AI virtual human product matching optimization method and system

Abstract

The invention provides an AI virtual human product matching optimization method and system. The method belongs to the technical field of artificial intelligence, and comprises the steps: obtaining multi-dimensional data of a user, carrying out the feature extraction and clustering analysis of the user data through a deep learning algorithm, and constructing a user portrait; dynamically adjusting a matching strategy according to the change of the user portrait and the market environment based on an adaptive deep reinforcement learning model; collecting user feedback and satisfaction data in real time according to the interaction process of the user and the AI virtual human; and user feedback is analyzed by using an adaptive deep reinforcement learning model, a matching strategy is dynamically adjusted, and accurate personalized recommendation is carried out. According to the method, feature extraction and clustering analysis are performed on user data through a deep learning algorithm, and a detailed user portrait is constructed, so that an AI virtual human can more accurately understand user requirements, and personalized product matching and recommendation are provided.

Description

Technical Field

[0001] The present invention proposes an AI virtual human product matching optimization method and system, belonging to the field of artificial intelligence technology. Background Art

[0002] Although the existing AI virtual human product matching methods have achieved certain results, they still face many challenges, such as the complexity of understanding user intent, the diversity of product features, and the rapid changes in the market environment. These problems lead to unstable matching results and uneven user experience. Therefore, an innovative optimization method is urgently needed to adapt to complex and changing product matching scenarios. Summary of the invention

[0003] The present invention provides an AI virtual human product matching optimization method and system to solve the problems mentioned in the above background technology:

[0004] The present invention proposes an AI virtual human product matching optimization method, the method comprising:

[0005] S1. Obtain multi-dimensional data of users, use deep learning algorithms to extract features and perform cluster analysis on user data, and build user portraits;

[0006] S2, based on the adaptive deep reinforcement learning model, dynamically adjust the matching strategy according to changes in user profiles and market environment;

[0007] S3. Collect user feedback and satisfaction data in real time based on the interaction process between users and AI virtual people; use adaptive deep reinforcement learning models to analyze user feedback, dynamically adjust matching strategies, and make accurate personalized recommendations;

[0008] S4. Based on cross-modal information fusion technology, user input information of multiple modalities is effectively integrated;

[0009] S5. Predict market trends based on big data analysis technology and identify potential product demand in advance; update product information and matching strategies in real time based on market trend forecast results.

[0010] The present invention proposes an AI virtual human product matching optimization system, the system comprising:

[0011] Data acquisition module: obtain multi-dimensional data of users; use deep learning algorithms to extract features and perform cluster analysis on user data to build user portraits;

[0012] Strategy matching module: Based on the adaptive deep reinforcement learning model, the matching strategy is dynamically adjusted according to changes in user profiles and market environment; and the optimal matching strategy is learned through continuous trial and error and reward mechanisms;

[0013] Precision recommendation module: collects user feedback and satisfaction data in real time based on the interaction process between users and AI virtual people; uses adaptive deep reinforcement learning models to analyze user feedback, dynamically adjust matching strategies, and make precise personalized recommendations;

[0014] Information integration module: Based on cross-modal information fusion technology, it effectively integrates user input information of multiple modalities;

[0015] Trend forecasting module: predict market trends based on big data analysis technology and identify potential product demand in advance; update product information and matching strategies in real time based on market trend forecast results.

[0016] The beneficial effects of the present invention are as follows: by using deep learning algorithms to extract features and cluster analysis of user data, a detailed user portrait is constructed, so that AI virtual people can understand user needs more accurately and provide personalized product matching and recommendations; based on an adaptive deep reinforcement learning model, the matching strategy is dynamically adjusted according to changes in user portraits and market environment to ensure that the recommendation strategy is always consistent with the latest market trends and user needs; by collecting user feedback and satisfaction data in real time, the adaptive deep reinforcement learning model is used for analysis to continuously optimize the matching strategy and improve user satisfaction and the accuracy of recommendations; by using cross-modal information fusion technology, user input information in multiple modes such as text, image, and voice is effectively integrated to provide a richer and more accurate user experience; by using big data analysis technology to predict market trends, identify potential product needs in advance, and update product information and matching strategies in real time, so that AI virtual people can better understand user needs and provide personalized product matching and recommendations; AI virtual people can better adapt to market changes; through machine learning algorithms, user portraits are continuously optimized, and combined with real-time data monitoring mechanisms, user portraits are updated regularly or on demand to reflect the latest changes in user behavior; stream processing technology and multiple methods are used to collect user satisfaction evaluation data to ensure the real-time and accuracy of data processing, and improve the response speed and processing capabilities of the system; through association rule mining algorithms and sentiment analysis algorithms, user behavior patterns and satisfaction evaluation data are deeply analyzed to provide a scientific basis for the adjustment of matching strategies; in combination with market trends and competitor dynamics, matching strategies are continuously optimized to ensure that the recommendation system of AI virtual people can maintain its advantage in the highly competitive market; through the cleaning, standardization and normalization of market data, as well as the selection and training of prediction models, the accuracy of market trend predictions is improved to provide support for the update of product information and the adjustment of matching strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a step diagram of the method of the present invention;

[0018] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0020] One embodiment of the present invention, as Figure 1 As shown, an AI virtual human product matching optimization method comprises:

[0021] S1. Obtain multi-dimensional data of the user, including basic information, historical behavior, preferences and emotional state of the multi-dimensional data; perform feature extraction and cluster analysis on the user data using a deep learning algorithm to construct a user portrait;

[0022] S2. Based on the adaptive deep reinforcement learning model, the matching strategy is dynamically adjusted according to the changes in user profiles and market environment; and the optimal matching strategy is learned through continuous trial and error and reward mechanism;

[0023] S3. Collect user feedback and satisfaction data in real time based on the interaction process between users and AI virtual people; use adaptive deep reinforcement learning models to analyze user feedback, dynamically adjust matching strategies, and make accurate personalized recommendations;

[0024] S4. Based on cross-modal information fusion technology, user input information of multiple modalities (such as text, pictures, and audio) is effectively integrated;

[0025] S5. Predict market trends based on big data analysis technology and identify potential product demand in advance; update product information and matching strategies in real time based on market trend forecast results.

