Interactive advertisement putting method, system, equipment and medium

Abstract

The invention is applicable to the field of advertisement putting, and discloses an interactive advertisement putting method, which comprises the following steps of: making advertisement content; setting an interaction element in the advertisement content; putting the made advertisement content to a target platform; collecting and analyzing interaction data of the user; and adjusting the display speed and the switching speed of the interactive elements based on the interactive data of the user. The interaction elements are arranged in the advertisement content, and the display speed and the switching speed are dynamically adjusted according to the real-time feedback of the user, so that the participation degree and the interaction experience of the user can be remarkably improved. For users with high interestingness and concentrated attention, the display speed is properly reduced and the interaction time is prolonged, so that the understanding and memory of the users on the advertisement content can be deepened; and for the users with low interestingness and distraction, the switching speed is accelerated to attract the attention of the users and maintain the participation degree of the users, so that the participation degree and retention rate of the users are improved, the click rate and conversion rate of the advertisement are increased, and the maximization of the advertisement effect is realized.

Description

Technical Field

[0001] The present invention relates to the field of advertisement delivery, and in particular to an interactive advertisement delivery method, system, device and medium. Background Art

[0002] With the rapid development of Internet technology and the booming game industry, Internet game operators are facing unprecedented challenges in advertising. Traditional graphic and video advertising can no longer meet the growing and diversified needs of users. Users generally have visual fatigue for such ads, which greatly reduces the advertising effect. Importantly, different users have different reactions and interests to advertising content, but traditional advertising cannot be dynamically adjusted according to real-time feedback from users, resulting in inconsistent user experience and some users feel bored or annoyed. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes an interactive advertising delivery method, system, device and medium.

[0004] In a first aspect, the present application proposes an interactive advertising delivery method, comprising: preparing advertising content;

[0005] providing interactive elements in the advertising content;

[0006] Delivering the prepared advertising content to the target platform;

[0007] Collect and analyze user interaction data;

[0008] Based on the user's interactive data, the display speed and switching speed of the interactive elements are adjusted.

[0009] More specifically, in the above technical solution, adjusting the display speed and switching speed of the interactive element based on the user's interactive data specifically includes:

[0010] The interaction data includes click, hover, slide interaction operations and dwell time;

[0011] Obtaining the user's interest and attention level on the current interactive element based on the interactive data;

[0012] Predict the user's interest and attention level and obtain the prediction results;

[0013] If the prediction result indicates that the user is highly interested and focused, then the display speed of the interactive element is reduced to extend the interactive time of the interactive element;

[0014] If the prediction result indicates that the user's interest is low and the user's attention is scattered, the display speed of the interactive element is increased, and the switching speed of the interactive element is accelerated.

[0015] More specifically, in the above technical solution, the user's interaction data is collected in real time through the embedding technology;

[0016] Using a data analysis platform to clean, process and analyze the collected interaction data;

[0017] By setting the threshold of behavioral indicators, the weighted average algorithm is used to calculate the user's current interest and attention level. Behavioral indicators include click rate, number of hovers, number of slides, and length of stay.

[0018] More specifically, in the above technical solution, after the advertisement content is delivered, the distance data between the user and the interactive element is obtained, and the distance data is sent to the speed adjustment module;

[0019] The speed adjustment module determines the speed parameter and the size parameter corresponding to the distance data according to a pre-established distance-speed mapping table and a distance-size mapping table;

[0020] If the distance data is less than a preset threshold, a corresponding speed parameter less than the preset threshold is obtained from the distance-speed mapping table, and a corresponding size parameter greater than the preset threshold is obtained from the distance-size mapping table;

[0021] If the distance data is greater than a preset threshold, a corresponding speed parameter greater than the preset threshold is obtained from the distance-speed mapping table, and a corresponding size parameter less than the preset threshold is obtained from the distance-size mapping table;

[0022] Sending the speed parameter and the size parameter output by the speed adjustment module to the interactive element rendering module;

[0023] The interactive element rendering module adjusts the scaling ratio and displacement increment of the mesh model of the interactive element according to the received speed parameter and the size parameter to obtain an adjusted interactive element;

[0024] The adjusted interactive element is presented to the user with corresponding speed parameters and size parameters.

