Intelligent digital media advertisement pushing system and method

Abstract

The invention relates to the technical field of advertisement pushing, in particular to an intelligent digital media advertisement pushing system and method. Comprising a user behavior analysis module, a content feature extraction module, an emotion resonance analysis module, an environment adaptability push module, a user portrait deep mining module, an advertisement effect real-time feedback module, an advertisement putting optimization module and an examination module. And the user behavior analysis module is used for collecting user behavior data and identifying a user behavior mode. According to the intelligent digital media advertisement pushing system and method, a traditional advertisement pushing mode is innovated through the advantages of accurate positioning, content individuation, emotion resonance, environmental adaptability, effect real-time feedback and optimization and privacy protection and compliance, a more efficient and more intelligent marketing means is provided for advertisers, and the advertisement pushing efficiency is improved. And more intimate and more interesting advertisement experience is brought to the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of advertisement pushing technology, in particular to an intelligent digital media advertisement pushing system and method. BACKGROUND

[0002] Digital media, as a new media form relying on digital technology and Internet platform, has penetrated into all aspects of our life. It covers network video, social media, online news, digital audio, e-books and various mobile applications, etc. With its convenience, interactivity and personalized customization, it is deeply loved by the majority of users. Advertisement pushing is a marketing method for businesses or advertisers to deliver information to target audiences through specific channels to promote products, services or brands.

[0003] However, in general, in traditional advertisement pushing, advertisements are usually based on a wide audience group, lacking in targeting and personalization, which often leads to poor advertising effectiveness and even causes users to be repelled. The main shortcomings of traditional advertisement pushing methods are that the target is not accurate, the advertisement is difficult to reach users who are really interested, the content is single and cannot meet the diverse needs of users, and there is a lack of interactivity, users cannot effectively interact with the advertisement, reducing the attractiveness of the advertisement.

[0004] In summary, an intelligent digital media advertisement pushing system and method is needed to solve the above problems. SUMMARY

[0005] The present application aims to provide an intelligent digital media advertisement pushing system and method to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] An intelligent digital media advertisement pushing system, comprising a user behavior analysis module, a content feature extraction module, an emotional resonance analysis module, an environmental adaptability pushing module, a user portrait depth mining module, an advertisement effectiveness real-time feedback module, an advertisement delivery optimization module and a review module;

[0008] The user behavior analysis module is used for user behavior data collection and user behavior pattern recognition;

[0009] The content feature extraction module is used for advertisement content text analysis and advertisement content visual feature extraction;

[0010] The emotional resonance analysis module is used for emotional resonance measurement and emotional resonance strategy generation;

[0011] The environmental adaptability pushing module is used for environmental information perception and environmental adaptability advertisement generation;

[0012] The user portrait deep mining module is used for user basic information integration and user potential demand mining;

[0013] The advertisement effect real-time feedback module is used for real-time click monitoring and advertisement conversion effect tracking;

[0014] The advertisement delivery optimization module is used for advertisement delivery timing optimization and advertisement channel combination optimization;

[0015] The review module is used for user data privacy protection and advertisement content compliance review.

[0016] Preferably, the user behavior analysis module further comprises a user behavior data collection unit and a user behavior pattern recognition unit;

[0017] The user behavior data collection unit collects user browsing, clicking, and purchasing behavior data on digital platforms through data crawler technology and API interface, to realize comprehensive user behavior data acquisition and provide a basis for subsequent analysis;

[0018] The user behavior pattern recognition unit classifies user behavior data through clustering algorithms to identify different user behavior patterns, to realize user group segmentation and provide a basis for precise advertisement pushing.

[0019] Preferably, the content feature extraction module further comprises an advertisement content text analysis unit and an advertisement content visual feature extraction unit;

[0020] The advertisement content text analysis unit extracts keywords and themes of advertisement content through TF-IDF algorithm, to realize text feature extraction of advertisement content and facilitate subsequent content matching;

[0021] The advertisement content visual feature extraction unit extracts color, shape, and texture visual features of advertisement pictures through convolutional neural network, to realize visual feature extraction of advertisement content and enhance the matching degree between advertisements and users.