[0026] The working principle of the above technical solution is as follows: obtaining multi-dimensional data of users from multiple sources (such as user registration information, historical behavior records, social media activities, sensor data, etc.); these data include basic information (such as age, gender, geographic location), historical behavior (such as purchase records, browsing history), preferences (such as brand preferences, color preferences) and emotional states (such as current mood, emotional changes); using deep learning algorithms (such as convolutional neural networks, recurrent neural networks, autoencoders, etc.) to extract features from user data and extract features that have a significant impact on user behavior, preferences and emotional states; applying clustering analysis techniques (such as K-means, hierarchical clustering, DBSCAN, etc.) to group users to form different user groups in order to more finely construct user portraits; based on the extracted features and clustering results, constructing a detailed user portrait, including user interests, needs, behavior patterns, etc.; setting an initial matching strategy based on the preliminary user portrait and market environment; based on the adaptive deep reinforcement learning model, according to changes in user portraits (such as changes in user preferences) and changes in the market environment (such as new product launches, seasonal demand The AI ​​​​virtual person can dynamically adjust the matching strategy according to the changes in the recommended products; learn the optimal matching strategy through continuous trial and error and reward mechanisms (such as improved user satisfaction and purchase conversion rate as positive feedback); collect user feedback and satisfaction data in real time during the interaction between users and AI virtual people; use adaptive deep reinforcement learning models to analyze user feedback and identify information such as user satisfaction with recommended products and changes in preferences; dynamically adjust matching strategies based on the analysis results to make more accurate personalized recommendations; based on cross-modal information fusion technology, effectively integrate user input information in multiple modes such as text, pictures, and audio; this helps to understand the user's intentions and needs more comprehensively; by integrating information from different modes, improve the comprehensiveness and accuracy of product matching, and ensure that recommended products are more in line with user expectations; based on big data analysis technology, predict market trends and identify potential product needs; this includes analyzing historical sales data, user behavior data, social media trends, etc.; based on market trend prediction results, update product information and matching strategies in real time; this ensures that AI virtual people can recommend products that meet market trends and improve the relevance and timeliness of recommendations.

[0027] The effects of the above technical solutions are as follows: through the collection and analysis of multi-dimensional data, a detailed user portrait is constructed, which can more deeply understand the user's preferences, needs and behavior patterns; the application of adaptive deep reinforcement learning models enables the matching strategy to be dynamically adjusted according to changes in user portraits and market environment, thereby providing more personalized recommendations and improving user satisfaction; real-time collection of feedback and satisfaction data during the interaction between users and AI virtual people, and dynamic adjustment of matching strategies based on these data, can ensure that the recommended products are more in line with user expectations; the application of cross-modal information fusion technology enables AI virtual people to better understand the user's input information, whether it is text, pictures or audio, and can be effectively integrated, thereby improving the comprehensiveness and accuracy of matching and enhancing user experience; based on big data analysis technology, market trends are predicted, which can identify potential product needs in advance, so that AI virtual people can Anthropomorphism can recommend products that are in line with market trends; this not only helps to improve the market competitiveness of products, but also ensures that AI virtual people always recommend the latest and most popular products, thereby enhancing users' trust in and willingness to buy products; the adaptive deep reinforcement learning model learns the optimal matching strategy through continuous trial and error and reward mechanisms, which means that AI virtual people can continuously learn and grow from user feedback, improve their matching capabilities and personalized recommendation levels; this ability to continuously learn and optimize enables AI virtual people to always stay at the forefront of the industry and provide users with better and more efficient services; accurate personalized recommendations can reduce users' screening costs and increase purchase conversion rates, thereby bringing higher marketing efficiency to companies; at the same time, by providing product recommendations that meet user expectations and market demand, it can enhance user satisfaction and loyalty, improve user stickiness, and lay the foundation for the long-term development of the company.

[0028] In one embodiment of the present invention, the S1 includes:

[0029] S11. Collect multi-dimensional data of users from multiple channels, including social media, e-commerce platforms, user registration information, and browser history, wherein the multi-dimensional data includes basic information, historical behavior, preferences, and emotional state; and pre-process the collected multi-dimensional data;

[0030] S12. Extract features from the pre-processed multi-dimensional data through deep learning algorithms (such as convolutional neural network CNN, recurrent neural network RNN, Transformer, etc.), including user behavior features, preference features, and sentiment features;

[0031] S13. Use clustering algorithms, such as K-means and DBSCAN, to group users and form user groups with similar characteristics. Build a detailed portrait for each user group, including group characteristic description, behavior patterns, preference tendencies, emotional states, etc., and generate a personalized portrait for each individual user.

[0032] The working principle of the above technical solution is as follows: collect multi-dimensional data of users from multiple channels such as social media (such as Weibo, WeChat, Tik Tok, etc.), e-commerce platforms (such as Taobao, JD.com, Amazon, etc.), user registration information (such as name, age, gender, email, etc.) and browser history (such as browsing history, search keywords, click behavior, etc.); these data cover users' basic information (such as age, gender, geographic location), historical behavior (such as purchase history, browsing history), preferences (such as brand preference, color preference, product category preference) and emotional state (such as current mood, emotional changes, attitude towards specific topics, etc.); clean the collected multi-dimensional data to remove duplicate, invalid or abnormal data; standardize or normalize the data to ensure that data from different sources and types are comparable in subsequent analysis; fill or delete missing data to ensure data integrity; use convolutional neural networks (CNN) to extract features in image or text data, such as the type and color of items in pictures uploaded by users; use loops to extract features from images or text data; use CNN ... Neural network (RNN) or Transformer model processes time series data, such as user browsing history, purchase history, etc., to capture user behavior patterns and preference changes; through the comprehensive application of deep learning algorithms, key information such as user behavior characteristics, preference characteristics, and emotional characteristics are extracted; clustering algorithms such as K-means and DBSCAN are used to group users to form user groups with similar characteristics; in the clustering process, similarity is calculated based on dimensions such as user behavior characteristics, preference characteristics, and emotional characteristics, and similar users are divided into the same group; a detailed portrait is constructed for each user group, including group characteristic descriptions (such as age distribution, gender ratio, geographical distribution, etc.), behavior patterns (such as purchase frequency, browsing habits, etc.), preference tendencies (such as brand preference, product category preference, etc.), and emotional states (such as overall emotional tendencies, attitudes towards specific topics, etc.); at the same time, a personalized portrait is generated for each individual user, combining their historical behavior, preferences, and emotional states to provide a more detailed user description.

[0033] The effects of the above technical solutions are: data is collected through multiple channels such as social media, e-commerce platforms, user registration information, and browser history, ensuring the comprehensiveness and diversity of user data. These channels cover multiple dimensions such as users' basic information, historical behaviors, preferences, and emotional states, providing rich materials for subsequent feature extraction and portrait construction; the collected multi-dimensional data is preprocessed, including cleaning, standardization or normalization, missing data filling and other steps to ensure the accuracy and comparability of the data. This helps to improve the accuracy and reliability of subsequent feature extraction and portrait construction; deep learning algorithms such as convolutional neural networks (CNN), recurrent neural networks (RNN), and Transformer are used to extract features from preprocessed multi-dimensional data. These algorithms can automatically learn the inherent laws and characteristics of data, extract key information such as user behavior characteristics, preference characteristics, and emotional characteristics, and provide strong support for subsequent clustering analysis and portrait construction; clustering algorithms such as K-means and DBSCAN are used to group users to form user groups with similar characteristics. This helps companies to have a deeper understanding of the characteristics and needs of different user groups, and provides a basis for subsequent precision marketing and personalized recommendations. While building detailed portraits for each user group, personalized portraits are also generated for each individual user. These portraits not only include the user's basic information, historical behavior, preference tendencies and other conventional content, but also incorporate more delicate information such as the user's emotional state, making the portraits more refined and personalized. Based on user portraits, companies can more accurately locate target user groups, formulate personalized marketing strategies and promotion plans, and improve marketing efficiency and conversion rates. By analyzing information such as preference tendencies and behavior patterns in user portraits, companies can promptly discover product deficiencies and conduct targeted optimization and improvement to enhance product market competitiveness and user satisfaction. Information such as emotional state in user portraits can also serve as an important basis for corporate risk warnings. When negative changes in the user's emotional state are found, companies can take timely measures to intervene and respond to avoid potential risks and losses.