[0025] More specifically, in the above technical solution, after the advertisement content is delivered, the interactive element image resource to be optimized is obtained, and the original image is compressed to obtain a compressed image file;

[0026] Acquire the user's network bandwidth through a network speed detection interface, and determine the target resolution of the image according to the user's network bandwidth;

[0027] Obtain device performance parameters through the device hardware information interface and determine the target quality level of the image based on the device performance;

[0028] Based on the compressed image file, according to the target resolution and the target quality level, a scaling algorithm and a quality adjustment algorithm are used to generate image resources adapted to the user network and device;

[0029] The generated image resource is applied to the interactive element to obtain the image loading speed parameter. If the loading completion time is greater than or equal to a preset threshold, the resolution or quality is reduced to reduce the size of the image file until the loading speed requirement is met.

[0030] More specifically, in the above technical solution, the optimized image resources are applied in the interactive elements, the user's interactive operation data is collected through embedding points, the interactive operation data is input into the pre-trained machine learning model, and the user's experience score is obtained through the machine learning model;

[0031] The image resource optimization strategy is adjusted according to the user's experience score. If the score is lower than a preset threshold, the image compression rate is further increased and the resolution is reduced during the next optimization.

[0032] More specifically, in the above technical solution, placing the prepared advertising content on the target platform includes:

[0033] Acquire effect data of the advertisement content, establish an advertisement delivery effect prediction model based on the effect data, predict delivery results according to the advertisement delivery effect prediction model, and adjust the delivery strategy;

[0034] Use clustering algorithms to aggregate and analyze advertising effects, classify advertising combinations with similar effects, and discover advertising combinations with the best click-through rate and conversion rate;

[0035] Adjust advertisement delivery parameters according to the optimal advertisement combination.

[0036] In a second aspect, the present application also proposes an interactive advertising delivery system, comprising:

[0037] The first processing module: creating advertising content;

[0038] The second processing module: setting interactive elements in the advertisement content;

[0039] The third processing module: delivering the prepared advertising content to the target platform;

[0040] Fourth processing module: collect and analyze user interaction data;

[0041] The fifth processing module: based on the user's interactive data, adjusts the display speed and switching speed of the interactive elements.

[0042] In a third aspect, the present application further proposes a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0043] In a fourth aspect, the present application further proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in any one of the above items is implemented.

[0044] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0045] 1. The present invention obtains the user's feedback data and behavior data (such as clicks, hovering, sliding and dwell time) in real time during the advertising interaction process, and dynamically adjusts the display speed and switching speed of interactive elements based on these data. For example, when the user shows high interest and concentration, the display speed is reduced to extend the interaction time; conversely, the switching speed is accelerated to maintain the user's enthusiasm for participation, which significantly improves the user experience and the attractiveness of the advertising content.

[0046] 2. By analyzing advertising performance data, we use the logistic regression algorithm to establish an advertising effect prediction model, and dynamically optimize the advertising strategy based on the model prediction results. This includes adjusting the delivery time, frequency and channel, and concentrating the advertising budget on the best advertising combination, thereby maximizing the return on investment of advertising.

[0047] 3. The present invention uses sensors such as depth cameras or laser radars to obtain real-time distance data between users and interactive elements, and dynamically adjusts the scale and displacement increment of the grid model of the interactive elements based on these data. For example, when a user approaches an interactive element, its display size is increased and its movement speed is reduced to enhance the user's immersion and interactivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flowchart of an interactive advertising delivery method provided by an embodiment of the present invention;

[0050] Figure 2 It is a structural diagram of an interactive advertising delivery system provided by an embodiment of the present invention;

[0051] Figure 3 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0053] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0054] It should also be understood that the term “and / or” used in the specification and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0055] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0056] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0057] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0058] See also Figure 1 The present application proposes an interactive advertising delivery method, comprising: S101, setting interactive elements in the advertising content; S102, setting interactive elements in the advertising content; S103, delivering the prepared advertising content to a target platform; S104, collecting and analyzing user interaction data; S105, adjusting the display speed and switching speed of the interactive elements based on the user interaction data.

