertisement API depends on the first online advertisement API, the flow difference between the first online advertisement API and the second online advertisement API in different time periods is larger, and when the influence of time on the flow difference between the first online advertisement API and the second online advertisement API is smaller, the first online advertisement API is judged to cause the too few request times of the second online advertisement API, and then the first online advertisement API is optimized, so that the accurate positioning of the optimization requirement of the online advertisement API is realized, and the monitoring effect of the online advertisement API is improved.
In some implementations, the method further includes obtaining categories of all online advertisement APIs of the same online advertisement, taking an online advertisement API with a category of a preset category as a first online advertisement API, and taking an online advertisement API dependent on the first online advertisement API as a second online advertisement API. The preset categories comprise an advertisement display category, an advertisement click category and an advertisement conversion category.
When the online advertisement API to be optimized is identified, the categories of all online advertisement APIs of the same online advertisement can be obtained first, and the online advertisement APIs to be optimized are identified according to the categories of the online advertisement APIs.
When the online advertisement API needing to be optimized is identified, the online advertisement API with the category being the preset category can be used as a first online advertisement API, the preset category comprises an advertisement display category, an advertisement click category and an advertisement conversion category, because the online advertisement API for processing the advertisement display requirement, the advertisement click requirement and the advertisement display requirement is generally used as a basic API to influence the execution of other online advertisement APIs, when the online advertisement API for processing the advertisement display requirement, the advertisement click requirement and the advertisement display requirement is insufficient in optimization, the execution of the online advertisement API which is dependent on the online advertisement API can be directly influenced, so the online advertisement API which is dependent on the first online advertisement API can be used as a second online advertisement API, and the optimization requirement of the first online advertisement API can be evaluated through the flow difference value of the first online advertisement API and the second online advertisement API.
Illustratively, when the first online advertisement API is an online advertisement API that handles advertisement exposure requirements, the second online advertisement API may be TARGETING API for user-targeting.
The online advertisement API for processing the advertisement display requirement, the advertisement click requirement and the advertisement display requirement is generally used as a basic API to influence the execution process of other online advertisement APIs depending on the basic API, the online advertisement APIs of the advertisement display category, the advertisement click category and the advertisement conversion category are used as the first online advertisement APIs, and the optimization decision is carried out on the first online advertisement APIs through the flow difference value of the first online advertisement APIs and the second online advertisement APIs, so that the accuracy and the scientificity for evaluating the optimization requirement of the first online advertisement APIs are improved.
In some implementations, the method further includes obtaining a category of the second online advertising API, and determining a preset traffic difference value 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.
When the preset flow difference value and the preset flow difference value fluctuation value are determined, the category of the second online advertisement API can be obtained, the category of the first online advertisement API is related to the flow of the first online advertisement API, the category of the second online advertisement API is related to the flow of the second online advertisement API, and then the preset flow difference value and the preset flow difference value fluctuation value can be determined according to the category of the first online advertisement API and the category of the second online advertisement API.
For example, when the category of the first online advertisement API is the advertisement exposure category and the category of the second online advertisement API is the advertisement click category, the number of requests of the first online advertisement API and the number of requests of the second online advertisement API may be not much different when the number of requests of the online advertisement API is measured, so that the number of requests of the first online advertisement API and the number of requests of the second online advertisement API are on an order of magnitude, further, the preset flow difference value may be determined to be 50% of the first online advertisement API flow according to the category of the first online advertisement API and the category of the second online advertisement API, and the preset flow difference fluctuation value may be determined to be 30% of the first online advertisement API flow according to the category of the first online advertisement API and the category of the second online advertisement API.
In an exemplary embodiment, when the category of the first online advertisement API is a video advertisement playing category and the category of the second online advertisement API is an effect measurement category, the first online advertisement API requests an advertisement when the video advertisement starts playing, so that the number of requests of the first online advertisement API is smaller, and the second online advertisement API is responsible for measuring advertisement effects, such as time and clicks of the user watching the advertisement, and the number of requests of the second online advertisement API is higher. When the traffic of the online advertisement 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 advertisement API and the number of requests of the second online advertisement API, so that the traffic of the first online advertisement API and the traffic of the second online advertisement API are not in one order of magnitude, further, the preset traffic difference value may be determined to be 300% of the traffic of the first online advertisement API according to the type of the first online advertisement API and the type of the second online advertisement API, and the preset traffic difference fluctuation value may be determined to be 200% of the traffic of the first online advertisement API according to the type of the first online advertisement API and the type of the second online advertisement API.
