Request processing method and device, equipment, medium and product

Abstract

The invention discloses a request processing method and device, equipment, a medium and a product in the technical field of the Internet, and the method comprises the steps that an SDK in a client receives a first request, and the first request indicates to obtain a target object; the SDK identifies the type of the first request; if the type of the first request is a first type, the SDK reads the target object from a cache pool; and if the type of the first request is a second type, the SDK downloads the target object from the server, so that the SDK can dynamically decide to use the object downloaded in real time and the object read from the cache pool for replying according to the request, thereby overcoming the defect caused by replying the request only through the mode of downloading the object in real time, and improving the user experience. And the cached objects can be used more reasonably, so that the functions of the cached objects can be played as much as possible.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a request processing method, apparatus, device, medium, and product. Background Art

[0002] For some scenarios (such as scenarios with advertisement display requirements), these scenarios have the following requirements: after an object request is triggered, the object (such as an advertisement) is obtained for display.

[0003] In some cases, the above objects can be retrieved by sending real-time requests to the server. However, this method takes a long time to retrieve, resulting in a high delay in displaying the object, which affects the user experience. In addition, this method may result in no object being returned due to network or other factors (such as policy, device performance, etc.), thus wasting the opportunity to display the object. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a request processing method, device, equipment, medium, and product.

[0005] In order to achieve the above objectives, the technical solutions provided by this application are as follows:

[0006] The present application provides a request processing method, which is applied to a software development kit (SDK), and the SDK is deployed on a client. The method includes: receiving a first request, where the first request indicates to obtain a target object; identifying the type of the first request; in response to the type of the first request being the first type, reading the target object from a cache pool; and in response to the type of the first request being the second type, downloading the target object from a server.

[0007] In one possible implementation, the process of identifying the type of the first request includes: obtaining at least one piece of information, the at least one piece of information including device performance, network status, request interval, request frequency, and part or all of the impact representation data of a historical object, where the display timing of the historical object is earlier than the triggering timing of the first request; determining a scoring result of the first request based on the at least one piece of information; and determining the type of the first request based on the scoring result.

[0008] In one possible implementation, determining the type of the first request based on the scoring result includes: in response to the scoring result not exceeding a threshold value, determining the first type as the type of the first request; and in response to the scoring result exceeding the threshold value, determining the second type as the type of the first request.

[0009] In one possible implementation, the type of the first request is determined based on the scoring result and the cutoff value corresponding to the first request; the process of obtaining the cutoff value corresponding to the first request includes: in response to the existence of a reference time period corresponding to the first request, determining the cutoff value corresponding to the first request based on the impact characterization data of the cutoff value used in the reference time period, the reference time period being earlier than the triggering timing of the first request; in response to the absence of the reference time period corresponding to the first request, determining the cutoff value corresponding to the first request based on a pre-set cutoff value.

[0010] In one possible implementation, determining the demarcation value corresponding to the first request based on the impact characterization data of the demarcation value used within the reference time period includes: in response to using multiple demarcation values ​​within the reference time period, selecting a target value from the multiple demarcation values ​​based on the impact characterization data of each demarcation value among the multiple demarcation values, the impact characterization data of the target value being not lower than the impact characterization data of each demarcation value among the multiple demarcation values; determining the target value as the central value corresponding to the first request; and determining the demarcation value corresponding to the first request based on the central value corresponding to the first request.

[0011] In one possible implementation, before reading the target object from the cache pool, the method further includes: respectively correcting the impact characterization data of each object in the cache pool to obtain a correction result for each object in the cache pool; and screening the target object from the cache pool based on the correction result of each object in the cache pool, wherein the correction result of the target object is not lower than the correction results of each object in the cache pool.

[0012] In a possible implementation manner, the target object is an advertisement.

[0013] The present application provides a request processing device, including: a receiving unit, used to receive a first request, wherein the first request indicates to obtain a target object; an identification unit, used to identify the type of the first request; a reading unit, used to read the target object from a cache pool in response to the type of the first request being a first type; and a downloading unit, used to download the target object from a server in response to the type of the first request being a second type.

[0014] The present application provides an electronic device, comprising: a processor and a memory; the memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory, so that the electronic device executes the request processing method provided in the present application.

[0015] The present application provides a computer-readable medium, wherein the computer-readable medium stores instructions or computer programs. When the instructions or computer programs are executed on a device, the device executes the request processing method provided in the present application.

[0016] The present application provides a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the request processing method provided by the present application.

[0017] Compared with the related art, this application has at least the following advantages:

[0018] In the technical solution provided by the present application, for the SDK deployed on the client, the working principle of the SDK includes: first receiving a first request, which indicates to obtain a target object (such as an advertisement); then identifying the type of the first request; if the type of the first request is the first type (such as the "cache" type), the target object is read from the cache pool; if the type of the first request is the second type (such as the "real-time" type), the target object is downloaded from the server, so as to use the SDK to dynamically decide whether to use the real-time downloaded object and the object read from the cache pool to respond to the request. This not only overcomes the defects caused by responding to requests only by downloading objects in real time, but also enables more reasonable use of cached objects to maximize the role of cached objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a request processing method provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a request processing flow provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of an intelligent decision-making module provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of an intelligent decision-making process provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the structure of a request processing device provided in an embodiment of the present application;

[0025] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] After research, it was found that in some scenarios, the defects of the real-time loading method (such as the method of "sending a request to the server in real time" as shown in the background technology section) can be overcome by means of a caching method. Among them, the caching method may include: after caching some objects (such as advertisements) into the memory by preloading, if it is detected that an object request is triggered, a cached object is read from the memory for display. In addition, because the caching method obtains objects by reading data from the cache, the object acquisition process implemented based on the caching method does not involve the data loading process, so that the object acquisition process implemented based on the caching method does not involve data communication between different ends, so as to overcome the defects of the object acquisition process implemented based on data communication between different ends (such as the real-time loading method). It can be seen that the object acquisition process implemented based on the caching method has the following advantages: shortening the overall time consumption of object display and improving the speed of object display, which not only improves the smoothness of object display, but also avoids the waste of object display opportunities due to object loading timeout.