[0034] In one embodiment of the present invention, the S13 includes:

[0035] K-means, DBSCAN and other clustering algorithms are used to preliminarily group the preprocessed multi-dimensional user data. Based on key indicators such as user behavior characteristics and preference characteristics, users are divided into several groups with similar characteristics.

[0036] Conduct in-depth analysis on each initially divided user group to extract common characteristics of the group, including behavior patterns, preferences, consumption habits, etc. Based on the initial division, further use deep learning algorithms (such as self-organizing maps SOM, hierarchical clustering, etc.) to segment the group internally, identify more subtle differences in user characteristics, and form more sophisticated user subgroups;

[0037] Combine group characteristics and individual user data to build a detailed portrait for each user subgroup, including group characteristics description, behavior patterns, preference tendencies, emotional state, consumption capacity and other dimensions. Based on the group portrait, according to the specific behavior and preference data of individual users, generate a personalized portrait for each user, including the user's unique behavior habits, preference changes, potential needs, etc.;

[0038] Based on the real-time data monitoring mechanism, we continuously collect new user behaviors and preference data. According to the real-time data monitoring results, we update the user portrait regularly or on demand, including adding new user features, adjusting the weights of existing features, etc.; and continuously optimize the user portrait through machine learning algorithms.

[0039] The working principle of the above technical solution is as follows: First, clustering algorithms such as K-means and DBSCAN are used to perform preliminary grouping on the preprocessed multi-dimensional user data. These clustering algorithms can divide users into several groups with similar characteristics based on key indicators such as user behavior characteristics and preference characteristics. The results of the preliminary grouping provide a basis for subsequent in-depth analysis; each preliminarily divided user group is deeply analyzed to extract the common characteristics of the group. These characteristics include behavior patterns, preference tendencies, consumption habits, etc., which reflect the overall characteristics and needs of the users in this group; on the basis of the preliminary division, deep learning algorithms (such as self-organizing maps SOM, hierarchical clustering, etc.) are further used to segment the group. These algorithms can identify more subtle differences in user characteristics, thereby forming more sophisticated user subgroups. The subdivided user subgroups are closer in characteristics, which helps companies grasp user needs more accurately; combining group characteristics and individual user data, a detailed portrait is constructed for each user subgroup. These portraits include group feature descriptions, behavior patterns, preference tendencies, emotional states, spending power and other dimensions, which fully reflect the characteristics and needs of the user subgroup; based on the group portrait, a personalized portrait is generated for each user according to the specific behavior and preference data of individual users. Personalized portraits include information such as users' unique behavior habits, preference changes, and potential needs, which reflect the personalized characteristics and needs of users; a real-time data monitoring mechanism is established to continuously collect new behavior and preference data of users. These data include users' latest purchase records, browsing history, evaluation feedback, etc., which reflect the dynamic changes of user needs; based on the results of real-time data monitoring, user portraits are updated regularly or on demand. Updates include adding new user features, adjusting existing feature weights, etc., to ensure that the portrait can accurately reflect the latest features and needs of users; and continuous optimization of user portraits through machine learning algorithms. These algorithms can automatically adjust the feature weights and parameters of the portraits based on the real-time and historical data of users, thereby improving the accuracy and reliability of the portraits.

[0040] The effect of the above technical solution is: by using clustering algorithms such as K-means and DBSCAN to preliminarily group the preprocessed multi-dimensional user data, and then combining it with deep learning algorithms for further segmentation, it is possible to identify more subtle differences in user characteristics and form more sophisticated user subgroups. This multi-level clustering analysis method improves the accuracy of user segmentation, which helps companies to have a deeper understanding of the characteristics and needs of different user groups; combining group characteristics and individual user data, a detailed portrait is constructed for each user subgroup, including group characteristic description, behavior patterns, preference tendencies, emotional state, consumption capacity and other dimensions. This comprehensive portrait description helps companies to have a more comprehensive understanding of the overall characteristics and needs of the user group; based on the group portrait, a personalized portrait is generated for each user according to the specific behavior and preference data of individual users. This personalized portrait can reflect the user's unique behavior habits, preference changes, potential needs and other information, and provide strong data support for enterprises to conduct personalized recommendations and precision marketing; based on the real-time data monitoring mechanism, the user's new behavior and preference data are continuously collected, and the user portrait is updated regularly or on demand based on these data. This dynamic adjustment method ensures the timeliness and accuracy of user portraits, and helps companies to capture the dynamic changes of user needs in a timely manner; through machine learning algorithms, user portraits are continuously optimized, and the feature weights and parameters of portraits can be automatically adjusted according to the user's real-time data and historical data, thereby improving the accuracy and reliability of portraits. This continuous optimization method helps companies to continuously improve the quality of user portraits and provide more accurate data support for corporate decision-making; based on accurate user segmentation and personalized user portraits, companies can formulate more accurate marketing strategies and promotion plans to improve marketing efficiency and conversion rates; by analyzing information such as preference tendencies and behavior patterns in user portraits, companies can promptly discover product deficiencies and carry out targeted optimization and improvement to enhance product market competitiveness and user satisfaction; information such as emotional status in user portraits can also serve as an important basis for corporate risk warning. When negative changes in user emotional status are found, companies can take timely measures to intervene and respond to avoid potential risks and losses.

[0041] In one embodiment of the present invention, the S2 includes:

[0042] S21. Use user portraits and market environment data (such as popular products, popular trends, competitor dynamics, etc.) as inputs to the state space and define a set of matching strategies, including product recommendations, discount strategies, interaction methods, etc.

