Advertising data processing method, device, electronic device and storage medium

Abstract

Embodiments of the present invention provide an advertising data processing method, apparatus, electronic device, and storage medium. The advertising data processing method includes calculating a predicted cached advertisement capacity of a first cache space that matches an advertisement display scenario; if the remaining cached advertisements in the first cache space are less than the predicted cached advertisement capacity, preloading a first cached advertisement for display in the advertisement display scenario into the first cache space until the remaining cached advertisements are no less than the predicted cached advertisement capacity. This improves the display effect of cached advertisements and increases the efficiency of advertisement content distribution.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of Internet technology, and in particular to an advertisement data processing method, device, electronic device, and storage medium. Background Art

[0002] In recent years, with the development of internet technology, traditional online advertising methods have become unsuitable for new media scenarios. Consequently, paid ranking advertising has emerged and continues to develop. This format allows for targeted advertising and generates substantial advertising revenue for media outlets. In the mobile internet landscape, mobile media developers can connect their mobile media ad spaces to mobile advertising platforms, and advertisers can obtain matching media ad spaces through paid ranking, thus enabling the distribution, placement, and commercialization of advertising content through mobile advertising platforms.

[0003] However, in this advertising content distribution model, there is still room for improvement in the advertising distribution efficiency of existing mobile advertising platforms. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an advertisement data processing method, apparatus, electronic device, and storage medium to solve or alleviate the above-mentioned problems.

[0005] According to a first aspect of an embodiment of the present invention, a method for processing advertising data is provided, including: calculating a predicted capacity of cached advertisements of a first cache space that matches an advertising display scenario; if the remaining amount of cached advertisements in the first cache space is less than the predicted capacity of cached advertisements, preloading a first cached advertisement for display in the advertising display scenario in the first cache space until the remaining amount of cached advertisements is no less than the predicted capacity of cached advertisements.

[0006] According to a second aspect of an embodiment of the present invention, there is provided an advertising data processing method, which is applied to a mobile terminal, and includes: sending information indicating a current advertising display scene to a server to obtain a current cached advertising predicted capacity of a cache space that matches the current advertising display scene; if the current cached advertising remaining amount in the cache space is less than the current cached advertising predicted capacity, obtaining a cached advertisement for display in the current advertising display scene from the server, and preloading it into the cache space until the current cached advertising remaining amount is not less than the current cached advertising predicted capacity.

[0007] According to a third aspect of an embodiment of the present invention, an advertising data processing device is provided, including: a calculation module, which calculates the cached advertisement predicted capacity of a first cache space matching an advertisement display scenario; and a preloading module, which preloads a first cached advertisement for display in the advertisement display scenario in the first cache space if the remaining cached advertisement in the first cache space is less than the cached advertisement predicted capacity, until the remaining cached advertisement is not less than the cached advertisement predicted capacity.

[0008] According to a fourth aspect of an embodiment of the present invention, there is provided an advertising data processing device, which is applied to a mobile terminal and includes: a transceiver module, which sends information indicating a current advertising display scene to a server to obtain a current cached advertising predicted capacity of a cache space that matches the current advertising display scene; and a preloading module, which obtains a cached advertisement for display in the current advertising display scene from the server if the current cached advertisement remaining amount in the cache space is less than the current cached advertisement predicted capacity, and preloads it into the cache space until the current cached advertisement remaining amount is not less than the current cached advertisement predicted capacity.

[0009] According to a fifth aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the method described in the first aspect or the second aspect.

[0010] According to a sixth aspect of an embodiment of the present invention, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect or the second aspect is implemented.

[0011] In the solution of the embodiment of the present invention, cached ads are preloaded when the remaining amount of cached ads is less than the predicted capacity of cached ads, and are kept until the remaining amount of cached ads is not less than the predicted capacity of cached ads. Thus, the remaining amount of cached ads is kept dynamically consistent with the predicted capacity of cached ads that meets the ad display scenario, thereby improving the display effect of cached ads and improving the efficiency of ad content distribution when the cached ads are displayed in the ad display scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0013] Figure 1 A schematic diagram of a software architecture for advertising data processing of an example mobile advertising platform;

[0014] Figure 2A is a schematic flow chart of an advertisement data processing method according to an embodiment of the present invention;

[0015] Figure 2B is a schematic diagram of an advertisement data processing method according to an embodiment of the present invention;

[0016] Figure 3A is a schematic flow chart of an advertisement data processing method according to another embodiment of the present invention;

[0017] Figure 3B is a schematic flow chart of an advertisement data processing method according to another embodiment of the present invention;

[0018] Figure 4 is a schematic flow chart of an advertisement data processing method according to another embodiment of the present invention;

[0019] Figure 5 is a schematic flow chart of an advertisement data processing method according to another embodiment of the present invention;

[0020] Figure 6 is a schematic block diagram of an advertisement data processing apparatus according to another embodiment of the present invention;

[0021] Figure 7 is a schematic block diagram of an advertisement data processing apparatus according to another embodiment of the present invention;