The implementation method has the advantages that the preset flow difference value and the preset flow difference value fluctuation value are determined according to the category of the first online advertisement API and the category of the second online advertisement API, the evaluation standard of the online advertisement API optimization requirement can be scientifically determined on the influence of the category of the first online advertisement API and the category of the second online advertisement API on the flow of the online advertisement API, and the accuracy and the scientificity of the online advertisement API optimization requirement evaluation are improved.
Fig. 3 is a flow chart of another online advertisement API optimization method based on traffic statistics according to an embodiment of the present application, as shown in fig. 3, the method further includes S210 to S220, and S210 to S220 are specifically described below.
S210, obtaining the error rate and the dependent number of the advertisement API on the first line.
When planning the optimization strategy of the first online advertisement API, the error rate and the dependence number of the first online advertisement API can be obtained, and the optimization strategy of the first online advertisement API is planned according to the error rate and the dependence number of the first online advertisement API.
Illustratively, the error rate of the first online advertising API may be calculated by the total number of API requests issued during the first period of time and the number of API requests returning errors during the first period of time, error rate = total number of requests/number of error requests.
Illustratively, the number of dependencies of the first online advertisement API may be a number of online advertisement APIs that depend on the first online advertisement API, the number of online advertisement APIs that depend on the first online advertisement API may be more than 1, and the optimization strategy of the first online advertisement API may be adjusted by the number of online advertisement APIs that depend on the first online advertisement API.
S220, when the traffic of the first online advertisement API is greater than or equal to the preset traffic, the error rate of the first online advertisement API is greater than or equal to the preset error rate, and the dependent number of the first online advertisement API is greater than or equal to the preset number, splitting the first online advertisement API into a plurality of online sub-line advertisement APIs, wherein the plurality of online sub-line advertisement APIs correspond to requests for processing different online sub-line advertisement APIs dependent on the first online advertisement API.
When the optimization strategy of the first online advertisement API is planned, when the flow of the first online advertisement API is larger than or equal to the preset flow and the error rate of the first online advertisement API is larger than or equal to the preset error rate, the flow of the first online advertisement API is larger, and the error rate of the first online advertisement API is higher, so that the optimization requirement on the first online advertisement API is met.
When the first online advertisement API has the optimized requirement, and the dependent number of the first online advertisement API is larger than or equal to the preset number, the online advertisement API which depends on the first online advertisement API is larger, and then the first online advertisement API can be split into a plurality of sub-online advertisement APIs, and the plurality of sub-online advertisement APIs correspond to the request for processing different sub-online advertisement APIs which depend on the first online advertisement API, so that the optimization of the first online advertisement API can meet the request of different sub-online advertisement APIs which depend on the first online advertisement API before, and the error rate of the online advertisement API can be reduced and the requirement of large flow of the first online advertisement API can be met by splitting the first online advertisement API into the plurality of sub-online advertisement APIs.
The implementation method has the beneficial effects that when the flow rate, the error rate and the dependence number of the first online advertisement API are larger, the first online advertisement API is split, so that the optimization of the first online advertisement API can adapt to the large flow characteristic of the first online advertisement API, the problems of larger error rate and larger dependence number can be solved, and the actual working effect of the first online advertisement API is improved.
In some implementations, the method further includes optimizing performance of the first on-line advertisement API when the traffic of the first on-line advertisement API is greater than a preset traffic, the error rate of the first on-line advertisement API is less than a preset error rate, and the lateral expansion index of the first on-line advertisement API is greater than or equal to a preset lateral expansion index. The lateral expansion index is used for representing the lateral expandability of the online advertisement API. When the flow of the first online advertisement API is larger than the preset flow, the error rate of the first online advertisement API is smaller than the preset error rate, and the transverse expansion index of the first online advertisement API is smaller than the preset transverse expansion index, the first online advertisement API is split into a plurality of sub-online advertisement APIs.
When the optimization strategy of the first online advertisement API is planned, when the flow of the first online advertisement API is larger than the preset flow and the error rate of the first online advertisement API is smaller than the preset error rate, the first online advertisement API is indicated to have larger flow and smaller error rate, and the first online advertisement API is in a better working state.
When the first online advertisement API is in a better working state and the optimization of the first online advertisement API is judged by the methods in S110 to S130, when the lateral expansion index of the first online advertisement API is larger than or equal to the preset lateral expansion index, the lateral expansion index is used for representing the lateral expandability of the online advertisement API, which indicates that the lateral expandability of the first online advertisement API is better, at the moment, the performance of the first online advertisement API can be optimized only, and the requirement that the optimization of the first online advertisement API is needed is met by judging by the methods in S110 to S130.