[0027] Research has also found that the object acquisition process implemented based on the caching method has the following defects: because the cached objects have a lag compared to the real-time loaded objects, the cached objects may have a deviation from the user's immediate interest, which causes the cached objects to have the following two defects: on the one hand, the conversion effect of the cached objects is theoretically inferior to the conversion effect of the real-time loaded objects; on the other hand, the display feedback of the cached objects (such as advertising value) is obviously overestimated, which results in the object acquisition process implemented by reading the cached objects showing a higher object billing ratio (such as advertising billing ratio).

[0028] The research also found that the object acquisition process achieved by real-time loading has the defects of being time-consuming and lossy in object display, and the object acquisition process achieved by caching has the defect of object billing ratio, which results in that neither of these two methods is the optimal solution for the object acquisition process. Based on this, the present application also provides an optimization scheme, and the optimization scheme includes: after caching some objects into memory by preloading, if it is detected that an object request (such as an advertisement request) is triggered, a request is first sent to the server in order to obtain a real-time loaded object, so that when the object cannot be loaded from the server in real time, a cached object is read from the memory for display, thereby realizing an object acquisition process based on real-time priority and cache backup.

[0029] The research also found that the object acquisition process based on real-time priority and cache backup has at least the following defects: it cannot maximize the role of cached objects, so that the global benefits of this method are not maximized, which makes this method not the optimal solution.

[0030] Based on the above research, in order to solve the above problems, the present application provides a request processing method for an SDK deployed on a client, the method including: first receiving a first request, the first request indicating the acquisition of a target object (such as an advertisement); then identifying the type of the first request; if the type of the first request is the first type (such as the "cache" type), the target object is read from the cache pool; if the type of the first request is the second type (such as the "real-time" type), the target object is downloaded from the server, so as to use the SDK to dynamically decide whether to use the real-time downloaded object and the object read from the cache pool to respond to the request, thereby overcoming the defects of the above three object acquisition processes, so that the cached objects can be used more reasonably to maximize the role of the cached objects as much as possible, which is conducive to maximizing the global benefits.

[0031] It can be seen that the SDK in this application makes an intelligent decision in the request dimension whether to use the real-time loaded object or the cached object, so that the SDK in this application can intelligently decide when to use the cached object, so that this application can present the following effects: if it is detected that the network environment is good, the device performance is high, and the object can be downloaded from the server with a high probability, then the real-time loaded object is obtained for display to avoid time-consuming and loss of display opportunity problems; if it is detected that the network environment is not good, the device performance is not high, the object cannot be downloaded from the server with a high probability, or the display feedback of the real-time loaded object is inferior to the display feedback effect of the cached object, then the cached object is read for display to maximize the role of the cached object.

[0032] In addition, this application does not limit the execution subject of the request processing method. For example, the method can be applied to a terminal device. For another example, the method can also be implemented by means of a data interaction process between a terminal device and a server. The terminal device can be a smartphone, a computer, a personal digital assistant (PDA), a tablet computer, etc. The server can be a standalone server, a cluster server, or a cloud server.

[0033] It is understandable that before using the technical solutions disclosed in the various embodiments of the present disclosure, the type, scope of use, usage scenarios, etc. of the information involved in the present disclosure should be informed to relevant users and authorization should be obtained from relevant users in an appropriate manner in accordance with relevant laws and regulations. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.

[0034] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the information of the relevant user, so that the relevant user can independently choose whether to provide information to the software or hardware such as the electronic device, application, server or storage medium that executes the operation of the technical solution of the present disclosure based on the prompt message.

[0035] As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, a prompt message may be sent to the relevant user in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "agree" or "disagree" to provide information to the electronic device.

[0036] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0037] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0038] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0039] In order to better understand the technical solution provided by this application, the request processing method provided by this application is described below with reference to some drawings. Figure 1 As shown, the request processing method for the SDK deployed on the client provided in the embodiment of the present application includes the following S1-S4.

[0040] S1: The SDK receives a first request, where the first request instructs to obtain a target object.

[0041] The SDK is deployed on the client and can make intelligent decisions based on the request dimension. This helps achieve intelligent decision-making without adjusting the application (APP) itself, thereby improving request processing performance. It should be noted that the APP is also deployed on the client.

[0042] The first request refers to the current scene (such as Figure 2 The SDK passively receives a request in the advertising scenario shown in FIG. 1 , so that the SDK needs to decide whether to use a real-time loaded object or a cached object for display based on the request.

[0043] It can be seen that in one possible implementation, when an APP with object display function (such as Figure 2 When an application uses an advertising scenario shown in the figure) and an SDK with intelligent decision-making capabilities, the first request may refer to a request triggered by the APP (such as a request triggered based on a user's operation on the APP, or a request automatically triggered by the APP, etc.), so that the request can convey to the SDK the acquisition of an object that can be displayed on the APP (such as an advertisement), so that the APP can subsequently obtain the object (such as a real-time loaded object or a cached object) through the SDK for display.