[0043] S22. A reward function is set based on user feedback (such as click-through rate, conversion rate, satisfaction evaluation, etc.), the effect of the matching strategy is evaluated, and a training set is constructed using historical user data and matching strategy data; the effect of the matching strategy is evaluated using the following formula:

[0044]

[0045] Among them, E represents the strategy effect evaluation score; CTR represents click-through rate; CVR represents conversion rate; SAT represents satisfaction evaluation; L represents page dwell time, that is, the average dwell time of users on the page; S represents user complaint rate, that is, the proportion of user complaints about the service; C represents user retention rate, that is, the proportion of users who are still active after a certain period of time; K represents refund rate, that is, the proportion of users applying for refunds after purchase; Z represents brand loyalty, that is, the user's loyalty score to the brand; P represents user churn rate, that is, the proportion of users who stop using the service; a, β, r, δ, ∈, ζ, η, θ, Indicates weight and adjustment coefficient, which represent the relative importance of each indicator in the overall evaluation and its relationship with other indicators; max(indicator) indicates the maximum value of the indicator, which is used for normalization processing;

[0046] S23. Train the model based on deep reinforcement learning algorithms (such as DQN, A3C, PPO, etc.), evaluate the effect of the matching strategy through simulation experiments or A / B tests, and adjust the model parameters and strategies according to the evaluation results;

[0047] S24. Update the state space of the model in real time according to changes in user profiles and market environment, and use the trained model to generate the optimal matching strategy according to the real-time status;

[0048] S25. Continuously monitor the effect of the matching strategy and adjust the matching strategy based on the monitoring results.

[0049] The working principle of the above technical solution is as follows: user portrait is used as the core input, including information on multiple dimensions such as user age, gender, region, occupation, hobbies, consumption habits, etc.; at the same time, market environment data such as popular products, popular trends, competitor dynamics, etc. are introduced to reflect the latest changes and trends in the market; a series of matching strategies are defined, including product recommendations, preferential strategies, interaction methods, etc., to meet the needs of different users and the market environment; reward functions are set based on user feedback, such as click-through rate, conversion rate, satisfaction evaluation, etc. The reward function is used to evaluate the effect of the matching strategy and is the goal of deep reinforcement learning algorithm optimization; historical user data and matching strategy data are used to construct a training set. The training set contains multiple state-action-reward pairs for training deep reinforcement learning models; the model is trained based on deep reinforcement learning algorithms (such as DQN, A3C, PPO, etc.). These algorithms can learn and optimize matching strategies so that the model selects the optimal action (i.e., matching strategy) under a given state; the effect of the matching strategy is evaluated through simulation experiments or A / B tests. A / B testing is a common method that compares different versions of matching strategies to find the best solution; adjusts model parameters and strategies based on evaluation results to continuously improve the effectiveness of matching strategies; and updates the state space of the model in real time based on changes in user portraits and market environment. This ensures that the model can reflect the latest user and market information; uses the trained model to generate the best matching strategy based on real-time status. These strategies can accurately meet user needs and adapt to market changes; and continuously monitor the effectiveness of matching strategies. This includes collecting user feedback, analyzing conversion rates, click-through rates and other indicators to evaluate the actual effectiveness of the strategy; and adjusting the matching strategy based on monitoring results. When it is found that the strategy is not effective or user needs have changed, adjust the strategy in a timely manner to adapt to the new market environment.

[0050] The effect of the above technical solution is: by taking user portraits and market environment data as inputs of the state space, the solution can comprehensively consider the user's personal characteristics and market dynamics to achieve more accurate matching. This helps to provide users with personalized product recommendations, preferential strategies and interaction methods, and improve user experience and satisfaction; the model is trained using deep reinforcement learning algorithms (such as DQN, A3C, PPO, etc.) so that the model can learn and optimize matching strategies. This algorithm can automatically explore and discover the optimal strategy, thereby improving the efficiency and effectiveness of the matching strategy; by continuously monitoring the effect of the matching strategy and adjusting it according to the monitoring results, the solution can ensure that the matching strategy is always in the optimal state. This helps enterprises respond to market changes in a timely manner and improve their competitiveness; according to changes in user portraits and market environment, the state space of the model is updated in real time. This ensures that the model can reflect the latest user and market information, thereby generating a matching strategy that is more in line with the current situation; using the trained model, the optimal matching strategy is generated according to the real-time status. This dynamic strategy generation method helps enterprises respond quickly to market changes and meet the diverse needs of users; the solution is data-centric and provides a basis for decision-making by collecting and analyzing user feedback, historical user data and matching strategy data. This helps enterprises realize intelligent operation and improve the scientificity and accuracy of decision-making; through the application of deep reinforcement learning algorithms, the solution can realize intelligent recommendation and optimization, improve product exposure and conversion rate, and create more business value for enterprises; through precise matching and personalized services, the solution can provide users with products and services that better meet their needs and preferences, thereby improving user experience; good user experience helps to improve user loyalty and stickiness, promote users' continuous consumption and word-of-mouth communication, and lay the foundation for the long-term development of enterprises. The above formula combines multiple key indicators such as click-through rate (CTR), conversion rate (CVR), satisfaction evaluation (SAT), etc., which can fully reflect the performance of matching strategies in user attraction, conversion and long-term satisfaction. This multi-dimensional evaluation method helps to more accurately judge the overall effect of the strategy; by introducing parameters such as page dwell time (L), user complaint rate (S), user retention rate (C), refund rate (K), brand loyalty (Z) and user churn rate (P), the formula not only takes into account the direct feedback of users (such as clicks and conversions), but also takes into account the deep meaning behind user behavior and market dynamics, such as service quality and user loyalty; the weights and adjustment coefficients in the formula can be adjusted according to actual conditions to adapt to different market environments and user needs. This flexibility enables the evaluation system to maintain accuracy as the market changes and user preferences change; through maximum value normalization (max(indicator)), the formula ensures the relative fairness of each indicator in the evaluation.This approach avoids evaluation bias caused by different indicator dimensions, making the comparison between different indicators more objective and accurate; based on the evaluation results of this formula, it is possible to clearly identify which strategies perform well in which aspects and which aspects need improvement. This provides a clear direction and basis for strategy optimization, helping companies to quickly respond to market changes and improve user experience and performance; the formula can be updated in real time as user profiles and market environments change, ensuring the timeliness and accuracy of evaluation results. At the same time, by continuously monitoring the effectiveness of matching strategies and making adjustments based on monitoring results, companies can continuously optimize strategies to adapt to the ever-changing market environment.

[0051] In one embodiment of the present invention, the S24 includes:

[0052] S241. Based on the real-time data stream access mechanism, continuously obtain the latest data from multiple data sources (such as user behavior logs, market trend reports, competitor analysis, etc.); and use stream processing technologies (such as Apache Kafka, Spark Streaming, etc.) to clean, transform and aggregate the data in real time;

[0053] S242. Dynamically adjust the state space of the model based on the real-time data monitoring results; including updating the dimensions and weights of the user portrait to reflect the user's latest behavior and preferences; at the same time, it also includes adjusting the input of market environment data, such as introducing new popular products, popular trends or competitor dynamics, etc., using the trained deep reinforcement learning model, and generating the initial matching strategy based on the real-time updated state space; including product recommendations, preferential strategies, interaction methods, etc.