It should be noted that the lateral extensibility (Horizontal Scalability) of the first online advertisement API refers to the ability of the server system to handle larger requests of the first online advertisement API by adding more machines or nodes without significantly affecting the performance or service disruption of the first online advertisement API in the server, as the traffic scale of the online advertisement increases, by which the ability of the first online advertisement API to add more instances (e.g., servers) in the distributed system to share the load can be characterized.
When the first online advertisement API is in a better working state and the optimization of the first online advertisement API is required by the method in the steps of S110 to S130, when the lateral expansion index of the first online advertisement API is smaller than the preset lateral expansion index, the lateral expansion of the first online advertisement API is poor, and the first online advertisement API can be split into a plurality of sub-online advertisement APIs at the moment, so that the influence of the first online advertisement API on the second online advertisement API after the first online advertisement API is split is reduced, and the performances of the first online advertisement API and the second online advertisement API are improved.
The implementation method has the advantages that when the flow of the first online advertisement API is large, the error rate is small and the lateral expansibility is large, the performance of the first online advertisement API can be optimized only, the optimization requirement on the first online advertisement API can be met, the influence of the first online advertisement API on the second online advertisement API is reduced, and the performance of the whole online advertisement API is improved.
The implementation method has the advantages that when the flow of the first online advertisement API is large, the error rate is small and the lateral expansibility is small, the first online advertisement API can be split into a plurality of sub-online advertisement APIs, so that the influence of the first online advertisement API on the second online advertisement API is reduced, and the performances of the first online advertisement API and the second online advertisement API are improved.
In some implementations, the method further includes obtaining traffic of a plurality of second online advertising APIs that are dependent on the first online advertising APIs, and determining a traffic average of the plurality of second online advertising APIs as a second traffic average, and optimizing a target online advertising API of the plurality of second online advertising APIs when the traffic of the target online advertising API is lower than the second traffic average.
When there are multiple second online advertising APIs that rely on the first online advertising API, the optimization strategy of the second online advertising API may be planned at the same time as the optimization strategy of the first online advertising API.
In operation, the flow of a plurality of second online advertisement APIs depending on the first online advertisement APIs can be obtained, and the flow average value of the plurality of second online advertisement APIs is determined as the second flow average value, so that the second online advertisement APIs which need to be optimized in the plurality of second online advertisement APIs can be identified according to the flow of the plurality of second online advertisement APIs.
When the flow of the target online advertisement API in the plurality of second online advertisement APIs is lower than the second flow average value, the condition that the flow of the target online advertisement API is lower may cause poor performance of the target online advertisement API due to insufficient performance of the target online advertisement API, so that optimization of the target online advertisement API is performed.
The implementation method has the advantages that when a plurality of second online advertisement APIs which depend on the first online advertisement APIs exist, the performance of the second online advertisement APIs is evaluated, so that the target online advertisement APIs which need to be optimized in the second online advertisement APIs are identified, and the second online advertisement APIs which are related to the first online advertisement APIs are optimized.
In some implementations, the method further includes optimizing an online advertisement API other than the first online advertisement API on which the target online advertisement API depends when traffic of the target online advertisement API is below a second traffic average among the plurality of second online advertisement APIs.
In operation, when the traffic of the target online advertisement API in the plurality of second online advertisement APIs is lower than the second traffic average value, it is indicated that the target online advertisement API may affect performance due to the online advertisement API other than the first online advertisement API that the target online advertisement API depends on, and thus the online advertisement API other than the first online advertisement API that the target online advertisement API depends on may be optimized.
The implementation method has the beneficial effects that when the flow of the on-line advertisement API of the target is lower than the flow average value of a plurality of second on-line advertisement APIs depending on the first on-line advertisement API, the on-line advertisement APIs except the first on-line advertisement APIs depending on the target on-line advertisement APIs are optimized, the on-line advertisement APIs needing to be optimized can be accurately positioned, and the optimization effect of the on-line advertisement APIs is improved.
The embodiment of the application also provides an online advertisement API optimizing device based on the traffic statistics, which comprises a unit for executing the method of any one of the above.