[0022] Figure 8 This is a hardware structure of an electronic device according to another embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0024] The specific implementation of the embodiment of the present invention is further described below with reference to the accompanying drawings of the embodiment of the present invention. Figure 1This diagram illustrates the software architecture for advertising data processing on an example mobile advertising platform. User device 100 includes a network interface 110, an application 120, an advertising module 121, and a human interface 130. Advertising resource server 200 includes a network interface 210 and multiple advertising resources 1, 2, ..., and N for delivery. These advertising resources can be further sent to advertising module 121 on user device 100 through methods such as competitive bidding. The software module that provides advertising resources 1, 2, ..., and N can be referred to as an advertising return mechanism. Advertising module 121 can be installed in the operating system of user device 100 or in at least one application via a method such as an SDK (Software Development Kit). Advertising module 121 can directly request ads that have been successfully ranked by competitive bidding from the advertising return mechanism and cache them in a corresponding cache area. Advertising module 121 can also request at least one successfully ranked ad from advertising resource server 200 in response to an ad cache request issued by an application. In addition, the advertising module 121 can display the cached advertisements in the cache space to the advertisement slots in the application according to the advertisement display request of the application. The proportion of the displayed advertisement materials in the total advertisement materials in the cache pool can be called the material utilization rate. A higher material utilization rate indicates a higher utilization of the advertisement materials, and the less waste. In addition, the proportion of the number of requests that can return advertisements each time when requesting an advertisement return device in the total number of requests can be called the advertisement fill rate. A higher advertisement fill rate indicates a higher advertisement replenishment efficiency. In other words, a higher advertisement fill rate indicates a higher SDK advertisement data processing performance. For example, if a certain advertisement slot of the media is triggered 100 times in a certain period of time, it means that 100 advertisements can be provided. If there is 1 time when the advertisement is not successfully displayed, the corresponding fill rate is (100-1) / 100, that is, 99%. For example, due to limitations such as the media's advertising requests (supply) and advertisers' advertising needs (demand) not always being equal or the advertising matching degree, the corresponding advertising materials may not be returned and displayed for every advertising request, that is, the advertising fill rate is insufficient. Therefore, material utilization rate and advertising fill rate are relatively important advertising content distribution efficiency indices for mobile advertising platforms.

[0025] To ensure a certain ad fill rate and material utilization, you can set the cache number. However, if the cache number is too short, the ad fill rate will be affected, and if it is too long, the utilization rate of the ad material will be affected.

[0026] Figure 2A The figure is a schematic flow chart of an advertisement data processing method according to an embodiment of the present invention. Figure 2AThe advertising data processing method is applicable to any appropriate electronic device with data processing capabilities, including but not limited to mobile terminals (such as mobile phones, PADs, etc.) and PCs. The advertising data processing method includes:

[0027] 210: Calculate the predicted capacity of cached advertisements of the first cache space that matches the advertisement display scenario.

[0028] It should be understood that the advertising display scene can be related to at least one of the user, mobile media platform, hardware environment or software environment. For example, changes in any one of user data, platform data, network environment data, hardware data or software data can indicate changes in the advertising display scene. For example, the switching of logged-in users can cause changes in the advertising display scene (for example, different users have different needs, and the advertisements required for personalized advertising display are also different). The switching of mobile media platforms can cause changes in the advertising display scene (for example, different types of applications have different target users, indicating different user groups, so the advertisements that need to be displayed will also be different). Changes in the network environment can cause changes in the advertising display scene (for example, when the network environment is good, advertisements with larger files or more advertisements can be displayed, and when the network environment is poor, advertisements with smaller files or fewer advertisements can be displayed).

[0029] It should also be understood that the mobile media platform can be an operating system installed on a terminal device such as a mobile terminal, or it can be an application installed on the above-mentioned terminal device. The advertising cache space can be managed by a module in the target platform or a function of the target platform (for example, an installed advertising SKD), for example, managed by an advertising module. The advertising module can be responsible for the request, cache, rendering or dotting of advertisements, etc. For example, the advertising module can send an advertising display request to request and read cached advertisements from the advertising cache space for display. The advertising display request can be triggered by any means such as page updates, operations at specific screen positions, arrival at specific time points, etc. In addition, the advertising module can also send a preloaded cached advertising request to the backend or middle-end server, and the multiple advertising return devices deployed by the server corresponding to multiple advertising delivery demanders can respond to the above-mentioned preloaded cached advertising request to perform operations such as bidding ranking, and return the advertisements that have successfully bid to the advertising cache space.

[0030] 220: If the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, preload the first cached ads for display in the ad display scene in the first cache space until the remaining amount of cached ads is no less than the predicted capacity of cached ads.

[0031] It should be understood that the advertising cache space can have a maximum capacity, and the maximum capacity is not less than the predicted cache capacity. The maximum capacity can be a fixed capacity or a variable capacity. For example, the predicted cache capacity can be calculated multiple times over a period of time, and the maximum value among the multiple predicted cache capacities can be set as the maximum capacity. In addition, a value less than the maximum value can be set as the maximum capacity. For example, before determining the predicted cache capacity, an initial predicted cache capacity can be determined, and the initial predicted cache capacity can be compared with the maximum capacity. If the initial predicted cache capacity is greater than the maximum capacity, the maximum capacity is determined as the predicted cache capacity.

[0032] It should also be understood that when the number of remaining ads in the ad cache space reaches the predicted cache capacity, the ad module stops sending cached ad preload requests and may instead send ad display requests. When the number of remaining cached ads in the ad cache space is less than the predicted cache capacity, the ad module may send both cached ad preload requests and ad display requests. When there are no remaining cached ads in the ad cache space, the ad module stops sending ad display requests and may instead send cached ad preload requests.

[0033] In the solution of the embodiment of the present invention, cached ads are preloaded when the remaining amount of cached ads is less than the predicted capacity of cached ads, and are kept until the remaining amount of cached ads is not less than the predicted capacity of cached ads. Thus, the remaining amount of cached ads is kept dynamically consistent with the predicted capacity of cached ads that meets the ad display scenario, thereby improving the display effect of cached ads and improving the efficiency of ad content distribution when the cached ads are displayed in the ad display scenario.