[0044] In addition, the first request may indicate obtaining a target object. The target object is the object in the current scene (such as Figure 2 The target object is an object that needs to be displayed in the advertising scene shown in the figure; and this application does not limit the target object. For example, if the current scene is an advertising scene, the target object can be an advertisement to meet the object display requirements in the advertising scene. For another example, if the current scene is a news scene, the target object can be news to meet the object display requirements in the news scene. For another example, if the current scene is a short video scene, the target object can be a short video to meet the object display requirements in the short video scene.

[0045] Based on the relevant content of S1 above, it can be known that in some scenarios, for the client, if an APP and an SDK are deployed on the client, the first request (such as an advertising request) is first triggered through the APP; then the APP sends the first request to the SDK, so that the SDK can subsequently intelligently decide how to obtain the feedback data of the first request. This is conducive to achieving intelligent decision-making without adjusting the APP itself, thereby simplifying optimization costs.

[0046] S2: The SDK identifies the type of the first request.

[0047] Among them, the type of the first request can indicate the characteristics of the request, so that the type can indicate the most applicable processing method for the request to a certain extent, and thus the type can indicate the most applicable acquisition method for the feedback data of the request to a certain extent (such as real-time loading method or reading cache method, etc.).

[0048] In addition, this application does not limit the implementation method of the type of the first request. For example, the type of the first request can be the first type or the second type. The first type refers to the "cache" type, so that the first type can indicate that the feedback data of the request is most suitable for acquisition by using the read cache method. The second type refers to the "real-time" type, so that the second type can indicate that the feedback data of the request is most suitable for acquisition by using the real-time loading method.

[0049] In addition, the present application does not limit the method for obtaining the type of the above-mentioned first request. For example, it can be implemented by any method that can identify the type of the request, such as a pre-built script with the above-mentioned function, a pre-built rule with the above-mentioned function, or a pre-built machine learning model with the above-mentioned function.

[0050] S3: In response to the first request being of the first type, the SDK reads the target object from the cache pool.

[0051] Among them, the cache pool can store some objects that have been loaded but not displayed; and this application does not limit the implementation method of the cache pool. For example, it can adopt any cache pool, such as Figure 2 The cache pool shown is implemented.

[0052] In addition, this application does not limit the acquisition method of each object in the cache pool. For example, for any object in the cache pool, the acquisition process of the object may include: a second request automatically initiated by the SDK to the server (such as a preload request or Figure 2 The SDK then creates a new request (shown as a new request) so that after the SDK receives the object returned by the server in response to the second request, the SDK directly stores the object in the cache pool for future use. The second request refers to a request automatically triggered by the SDK. This indicates that the cache pool may contain some objects automatically requested by the SDK (such as preloaded objects).

[0053] After research, it was found that for some scenarios, there is a need for the following: there is a certain time difference between the object loading process and the object display process. Therefore, in order to better meet this need, for any object, after loading it in real time, the object is first stored in the cache pool (such as Figure 2The cache pool shown in FIG2 is used to read the object from the cache pool for display when the display conditions of the object are determined to be met. As can be seen, the cache pool records at least objects loaded in real time by the SDK in response to certain requests (such as passively triggered requests or actively triggered requests).

[0054] Research has also revealed that some scenarios require that the same object not be displayed repeatedly. To better meet this requirement, for any object that needs to be displayed to the user, it can be destroyed from the cache pool. This ensures that the cache pool only records undisplayed objects, not displayed objects, ensuring that the object is only displayed once. This indicates that the cache pool contains at least some undisplayed objects that were loaded in real time by the SDK in response to certain requests (such as passively triggered or actively triggered requests).

[0055] In addition, the present application does not limit the implementation of the above S3. For example, it may specifically be: in response to the first request being of the first type, a cached object is randomly read from the cache pool as the target object for display.

[0056] S4: In response to the first request being of the second type, the SDK downloads the target object from the server.

[0057] Among them, data communication can be carried out between the server and the SDK. In addition, this application does not limit the implementation method of the server. For example, the server can use any server, such as an independent server, a cluster server, a cloud server, or Figure 2 The server shown is implemented.

[0058] In addition, the present application does not limit the implementation method of the above-mentioned S3. For example, it can be specifically: in response to the type of the first request being the second type, the SDK sends the first request to the server, so that the server obtains the target object for the first request and feeds it back to the SDK, so that after the SDK receives the target object fed back by the server, the SDK feeds back the target object to the APP that triggered the first request, so that the APP can display the target object to the user.

[0059] It should be noted that this application does not limit the implementation method of the above-mentioned step of "the server obtains the target object for the first request". For example, it can be specifically: the server performs object recommendation processing on some candidate objects based on the relevant information of the user of the APP to obtain the target object that is most suitable for the user. Among them, the relevant information can describe some characteristics of the user; and this application does not limit the implementation method of the relevant information. For example, the relevant information can at least include some operations performed by the user in the APP, such as operations that have been performed during the current operation of the APP (such as browsing operations, collection operations, like operations, comment operations, etc.). For another example, the relevant information may include descriptive information of the user (such as login account, some attributes configured for the login account, etc.).

[0060] It should also be noted that this application does not limit the implementation method of the above-mentioned "object recommendation processing". For example, it can adopt any method that can achieve object recommendation, such as a pre-built script with the aforementioned function, a pre-built rule with the aforementioned function, or a pre-built machine learning model with the aforementioned function.

[0061] Based on the relevant contents of S1 to S4 above, it can be seen that the request processing scheme executed by the SDK provided in this application includes: first receiving a first request, which indicates to obtain a target object (such as an advertisement); then identifying the type of the first request; if the type of the first request is the first type (such as the "cache" type), then reading the target object from the cache pool; if the type of the first request is the second type (such as the "real-time" type), then downloading the target object from the server to dynamically decide whether to use the real-time downloaded object and the object read from the cache pool to respond to the request. This can overcome the defects of the above three object acquisition processes, so that the cached objects can be used more reasonably to maximize the role of the cached objects as much as possible, which is conducive to maximizing the global benefits.