[0054] S243. Before implementing the matching strategy, use the simulation environment or historical data to conduct a preliminary assessment; by comparing the expected benefits (such as click-through rate, conversion rate, user satisfaction, etc.) under different strategies, screen out the potential optimal strategy;

[0055] S244. Fine-tune the initially generated matching strategy based on real-time user feedback and market changes, including adjusting the ranking of recommended products, optimizing the strength of preferential policies, improving the user experience of interactive methods, etc., and deploy the optimized matching strategy to actual business scenarios, such as product recommendation systems and advertising delivery systems of e-commerce platforms, and collect user feedback and business data in real time;

[0056] S245. Based on the effect monitoring system, the implementation effect of the matching strategy is tracked in real time; including monitoring user behavior data (such as click-through rate, conversion rate, dwell time, etc.), business indicators (such as sales, user growth rate, etc.) and user satisfaction evaluation, etc., and the matching strategy is iteratively optimized according to the real-time monitoring results.

[0057] The working principle of the above technical solution is as follows: based on the real-time data stream access mechanism, the latest data is continuously obtained from multiple data sources (such as user behavior logs, market trend reports, competitor analysis, etc.). These data are the basis for the subsequent generation of matching strategies; use stream processing technologies (such as Apache Kafka, Spark Streaming, etc.) to clean, transform and aggregate real-time data to ensure the accuracy and availability of data; dynamically adjust the state space of the model according to the real-time data monitoring results. This includes updating the dimensions and weights of user portraits to reflect the latest user behaviors and preferences; at the same time, adjust the input of market environment data, such as introducing new popular products, popular trends or competitor dynamics, to ensure that the model can capture the latest changes in the market; use the trained deep reinforcement learning model to generate the initial matching strategy based on the real-time updated state space. These strategies may include product recommendations, preferential strategies, interaction methods, etc.; before implementing the matching strategy, use the simulation environment or historical data for pre-evaluation. By comparing the expected benefits (such as click-through rate, conversion rate, user satisfaction, etc.) under different strategies, the potential optimal strategy is screened out; combined with real-time user feedback and market changes, the initially generated matching strategy is fine-tuned. This may include adjusting the ranking of recommended products, optimizing the strength of preferential strategies, improving the user experience of interactive methods, etc.; deploying the optimized matching strategy to actual business scenarios, such as the product recommendation system and advertising delivery system of e-commerce platforms, and collecting user feedback and business data in real time; tracking the implementation effect of the matching strategy in real time based on the effect monitoring system. This includes monitoring user behavior data (such as click-through rate, conversion rate, dwell time, etc.), business indicators (such as sales, user growth rate, etc.) and user satisfaction evaluation; iteratively optimizing the matching strategy based on real-time monitoring results. This may involve adjusting model parameters, updating state space, optimizing matching strategies, and other aspects.

[0058] The effects of the above technical solution are as follows: through the real-time data stream access mechanism, the solution can quickly capture and process the latest information from multiple data sources to ensure the timeliness and accuracy of the data; according to the real-time data monitoring results, dynamically adjust the state space of the model, including user portraits and market environment data, so that the matching strategy can keep up with the changes in the market and users and maintain a high degree of adaptability; use the trained deep reinforcement learning model to generate initial matching strategies based on the real-time updated state space, which more accurately reflect the current status of users and the market; conduct pre-evaluation before implementing the matching strategy, screen out potential optimal strategies, and fine-tune them in combination with real-time user feedback and market changes to ensure the effectiveness and pertinence of the strategy; simulate the environment or history Pre-evaluation of strategies based on data provides a scientific basis for decision-making and reduces the cost of trial and error. An effect monitoring system is established to track the implementation effect of matching strategies in real time, and iterative optimization is performed based on monitoring results, thus realizing closed-loop management of intelligent operations and decision-making. Through precise matching and personalized recommendations, user experience is improved, and user satisfaction and loyalty are increased. Strategy adjustments are made based on real-time user feedback to ensure that products and services can continue to meet user needs and increase user participation and activity. By optimizing matching strategies, key business indicators such as click-through rate, conversion rate, and sales are improved, which drives sustained business growth. Market dynamics and competitor information are captured in real time, strategies are adjusted quickly, and the company's market competitiveness and resilience are enhanced.

[0059] In one embodiment of the present invention, the S242 includes:

[0060] Conduct in-depth analysis of real-time data to uncover the latest trends in user behavior, key signals of market changes, and competitor movements (e.g., competitors launching new products); and use machine learning algorithms to obtain hidden patterns and associations in the data;

[0061] Dynamically update the dimensions and weights of user portraits based on real-time data analysis results; this includes introducing new user behavior features, adjusting the weights of existing features to reflect the user's latest preferences and interests, removing outdated or no longer relevant features, and using deep learning algorithms to continuously optimize user portraits;

[0062] Dynamically adjust the input of market environment data based on market trend reports and competitor analysis, including the introduction of key information such as new popular products, popular trends or competitor dynamics, and updating the weight and timeliness of existing market data; define the space of matching strategies based on the dynamically adjusted state space, including multiple dimensions such as product recommendations, preferential strategies, interaction methods, and possible different strategy options under each dimension;

[0063] Using the trained deep reinforcement learning model, the initial matching strategy is generated according to the real-time updated state space.

[0064] The working principle of the above technical solution is: collect data in real time from multiple data sources (such as user behavior logs, market trend reports, competitor analysis, etc.); use stream processing technology (such as Apache Kafka, Spark Streaming, etc.) to clean, transform and aggregate data in real time to ensure data accuracy and availability; conduct in-depth analysis of real-time data to explore the latest trends in user behavior, such as changes in user preferences, patterns of purchasing behavior, etc.; identify key signals of market changes, such as the rise of popular products, the evolution of popular trends, etc.; track competitors' movements, such as new product launches, marketing strategy adjustments, etc.; apply machine learning algorithms to extract hidden patterns and associations from data to provide a scientific basis for subsequent dynamic adjustments; based on the results of real-time data analysis, introduce new features that can reflect the user's latest preferences and interests, such as new browsing behaviors and purchasing preferences; adjust the weights of existing features to reflect changes in user preferences. For example, if a user has recently shown a strong interest in a certain category of products, the weight of the features of that category will be increased accordingly; features that are outdated or no longer relevant to user behavior will be eliminated to maintain the simplicity and accuracy of the user portrait; deep learning algorithms will be used to continuously optimize the user portrait to improve the predictive power and accuracy of the user portrait; new hot products, popular trends, or competitor dynamics and other key information will be introduced based on market trend reports and competitor analysis; the weight of existing market data will be adjusted to reflect its importance and timeliness. For example, a higher weight should be given to the recently released market trend report; the space of matching strategies is defined based on the dynamically adjusted state space. This includes multiple dimensions such as product recommendations, preferential strategies, and interaction methods; under each dimension, the possible different strategy options are refined. For example, under the product recommendation dimension, there can be multiple strategies such as recommendations based on user historical purchase behavior and recommendations based on popular products; using the trained deep reinforcement learning model, the initial matching strategy is generated based on the real-time updated state space and the defined matching strategy space; before implementing the matching strategy, a simulation environment or historical data can be used for pre-evaluation to screen out the potential optimal strategy. At the same time, the matching strategy is fine-tuned based on real-time user feedback and market changes to improve its targeting and effectiveness.