[0034] In the first example, the first advertisement prediction capacity of the first cache space can match the first advertisement display scene, and the second advertisement prediction capacity of the first cache space can match the second advertisement display scene. It should be understood that the first advertisement display scene and the second advertisement display scene can be different applications (for example, applications installed in mobile terminals or Internet of Things (IOT) devices) or application modules in IoT devices; the first advertisement display scene and the second advertisement display scene can also be different pages of the same application; the first advertisement display scene and the second advertisement display scene can also be different users of the same application, etc. In this first example, when switching from the first advertisement display scene to the second advertisement display scene, if the second advertisement prediction capacity of the first cache space is greater than the first advertisement prediction capacity, the cached advertisement for display in the second advertisement display scene will not be loaded, and the cached advertisement for display in the first advertisement display scene will be used as the cached advertisement for display in the second advertisement display scene. Thus, the data processing amount of the cached advertisement loaded for display in the second advertisement display scene is saved.

[0035] In the second example, when switching from the first ad display scenario to the second ad display scenario, if the second ad predicted capacity of the first cache space is greater than the first ad predicted capacity, the cached ads used for the first ad display scenario are deleted, and the cached ads used for the second ad display scenario are loaded until the remaining cached ads are no less than the second cached ad predicted capacity. This allows for provision of cached ads suitable for different ad display scenarios.

[0036] In the third example, the first advertisement display scene and the second advertisement display scene correspond to different applications among multiple applications, and the cached advertisements of each of the multiple applications include a group of shared cached advertisements (for example, advertisements applicable to various applications) and respective personalized cached advertisements (for example, financial advertisements for financial applications, product advertisements for e-commerce applications, course advertisements for educational applications, interactive advertisements for social applications, news push for news applications, etc.). When switching from the first advertisement display scene to the second advertisement display scene, a group of shared cached advertisements is maintained, the personalized cached advertisements of the first advertisement display scene are deleted, and personalized cached advertisements for display in the second advertisement display scene are loaded until the remaining amount of cached advertisements is not less than the predicted capacity of the second cached advertisements. Since a group of shared cached advertisements is maintained, the data storage efficiency and data processing efficiency are improved while ensuring the personalization of advertisement display in different scenes.

[0037] In the fourth example, the first advertising display scenario and the second advertising display scenario correspond to different pages in the same or different applications (for example, applications installed in IoT devices, mobile devices, or embedded devices (offline or online)), and the cached advertisements of the multiple pages include a set of shared device cached advertisements (for example, advertisements applicable to various pages) and respective personalized cached advertisements (for example, financial management advertisements on financial application pages, product advertisements on e-commerce application pages, course advertisements on educational application pages, interactive advertisements on social application pages, news push on news application pages, etc.).

[0038] In one example, where a first ad display scenario and a second ad display scenario correspond to different pages in different applications, when switching from the first ad display scenario to the second ad display scenario, a set of shared device cached ads is maintained, personalized cached ads for the first ad display scenario are deleted, and personalized cached ads for display in the second ad display scenario are loaded until the remaining cached ads are no less than the predicted capacity of the second cached ads. Because a set of shared device cached ads is maintained, data storage and processing efficiency is improved while ensuring personalized ad display for different applications on the same device.

[0039] In another example, when the first advertisement display scene and the second advertisement display scene correspond to different pages in the same application, the first advertisement display scene and the second advertisement display scene have a set of shared application cache advertisements in addition to the above-mentioned set of shared device cache advertisements. When switching from the first advertisement display scene to the second advertisement display scene, a set of shared device cache advertisements and shared application cache advertisements are maintained, the personalized cache advertisements of the first advertisement display scene are deleted, and personalized cache advertisements for display in the second advertisement display scene are loaded until the remaining amount of cache advertisements is not less than the predicted capacity of the second cache advertisements. Since a set of shared device cache advertisements and a set of shared application cache advertisements are maintained, while ensuring the personalized display of advertisements on different pages in the same application on the same device, the data storage efficiency and data processing efficiency are further improved.

[0040] It should also be understood that the advertising data processing method can be applied to Internet of Things (IoT) devices, which can be installed with embedded operating systems or real-time operating systems. These operating systems can also be installed with applications or software-defined devices (SKDs). IoT devices interact with corresponding advertising resource servers and voice recognition servers.

[0041] In one example, an IoT device receives a voice command and, in response to the voice command, sends a voice recognition request to a voice recognition server. The IoT device receives a recognition result returned in response to the voice recognition request, triggering a request to display an ad that matches the recognition result. For example, the recognition result may be thematically or keyword-relevant to the ad.

[0042] In another example, the IoT device receives a voice command, and in response to the voice command, triggers a display request for an advertisement to be displayed, and sends a voice recognition request to a voice recognition server.

[0043] In another example, a speech recognition server deploys a pre-trained cache capacity prediction model. The speech recognition server inputs historical speech recognition data into the cache capacity prediction model to obtain the current predicted cache capacity. The speech recognition server then sends information indicating the current predicted cache capacity to an IoT device. The IoT device then determines whether the number of currently cached ads is less than the current predicted cache capacity. If so, it requests ads from an ad resource server.

[0044] In another example, the speech recognition server sends a historical speech recognition request to the IoT device. The IoT device is deployed with a pre-trained cache capacity prediction model. The IoT device inputs the historical speech recognition data into the cache capacity prediction model to obtain the current predicted cache capacity. The IoT device determines whether the number of currently cached advertisements is less than the current predicted cache capacity. If so, it requests advertisements from the advertising resource server. In addition, the IoT device can receive a voice instruction and, in response to the voice instruction, send a speech recognition request to the speech recognition server. The IoT device receives the recognition result returned in response to the speech recognition request and triggers a display request for the advertisement to be displayed that matches the recognition result. For example, the recognition result has a topic relevance or a keyword relevance to the advertisement to be displayed. For another example, the IoT device can receive a voice instruction and, in response to the voice instruction, trigger a display request for the advertisement to be displayed and send a speech recognition request to the speech recognition server.