[0062] In addition, in order to better improve the accuracy of intelligent decision-making, the present application also provides a possible implementation method of the above-mentioned S2. In this method, the S2 may specifically include the following steps 11 to 13.

[0063] Step 11: SDK obtains at least one piece of information (such as Figure 3 The at least one information includes device performance, network status, request interval, request frequency, and part or all of the impact representation data of the historical object, and the display timing of the historical object is earlier than the triggering timing of the first request.

[0064] Among them, the above-mentioned at least one information can describe some characteristics of the first request; and the at least one information may include one or more of the following information: device performance (such as the performance of the client itself, etc.), network status (such as the data loading performance presented by the SDK at the current moment), request interval (such as the time difference between two adjacent triggered advertising requests), request frequency (such as the number of times the advertising request is triggered within a unit time), and historical object impact representation data (such as display feedback of ads that have been displayed).

[0065] For the above-mentioned device performance, the device performance can describe the performance of the client on which the APP is deployed (such as computing performance, remaining resources, device model, etc.), and in particular can describe the performance presented by the client at the triggering moment of the first request; and the present application does not limit the implementation method of the device performance, for example, it can be implemented by any method that can obtain the device performance of the client. In addition, the present application does not limit the method of obtaining the device performance, for example, it can be: performing performance analysis on the client to obtain the device performance, so that the device performance can be provided to the server later, and the server scores the device performance to obtain a score value for the device performance, so that the score value can represent the performance presented by the client itself in a numerical way. In addition, the present application does not limit the effect of the device performance on the first request, for example, the score result of the first request can be positively correlated with the device performance, so that if the device performance is better, the score value of the device performance is higher, so that the score result of the first request determined based on the score value is also higher.

[0066] For the above-mentioned network status, the network status can describe the data communication performance between the SDK and the server at the time of triggering the first request, so that the network status can describe the data loading performance presented by the SDK at the time of triggering the first request; and the present application does not limit the implementation method of the network status. For example, the network status includes the network parameters (such as 2G, 5G, etc.) presented by the client at the time of triggering the first request. In addition, the present application does not limit the method for obtaining the network status. For example, it can be specifically: in response to triggering the first request, the network status of the client is analyzed to obtain the network status corresponding to the first request, so that the network status score table downloaded from the server can be used to determine the score value of the network status corresponding to the first request, so that the score value can represent the network status corresponding to the first request in a numerical way. In addition, the present application does not limit the impact of the network status on the first request. For example, the score result of the first request can be positively correlated with the network status corresponding to the first request, so that if the network status corresponding to the first request is better, the score value of the network status corresponding to the first request is higher, so that the score result of the first request determined based on the score value is also higher.

[0067] The request interval can describe the time difference between the triggering times of two consecutive requests of the same type (e.g., the first request), so that the request interval can describe the difference between the triggering time of the first request and the triggering time of the previous request corresponding to the first request, thereby enabling the request interval to describe the triggering time difference between the two requests, and further enabling the request interval to indicate, to a certain extent, whether such requests are frequently triggered in a short period of time. The triggering time of the previous request is earlier than the triggering time of the first request; and both the previous request and the first request are used to request the display of the same object (e.g., an advertisement).

[0068] In addition, this application does not limit the implementation method of the above request interval. In addition, this application does not limit the method for obtaining the request interval. For example, it can be specifically: in response to triggering the first request, requesting records of the target object stored in the client (such as Figure 3The advertising request record shown in the figure is used to perform request interval analysis to obtain the request interval corresponding to the first request, so that the request interval scoring table downloaded from the server can be used to determine the score value of the request interval corresponding to the first request later, so that the score value can numerically represent whether the first request is a request that is frequently triggered in a short period of time. In addition, the present application does not limit the effect of the request interval on the first request. For example, the score result of the first request can be positively correlated with the request interval, so that if the request interval corresponding to the first request is smaller, the score value of the request interval corresponding to the first request is lower, and thus the score result of the first request determined based on the score value is also lower.

[0069] For the above request frequency, the request frequency can describe the triggering frequency of the same request; and this application does not limit the implementation method of the request frequency. In addition, this application does not limit the method of obtaining the request frequency. For example, it can be specifically: in response to triggering the first request, request records (such as Figure 3 The advertising request record shown in the figure is used to perform request frequency analysis to obtain the request frequency corresponding to the first request, so that the request frequency scoring table downloaded from the server can be used to determine the score value of the request frequency corresponding to the first request later, so that the score value can numerically represent whether the first request is a high-frequency triggered request. In addition, the present application does not limit the effect of the request frequency on the first request. For example, the score result of the first request can be negatively correlated with the request frequency, so that if the request frequency corresponding to the first request is higher (such as the more requests are triggered continuously), the score value of the request frequency corresponding to the first request is lower, so that the score result of the first request determined based on the score value is also lower.

[0070] For the above-mentioned historical object, the historical object refers to an object (such as an advertisement) that has been displayed before the triggering moment of the first request; and the historical object belongs to the same type of object as the object requested by the first request. In addition, this application does not limit the method of obtaining the historical object. For example, it can be: in response to triggering the first request, according to the target object display record stored in the client (such as Figure 3 The advertisement display record shown is determined, so that the historical object can represent the object of the same type that has been displayed before the first request is triggered.