[0059] Obtain real-time feedback data and behavior data of users during the advertising interaction process, including click, hover, slide interaction operations and dwell time data; use website analysis tools to perform statistical analysis on the user feedback and behavior data to obtain the user's interest and attention level for the current interactive element; use a logistic regression algorithm to model the user feedback and behavior data to obtain a logistic regression model; predict the user's interest and attention level based on the logistic regression model to obtain a prediction result; if the prediction result shows that the user is highly interested and focused, reduce the display speed of the interactive element and extend the interaction time of the element; if the prediction result shows that the user is not interested and distracted, increase the display speed of the interactive element and speed up the element switching rhythm to maintain the user's participation and interactive enthusiasm. Through dynamic adjustment of speed, the interactive experience of advertising content and user participation can be improved.

[0060] Optionally, while adjusting the display speed of interactive elements, a collaborative filtering algorithm is used to obtain personalized interactive content recommendation results based on the user's historical interactive behavior data and the preferences of other similar users; the personalized interactive content recommendation results are displayed to users to improve the attractiveness and conversion rate of the advertisement; by embedding code in the advertisement page, the user's interactive behavior and feedback in the optimized advertisement are continuously monitored to obtain key data indicators such as user engagement, interaction duration, and completion rate; the collected user interaction data is imported into a data analysis tool to compare and analyze the interactive performance of different user groups to obtain analysis results; according to the analysis results, the advertising delivery time period, frequency, and channel on the target platform are dynamically adjusted to improve delivery accuracy and return on investment; database query statements are used to perform regular aggregate query and statistical analysis on the interactive data to obtain user behavior patterns and preference change trends; a visualization library is used to generate charts to intuitively display the analysis results; based on the data analysis results, the creative strategies and technical implementation solutions of interactive advertisements are continuously iterated and optimized to continuously improve user experience and advertising effectiveness.

[0061] Specifically, during the advertising interaction process, the user's interactive operation data such as clicks, hovering, sliding, and behavioral data such as dwell time can be collected in real time through the embedding technology. The collected user feedback and behavioral data are cleaned, processed and analyzed using the data analysis platform, and the user's interest and attention level for the current interactive elements are judged by setting thresholds for key behavioral indicators (such as click-through rate, hovering time, interaction depth, etc.). For the processed user behavior data, decision tree algorithms (such as CART, C5, etc.) or neural network algorithms (such as BP neural network) can be used for modeling and prediction, and user behavior features can be extracted through feature engineering to train a machine learning model that can accurately predict the user's interest and attention level. According to the user's interest and attention level predicted by the model, the display speed of the interactive elements is dynamically adjusted. For example, for users with high interest (such as interest index>80%) and concentrated attention (such as average dwell time>15 seconds), the display speed is reduced to 50% to extend the interaction time; for users with low interest (such as interest index<50%) and distracted attention (such as average dwell time<5 seconds), the display speed is increased to 150% to speed up the element switching rhythm. At the same time, collaborative filtering, content recommendation and other algorithms are used, combined with user historical behavior data and preference tags, to dynamically recommend personalized interactive content to different users, thereby increasing advertising appeal and conversion rates. By real-time monitoring of user interaction data of optimized ads, key data indicators (such as engagement, interaction time, completion rate, etc.) are calculated to evaluate the optimization effect. For example, the average interaction time of optimized ads is increased by 20%, and the completion rate is increased by 15%. Finally, the interactive data is used to dynamically optimize the advertising delivery strategy, and parameters such as the time period, frequency, and channel of advertising delivery are adjusted according to the differences in the interactive performance of user groups. For example, for young user groups with active interactions, the delivery of short video platforms is increased, and the delivery time period is concentrated between 8 and 10 pm. At the same time, user behavior mining and analysis are regularly carried out to gain insight into the changing trends of user preferences, and interactive advertising strategies are continuously iterated and optimized to provide users with a more personalized and immersive interactive advertising experience.

[0062] Interactive elements are well-known technologies in the field of advertising, and are not described in detail in this application. Those skilled in the art may adopt existing interactive elements according to actual needs.