[0045] In another example, a speech recognition server sends historical speech recognition data to an advertising resource server, which is equipped with a pre-trained cache capacity prediction model. The advertising resource server inputs the historical speech recognition data into the cache capacity prediction model, obtains the current predicted cache capacity, and sends it to the IoT device.

[0046] It should be understood that the IoT device can provide feedback to the advertising resource server regarding the percentage of time that the number of currently cached ads is less than the current predicted cache capacity. The advertising resource server can then update the cache capacity prediction model based on this percentage of time. Furthermore, for each of the above examples, the cache capacity prediction model can be updated on the server or IoT device where it is deployed.

[0047] In addition, the cache capacity prediction model can also use historical speech recognition data input by the speech recognition server to update the cache capacity prediction model.

[0048] In addition, alternatively, the voice recognition server may also be a gesture recognition server or other recognition server, which is not limited in the embodiment of the present invention.

[0049] Additionally, alternatively, the historical voice recognition data may be historical gesture recognition data or other operation data.

[0050] In another implementation of the present invention, the predicted capacity of cached advertisements of a first cache space matching an advertisement display scenario is calculated, including: inputting information indicating the advertisement display scenario into a capacity dynamic prediction model to obtain the predicted capacity of cached advertisements, wherein the capacity dynamic prediction model is obtained by classification training using advertisement display scenario training samples.

[0051] Since the capacity dynamic prediction model is obtained by classification training through advertising display scene training samples, the cached advertising prediction capacity is obtained, thereby improving the accuracy of the capacity dynamic prediction.

[0052] In one example, the method further includes: obtaining information indicating an advertisement display scenario based on an advertisement preloading request of a cached advertisement, the information indicating the advertisement display scenario including at least one of user advertisement consumption information, network environment information, and hardware configuration information.

[0053] Since at least one of the user's advertising consumption information, network environment information, and hardware configuration information usually changes dynamically and has a strong correlation with the delivery of cached advertisements, the above information effectively indicates the advertisement display scenario. In addition, based on the advertisement preloading request of the cached advertisement, information indicating the advertisement display scenario is obtained, thereby avoiding the inaccuracy of obtaining information about the dynamically changing advertisement display scenario and improving the accuracy of information acquisition.

[0054] In another implementation of the present invention, the predicted capacity of cached advertisements of a first cache space that matches an advertisement display scenario is calculated, including: monitoring dynamic changes of the advertisement display scenario to obtain information indicating the current advertisement display scenario; and calculating the predicted capacity of cached advertisements of a first cache space that matches the current advertisement display scenario based on a preloading request of the cached advertisements.

[0055] Since the information indicating the current advertisement display scene has been obtained before the preloading request of the cached advertisement is made, the preloading efficiency of the cached advertisement is improved.

[0056] In another implementation of the present invention, the method further includes: pre-caching a second cached ad in the second cache space after a previous bidding round, wherein the ad to be cached in the first cache space had a higher bid ranking than the second cached ad in the previous bidding round. Pre-loading the first cached ad in the first cache space for display in the ad display scenario includes: after a current bidding round, moving the second cached ad from the second cache space to the first cache space as the first cached ad, wherein the second cached ad had a higher ranking than the ad to be cached in the first cache space in the current bidding round.

[0057] Because the ads in the first cache space had a higher bid ranking than the ads in the second cache space in the previous bidding round, and because the ads in the second cache space have a higher ranking than the ads in the first cache space in the current bidding round, this ensures that the ads that successfully ranked in each bidding round are delivered. Furthermore, the move from the second cache space to the first cache space improves preloading efficiency compared to returning cached ads from the server.

[0058] In one example, the second cache space may also have a corresponding dynamic capacity prediction model and be configured independently from the first cache space. For example, if the current initial predicted cache capacity is greater than the maximum capacity, the maximum capacity may be determined as the current predicted cache capacity, and advertisements corresponding to the difference between the current initial predicted cache capacity and the maximum capacity may be temporarily stored in the second cache space. Furthermore, the next time the initial predicted cache capacity is less than the maximum capacity, the advertisements temporarily stored in the second cache space may be preferentially moved to the first cache space.

[0059] Figure 2B This is a schematic diagram of an advertising data processing method according to one embodiment of the present invention. As shown, a mobile device sends an ad display request and reads the remaining cached ads from the ad cache. The ad cache capacity can be configured to be greater than or equal to the predicted cached ad capacity, preferably greater than the maximum predicted cached ad capacity among multiple predicted cached ad capacities within a specific time period.

[0060] In addition, the ad cache space can respond to the mobile media's ad preload request and obtain the currently remaining cached ads. The ad cache space can also read the currently remaining cached ads in response to changes in the mobile media's ad display scene.

[0061] Furthermore, the ad cache space can obtain the current ad cache predicted capacity from the elastic storage medium capacity device (an example of a dynamic capacity prediction model) in response to an ad preload request from the mobile media. The ad cache space can also obtain the current ad cache predicted capacity from the elastic storage medium capacity device in response to changes in the ad display scenario on the mobile media.