[0071] Among them, the impact characterization data of the above-mentioned historical object (such as advertising value or advertising display feedback, etc.) can represent the impact presented after the display of the historical object, so that the impact characterization data can indicate the value of the historical object, so that the historical object impact score table downloaded from the server can be used to determine the score value of the impact characterization data of the historical object corresponding to the first request. In addition, the present application does not limit the implementation method of the impact characterization data. For example, the impact characterization data can be implemented using any indicator that can describe the value of the object, such as Effective Cost Per Mille (ECPM). It can be seen that under one possible implementation method, the impact characterization data of the historical object can include the ECPM of the historical object. In addition, the present application does not limit the impact effect of the impact characterization data on the first request. For example, the score result of the first request can be positively correlated with the impact characterization data of the historical object, so that if the impact characterization data is lower, the score value of the impact characterization data of the historical object is lower, thereby making the score result of the first request determined based on the score value lower.

[0072] Step 12: The SDK determines the scoring result of the first request based on the at least one information (e.g. Figure 3 The rating value requested in or Figure 4 The scoring result can indicate the possibility that the first request belongs to various types.

[0073] It should be noted that the present application does not limit the implementation method of the above-mentioned step 12. For example, it can be specifically: first score each information in the above-mentioned at least one information to obtain a scoring value of each information; then calculate the scoring result of the first request based on the scoring value of each information, such as adding up the scoring values ​​of these information to obtain the scoring result of the first request, or using a pre-built machine learning model to conduct a comprehensive analysis of the scoring values ​​of these information to obtain the scoring result of the first request, etc.

[0074] Step 13: The SDK determines the type of the first request based on the scoring result of the first request (e.g. Figure 4 ad type shown).

[0075] It should be noted that this application does not limit the implementation of the above step 13. For example, it can be: searching for a type that matches the scoring result of the first request from a preset mapping relationship as the type of the first request. The mapping relationship can record the types corresponding to some score values.

[0076] Based on the above steps 11 to 13, it can be seen that for SDK (such as Figure 3 For the SDK shown in FIG, when the SDK receives the first request (such as Figure 3 After receiving the request triggered by the advertising scene), the SDK first obtains at least one information corresponding to the first request (such as Figure 3 Various information recorded by the rule module in the first request) so that these information can describe some characteristics of the first request (such as device performance, network status, etc.); and then determine the scoring result of the first request based on this information (such as Figure 3 Then, the type of the first request is determined based on the scoring result, so that the type can more accurately indicate how the feedback data of the first request is most suitable for obtaining, so that the subsequent method of processing the first request can be more accurately decided based on the type, so as to better achieve intelligent decision-making.

[0077] After research, it was found that the SDK needs to decide how to handle the first request from the two methods of real-time loading and reading the cache, so that the intelligent decision implemented by the SDK belongs to a binary classification decision. Therefore, in order to better improve the decision-making efficiency, this application also provides a possible implementation of the above step 13. In this way, the step 13 can be specifically as follows: in response to the first request, the scoring result does not exceed the cutoff value (such as Figure 3 The first type (such as the "cache" type) is determined as the type of the first request; in response to the score result of the first request exceeding the cutoff value, the second type (such as the "real-time" type) is determined as the type of the first request. In this way, the type of the first request can be accurately determined by a binary classification method based on the cutoff value. The cutoff value refers to the threshold value that distinguishes different types; and this application does not limit the cutoff value. For example, the cutoff value can be set according to the actual application scenario.

[0078] In addition, in order to better improve the accuracy of decision-making, the present application also provides a method for obtaining the above-mentioned boundary value. Under this method, when the type of the above-mentioned first request is determined based on the scoring result of the first request and the boundary value corresponding to the first request, the process of obtaining the boundary value corresponding to the first request may include: in response to the existence of a reference time period corresponding to the first request, determining the boundary value corresponding to the first request based on the influence characterization data of the boundary value used in the reference time period, and the reference time period is earlier than the triggering timing of the first request; in response to the absence of a reference time period corresponding to the first request, determining the boundary value corresponding to the first request based on a pre-set boundary value (such as the default value of 50). In this way, the boundary value can be obtained through dynamic optimization, thereby ensuring the accuracy of the boundary value, so as to better improve the accuracy of intelligent decision-making.

[0079] It should be noted that, for the reference time period corresponding to the first request, the reference time period is earlier than the triggering timing of the first request, so that the APP users appearing in the reference time period are divided into two parts. One part (such as 90% of APP users) performs intelligent decision-making according to the same boundary value (such as the central value corresponding to the reference time period), and the other part (such as 10% of APP users) is used to execute the experimental process for multiple boundary values ​​(such as the upper limit, central value, lower limit and other boundary values ​​corresponding to the reference time period). It is expected that the usage feedback data of each of the multiple boundary values ​​can be collected through the experimental process, so that the impact characterization data (such as ECPM) of each boundary value can be calculated separately based on the usage feedback data of each boundary value, and the boundary value corresponding to the first request can be determined based on the impact characterization data of these boundary values, so that the boundary value corresponding to the first request is more accurate.

[0080] It can be seen that, in one possible implementation, the process of determining the boundary value corresponding to the above-mentioned first request (such as the boundary value corresponding to the current time period) can include: in response to using multiple boundary values ​​within the reference time period (such as the previous time period) corresponding to the first request, selecting a target value from the multiple boundary values ​​based on the influence characterization data of each boundary value in the multiple boundary values, the influence characterization data of the target value being not lower than the influence characterization data of each boundary value in the multiple boundary values, so that the target value can represent the boundary value with the highest influence identified through the reference time period; determining the target value as the central value corresponding to the first request (such as the central value corresponding to the current time period); determining the boundary value corresponding to the first request (such as the upper limit, central value, lower limit and waiting boundary value corresponding to the current time period) based on the central value corresponding to the first request, so that the experimental process can be continued based on the boundary value corresponding to the first request to find the boundary value corresponding to the next time stage, so that the boundary value can be continuously optimized in an iterative manner to improve the decision accuracy.