[0063] Optionally, during the display of interactive elements, the system collects the user's interactive operation data such as clicks, hovers, and slides and the dwell time data every 2 seconds. By counting the number of clicks, hovers, slides, and average dwell time of the user in the past minute, the weighted average algorithm is used to calculate the user's current interest and attention level, where the click operation weight is 4, the hover operation weight is 3, the slide operation weight is 2, and the dwell time weight is 1 (the weight distribution here is assumed to simplify the calculation, and can be adjusted according to the specific situation in actual application). Then, for each interactive element, the system will count the number of user interaction operations and dwell time, and compare them with the preset threshold. For example, if the user clicks on an element more than 10 times and the average dwell time exceeds 5 seconds, it is judged that the user has a high interest in the element. Taking into account the interest and attention level, the system will generate a user preference vector, such as using the TF-IDF algorithm to extract features and weight the user behavior data.

[0064] The interest and attention levels measured in real time may be affected by a variety of factors, such as accidental user behavior, device performance fluctuations, etc., resulting in large volatility and errors in the data. Prediction models usually smooth the input data to reduce the impact of noise and outliers, thereby obtaining more stable and reliable prediction results. This helps avoid frequent adjustments to the display and switching speeds of interactive elements due to short-term data fluctuations, and improves the stability of the user experience.

[0065] Advertising can be placed on different platforms such as social media, gaming platforms, video sites, etc.

[0066] In some embodiments, delivering the prepared advertising content with interactive elements to a target platform includes:

[0067] Acquire advertising effect data, use logistic regression algorithm to extract effect features, establish advertising effect prediction model, use historical data to train and evaluate the model, and dynamically optimize and adjust the delivery strategy according to the prediction results of the model; use K-means clustering algorithm to aggregate and analyze advertising delivery effects, classify advertising combinations with similar effects, and mine the delivery combinations with the best click-through rate and conversion rate indicators; according to the best delivery combination, dynamically adjust advertising delivery parameters, continuously optimize delivery strategy, concentrate advertising budget on the advertising combination with the best effect, and maximize the return on advertising investment.

[0068] Specifically, after obtaining the prepared advertising content with interactive elements, the target platform suitable for delivery is determined by analyzing the material type, interactive form, etc. of the advertising content. For example, for an advertisement containing interactive elements such as videos and questionnaires, social media platforms such as WeChat official accounts and Weibo can be selected for delivery. For the target platform WeChat official account, the advertising delivery system provided by it is used to set the audience targeting conditions for advertising delivery, such as female users aged 18-35 who are interested in beauty and makeup, and accurate audience screening is performed based on user attribute data. The screened target audience is grouped according to dimensions such as age group and region, and the advertising materials are optimized and dynamically displayed for different audience groups through the collaborative filtering algorithm model to improve the click-through rate and conversion rate of the advertisement. During the advertising delivery process, the effect data such as advertising exposure, clicks, and conversions are continuously obtained, and real-time data statistics and analysis are performed through the effect monitoring platform. According to the results of the advertising effect data analysis, a logistic regression algorithm is used to establish an advertising delivery effect prediction model, and the advertising click-through rate is predicted through feature variables such as advertising creativity, delivery time period, and user attributes, and the delivery strategy is dynamically optimized and adjusted based on the model prediction results. Through the advertising effect monitoring platform, we obtain the effect data of different dimensions such as advertising creativity, delivery time, and delivery area, and use the K-means clustering algorithm to conduct multi-dimensional aggregation analysis of advertising delivery effects, and find out the best delivery combinations with the highest click-through rate, such as the delivery time period of 20:00-22:00, the delivery area of ​​first-tier cities, and the advertising creativity of short videos. According to the best delivery combination obtained by aggregation analysis, we dynamically adjust the time period, area, creativity, etc. of advertising delivery, continuously optimize the delivery strategy, and maximize the ROI of advertising delivery. After one month of delivery optimization, the advertising click-through rate increased from 1% to 5%, the conversion rate increased from 5% to 2%, and the advertising delivery ROI increased by 300%.

[0069] In some embodiments, when setting interactive elements in advertising content, image coding optimization technology is used to efficiently compress and encode the image resources of the interactive elements, reduce the file size, and improve the loading speed. At the same time, the resolution and quality of the image are dynamically adjusted in combination with the user's network conditions and device performance to ensure the clarity and smoothness of the interactive elements. Through the integration of image coding optimization and loading speed improvement, users can be provided with a fast and high-quality interactive experience, improving the user experience and conversion rate of advertising content.