[0062] In addition, the elastic storage medium capacity device can determine the current advertising cache prediction capacity based on at least one of the user's historical advertising consumption data, the network environment data, and the hardware environment data (for example, the hardware environment of the mobile media). For example, the above data can be obtained when the mobile media responds to the mobile media's advertising preloading request. For example, the above data can also be obtained in response to changes in the advertising display scene of the mobile media. At least one of the above-mentioned user's historical advertising consumption data, the network environment data, and the hardware environment data can indicate the advertising display scene. In other words, for example, the above data can be obtained when the logged-in user switches, or when the mobile media is changed, or when the hardware platform on which the mobile media is installed is changed.

[0063] In addition, the capacity dynamic prediction model of the elastic storage medium capacity device based on the current advertising display scenario prediction can be classified and trained based on the data of the previous advertising display scenario (for example, supervised classification learning). The cache capacity label of the previous advertising display scenario can be labeled based on the advertising material utilization rate, for example, so that the labeled cache capacity label can obtain the advertising material utilization rate threshold. The advertising material utilization rate can be the previous advertising material utilization rate corresponding to the previous advertising display scenario. The previous advertising material utilization rate can be reported to the server through the mobile terminal. The server can deploy the trained or updated model on the edge (for example, the mobile terminal).

[0064] In addition, as shown in the figure, when the number of remaining advertisements is less than the predicted capacity of the advertisement cache, advertisement preloading is performed. For example, any number of advertisements can be preloaded.

[0065] In addition, when the number of remaining ads is not less than the predicted capacity of the ad cache, the process ends.

[0066] Figure 3A This is a schematic flowchart of an advertisement data processing method according to another embodiment of the present invention. Figure 3A The advertising data processing methods include:

[0067] 310: Calculate the predicted capacity of cached advertisements of the first cache space that matches the advertisement display scenario.

[0068] 320: Determine whether the current cached advertisement remaining amount is less than the current cached advertisement predicted capacity. If the current cached advertisement remaining amount is less than the current cached advertisement predicted capacity, preload a single first cached advertisement in the first cache space and update the first cache space. If the current cached advertisement remaining amount is not less than the current cached advertisement predicted capacity, stop preloading the first cached advertisement.

[0069] Since a single first cached advertisement is preloaded when the remaining amount of the current cached advertisement is less than the current cached advertisement predicted capacity, the dynamic adjustment accuracy of the current cached advertisement predicted capacity is improved, thereby further improving the accuracy of advertisement content distribution.

[0070] Figure 3B This is a schematic flow chart of an advertisement data processing method according to another embodiment of the present invention. As shown in the figure, in step 301, the elastic storage medium capacity device calculates the current cached advertisement predicted capacity.

[0071] In step 302 , the elastic storage medium capacity device determines whether the current cached advertisement remaining amount is less than the current cached advertisement predicted capacity. If so, the process proceeds to step 303 ; if not, the process proceeds to step 304 .

[0072] In step 303, the elastic storage medium capacity device preloads a single cached advertisement, and the process returns to step 301. It should be understood that, in one example, the current number of remaining advertisements may be obtained after step 303 is executed and before step 301 is executed. In another example, the current number of remaining advertisements may also be obtained after step 301 is executed and before step 302 is executed.

[0073] In step 304 , the elastic storage medium capacity device stops loading advertisements.

[0074] It should also be understood that in this example, the preloading of a single cached advertisement is used as an example for explanation, but in other examples, other numbers of cached advertisements can also be preloaded, for example, two cached advertisements or four cached advertisements. It should also be understood that it is also possible to preload a certain proportion of the maximum capacity of the advertisement cache space. For example, the maximum capacity of the advertisement cache space is a cache space for ten advertisements, and the current cache prediction capacity is six advertisements. The number of advertisements preloaded each time can be 20% of the maximum capacity, so 2 cached advertisements can be preloaded each time, and then the next prediction can be performed. It should be understood that the advantage of performing multiple predictions before the cached advertisements meet the cache prediction capacity is that it can meet the changes in the real-time advertisement display scene or the update of the dynamic prediction model of the capacity of the elastic storage medium, thereby improving the accuracy of advertisement delivery (the dynamic most appropriate number of remaining advertisements improves the utilization rate of the advertisement material and the advertisement fill rate). For example, during the prediction process, the model can also be updated or the advertisement display scene can be changed.

[0075] Figure 4 This is a schematic flowchart of an advertisement data processing method according to another embodiment of the present invention. Figure 4 The advertising data processing methods include:

[0076] 410: Calculate the predicted capacity of cached advertisements of the first cache space that matches the advertisement display scenario.

[0077] 420: If the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, then based on the difference between the predicted capacity of cached ads and the remaining amount of cached ads, preload the first cached ads for display in the ad display scenario in the first cache space so that the remaining amount of cached ads is not less than the predicted capacity of cached ads.

[0078] When the remaining amount of cached ads is less than the current predicted capacity of cached ads, the preloading of cached ads is completed at one time based on the difference between the predicted capacity of cached ads and the remaining amount of cached ads. This improves the efficiency of dynamic adjustment of the predicted capacity of cached ads, thereby further improving the efficiency of advertising content distribution.

[0079] Figure 5This is a schematic flowchart of an advertisement data processing method according to another embodiment of the present invention. Figure 5 The advertising data processing method is applicable to any appropriate electronic device with data processing capabilities, including but not limited to mobile terminals (such as mobile phones, PADs, etc.) and PCs. Figure 5 The advertising data processing methods include:

[0080] 510: Send information indicating the current advertisement display scenario to the server to obtain the current cached advertisement predicted capacity of the cache space that matches the current advertisement display scenario.