[0065] The effects of the above technical solutions are as follows: through in-depth analysis of real-time data, enterprises can quickly capture the latest trends in user behavior, key signals of market changes, and the movements of competitors, ensuring that decisions are based on the latest and most accurate information; based on the results of real-time data analysis, dynamically update the dimensions and weights of user portraits, so that user portraits can more accurately reflect the user's latest preferences and interests, improving the accuracy of personalized recommendations and user satisfaction; based on market trends and competitor analysis, dynamically adjust the input of market environment data to ensure that matching strategies can keep up with market changes and maintain a high degree of adaptability; through machine learning algorithms, extract hidden patterns and associations from data, providing a scientific basis for formulating more accurate matching strategies; based on the dynamically adjusted state space, define the space of matching strategies, including multiple dimensions and possible different strategy options under each dimension, providing the possibility of formulating diversified matching strategies; use the trained deep reinforcement learning model to generate initial matching strategies based on the real-time updated state space, and these strategies are more accurate It reflects the current status of users and the market and improves the effectiveness of matching strategies. Through the application of real-time data analysis and machine learning algorithms, it realizes intelligent analysis of user behavior and market changes, providing a scientific and objective basis for decision-making. The dynamic update of user portraits and market environment data, as well as the definition of matching strategy space and the generation of initial matching strategies, are all automated, greatly improving the efficiency of decision-making. The use of deep learning algorithms to continuously optimize user portraits and fine-tune matching strategies based on real-time feedback ensures continuous optimization and improvement of decisions. Through real-time data analysis and dynamic adjustment of strategies, enterprises can quickly respond to market changes, seize market opportunities, and improve market competitiveness. Based on accurate user portraits and matching strategies, enterprises can provide personalized products and services to meet the diverse needs of users and improve user satisfaction and loyalty. By deeply exploring hidden patterns and correlations in data and using advanced technologies such as deep reinforcement learning, enterprises can continuously innovate products and services to promote business development and innovation.

[0066] In one embodiment of the present invention, the S243 includes:

[0067] Determine key indicators for pre-evaluation of matching strategies based on business needs and goals, including but not limited to click-through rate, conversion rate, user satisfaction, average dwell time, sales, user growth rate, etc., and assign weights to each key indicator based on business priorities and strategic directions;

[0068] Using existing data and system architecture, we build a simulation environment similar to the actual business scenario, including user behavior simulation, market environment simulation, etc. We organize and analyze historical user behavior data and market trend data to build a comprehensive historical data set.

[0069] In the simulation environment, implement simulation according to the predetermined strategy plan, including adjusting the ranking of recommended products, optimizing the strength of preferential strategies, and improving the user experience of interactive methods. Through simulation implementation, collect and analyze data changes of key indicators, and predict and evaluate the implementation effect of the strategy;

[0070] Use machine learning algorithms (such as regression analysis, classification algorithms, etc.) to model and predict indicator data and quantify the potential impact of strategies on the business;

[0071] Based on the preliminary evaluation results, compare the performance of different strategies on key indicators, and find out the potential optimal strategy through visual analysis and statistical analysis.

[0072] The working principle of the above technical solution is as follows: according to business needs and goals, key indicators for pre-evaluation of matching strategies are clearly defined, such as click-through rate, conversion rate, user satisfaction, average stay time, sales, user growth rate, etc.; according to business priorities and strategic directions, reasonable weights are assigned to each key indicator to reflect their importance in business success; using existing data and system architecture, a simulation environment similar to the actual business scenario is constructed; the environment includes user behavior simulation and market environment simulation, which can fully reflect the interaction between users and the market; sorting and analyzing historical user behavior data and market trend data, and constructing a comprehensive historical data set for strategy pre-evaluation; in the simulation environment, implementation simulation is carried out according to the predetermined strategy plan, such as adjusting the ranking of recommended products, optimizing the strength of preferential strategies, and improving the user experience of interactive methods; through simulation implementation, data changes of key indicators are collected and analyzed to evaluate the implementation effect of the strategy; using machine learning algorithms (such as regression analysis, classification algorithms, etc.) to model and predict indicator data; through models, the potential impact of strategies on the business is quantified to provide a basis for decision-making; according to the pre-evaluation results, the performance of different strategies on key indicators is compared; through visualization analysis and statistical analysis methods, potential optimal strategies are found to provide reference for actual business decisions. The S243 technical solution emphasizes data-based decision-making. By collecting and analyzing historical data and building a comprehensive data set, a reliable basis is provided for strategy pre-evaluation. Pre-evaluating strategies in a simulation environment can avoid the risks that may be caused by directly implementing new strategies in actual business. Through verification in a simulation environment, the effectiveness of strategies can be evaluated more accurately. Using machine learning algorithms to model and predict indicator data can quantify the potential impact of strategies on the business. This predictive capability provides a more scientific basis for decision-making. Through visual analysis and statistical analysis methods, the performance of different strategies on key indicators can be intuitively displayed. This helps decision makers understand the effectiveness of strategies more clearly and make more informed decisions.

[0073] The effect of the above technical solution is: by determining the key indicators of matching strategy pre-evaluation and assigning reasonable weights to these indicators, the S243 technical solution provides a clear direction and basis for business decision-making. The simulation environment built using existing data and system architecture can fully and truly reflect the actual business scenario, making the strategy pre-evaluation closer to reality. By implementing the predetermined strategy plan in the simulation environment and collecting and analyzing the data changes of key indicators, the implementation effect of the strategy can be accurately predicted and evaluated, thereby improving the scientificity and accuracy of decision-making. Directly implementing new strategies in actual business may bring certain risks, such as user loss and sales decline. Through the simulation environment, strategy pre-evaluation can be carried out without actually affecting the business. Different strategies can be compared and analyzed to find the potential optimal strategy. This method greatly reduces business risks and enables enterprises to promote business development more steadily. By adjusting the ranking of recommended products, optimizing the strength of preferential strategies, and improving the user experience of interactive methods, user needs can be met more accurately and user satisfaction and loyalty can be improved. At the same time, these strategy solutions also help to improve business results, such as increasing sales and increasing user growth rate. Using machine learning algorithms to model and predict indicator data can quantify the potential impact of strategies on the business, providing a more intuitive and quantitative basis for decision-making. In addition, through visual analysis and statistical analysis methods, the performance of different strategies on key indicators can be more clearly displayed, making the decision-making process more efficient and convenient. Through the application of simulation environments and machine learning algorithms, strong support is provided for business innovation and sustainable development. By continuously trying and optimizing strategic solutions, enterprises can respond to market changes more flexibly, seize new business opportunities, and achieve sustainable development and growth.