[0081] Based on the above three paragraphs, it can be seen that the present application provides an iterative optimization scheme for the demarcation value, which may include: first determining a preset default value (such as 50) as the center value of the first round; then subtracting a preset offset (such as 10 or Figure 3N shown) to obtain the lower limit of the first round, and the central value of the first round plus the preset offset to obtain the upper limit of the first round; then, based on the lower limit of the first round, the central value of the first round, and the upper limit of the first round, multiple cutoff values ​​of the first round (such as 40, 50, and 60) are determined, so that within the time period corresponding to the first round (such as March 1, 2025), 90% of the APP users are set to perform intelligent decision-making according to the central value of the first round, and 10% of the APP users are set to be used to perform an experimental process for multiple cutoff values ​​of the first round (hereinafter referred to as "the first round experimental process"), such as "the 10% of the APP users are divided into three parts, one part performs intelligent decision-making according to the lower limit of the first round, another part performs intelligent decision-making according to the central value of the first round, and the remaining part performs intelligent decision-making according to the upper limit of the first round", so as to obtain the influence characterization data of each of the multiple cutoff values ​​of the first round through the first round experimental process;

[0082] Then, based on the impact characterization data of each of the multiple cut-off values ​​in the first round, the cut-off value with the highest impact (such as 60) is selected from the multiple cut-off values ​​in the first round as the center value of the second round; then, the preset offset is subtracted from the center value of the second round to obtain the lower limit of the second round (such as 50), and the preset offset is added to the center value of the second round to obtain the upper limit of the second round (such as 70); then, based on the lower limit of the second round, the center value of the second round, and the upper limit of the second round, multiple cut-off values ​​of the second round (such as 50, 60, 70) are determined so that within the time period corresponding to the second round (such as March 2, 2025), , setting 90% of the APP users to perform intelligent decision-making according to the central value of the second round, and setting 10% of the APP users to be used to perform the experimental process for multiple cutoff values ​​of the second round (hereinafter referred to as the "second round experimental process"), such as "the 10% of the APP users are divided into three parts, one part performs intelligent decision-making according to the lower limit of the second round, another part performs intelligent decision-making according to the central value of the second round, and the remaining part performs intelligent decision-making according to the upper limit of the second round". This is to obtain the impact characterization data of each of the multiple cutoff values ​​of the second round through the second round experimental process;

[0083] ...(and so on)

[0084] Then, based on the impact characterization data of each of the multiple cutoff values ​​in the M-1th round, the cutoff value with the highest impact (such as 80) is selected from the multiple cutoff values ​​in the M-1th round as the center value of the Mth round; then, the preset offset is subtracted from the center value of the Mth round to obtain the lower limit of the Mth round (such as 70), and the preset offset is added to the center value of the Mth round to obtain the upper limit of the Mth round (such as 90); then, based on the lower limit of the Mth round, the center value of the Mth round, and the upper limit of the Mth round, multiple cutoff values ​​of the Mth round (such as 70, 80, 90) are determined, so as to be used in the time period corresponding to the Mth round (such as from March 1, 2025 to the M-1th round). 1 day), set 90% of the APP users to perform intelligent decision-making according to the central value of the Mth round, and set 10% of the APP users to be used to perform an experimental process for multiple cutoff values ​​of the Mth round (hereinafter referred to as the "Mth round experimental process"), such as "the 10% of the APP users are divided into three parts, one part performs intelligent decision-making according to the lower limit of the Mth round, another part performs intelligent decision-making according to the central value of the Mth round, and the remaining part performs intelligent decision-making according to the upper limit of the Mth round". The purpose is to obtain the impact representation data of each of the multiple cutoff values ​​of the Mth round through the Mth round experimental process. Wherein, M is a positive integer.

[0085] It should be noted that, for the above-mentioned M rounds, when the triggering timing of the above-mentioned first request is within the time period corresponding to the jth round, if j is 1, then there is no reference time period corresponding to the first request, so that the demarcation value corresponding to the first request is determined according to a pre-set demarcation value (such as the above-mentioned default value), so that the demarcation value corresponding to the first request can be the default value, the default value-N, or the default value+N; if j ≥ 2, then there is a reference time period corresponding to the first request, and the reference time period corresponding to the first request is the time period corresponding to the j-1th round, so that The cutoff value corresponding to the first request is determined based on multiple cutoff values ​​used in the time period corresponding to the j-1th round, so that the cutoff value corresponding to the first request can be: the cutoff value with the highest impact (such as the target value described above) selected from the multiple cutoff values ​​used in the time period corresponding to the j-1th round, the "cutoff value with the highest impact" - N, or the "cutoff value with the highest impact" + N. In this way, the cutoff value corresponding to the first request is determined through a dynamic optimization method, making the cutoff value corresponding to the first request more accurate and better improving the intelligent decision-making effect. Wherein, j is a positive integer, j≤M.

[0086] Research has found that different objects may have different display feedback, so that different objects can show different impacts after display. Based on this, in order to better improve the object display effect, this application also provides a possible implementation of the above-mentioned S3. In this way, the S3 can specifically include: the SDK responds to the first request type of the first type, reads the target object from the cache pool, so that the impact representation data (such as ECPM) of the target object is not lower than the impact representation data of each object in the cache pool, so that the target object can represent the object with the highest impact recorded in the cache pool, which is conducive to improving the object display effect.