[0081] It should be understood that the dynamic capacity prediction model can be deployed on the server side and trained and updated via the server side. The cache capacity tags of previous ad display scenarios can be annotated based on the advertising material utilization rate. For example, the annotated cache capacity tags can be used to obtain an advertising material utilization threshold. The advertising material utilization rate can be the previous advertising material utilization rate corresponding to the previous ad display scenario. The previous advertising material utilization rate can be reported to the server side via the mobile terminal.

[0082] 520: If the current remaining amount of cached ads in the cache space is less than the current predicted capacity of cached ads, cached ads for display in the current ad display scenario are obtained from the server and preloaded into the cache space until the current remaining amount of cached ads is not less than the current predicted capacity of cached ads.

[0083] It should be understood that the advertising cache space can have a maximum capacity, and the maximum capacity is not less than the predicted cache capacity. The maximum capacity can be a fixed capacity or a variable capacity. For example, the predicted cache capacity can be calculated multiple times over a period of time, and the maximum value among the multiple predicted cache capacities can be set as the maximum capacity. In addition, a value less than the maximum value can be set as the maximum capacity. For example, before determining the predicted cache capacity, an initial predicted cache capacity can be determined, and the initial predicted cache capacity can be compared with the maximum capacity. If the initial predicted cache capacity is greater than the maximum capacity, the maximum capacity is determined as the predicted cache capacity.

[0084] It should also be understood that when the number of remaining advertisements in the advertisement cache space is the predicted cache capacity, the advertisement module stops sending cache advertisement preloading requests, and the advertisement module can send advertisement display requests. When the number of remaining cached advertisements in the advertisement cache space is less than the predicted cache capacity, the advertisement module can send cache advertisement preloading requests and advertisement display requests. When there are no remaining cached advertisements in the advertisement cache space, the advertisement module stops sending advertisement display requests and can send cache advertisement preloading requests. In the scheme of the embodiment of the present invention, when the remaining amount of cached advertisements is less than the predicted capacity of cached advertisements, cached advertisements are preloaded until the remaining amount of cached advertisements is not less than the predicted capacity of cached advertisements, thereby keeping the remaining amount of cached advertisements dynamically consistent with the predicted capacity of cached advertisements that meets the advertisement display scenario, thereby improving the display effect of cached advertisements when the cached advertisements are displayed in the advertisement display scenario, and improving the efficiency of advertisement content distribution. In addition, since the current predicted capacity of cached advertisements is obtained from the server, the local computing resources of the mobile terminal are reduced.

[0085] Figure 6 This is a schematic block diagram of an advertisement data processing device according to another embodiment of the present invention. Figure 6 The advertising data processing device is suitable for execution by any appropriate electronic device with data processing capabilities, including but not limited to mobile terminals (such as mobile phones, PADs, etc.) and PCs. The advertising data processing device includes:

[0086] A calculation module 610 calculates a predicted capacity of cached advertisements of a first cache space that matches an advertisement display scenario;

[0087] The preloading module 620 preloads the first cached advertisement for display in the advertisement display scene in the first cache space if the remaining amount of the cached advertisement in the first cache space is less than the predicted capacity of the cached advertisement, until the remaining amount of the cached advertisement is not less than the predicted capacity of the cached advertisement.

[0088] In the solution of the embodiment of the present invention, cached ads are preloaded when the remaining amount of cached ads is less than the predicted capacity of cached ads, and are kept until the remaining amount of cached ads is not less than the predicted capacity of cached ads. Thus, the remaining amount of cached ads is kept dynamically consistent with the predicted capacity of cached ads that meets the ad display scenario, thereby improving the display effect of cached ads and improving the efficiency of ad content distribution when the cached ads are displayed in the ad display scenario.

[0089] In another implementation of the present invention, the preloading module is specifically used to: determine whether the current cached advertisement remaining amount is less than the current cached advertisement predicted capacity; if the current cached advertisement remaining amount is less than the current cached advertisement predicted capacity, preload a single first cached advertisement in the first cache space and update the first cache space; if the current cached advertisement remaining amount is not less than the current cached advertisement predicted capacity, stop preloading the first cached advertisement.

[0090] Since at least one of the user's advertising consumption information, network environment information, and hardware configuration information usually changes dynamically and has a strong correlation with the delivery of cached advertisements, the above information effectively indicates the advertisement display scenario. In addition, based on the advertisement preloading request of the cached advertisement, information indicating the advertisement display scenario is obtained, thereby avoiding the inaccuracy of obtaining information about the dynamically changing advertisement display scenario and improving the accuracy of information acquisition.

[0091] In another implementation of the present invention, the preloading module is specifically used to: if the remaining amount of cached advertisements in the first cache space is less than the predicted capacity of cached advertisements, then based on the difference between the predicted capacity of cached advertisements and the remaining amount of cached advertisements, preload the first cached advertisement for display in the advertisement display scene in the first cache space, so that the remaining amount of cached advertisements is not less than the predicted capacity of cached advertisements.

[0092] In another implementation of the present invention, the calculation module is specifically used to: input information indicating the advertisement display scene into the capacity dynamic prediction model to obtain the cached advertisement prediction capacity, wherein the capacity dynamic prediction model is obtained by classification training of advertisement display scene training samples.

[0093] In another implementation of the present invention, the device also includes: an acquisition module, which obtains information indicating the advertisement display scene based on the advertisement preloading request of the cached advertisement, and the information indicating the advertisement display scene includes at least one of user advertisement consumption information, network environment information and hardware configuration information.

[0094] In another implementation of the present invention, the preloading module is further configured to: pre-cache a second cached advertisement in the second cache space after a previous bidding round. In the previous bidding round, the cached advertisement in the first cache space had a higher bid ranking than the second cached advertisement. The preloading module is specifically configured to: move the second cached advertisement from the second cache space to the first cache space as the first cached advertisement after a current bidding round. In the current bidding round, the second cached advertisement had a higher ranking than the cached advertisement in the first cache space.