[0070] Obtain the interactive element image resources to be optimized, compress the original image in JPEG or PNG format, and obtain a compressed image file; obtain the user's network bandwidth through the network speed detection interface, and determine the target resolution of the image based on the network bandwidth; obtain the device CPU / GPU performance parameters through the device hardware information interface, and determine the target quality level of the image based on the device performance; for the compressed image file, use the scaling algorithm and quality adjustment algorithm to generate image resources adapted to the user's network and device according to the target resolution and target quality level; apply the generated image resources to the interactive elements, and obtain the image loading speed parameters through the front-end performance monitoring tool. If the loading completion time is greater than or equal to the preset threshold, reduce the image file size by reducing the resolution or quality until the loading speed requirements are met.

[0071] Preferably, the optimized image resources are applied in the interactive elements, the user's interactive operation data is collected through embedding points, the interactive data is input into the pre-trained machine learning model, and the user experience score is obtained through model reasoning; the image resource optimization strategy is dynamically adjusted according to the user experience score, and if the score is lower than the preset threshold, the image compression rate is further increased and the resolution is reduced during the next optimization, so as to improve the user's interactive experience through continuous optimization iteration.

[0072] Specifically, in the process of optimizing interactive element image resources, first obtain the image resources to be optimized, and use efficient compression algorithms such as JPEG and PNG to perform encoding optimization processing on the image. For example, for an image with a resolution of 1920×1080, the JPEG algorithm can be used for compression, and the compression quality is set to 80% to obtain a compressed image file. Then, according to the preset file size threshold (such as 500KB), it is judged whether the size of the compressed image file meets the requirements. If not, the compression parameters are adjusted (such as reducing the compression quality to 70%), and the compression is re-executed until the file size requirements are met. Next, the user's network status parameters (such as network bandwidth, delay, etc.) and device performance parameters (such as CPU, memory, etc.) are obtained, and the target resolution of the image is determined according to the network status (such as reducing the resolution to 1280×720 in a low-bandwidth environment), and the target quality level of the image is determined according to the device performance (such as reducing the quality level to medium on a low-end device). For the optimized image resources, the bilinear interpolation algorithm is used to scale the image to the target resolution, and the image quality is adjusted according to the target quality level to generate image resources adapted to the user's network and device. In order to verify the optimization effect, the adjusted image resources are loaded and tested to obtain the loading speed parameters (such as loading time, frame rate, etc.). If the loading time exceeds 500ms or the frame rate is lower than 30fps, it is considered that the loading speed does not meet the requirements and further optimization of image resources is required (such as reducing resolution, compression quality, etc.). The optimization steps are repeated until the loading speed requirements are met. Finally, the optimized image resources are applied to the interactive elements. By monitoring the user's interactive behavior data (such as click-through rate, dwell time, etc.), the decision tree algorithm is used to evaluate the user experience and obtain the experience score. If the score is lower than 5 points (out of 5 points), it is considered that the user experience is not good and the image resources need to be further optimized, such as increasing the compression rate by 10% and reducing the resolution by 20%; if the score is higher than 4 points, it is considered that the user experience is good and the current optimization strategy can be maintained. Through continuous optimization iteration, the user's interactive experience is continuously improved, and user satisfaction and retention rate are improved.

[0073] In some embodiments, in the interactive design of advertising content, speed distance perception technology can be used to dynamically adjust the display speed and size of interactive elements according to the distance between the user and the interactive element. Through speed distance perception, a more three-dimensional and realistic interactive experience can be provided to users, enhancing the user's sense of presence and participation. For example, when a user is close to an interactive element, the size of the element can be appropriately enlarged and the display speed can be reduced; when the user is away from the element, the size can be reduced and the speed can be increased. Speed ​​distance perception can create a sense of spatial depth and enhance the visual impact and appeal of advertising content.