[0074] In one embodiment of the present invention, S3 includes:

[0075] S31, collecting the user's behavior data in the process of AI virtual human interaction in real time, the behavior data including clicks, browsing and purchases; and collecting the user's satisfaction evaluation data on the recommendation results through multiple methods, including questionnaires and user comments;

[0076] S32, using association rule mining algorithms to analyze user behavior patterns, identify user preferences and purchasing habits, and using sentiment analysis algorithms to conduct in-depth analysis of user satisfaction evaluation data to extract key indicators of user recommendations; the key indicators include preference and satisfaction;

[0077] S33. Evaluate the effectiveness of the current matching strategy based on the user feedback analysis results, strengthen the products and strategies that have received positive user feedback, and weaken or adjust the products and strategies that have received negative user feedback;

[0078] S34. Combine market trends and competitor dynamics to continuously optimize matching strategies.

[0079] The working principle of the above technical solution is: collect the user's behavior data in the process of AI virtual human interaction in real time, including but not limited to the user's click behavior, browsing behavior and purchase behavior. These data can intuitively reflect the user's interest and preference for the recommended content; collect user satisfaction evaluation data on the recommended results through various methods such as questionnaires and user comments. These data are crucial to understanding key indicators such as the user's preference and satisfaction with the recommended content; use association rule mining algorithms to conduct in-depth analysis of user behavior patterns to identify user preferences and purchasing habits. This helps companies understand user needs more accurately and provide more personalized recommendation services; use sentiment analysis algorithms to conduct in-depth analysis of user satisfaction evaluation data and extract key indicators of users' recommendation results, such as preference and satisfaction. These indicators are important bases for measuring the effectiveness of recommendations. Based on the results of user feedback analysis, conduct a comprehensive evaluation of the effectiveness of the current matching strategy. This helps enterprises understand the advantages and disadvantages of current strategies and provide direction for subsequent adjustments; strengthen products and strategies with positive user feedback to increase their recommendation weight and exposure; weaken or adjust products and strategies with negative user feedback to reduce their impact or improve their recommendation effect; pay close attention to market dynamics and changes in competitors' recommendation strategies to adjust their own matching strategies in a timely manner; continuously optimize matching strategies in combination with market trends and competitor dynamics to improve the accuracy of recommendations and user satisfaction. For example, new recommendation algorithms can be introduced, recommendation weights can be adjusted, or new recommendation dimensions can be added; based on real-time collected user behavior data and satisfaction evaluation data, data analysis can be used to guide the formulation and adjustment of matching strategies. This ensures the scientificity and accuracy of decision-making; through in-depth analysis of user behavior patterns, users' personalized needs can be identified, thereby providing more accurate personalized recommendation services. This helps to improve user satisfaction and loyalty; emphasizes the continuous optimization and iteration of matching strategies. By continuously collecting user feedback and market dynamic information, enterprises can adjust strategies in a timely manner to adapt to market changes and changes in user needs. This ensures the timeliness and competitiveness of recommendation services. For example, suppose an e-commerce platform introduces AI virtual people to recommend products. Under the guidance of the S3 technical solution, the platform can collect real-time data on users' clicks, browsing and purchasing behaviors during the interaction with AI virtual humans, and collect users' satisfaction evaluations on recommendation results through questionnaires and user comments. Then, the association rule mining algorithm and sentiment analysis algorithm are used to analyze these data to identify users' purchase preferences and key indicators of satisfaction. Based on these analysis results, the platform can evaluate and adjust the current matching strategy, such as increasing the recommendation weight of users' favorite products and reducing the exposure of products with poor user feedback. At the same time, the platform can also pay close attention to market dynamics and changes in competitors' recommendation strategies, and adjust its own matching strategy in a timely manner to maintain competitiveness.Through this continuous optimization and iteration approach, the platform can continuously improve the accuracy of recommendations and user satisfaction.

[0080] The effects of the above technical solutions are: by collecting the user's behavior data (such as clicks, browsing, purchases) and satisfaction evaluation data (through questionnaires, user comments, etc.) in the process of AI virtual human interaction in real time, it can quickly capture changes in user preferences and needs, thereby providing more personalized recommendation services; using association rule mining algorithms and sentiment analysis algorithms, S3 can deeply mine key information in user behavior patterns and satisfaction evaluations, and identify key indicators such as user preferences and satisfaction. This helps to achieve more accurate recommendations and improve user satisfaction and loyalty; based on the results of user feedback analysis, it can evaluate the effect of the current matching strategy, strengthen products and strategies with positive user feedback, and weaken or adjust products and strategies with negative user feedback. This dynamic adjustment mechanism helps to optimize the recommendation strategy and improve business results; combined with market trends and competitor dynamics, the matching strategy is continuously optimized. This helps companies maintain market sensitivity and adjust the recommendation strategy in a timely manner to adapt to market changes and changes in user needs, thereby maintaining a leading position in the fierce market competition; by collecting and analyzing user behavior data and satisfaction evaluation data in real time, it provides companies with rich data support, which helps companies make more scientific and reasonable decisions; through automated and intelligent data analysis methods, decision-making efficiency is improved. Enterprises can more quickly identify user needs and market changes, and make timely and effective adjustments; by collecting and analyzing user data in real time, they can provide users with more personalized recommendation services. This personalized service helps to enhance user stickiness and loyalty, and improve user repurchase and retention rates; collecting user satisfaction evaluation data through questionnaires, user comments, etc. can not only understand user satisfaction with recommendation results, but also enhance user participation and sense of belonging. This helps to establish good user relationships and enhance brand image.

[0081] In one embodiment of the present invention, the S4 includes:

[0082] S41, collecting information of various modes input by the user, including text, pictures, and audio, and performing corresponding preprocessing on the information of various modes; for example, performing word segmentation, stop word removal, stem extraction, etc. on the text; performing denoising, scaling, color correction, etc. on the picture; performing feature extraction, noise reduction, etc. on the audio;

[0083] S42, extracting feature vectors of different modal data based on a deep learning algorithm, and fusing the feature vectors of different modalities based on a feature fusion algorithm;

[0084] S43. Use natural language processing and computer vision technology to deeply understand the fused information and extract user intentions and needs;

[0085] S44. According to user intentions and needs, the integrated information is matched with the product library to screen out products that meet user needs. Combined with user portraits and market trends, the recommendation strategy is optimized.