[0022] Preferably, the emotional resonance analysis module further comprises an emotional resonance measurement unit and an emotional resonance strategy generation unit;

[0023] The emotional resonance measurement unit quantifies the degree of emotional resonance of users to advertisement content through emotional resonance algorithm combined with psychological principles, to evaluate the connection strength between advertisement content and users at the emotional level and optimize the emotional expression of advertisements;

[0024] The emotional resonance strategy generation unit provides emotional resonance enhanced suggestions for the advertisement creative based on the emotional resonance measurement results through a strategy generation algorithm, for guiding the production of the advertisement creative, making the advertisement closer to the emotional needs of the user, and improving the attractiveness and resonance of the advertisement.

[0025] Preferably, the environmental adaptability pushing module further comprises an environmental information perception unit and an environmental adaptability advertisement generation unit.

[0026] The environmental information perception unit collects real-time data of the environment where the user is through Internet of Things devices, including weather, temperature, and noise level, to obtain the current environmental information of the user and provide data support for environmental adaptability pushing.

[0027] The environmental adaptability advertisement generation unit generates advertisement content matched with the current environment through an environmental adaptability algorithm in combination with the environmental information perception data, to realize seamless integration of the advertisement content and the environment where the user is, and improve the timeliness and relevance of the advertisement.

[0028] Preferably, the user portrait depth mining module further comprises a user basic information integration unit and a user potential demand mining unit.

[0029] The user basic information integration unit integrates the age, gender, and occupation basic information of the user through data integration technology to form a user basic portrait, for realizing systematic management of the user basic information and providing a basis for subsequent portrait construction.

[0030] The user potential demand mining unit deeply mines the potential demands and future possible interest points of the user through deep learning algorithm and association rule mining technology, for realizing depth mining of the user portrait and providing a basis for forward-looking advertisement pushing.

[0031] Preferably, the advertisement effect real-time feedback module further comprises an advertisement real-time click monitoring unit and an advertisement conversion effect tracking unit.

[0032] The advertisement real-time click monitoring unit monitors the click situation of the advertisement, including the number of clicks, click time, and click user, through real-time data stream processing technology, for realizing real-time acquisition of the advertisement click data and providing immediate feedback for advertisement effect evaluation.

[0033] The advertisement conversion effect tracking unit tracks the conversion effect of the advertisement, including purchase behavior and registration behavior, through user behavior tracking technology and data analysis algorithm, for evaluating the actual conversion effect of the advertisement and providing data support for advertisement putting strategy adjustment.

[0034] Preferably, the advertisement putting optimization module further comprises an advertisement putting timing optimization unit and an advertisement channel combination optimization unit.

[0035] The advertisement delivery time optimization unit predicts the time period when the user is most likely to respond to the advertisement through time series analysis and prediction algorithms, optimizes the advertisement delivery time, and is used to realize the intelligent selection of advertisement delivery time, improve the reach rate and response rate of the advertisement.

[0036] The advertisement channel combination optimization unit determines the optimal advertisement channel combination through multi-channel delivery effect evaluation algorithms and combination optimization techniques, improves the overall coverage effect of the advertisement, and is used to realize the intelligent configuration of advertisement channel combination and optimize the efficiency and effect of advertisement delivery.

[0037] Preferably, the review module further comprises a user data privacy protection unit and an advertisement content compliance review unit;

[0038] The user data privacy protection unit ensures the privacy and security of user data through data encryption technology, anonymization processing algorithms and privacy protection protocols, and is used to realize the comprehensive protection of user data and comply with relevant laws and regulations and privacy policies;

[0039] The advertisement content compliance review unit automatically checks whether the advertisement content complies with laws and regulations, industry standards and ethical standards through natural language processing, image recognition technology and compliance review algorithms, and is used to realize the comprehensive compliance review of advertisement content and ensure the legality and legitimacy of the advertisement.

[0040] Based on the above system, the present application further proposes an intelligent digital media advertisement pushing method, comprising the following steps:

[0041] S1. Collect user behavior data: Collect user browsing, clicking, and purchasing behavior data on digital platforms through data crawler technology and API interface to provide a basis for subsequent user behavior pattern recognition;

[0042] S2. Identify user behavior patterns: Use clustering algorithms to classify the collected user behavior data to identify different user behavior patterns, so as to subdivide user groups and prepare for precise advertisement pushing;

[0043] S3. Extract advertisement content features: Extract keywords and themes of advertisement content through TF-IDF algorithm to realize text feature extraction of advertisement content; at the same time, use convolutional neural network to extract color, shape, and texture visual features of advertisement pictures to enhance the matching degree between the advertisement and the user;