[0074] Acquire distance data between the user and the interactive element, and transmit the distance data to the speed adjustment module;

[0075] The speed adjustment module uses a K-nearest neighbor algorithm to determine the speed parameter and size parameter corresponding to the distance data according to a pre-established distance-speed mapping table and a distance-size mapping table;

[0076] If the distance data is less than a preset threshold, a corresponding speed parameter less than the preset threshold is obtained from the distance-speed mapping table, and a corresponding size parameter greater than the preset threshold is obtained from the distance-size mapping table;

[0077] If the distance data is greater than a preset threshold, a corresponding speed parameter greater than the preset threshold is obtained from the distance-speed mapping table, and a corresponding size parameter less than the preset threshold is obtained from the distance-size mapping table;

[0078] Passing the speed parameter and size parameter output by the speed adjustment module to the interactive element rendering module;

[0079] The interactive element rendering module dynamically adjusts the scale and displacement increment of the grid model of the interactive element according to the received speed parameter and size parameter to obtain the adjusted interactive element;

[0080] Presenting the adjusted interactive element to the user with corresponding speed parameters and size parameters;

[0081] Optionally, continuously obtain data on changes in the distance between the user and the interactive element, obtain the latest distance data at a preset time interval, and pass the latest distance data to the speed adjustment module to trigger real-time adjustment of the speed and size of the interactive element.

[0082] Specifically, the distance data between the user and the interactive element is obtained in real time through sensors such as depth cameras or laser radars, and the distance data is input into the speed adjustment model based on the neural network. The speed adjustment model adopts a three-layer fully connected network structure, the input layer receives the distance data, the middle layer contains 64 neurons, and the output layer outputs the speed parameter and size parameter. When the distance between the user and the interactive element is less than 1 meter, the model outputs a speed parameter of 5 and a size parameter of 2; when the distance is greater than 3 meters, the model outputs a speed parameter of 0 and a size parameter of 8. The training data set of the speed adjustment model contains 1,000 sets of optimal speed and size parameters at different distances. The Adam optimizer is used with a learning rate of 0.01 and 100 epochs for training, and finally an accuracy rate of 95% is achieved on the test set. The interactive element rendering module is developed based on the Unity3D engine, receives the speed parameters and size parameters output by the speed adjustment model, and realizes the dynamic adjustment of the speed and size of the interactive element by setting the scaling ratio and displacement increment of the GameObject. For example, when the speed parameter is 5 and the size parameter is 9, the moving speed of the interactive element increases by 50% and the display size decreases by 10%. The rendering module also uses lighting and shadow effects to create a realistic sense of spatial depth. The system obtains the distance data between the user and the interactive element every 100 milliseconds, and adjusts the speed and size of the interactive element in real time to ensure the smoothness and continuity of the interactive experience. Through this distance-based speed and size adaptive adjustment technology, the spatial sense and realism of the virtual interactive scene are enhanced, and the user's immersion and participation are improved.

[0083] See also Figure 2 , the present application also proposes an interactive advertising delivery system, comprising:

[0084] The first processing module 201: creating advertisement content;

[0085] Second processing module 202: setting interactive elements in the advertisement content;

[0086] The third processing module 203: delivering the prepared advertising content to the target platform;

[0087] Fourth processing module 204: collecting and analyzing user interaction data;

[0088] The fifth processing module 205: based on the user's interactive data, adjusts the display speed and switching speed of the interactive element.

[0089] It is understandable that if Figure 1 The contents of the interactive advertising delivery method embodiment shown in the figure are applicable to the interactive advertising delivery system embodiment. The functions specifically implemented by the interactive advertising delivery system embodiment are similar to those in the embodiment shown in the figure. Figure 1 The interactive advertising delivery method embodiment shown is the same as that shown in the embodiment, and the beneficial effects achieved are the same as those Figure 1 The beneficial effects achieved by the illustrated interactive advertising delivery method embodiment are also the same.

[0090] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0091] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0092] See also Figure 3 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, an interactive advertising delivery method as described in any one of the above methods is implemented.

[0093] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0094] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0095] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (Smart Med iaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0096] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the interactive advertising delivery method as described in any one of the above methods is implemented.

[0097] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0098] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An interactive advertising delivery method, characterized in that: include: Create advertising content; providing interactive elements in the advertising content; Delivering the prepared advertising content to the target platform; Collect and analyze user interaction data; Based on the user's interactive data, the display speed and switching speed of the interactive elements are adjusted.