[0087] Research has also found that for any object, the impact of the object on the same user is not static, but changes, so that the impact representation data of the object recorded in the cache pool may be inaccurate.

[0088] Based on the above research, in order to better improve the object display effect, this application also provides a possible implementation method of the above request processing method. In this way, the request processing method can at least include the following steps: SDK corrects the impact representation data of each object in the cache pool (such as Figure 2 、 Figure 3 or Figure 4 The SDK selects the target object from the cache pool based on the correction result of each object in the cache pool, and the correction result of the target object is not lower than the correction result of each object in the cache pool, so that the target object can represent the object with the highest impact at the current moment recorded in the cache pool, which is conducive to improving the object display effect.

[0089] It should be noted that this application does not limit the implementation method of the above-mentioned correction. For example, it can be implemented in any correction method. For example, in order to better improve the correction effect, for any object in the cache pool, if the impact representation data of the object is determined by the server using the first model (such as a machine learning model with an object recommendation function), the correction result of the object can be determined by the SDK using the second model (such as Figure 2 or Figure 4. The working principle of the second model is the same as that of the first model, so that the two models can process data according to the same set of processes, so that the impact assessment processes implemented by the two models are consistent; and because the input data of the second model is increased by some data generated during the cache period of the object (such as user operation data) compared with the input data of the first model, the input data of the second model can more accurately represent the characteristics presented by the client at the triggering moment of the first request (such as the real-time demand for advertising recommendation, etc.), so that the impact characterization data re-determined for each cached object based on the input data of the second model (such as the correction results of each object) is more accurate, so that real-time correction of the impact of the cached object can be achieved, so that the impact of the object can be corrected without replacing the cached object, reducing defects caused by overestimation of the impact (such as value).

[0090] It should also be noted that this application does not limit the triggering time of the above-mentioned correction. For example, in some scenarios (such as scenarios with sufficient computing resources), if it is detected that the SDK passively receives a target object request (such as an advertising request or the above-mentioned first request), the SDK will perform a correction to ensure that the impact characterization data of each object in the cache pool is as accurate as possible. For another example, in some scenarios (such as scenarios with insufficient computing resources), if the SDK decides to process the first request in a cache manner, the SDK will first perform a correction so that it can subsequently sort the objects in the cache pool according to the impact characterization data after correction to obtain the target object; the SDK will then read the target object from the cache pool and provide it to the APP for display.

[0091] Based on the relevant content of the above request processing method and Figures 2 to 4 As can be seen from the content shown, for the technical solution provided in this application, the solution uses SDK to make intelligent decisions to decide whether to use real-time loaded objects or cached objects for display in the request dimension. In this way, the SDK can be used to intelligently decide the timing of using cached objects, so that cached objects can be used more reasonably; the solution corrects the impact of cached objects when using them, so as to overcome the defects caused by the overestimation of the impact of these cached objects.

[0092] It should be noted that for Figure 2 For example, the Figure 2 The "first request" in the above text refers to the request for obtaining advertisements that is triggered for the first time in an advertisement scenario (such as an APP). At this time, since there are no cached advertisements in the cache pool, the "first request" can use real-time loading to obtain advertisements. Figure 2The "non-first request" here refers to a request for obtaining an advertisement that is not triggered for the first time (such as the Qth time, Q≥2) in the advertising scenario. At this time, since there is already at least one cached advertisement in the cache pool, the "non-first request" can achieve advertisement acquisition with the help of the intelligent decision-making method provided by this application.

[0093] Based on the request processing method provided in the embodiment of the present application, the embodiment of the present application also provides a request processing device. Figure 5 Explain and illustrate. Figure 5 This is a schematic diagram of the structure of a request processing device provided in an embodiment of the present application. It should be noted that for the technical details of the request processing device provided in an embodiment of the present application, please refer to the relevant content of the request processing method above.

[0094] like Figure 5 As shown, the request processing device 500 provided in this embodiment of the application includes:

[0095] A receiving unit 501 is configured to receive a first request, where the first request indicates obtaining a target object;

[0096] an identification unit 502, configured to identify a type of the first request;

[0097] A reading unit 503 is configured to read the target object from a cache pool in response to the type of the first request being the first type;

[0098] The downloading unit 504 is configured to download the target object from the server in response to the type of the first request being the second type.

[0099] In one possible implementation, the request processing device 500 is deployed on a software development kit (SDK); or, the request processing device 500 is independent of the SDK, and data communication can be performed between the request processing device 500 and the SDK, which is deployed on the client.

[0100] In a possible implementation manner, the request processing device 500 may be implemented by software or hardware (such as a circuit board, etc.).

[0101] In one possible implementation, the identification unit 502 is specifically used to: obtain at least one piece of information, the at least one piece of information including device performance, network status, request interval, request frequency, and part or all of the impact representation data of the historical object, where the display timing of the historical object is earlier than the triggering timing of the first request; determine the scoring result of the first request based on the at least one piece of information; and determine the type of the first request based on the scoring result.

[0102] In one possible implementation, the identification unit 502 is specifically configured to: in response to the scoring result not exceeding a cutoff value, determine the first type as the type of the first request; and in response to the scoring result exceeding the cutoff value, determine the second type as the type of the first request.

[0103] In one possible implementation, the type of the first request is determined based on the scoring result and the cutoff value corresponding to the first request; the process of obtaining the cutoff value corresponding to the first request includes: in response to the existence of a reference time period corresponding to the first request, determining the cutoff value corresponding to the first request based on the impact characterization data of the cutoff value used in the reference time period, the reference time period being earlier than the triggering timing of the first request; in response to the absence of the reference time period corresponding to the first request, determining the cutoff value corresponding to the first request based on a pre-set cutoff value.