[0044] S4. Analyze emotional resonance: Based on psychological principles, use emotional resonance algorithms to quantify the emotional resonance degree of users to advertisement content, evaluate the connection strength between the advertisement and the user at the emotional level; and based on the emotional resonance measurement results, provide suggestions for enhancing emotional resonance of advertisement creativity to guide the production of advertisement creativity;

[0045] S5. Sensing environmental information: Collect real-time data of the environment where the user is located through IoT devices, including weather, temperature, noise level, to provide data support for environmental adaptability push;

[0046] S6. Generating environmentally adaptive advertisements: Combine environmental information perception data and generate advertisements that match the current environment through environmental adaptability algorithms, achieving seamless integration of advertisement content with the environment where the user is located;

[0047] S7. Integrating basic user information: Use data integration technology to integrate user's age, gender, occupation, and other basic information to form a user profile;

[0048] S8. Mining potential user needs: Use deep learning algorithms and association rule mining techniques to deeply mine potential user needs and future possible interest points to provide a basis for forward-looking advertisement push;

[0049] S9. Monitoring real-time advertisement clicks: Use real-time data stream processing technology to monitor advertisement clicks, including click count, click time, and click users, to provide immediate feedback for advertisement effectiveness evaluation;

[0050] S10. Tracking advertisement conversion effect: Use user behavior tracking technology and data analysis algorithms to track the conversion effect of advertisements, including purchase behavior and registration behavior, to evaluate the actual conversion effect of advertisements;

[0051] S11. Optimizing advertisement delivery timing: Use time series analysis and prediction algorithms to predict the time period when users are most likely to respond to advertisements, optimize advertisement delivery timing, and improve advertisement reach and response rates;

[0052] S12. Optimizing advertisement channel combination: Use multi-channel delivery effect evaluation algorithms and combination optimization techniques to determine the best advertisement channel combination to improve the overall coverage effect of advertisements;

[0053] S13. Protecting user data privacy: Use data encryption technology, anonymization processing algorithms, and privacy protection protocols to ensure user data privacy and security;

[0054] S14. Reviewing advertisement content compliance: Use natural language processing, image recognition technology, and compliance review algorithms to automatically check whether the advertisement content complies with laws, regulations, industry standards, and ethical standards to ensure the legality and legitimacy of the advertisement.

[0055] Compared with the prior art, the present application has the beneficial effects that: the present application can collect and analyze user behavior data on digital platforms through user behavior analysis, identify different user behavior patterns, and thus realize user group segmentation, so that advertisements can more accurately reach target audiences, improve the reach and response rates of advertisements, and also extract text and visual features of advertisement content, obtain user basic information and potential needs from the user portrait depth mining module, and push personalized advertisement content that meets the interests and needs of different users, provide suggestions for enhancing emotional resonance of advertisement ideas by quantifying the emotional resonance degree of users to advertisement content, make advertisements more close to the emotional needs of users, improve the attractiveness and resonance of advertisements, can perceive real-time data of the environment of users, generate advertisement content that matches the current environment, which enhances the timeliness and relevance of advertisements and improves the acceptance of users, the present application can also monitor the click-through rate and conversion effect of advertisements to provide data support for advertisement placement strategy adjustment, and the advertisement placement optimization module optimizes advertisement placement timing and channel combination based on these data to improve the overall coverage effect and return on investment of advertisements, while ensuring the privacy and security of user data, and checking the compliance of advertisement content. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The intelligent digital media advertisement pushing system topology of the present application is shown;

[0057] Figure 2 The intelligent digital media advertisement pushing method flowchart of the present application is shown. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0059] Embodiment 1

[0060] Please refer to Figure 1 The present application proposes an intelligent digital media advertisement pushing system, which includes a user behavior analysis module, a content feature extraction module, an emotional resonance analysis module, an environmental adaptability pushing module, a user portrait depth mining module, an advertisement effect real-time feedback module, an advertisement placement optimization module, and a review module.

[0061] It should be noted that the user behavior analysis module of the system is used for user behavior data collection and user behavior pattern recognition, the content feature extraction module of the system is used for advertisement content text analysis and advertisement content visual feature extraction, the emotional resonance analysis module of the system is used for emotional resonance measurement and emotional resonance strategy generation, the environmental adaptability pushing module of the system is used for environmental information perception and environmental adaptability advertisement generation, the user portrait deep mining module of the system is used for user basic information integration and user potential demand mining, the advertisement effect real-time feedback module of the system is used for advertisement real-time click monitoring and advertisement conversion effect tracking, the advertisement launching optimization module of the system is used for advertisement launching time optimization and advertisement channel combination optimization, and the review module of the system is used for user data privacy protection and advertisement content compliance review.