2. The interactive advertising delivery method according to claim 1, characterized in that: Based on the user's interactive data, the display speed and switching speed of the interactive element are adjusted, specifically including: The interaction data includes click, hover, slide interaction operations and dwell time; Obtaining the user's interest and attention level on the current interactive element based on the interactive data; Predict the user's interest and attention level and obtain the prediction results; If the prediction result indicates that the user is highly interested and focused, then the display speed of the interactive element is reduced to extend the interactive time of the interactive element; If the prediction result indicates that the user's interest is low and the user's attention is scattered, the display speed of the interactive element is increased, and the switching speed of the interactive element is accelerated.

3. The interactive advertising delivery method according to claim 2, characterized in that: Collect the user's interaction data in real time through embedding technology; Using a data analysis platform to clean, process and analyze the collected interaction data; By setting the threshold of behavioral indicators, the weighted average algorithm is used to calculate the user's current interest and attention level. Behavioral indicators include click rate, number of hovers, number of slides, and length of stay.

4. The interactive advertising delivery method according to claim 1, characterized in that: After the advertisement content is delivered, the distance data between the user and the interactive element is obtained, and the distance data is sent to the speed adjustment module; The speed adjustment module determines the speed parameter and the size parameter corresponding to the distance data according to a pre-established distance-speed mapping table and a distance-size mapping table; If the distance data is less than a preset threshold, a corresponding speed parameter less than the preset threshold is obtained from the distance-speed mapping table, and a corresponding size parameter greater than the preset threshold is obtained from the distance-size mapping table; If the distance data is greater than a preset threshold, a corresponding speed parameter greater than the preset threshold is obtained from the distance-speed mapping table, and a corresponding size parameter less than the preset threshold is obtained from the distance-size mapping table; Sending the speed parameter and the size parameter output by the speed adjustment module to the interactive element rendering module; The interactive element rendering module adjusts the scaling ratio and displacement increment of the mesh model of the interactive element according to the received speed parameter and the size parameter to obtain an adjusted interactive element; The adjusted interactive element is presented to the user with corresponding speed parameters and size parameters.

5. The interactive advertising delivery method according to claim 1, characterized in that: After the advertisement content is delivered, the interactive element image resource to be optimized is obtained, and the original image is compressed to obtain a compressed image file; Acquire the user's network bandwidth through a network speed detection interface, and determine the target resolution of the image according to the user's network bandwidth; Obtain device performance parameters through the device hardware information interface and determine the target quality level of the image based on the device performance; Based on the compressed image file, according to the target resolution and the target quality level, a scaling algorithm and a quality adjustment algorithm are used to generate image resources adapted to the user network and device; The generated image resource is applied to the interactive element to obtain the image loading speed parameter. If the loading completion time is greater than or equal to a preset threshold, the resolution or quality is reduced to reduce the size of the image file until the loading speed requirement is met.

6. The interactive advertising delivery method according to claim 5, characterized in that: Applying optimized image resources in the interactive elements, collecting user interaction operation data through embedding points, inputting the interaction operation data into a pre-trained machine learning model, and obtaining the user experience score through the machine learning model; The image resource optimization strategy is adjusted according to the user's experience score. If the score is lower than a preset threshold, the image compression rate is further increased and the resolution is reduced during the next optimization.

7. The interactive advertising delivery method according to claim 1, characterized in that: Delivering the prepared advertising content to the target platform, including: Acquire effect data of the advertisement content, establish an advertisement delivery effect prediction model based on the effect data, predict delivery results according to the advertisement delivery effect prediction model, and adjust the delivery strategy; Use clustering algorithms to aggregate and analyze advertising effects, classify advertising combinations with similar effects, and discover advertising combinations with the best click-through rate and conversion rate; Adjust advertisement delivery parameters according to the optimal advertisement combination.

8. An interactive advertising delivery system, characterized in that: include: The first processing module: creating advertising content; The second processing module: setting interactive elements in the advertisement content; The third processing module: delivering the prepared advertising content to the target platform; Fourth processing module: collect and analyze user interaction data; The fifth processing module: based on the user's interactive data, adjusts the display speed and switching speed of the interactive elements.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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

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