[0095] In another embodiment of the present invention, the calculation module is specifically configured to: monitor dynamic changes in the advertisement display scene to obtain information indicating the current advertisement display scene; and calculate, based on a preload request for the cached advertisement, a predicted capacity of a first cache space that matches the current advertisement display scene.

[0096] The device of this embodiment is used to implement the corresponding methods in the aforementioned multiple method embodiments and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here. In addition, the functional implementation of each module in the device of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be described in detail here.

[0097] Figure 7 This is a schematic block diagram of an advertisement data processing device according to another embodiment of the present invention. Figure 7 The advertising data processing device is suitable for execution by any appropriate electronic device with data processing capabilities, including but not limited to mobile terminals (such as mobile phones, PADs, etc.) and PCs. The advertising data processing device includes:

[0098] The transceiver module 710 sends information indicating the current advertisement display scenario to the server to obtain the current cached advertisement predicted capacity of the cache space matching the current advertisement display scenario;

[0099] The preloading module 720 obtains cached ads for display in the current ad display scene from the server if the current cached ad remaining amount in the cache space is less than the current cached ad predicted capacity, and preloads the cached ads into the cache space until the current cached ad remaining amount is not less than the current cached ad predicted capacity.

[0100] In the solution of the embodiment of the present invention, when the remaining cached ad quantity is less than the predicted cached ad capacity, cached ads are preloaded until the remaining cached ad quantity is no less than the predicted cached ad capacity. This ensures that the remaining cached ad quantity is dynamically consistent with the predicted cached ad capacity that matches the ad display scenario. This improves the display quality of the cached ad when the cached ad is displayed in the ad display scenario and enhances the efficiency of ad content distribution. Furthermore, since the current predicted cached ad capacity is obtained from the server, local computing resources on the mobile terminal are reduced.

[0101] The device of this embodiment is used to implement the corresponding methods in the aforementioned multiple method embodiments and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here. In addition, the functional implementation of each module in the device of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be described in detail here.

[0102] Figure 8 The hardware structure of the electronic device of another embodiment of the present invention is as follows; Figure 8As shown, the hardware structure of the electronic device may include: a processor 801, a communication interface 802, a storage medium 803 and a communication bus 804;

[0103] The processor 801, the communication interface 802, and the storage medium 803 communicate with each other via the communication bus 804;

[0104] Optionally, the communication interface 802 may be an interface of a communication module;

[0105] The processor 801 may be specifically configured to: calculate a predicted cached advertisement capacity of a first cache space that matches an advertisement display scenario; if the remaining cached advertisement amount in the first cache space is less than the predicted cached advertisement capacity, preload a first cached advertisement for display in the advertisement display scenario into the first cache space until the remaining cached advertisement amount is no less than the predicted cached advertisement capacity;

[0106] Alternatively, information indicating the current advertising display scenario is sent to the server to obtain the current cached advertising predicted capacity of the cache space that matches the current advertising display scenario; if the current cached advertising remaining amount in the cache space is less than the current cached advertising predicted capacity, the cached advertising for display in the current advertising display scenario is obtained from the server and preloaded into the cache space until the current cached advertising remaining amount is not less than the current cached advertising predicted capacity.

[0107] Processor 1001 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0108] The storage medium 1003 can be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0109] 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 storage medium, and the computer program includes a program code configured to execute the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present invention are executed. It should be noted that the storage medium described in the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, 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), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, 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, an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any storage medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program configured for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical cable, RF, etc., or any suitable combination of the foregoing.

[0110] Computer program code configured to perform the operations of the present invention may be written in one or more programming languages, or a combination thereof, including 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 a 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).

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or portion of code, which contains one or more executable instructions configured to implement the specified logical function. The above-mentioned specific embodiments have specific sequential relationships, but these sequential relationships are merely exemplary. During the specific implementation, these steps may be fewer, more, or the execution order may be adjusted. In other words, in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, or 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, as well as combinations of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0112] The modules involved in the embodiments of the present invention may be implemented in software or hardware, and the names of these modules do not necessarily limit the modules themselves.

[0113] As another aspect, the present invention further provides a storage medium storing a computer program, which implements the method described in the above embodiment when executed by a processor.

[0114] As another aspect, the present invention further provides a storage medium, which may be included in the apparatus described in the above embodiment; or it may exist independently and not be assembled into the apparatus. The above storage medium carries one or more programs, and when the above one or more programs are executed by the apparatus, the apparatus: calculates the predicted capacity of cached ads in a first cache space that matches an ad display scenario; if the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, preloads the first cached ads for display in the ad display scenario into the first cache space until the remaining amount of cached ads is no less than the predicted capacity of cached ads;

[0115] Alternatively, information indicating the current advertising display scenario is sent to the server to obtain the current cached advertising predicted capacity of the cache space that matches the current advertising display scenario; if the current cached advertising remaining amount in the cache space is less than the current cached advertising predicted capacity, the cached advertising for display in the current advertising display scenario is obtained from the server and preloaded into the cache space until the current cached advertising remaining amount is not less than the current cached advertising predicted capacity.

[0116] The terms "first," "second," "the first," or "the second" used in various embodiments of the present disclosure may modify various components regardless of order and / or importance, but these terms do not limit the corresponding components. The above terms are configured solely for the purpose of distinguishing an element from other elements. For example, a first user device and a second user device represent different user devices, even though both are user devices. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element without departing from the scope of the present disclosure.