[0062] In the embodiment, it should be further noted that the user behavior analysis module further includes a user behavior data collection unit and a user behavior pattern recognition unit.

[0063] Further, the user behavior data collection unit collects user browsing, clicking, and purchasing behavior data on digital platforms through data crawler technology and API interfaces, so as to realize comprehensive user behavior data acquisition and provide a basis for subsequent analysis.

[0064] Further, the user behavior pattern recognition unit classifies user behavior data through a clustering algorithm to identify the behavior patterns of different users, so as to realize user group segmentation and provide a basis for precise advertisement pushing.

[0065] In the embodiment, it should be further noted that the content feature extraction module further includes an advertisement content text analysis unit and an advertisement content visual feature extraction unit.

[0066] Further, the advertisement content text analysis unit extracts keywords and themes of advertisement content through a TF-IDF algorithm, so as to realize text feature extraction of advertisement content and facilitate subsequent content matching.

[0067] Further, the advertisement content visual feature extraction unit extracts color, shape, and texture visual features of advertisement pictures through a convolutional neural network, so as to realize visual feature extraction of advertisement content and enhance the matching degree between the advertisement and the user.

[0068] In the embodiment, it should be further noted that the emotional resonance analysis module further includes an emotional resonance measurement unit and an emotional resonance strategy generation unit.

[0069] Further, the emotional resonance measurement unit quantifies the degree of emotional resonance of users to advertisement content through an emotional resonance algorithm combined with psychological principles, so as to evaluate the connection strength between advertisement content and users at the emotional level and optimize emotional expression of the advertisement.

[0070] Further, the emotional resonance strategy generation unit generates suggestions for emotional resonance enhancement for the advertisement creative based on the emotional resonance measurement results through a strategy generation algorithm, to guide the production of the advertisement creative, make the advertisement closer to the emotional needs of the user, and improve the attractiveness and resonance of the advertisement.

[0071] In this embodiment, it should also be noted that the environmental adaptability pushing module further comprises an environmental information perception unit and an environmental adaptability advertisement generation unit.

[0072] Further, the environmental information perception unit collects real-time data of the environment in which the user is located through Internet of Things devices, including weather, temperature, and noise level, to obtain the current environmental information of the user and provide data support for environmental adaptability pushing.

[0073] Further, the environmental adaptability advertisement generation unit generates advertisement content matched with the current environment through an environmental adaptability algorithm in combination with the environmental information perception data, to realize seamless integration of the advertisement content and the environment in which the user is located, and improve the timeliness and relevance of the advertisement.

[0074] In this embodiment, it should also be noted that the user portrait depth mining module further comprises a user basic information integration unit and a user potential demand mining unit.

[0075] Further, the user basic information integration unit integrates the age, gender, and occupation basic information of the user through data integration technology to form a user basic portrait, to realize systematic management of the user basic information and provide a basis for subsequent portrait construction.

[0076] Further, the user potential demand mining unit deeply mines the potential demands and future possible interest points of the user through deep learning algorithms and association rule mining technology, to realize deep mining of the user portrait and provide a basis for forward-looking advertisement pushing.

[0077] In this embodiment, it should also be noted that the advertisement effect real-time feedback module further comprises an advertisement real-time click monitoring unit and an advertisement conversion effect tracking unit.

[0078] Further, the advertisement real-time click monitoring unit monitors the click situation of the advertisement, including the number of clicks, click time, and click users, through real-time data stream processing technology, to realize real-time acquisition of advertisement click data and provide immediate feedback for advertisement effect evaluation.

[0079] Further, the advertisement conversion effect tracking unit tracks the conversion effect of the advertisement, including purchase behavior and registration behavior, through user behavior tracking technology and data analysis algorithms, to evaluate the actual conversion effect of the advertisement and provide data support for advertisement putting strategy adjustment.