[0104] In one possible implementation, the process of determining the demarcation value corresponding to the first request includes: in response to using multiple demarcation values ​​within the reference time period, selecting a target value from the multiple demarcation values ​​based on the influence characterization data of each demarcation value in the multiple demarcation values, the influence characterization data of the target value being not lower than the influence characterization data of each demarcation value in the multiple demarcation values; determining the target value as the central value corresponding to the first request; and determining the demarcation value corresponding to the first request based on the central value corresponding to the first request.

[0105] In a possible implementation manner, the request processing device 500 further includes:

[0106] a correction unit, configured to correct the impact characterization data of each object in the buffer pool respectively to obtain a correction result for each object in the buffer pool;

[0107] The screening unit is configured to screen out the target object from the buffer pool according to the correction results of each object in the buffer pool, wherein the correction result of the target object is not lower than the correction results of each object in the buffer pool.

[0108] In a possible implementation manner, the target object is an advertisement.

[0109] Based on the relevant content of the above-mentioned request processing device 500, it can be known that the working principle of the device 500 includes: first receiving a first request, which indicates to obtain a target object (such as an advertisement); then identifying the type of the first request; if the type of the first request is the first type (such as the "cache" type), then reading the target object from the cache pool; if the type of the first request is the second type (such as the "real-time" type), then downloading the target object from the server to achieve dynamic decision-making for the request using the real-time downloaded object and the object read from the cache pool to respond. This not only overcomes the defects caused by responding to requests only by downloading objects in real time, but also enables more reasonable use of cached objects to maximize the role of cached objects.

[0110] In addition, an embodiment of the present application also provides an electronic device, which includes a processor and a memory: the memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory, so that the electronic device executes any implementation of the request processing method provided in the embodiment of the present application.

[0111] See also Figure 6 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0112] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0113] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0114] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0115] The electronic device provided by the embodiment of the present disclosure and the method provided by the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0116] An embodiment of the present application further provides a computer-readable medium, in which instructions or computer programs are stored. When the instructions or computer programs are executed on a device, the device executes any implementation of the request processing method provided in the embodiment of the present application.

[0117] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0118] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0119] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0120] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can perform the method.

[0121] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit / module does not, in some cases, limit the unit itself.

[0124] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0125] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0126] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0127] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0128] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0129] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0130] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A request processing method, characterized in that: The method is applied to a software development kit (SDK), which is deployed on a client. The method includes: receiving a first request, wherein the first request instructs obtaining a target object; identifying a type of the first request; In response to the type of the first request being a first type, reading the target object from a cache pool; In response to the type of the first request being the second type, the target object is downloaded from the server.

2. The method according to claim 1, characterized in that The process of identifying the type of the first request includes: Obtaining at least one piece of information, the at least one piece of information including device performance, network status, request interval, request frequency, and part or all of impact representation data of a historical object, where a display timing of the historical object is earlier than a triggering timing of the first request; determining a scoring result of the first request based on the at least one information; The type of the first request is determined according to the scoring result.

3. The method according to claim 2, characterized in that Determining the type of the first request according to the scoring result includes: In response to the scoring result not exceeding a cutoff value, determining the first type as the type of the first request; In response to the scoring result exceeding the cutoff value, the second type is determined as the type of the first request.

4. The method according to claim 2, characterized in that The type of the first request is determined according to the scoring result and a cutoff value corresponding to the first request; The process of obtaining the delimiting value corresponding to the first request includes: In response to the existence of a reference time period corresponding to the first request, determining the delimiting value corresponding to the first request based on the impact characterization data of the delimiting value used in the reference time period, wherein the reference time period is earlier than the triggering timing of the first request; In response to the absence of a reference time period corresponding to the first request, a demarcation value corresponding to the first request is determined according to a preset demarcation value.

5. The method according to claim 4, characterized in that The determining, based on the impact characterization data of the demarcation value used within the reference time period, the demarcation value corresponding to the first request includes: In response to using a plurality of cutoff values ​​within the reference time period, selecting a target value from the plurality of cutoff values ​​based on the impact characterization data of each of the plurality of cutoff values, the impact characterization data of the target value being not less than the impact characterization data of each of the plurality of cutoff values; Determining the target value as a central value corresponding to the first request; A demarcation value corresponding to the first request is determined based on the central value corresponding to the first request.

6. The method according to claim 1, wherein Before reading the target object from the cache pool, the method further includes: Correcting the impact representation data of each object in the buffer pool respectively to obtain a correction result for each object in the buffer pool; The target object is screened out from the buffer pool according to the correction results of each object in the buffer pool, and the correction result of the target object is not lower than the correction results of each object in the buffer pool.

7. The method according to any one of claims 1 to 6, characterized in that The target object is an advertisement.

8. A request processing device, characterized in that: include: A receiving unit, configured to receive a first request, where the first request indicates obtaining a target object; an identification unit, configured to identify a type of the first request; a reading unit, configured to read the target object from a cache pool in response to the type of the first request being a first type; A downloading unit is configured to download the target object from a server in response to the type of the first request being the second type.

9. An electronic device, characterized in that: The device includes: a processor and a memory; The memory is used to store instructions or computer programs; The processor is configured to execute the instructions or computer program in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer-readable medium stores instructions or a computer program, and when the instructions or the computer program are executed on a device, the device is caused to execute the method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises a computer program carried on a non-transitory computer-readable medium, the computer program comprising a program code for executing the method according to any one of claims 1 to 7.

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