Fig. 4 is a schematic logic structure diagram of an online advertisement API optimizing device according to an embodiment of the present application, as shown in fig. 4, where the device 1 of the embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13, the processing unit 11 is configured to process data, the storage unit 12 is configured to store data, the transceiver unit 13 is configured to transmit and receive data, and the processing unit 11, the storage unit 12, and the transceiver unit 13 are mutually matched to implement the above method. The beneficial effects brought by the embodiment of the present application have been described in the above method, and are not described here again.
The embodiment of the application also provides an online advertisement API optimizing device based on flow statistics, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the method according to any one of the above when executing the computer program.
Fig. 5 is a schematic physical structure diagram of an online advertising API optimizing apparatus based on traffic statistics according to an embodiment of the present application, as shown in fig. 5, where the apparatus 2 of this embodiment includes at least one process 20 (only one processor 20 is shown in fig. 5), a memory 21, and a computer program 22 stored in the memory 21 and capable of running on the at least one processor 20, and the steps in any of the foregoing method embodiments are implemented when the processor 20 executes the computer program 22. The beneficial effects brought by the embodiment of the present application have been described in the above method, and are not described here again.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the various method embodiments described above.
Embodiments of the present application provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least any entity or device capable of carrying computer program code to a camera device/terminal equipment, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.
The foregoing embodiments are merely illustrative of the technical solutions of the present application, and not restrictive, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that modifications may still be made to the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. An online advertisement API optimization method based on traffic statistics, the method comprising:
acquiring the flow of a first online advertisement API in a first time period and a second time period respectively, and acquiring the flow of a second online advertisement API in the first time period and the second time period, wherein the first online advertisement API and the second online advertisement API belong to the same online advertisement, and the second online advertisement API depends on the execution of the first online advertisement API;
determining a first flow difference value of the flows of the first online advertisement API and the second online advertisement API in a first time period, determining a second flow difference value of the flows of the first online advertisement API and the second online advertisement API in a second time period, and determining the difference value of the first flow difference value and the second flow difference value as a flow difference fluctuation value;
And when the first flow difference value and the second flow difference value are both larger than or equal to the preset flow difference value and the flow difference fluctuation value is smaller than the preset flow difference value fluctuation value, optimizing the advertisement API on the first line.
2. The method of claim 1, wherein the method further comprises:
The method comprises the steps of obtaining the categories of all online advertisement APIs of the same online advertisement, taking the online advertisement APIs with the categories of preset categories as a first online advertisement API, and taking the online advertisement APIs depending on the first online advertisement API as a second online advertisement API. The preset categories comprise an advertisement display category, an advertisement click category and an advertisement conversion category.
3. The method of claim 2, wherein the method further comprises:
And obtaining the category of the second online advertisement API, and determining a preset flow difference value and a preset flow difference fluctuation value according to the category of the first online advertisement API and the category of the second online advertisement API.
4. A method as claimed in claim 3, wherein the method further comprises:
Acquiring the error rate and the dependence number of the advertisement API on the first line;
When the traffic of the first on-line advertisement API is greater than or equal to the preset traffic, the error rate of the first on-line advertisement API is greater than or equal to the preset error rate, and the dependent number of the first on-line advertisement API is greater than or equal to the preset number, splitting the first on-line advertisement API into a plurality of sub-line advertisement APIs, the plurality of sub-line advertisement APIs corresponding to processing requests of different sub-line advertisement APIs dependent on the first on-line advertisement API.
5. The method of claim 4, wherein the method further comprises:
optimizing the performance of the first on-line advertisement API when the flow of the first on-line advertisement API is greater than the preset flow, the error rate of the first on-line advertisement API is less than the preset error rate, and the lateral expansion index of the first on-line advertisement API is greater than or equal to the preset lateral expansion index, wherein the lateral expansion index is used for characterizing the lateral expandability of the on-line advertisement API, and splitting the first on-line advertisement API into a plurality of sub-line advertisement APIs when the flow of the first on-line advertisement API is greater than the preset flow, the error rate of the first on-line advertisement API is less than the preset error rate, and the lateral expansion index of the first on-line advertisement API is less than the preset lateral expansion index.
6. The method of claim 5, wherein the method further comprises:
And obtaining the flow of a plurality of second online advertisement APIs depending on the first online advertisement APIs, determining the flow average value of the plurality of second online advertisement APIs as a second flow average value, and optimizing the target online advertisement APIs when the flow of the target online advertisement APIs in the plurality of second online advertisement APIs is lower than the second flow average value.
7. An online advertising API optimizing device based on traffic statistics, characterized by comprising means for performing the method of any of claims 1 to 6.
8. An on-line advertising API optimizing 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 the processor implements the method according to any one of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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