[0117] When one element (for example, a first element) is referred to as being “(operably or communicably) coupled” or “(operably or communicably) coupled to” or “connected to” another element (for example, a second element), it should be understood that the one element is directly connected to the other element or that the one element is indirectly connected to the other element via yet another element (for example, a third element). Conversely, it should be understood that when an element (for example, a first element) is referred to as being “directly connected” or “directly coupled” to another element (the second element), there is no element (for example, a third element) interposed therebetween.

[0118] The above description is merely an illustration of the preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

Claims

1. A method for processing advertising data, comprising: Calculating a predicted capacity of cached advertisements of a first cache space that matches the advertisement display scenario; If the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, preloading a first cached ad for display in the ad display scenario into the first cache space until the remaining amount of cached ads is no less than the predicted capacity of cached ads; Among them, the calculation of the cache advertisement prediction capacity of the first cache space matching the advertisement display scene includes: inputting information indicating the advertisement display scene into a capacity dynamic prediction model to obtain the cache advertisement prediction capacity, wherein the capacity dynamic prediction model is obtained by classification training through advertisement display scene training samples.

2. The method according to claim 1, wherein If the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, preloading a first cached ad for display in the ad display scenario into the first cache space until the remaining amount of cached ads is no less than the predicted capacity of cached ads, including: determining whether the current remaining amount of cached advertisements is less than the predicted capacity of the cached advertisements, and if so, preloading a single first cached advertisement into the first cache space and updating the first cache space; If the remaining amount of the current cached advertisement is not less than the predicted capacity of the cached advertisement, preloading the first cached advertisement is stopped.

3. The method according to claim 1, wherein If the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, preloading a first cached ad for display in the ad display scenario into the first cache space until the remaining amount of cached ads is no less than the predicted capacity of cached ads, including: If the remaining amount of cached ads in the first cache space is less than the predicted capacity of cached ads, then based on the difference between the predicted capacity of cached ads and the remaining amount of cached ads, the first cached ads for display in the ad display scenario are preloaded in the first cache space so that the remaining amount of cached ads is not less than the predicted capacity of cached ads.

4. The method according to claim 1, wherein Also includes: According to the advertisement preloading request of the cached advertisement, information indicating the advertisement display scenario is obtained, where the information indicating the advertisement display scenario includes at least one of user advertisement consumption information, network environment information, and hardware configuration information.

5. The method according to claim 1, wherein The method further comprises: After a previous round of bidding, a second cached advertisement is pre-cached in a second cache space, wherein in the previous round of bidding, the to-be-cached advertisement in the first cache space has a higher bidding ranking than the second cached advertisement, The step of preloading the first cached advertisement for display in the advertisement display scene into the first cache space includes: After the current round of bidding, the second cached advertisement is moved from the second cache space to the first cache space as the first cached advertisement, wherein, in the current round of bidding, the second cached advertisement has a higher ranking than the to-be-cached advertisement in the first cache space.

6. The method according to claim 1, wherein The calculating of the predicted capacity of cached advertisements of the first cache space matching the advertisement display scenario further includes: monitoring dynamic changes of the advertisement display scene to obtain information indicating the current advertisement display scene; According to the preloading request of the cached advertisement, a predicted capacity of the cached advertisement of a first cache space matching the current advertisement display scenario is calculated.

7. A method for processing advertising data, applied to a mobile terminal, comprising: Sending information indicating the current advertisement display scene to a server to obtain a current cached advertisement predicted capacity of a cache space that matches the current advertisement display scene, wherein the current cached advertisement predicted capacity is obtained by the server inputting the information indicating the current advertisement display scene into a capacity dynamic prediction model, the capacity dynamic prediction model being obtained by classification training using advertisement display scene training samples; If the current cached advertisement remaining amount in the cache space is less than the current cached advertisement predicted capacity, cached advertisements for display in the current advertisement display scene are obtained from the server and preloaded into the cache space until the current cached advertisement remaining amount is no less than the current cached advertisement predicted capacity.

8. An advertising data processing device, comprising: A calculation module, calculating a predicted capacity of cached advertisements of a first cache space that matches an advertisement display scenario; a preloading module, configured to preload a first cached advertisement for display in the advertisement display scene into the first cache space if the remaining amount of cached advertisements in the first cache space is less than the predicted capacity of cached advertisements, until the remaining amount of cached advertisements is no less than the predicted capacity of cached advertisements; Among them, the calculation module calculates the cache advertisement prediction capacity of the first cache space matching the advertisement display scene, including: inputting information indicating the advertisement display scene into the capacity dynamic prediction model to obtain the cache advertisement prediction capacity, wherein the capacity dynamic prediction model is obtained by classification training through advertisement display scene training samples.

9. An advertisement data processing device, applied to a mobile terminal, comprising: a transceiver module, configured to send information indicating a current advertisement display scenario to a server, so as to obtain a current cache advertisement predicted capacity of a cache space that matches the current advertisement display scenario, wherein the current cache advertisement predicted capacity is obtained by the server inputting the information indicating the current advertisement display scenario into a capacity dynamic prediction model, the capacity dynamic prediction model being obtained by classification training using advertisement display scenario training samples; A preloading module obtains cached advertisements for display in the current advertisement display scene from the server if the current cached advertisement remaining amount in the cache space is less than the current cached advertisement predicted capacity, and preloads the cached advertisements into the cache space until the current cached advertisement remaining amount is not less than the current cached advertisement predicted capacity.

10. An electronic device, comprising: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, where the executable instruction enables the processor to perform an operation corresponding to the method according to any one of claims 1 to 7.

11. A storage medium storing a computer program, wherein when the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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