[0080] In this embodiment, it also needs to be explained that the advertisement delivery optimization module further includes an advertisement delivery timing optimization unit and an advertisement channel combination optimization unit;

[0081] Further, the advertisement delivery timing optimization unit predicts the time period when the user is most likely to respond to the advertisement through time series analysis and prediction algorithms, optimizes the advertisement delivery timing, and is used to realize the intelligent selection of advertisement delivery timing, improve the reach rate and response rate of the advertisement;

[0082] Further, the advertisement channel combination optimization unit determines the optimal advertisement channel combination through multi-channel delivery effect evaluation algorithms and combination optimization techniques, improves the overall coverage effect of the advertisement, and is used to realize the intelligent configuration of advertisement channel combination, and optimize the efficiency and effect of advertisement delivery.

[0083] In this embodiment, it also needs to be explained that the review module further includes a user data privacy protection unit and an advertisement content compliance review unit;

[0084] Further, the user data privacy protection unit ensures the privacy and security of user data through data encryption technology, anonymization processing algorithms and privacy protection protocols, and is used to realize the comprehensive protection of user data and comply with relevant laws and regulations and privacy policies;

[0085] Further, the advertisement content compliance review unit automatically checks whether the advertisement content complies with laws and regulations, industry standards and ethical standards through natural language processing, image recognition technology and compliance review algorithms, and is used to realize the comprehensive compliance review of advertisement content and ensure the legality and legitimacy of the advertisement.

[0086] Embodiment 2

[0087] Please refer to Figure 2 In actual application, the intelligent digital media advertisement pushing method based on the above system specifically includes the following steps:

[0088] 1. Collect user behavior data:

[0089] (1) Configure the data crawler system: set the crawling rules, perform data crawling on the user behavior on the digital platform, determine the crawling range, including browsing pages, clicking links, purchasing goods, set the crawling frequency, and ensure that the digital platform does not cause too much burden;

[0090] (2) Obtain data through API interface: negotiate the use permission of API interface with the digital platform, regularly obtain user behavior data through API interface, and safely store in the database;

[0091] 2. Classify user behavior patterns:

[0092] (1) Data preprocessing: Clean, deduplicate, and format the collected user behavior data, and delete invalid or erroneous data records;

[0093] (2) Apply clustering algorithm: Classify user behavior data through clustering algorithms, including K-means or DBSCAN, to identify different user behavior patterns and subdivide user groups;

[0094] 3. Extract ad content features:

[0095] (1) Text feature extraction: Perform word segmentation and stop word filtering on ad text using the TF-IDF algorithm, calculate the TF-IDF values of each word, and extract keywords and topics;

[0096] (2) Visual feature extraction: Preprocess ad images, including scaling, cropping, and normalization, and use convolutional neural networks to extract color, shape, and texture visual features;

[0097] 4. Analyze user emotional resonance:

[0098] (1) Use emotional resonance algorithm: Determine the measurement indicators and calculation methods of emotional resonance based on psychological principles, and consider the influence of individual differences on emotional resonance;

[0099] (2) Quantify emotional resonance: Conduct emotional resonance tests on users, collect test data, and calculate the degree of emotional resonance based on test results;

[0100] (3) Provide creative improvement suggestions: Analyze the relationship between emotional resonance and ad effectiveness, and provide suggestions for improving ad creativity to enhance emotional resonance;

[0101] 5. Collect real-time environmental data:

[0102] (1) Configure IoT devices: Calibrate and test IoT devices, including sensors and cameras, to ensure data accuracy;

[0103] (2) Collect environmental data: Set data collection frequency and range, and store collected environmental data in a database to support environmental adaptability;

[0104] 6. Generate environmentally adaptive ads:

[0105] (1) Analyze environmental characteristics: Preprocess and analyze environmental data to extract environmental features, including weather, temperature, and noise level;

[0106] (2) Generate adaptive ads: Use environmental adaptability algorithms to consider the impact of environmental characteristics on ad content, and generate ad content that matches the current environment based on the algorithm;

[0107] 7. Forming a user base portrait:

[0108] (1) Collecting basic user information: Collecting basic information such as age, gender, and occupation through questionnaires and registration information, verifying and cleaning the information to ensure data accuracy;

[0109] (2) Integrating user information: Integrating basic user information through data integration solutions, considering the relevance and consistency between data, and forming a user base portrait;

[0110] 8. Mining potential user needs:

[0111] (1) Mining potential needs through deep learning: Training and learning user behavior data through deep learning models, including neural networks and recurrent neural networks, to mine potential needs;

[0112] (2) Discovering future interest points: Setting parameters and thresholds for association rule mining, and performing association rule mining on user behavior data to discover potential future interest points;

[0113] 9. Monitoring ad click-through rates:

[0114] (1) Real-time data stream processing: Configuring real-time data stream processing systems to ensure timely and accurate data processing, and performing real-time data capture and processing on ad click-through data;

[0115] (2) Recording click information: Considering data completeness and traceability, storing click counts, click times, and user information in the database through data recording solutions;

[0116] 10. Tracking ad conversion effectiveness:

[0117] (1) User behavior tracking: Configuring user behavior tracking systems to ensure accurate and reliable tracking, and performing real-time tracking and recording of ad conversion behavior;

[0118] (2) Evaluating conversion effectiveness: Considering the relationship between conversion effectiveness and ad investment through data analysis solutions, quantitatively evaluating conversion effectiveness, and providing data support for ad placement strategy adjustments;

[0119] 11. Optimizing ad placement timing:

[0120] (1) Predicting user response time period: Collecting historical data on user responses to ads, and applying time series analysis and prediction algorithms for prediction;

[0121] (2) Optimize the timing of delivery: Through the optimization of the timing of the delivery of advertisements, consider the user's activity level and the competition of the advertisement, adjust the time of the delivery of the advertisement, and improve the reach and response rate of the advertisement;

[0122] 12. Determine the optimal combination of advertising channels:

[0123] (1) Evaluate the effectiveness of channel delivery: Collect delivery data and conversion data from each channel, and apply a multi-channel delivery effectiveness evaluation algorithm to evaluate;

[0124] (2) Determine the optimal combination: Through the combination optimization scheme, consider the complementarity and synergy between channels, determine the optimal channel combination, and improve the overall coverage effect of the advertisement;

[0125] 13. Ensure user data privacy and security:

[0126] (1) Data encryption processing: Through encryption algorithms and key management methods, encrypt and store and transmit user data;

[0127] (2) Data anonymization processing: Through the anonymization processing scheme, ensure that the data cannot be traced back to a specific user, and process and analyze the anonymized data;

[0128] (3) Comply with privacy protection agreements: Understand and comply with relevant laws and regulations and privacy policies, sign privacy protection agreements with users, and clearly define the scope and purpose of data use;

[0129] 14. Check the compliance of the advertisement content:

[0130] (1) Text content analysis: Through natural language processing models and algorithms, perform syntax, semantics, and sentiment analysis on the text of the advertisement;

[0131] (2) Picture content recognition: Configure an image recognition system to ensure accuracy and reliability of recognition, and perform content recognition and analysis on the advertisement pictures;

[0132] (3) Compliance review: Through the compliance review scheme, consider the legality and legitimacy of the advertisement content, and conduct a compliance review of the advertisement content to ensure the legality and legitimacy of the advertisement.

[0133] Through the above steps, the intelligent digital media advertisement pushing system and method of the present application have the advantages of precise positioning, content personalization, emotional resonance, environmental adaptability, real-time feedback and optimization of effects, and privacy protection and compliance, which revolutionize the traditional advertisement pushing method, provide more efficient and intelligent marketing means for advertisers, and also bring more thoughtful and interesting advertisement experience for users.

[0134] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. An intelligent digital media advertising push system, characterized in that: It includes user behavior analysis module, content feature extraction module, emotional resonance analysis module, environmental adaptability push module, user portrait in-depth mining module, advertising effect real-time feedback module, advertising delivery optimization module and review module; The user behavior analysis module is used for collecting user behavior data and identifying user behavior patterns; The content feature extraction module is used for analyzing the text of advertisement content and extracting visual features of advertisement content; The emotional resonance analysis module is used for emotional resonance measurement and emotional resonance strategy generation; The environment adaptability push module is used for environmental information perception and environment adaptability advertisement generation; The user portrait deep mining module is used to integrate basic user information and mine potential user needs; The advertising effect real-time feedback module is used for real-time advertising click monitoring and advertising conversion effect tracking; The advertising delivery optimization module is used to optimize the timing of advertising delivery and the combination of advertising channels; The review module is used for user data privacy protection and advertising content compliance review.

2. The intelligent digital media advertising push system according to claim 1, characterized in that: The user behavior analysis module also includes a user behavior data collection unit and a user behavior pattern recognition unit; The user behavior data collection unit is used to obtain comprehensive user behavior data; The user behavior pattern recognition unit is used to achieve segmentation of user groups.

3. The intelligent digital media advertising push system according to claim 2, characterized in that: The content feature extraction module also includes an advertisement content text analysis unit and an advertisement content visual feature extraction unit; The advertisement content text analysis unit is used to extract text features of the advertisement content; The advertisement content visual feature extraction unit is used to extract the visual features of the advertisement content.

4. The intelligent digital media advertising push system according to claim 3, characterized in that: The emotional resonance analysis module also includes an emotional resonance measurement unit and an emotional resonance strategy generation unit; The emotional resonance measurement unit is used to evaluate the connection strength between the advertising content and the user at the emotional level; The emotional resonance strategy generation unit is used to guide the production of advertising creativity.

5. The intelligent digital media advertising push system according to claim 4, characterized in that: The environment adaptability push module also includes an environment information perception unit and an environment adaptability advertisement generation unit; The environmental information sensing unit is used to obtain the user's current environmental information; The environment-adaptive advertisement generating unit is used to achieve seamless integration of advertisement content and the user's environment.

6. The intelligent digital media advertising push system according to claim 5, characterized in that: The user portrait deep mining module also includes a user basic information integration unit and a user potential demand mining unit; The user basic information integration unit is used to achieve systematic management of user basic information; The user potential demand mining unit is used to achieve in-depth mining of user portraits.

7. The intelligent digital media advertising push system according to claim 6, characterized in that: The advertising effect real-time feedback module also includes an advertising real-time click monitoring unit and an advertising conversion effect tracking unit; The real-time advertisement click monitoring unit is used to obtain advertisement click data in real time; The advertisement conversion effect tracking unit is used to evaluate the actual conversion effect of the advertisement.

8. The intelligent digital media advertising push system according to claim 7, characterized in that: The advertising delivery optimization module also includes an advertising delivery timing optimization unit and an advertising channel combination optimization unit; The advertisement delivery timing optimization unit is used to optimize the selection of advertisement delivery timing; The advertising channel combination optimization unit is used to realize the configuration of advertising channel combination.

9. The intelligent digital media advertising push system according to claim 8, characterized in that: The review module also includes a user data privacy protection unit and an advertising content compliance review unit; The user data privacy protection unit is used to achieve comprehensive protection of user data; The advertising content compliance review unit is used to implement a comprehensive compliance review of advertising content.

10. An intelligent digital media advertising push method, according to an intelligent digital media advertising push system according to any one of claims 1-9, characterized in that: The following steps are involved: S1. Collect user browsing, clicking, and purchasing behavior data on digital platforms through data crawling technology and API interfaces, providing a basis for subsequent user behavior pattern identification; S2. Use clustering algorithms to classify the collected user behavior data and identify the behavior patterns of different users; S3. Extract keywords and themes from the ad content using the TF-IDF algorithm to extract text features from the ad content. Simultaneously, use a convolutional neural network to extract the color, shape, and texture visual features of the ad image. S4. Quantify the degree of emotional resonance users experience with advertising content through an emotional resonance algorithm, assessing the strength of the emotional connection between the ad and the user. Based on the emotional resonance measurement results, provide recommendations for enhancing emotional resonance in advertising creatives, guiding the development of creative advertising. S5. Collect real-time data about the user's environment, including weather, temperature, and noise levels, through IoT devices to provide data support for environmental adaptability push notifications; S6. Combined with environmental information perception data, an environmental adaptability algorithm is used to generate advertising content that matches the current environment, achieving seamless integration of advertising content with the user's environment. S7. Use data integration technology to integrate the user's age, gender, and occupation basic information to form a basic user profile; S8. Through deep learning algorithms and association rule mining technology, we can deeply explore users' potential needs and future interests, providing a basis for proactive advertising push; S9. Use real-time data stream processing technology to monitor ad clicks, including the number of clicks, click time, and click users, providing instant feedback for ad effectiveness evaluation. S10 uses user behavior tracking technology and data analysis algorithms to track the conversion effect of advertisements, including purchase behavior and registration behavior, to evaluate the actual conversion effect of advertisements; S11. Use time series analysis and prediction algorithms to predict the time period when users are most likely to respond to ads and optimize ad placement timing; S12. Determine the optimal advertising channel combination through multi-channel delivery effect evaluation algorithms and combination optimization technology; S13. Use data encryption technology, anonymization algorithms, and privacy protection protocols; S14. Utilize natural language processing, image recognition technology, and compliance review algorithms to automatically check whether advertising content complies with laws, regulations, industry standards, and ethical norms.

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