[0086] The working principle of the above technical solution is as follows: first, collect information of multiple modes input by users, including text, pictures, and audio; perform corresponding preprocessing for information of different modes. For text, perform word segmentation, stop word removal, stem extraction and other processing to extract key information in the text; for pictures, perform denoising, scaling, color correction and other processing to improve the quality and readability of pictures; for audio, perform feature extraction, noise reduction and other processing to extract useful information in audio and reduce noise interference; based on deep learning algorithms, extract feature vectors of different modal data. These feature vectors can reflect the essential attributes and key information of the data; use feature fusion algorithms to fuse feature vectors of different modalities. This fusion can comprehensively consider information of multiple modalities and improve the comprehensiveness and accuracy of information processing; use natural language processing and computer vision technology to deeply understand the fused information. This includes semantic analysis of text content, image recognition of picture content, and speech recognition of audio content. Through deep understanding, extract the user's intentions and needs. This helps the system to more accurately understand what the user wants to express and what goals they want to achieve; match the fused information with the product library according to the user's intentions and needs. Through comparison and analysis, select products that meet user needs; combine user portraits and market trends to optimize the recommendation strategy. This includes adjusting the ranking of recommended products, optimizing recommendation algorithms, etc., to improve the accuracy of recommendations and user satisfaction; collect and preprocess the multi-modal information input by users. The purpose of this step is to extract the key content in the information and provide a basis for subsequent processing; use deep learning algorithms to extract feature vectors of different modal data and perform feature fusion. Then, use natural language processing and computer vision technology to deeply understand the fused information and extract user intentions and needs; match the fused information with the product library according to user intentions and needs, and select products that meet user needs. At the same time, combine user portraits and market trends to optimize the recommendation strategy to improve the accuracy of recommendations and user satisfaction.

[0087] The effect of the above technical solution is: S4 can collect multiple modal information input by users, including text, pictures and audio. This way of collecting multimodal information can more comprehensively reflect the needs and intentions of users and provide a rich data foundation for subsequent processing and analysis; by performing targeted preprocessing on information of different modalities, such as word segmentation, stop word removal, stem extraction of text, denoising, scaling, color correction of pictures, and feature extraction and noise reduction of audio, the quality and readability of information can be improved, laying the foundation for subsequent feature extraction and deep understanding; extracting feature vectors of different modal data based on deep learning algorithms. Deep learning algorithms have powerful feature extraction capabilities and can extract high-level feature representations from raw data, providing strong support for subsequent information processing and analysis; through feature fusion algorithms, feature vectors of different modalities can be fused. This fusion can comprehensively consider information of multiple modalities and improve the comprehensiveness and accuracy of information processing. At the same time, feature fusion can also reduce the redundancy and conflict of information and improve the efficiency and effect of subsequent processing; natural language processing and computer vision technology are used to deeply understand the fused information. These two technologies can process text and image information respectively, extract semantic and visual features, and provide strong support for the subsequent extraction of user intentions and needs; through deep understanding, user intentions and needs can be extracted. This helps the system to more accurately understand what the user wants to express and what goals they want to achieve, and provides a strong basis for subsequent product matching and recommendation strategy optimization; according to user intentions and needs, the fused information can be matched with the product library. This matching method can more accurately screen out products that meet user needs, improve the accuracy of recommendations and user satisfaction; combined with user portraits and market trends, the recommendation strategy can be optimized. This includes adjusting the ranking of recommended products, optimizing recommendation algorithms, etc., to improve the personalization and diversity of recommendations, and further meet user needs and expectations; by comprehensively considering factors such as multimodal information input by users, user portraits and market trends, more personalized recommendation services can be provided to users. This personalized recommendation can more accurately meet user needs and preferences, and improve user satisfaction and loyalty; this technical solution can process user input information in multiple modes, allowing users to interact with the system in a more natural and convenient way. The improvement of this interactive method can enhance the user experience and participation, and further improve user satisfaction and stickiness.

[0088] In one embodiment of the present invention, the S42 includes:

[0089] According to the characteristics of different modal data such as text, pictures and audio, deep learning algorithms are selected, and features are extracted based on the selected deep learning algorithms; for example, for text data, convolutional neural networks (CNN) or recurrent neural networks (RNN) and their variants (such as LSTM, GRU) can be selected for feature extraction; for picture data, convolutional neural networks (CNN) can be selected for feature extraction; for audio data, long short-term memory networks (LSTM) or convolutional recurrent networks (CRN) can be selected for feature extraction;

[0090] Use large-scale labeled data sets to train the selected deep learning algorithm. During the training process, transfer learning is used to improve the generalization ability and robustness of the model.

[0091] Input the preprocessed text data into the trained deep learning model to extract the feature vector of the text; the feature vector of the text can reflect the semantic information and contextual relationship in the text;

[0092] Input the preprocessed image data into the trained convolutional neural network model to extract the feature vector of the image. The feature vector of the image can reflect the visual information and spatial structure in the image;

[0093] Input the preprocessed audio data into the trained deep learning model to extract the audio feature vector. The audio feature vector can reflect the sound characteristics, rhythm, melody and other information in the audio;

[0094] Select feature fusion strategies based on business needs and the characteristics of data of different modalities. This includes early fusion and late fusion. In the feature extraction stage, early fusion is used to fuse data of different modalities. In the decision-making stage, late fusion is used to fuse feature vectors of different modalities.

[0095] According to the selected feature fusion strategy, the fusion of feature vectors of different modalities is realized, and the fused feature vectors are evaluated. If the fusion effect is not good, the feature extraction algorithm, feature fusion strategy or fusion algorithm is adjusted and optimized.

[0096] The working principle of the above technical solution is: according to the characteristics of different modal data such as text, picture and audio, select a suitable deep learning algorithm for feature extraction; for text data, you can choose convolutional neural network (CNN) or recurrent neural network (RNN) and its variants (such as LSTM, GRU), etc. These algorithms can capture semantic information and contextual relationships in text; for picture data, convolutional neural network (CNN) is a common choice, which can extract visual information and spatial structure in pictures; for audio data, you can choose long short-term memory network (LSTM) or convolutional recurrent network (CRN), etc. These algorithms can extract sound features, rhythm, melody and other information in audio; use large-scale annotated data sets to train the selected deep learning algorithm to ensure that the model can accurately extract features; during the training process, use technical means such as transfer learning to improve the generalization ability and robustness of the model, so that it can be more Better adapt to different scenarios and tasks; input the preprocessed text data into the trained deep learning model to extract the feature vectors of the text; these feature vectors can reflect the semantic information and contextual relationships in the text, and provide strong support for subsequent information processing and analysis; input the preprocessed image data into the trained convolutional neural network model to extract the feature vectors of the image; these feature vectors can reflect the visual information and spatial structure in the image, which helps to realize functions such as image recognition, classification and retrieval; input the preprocessed audio data into the trained deep learning model to extract the feature vectors of the audio; these feature vectors can reflect the sound characteristics, rhythm and melody information in the audio, and provide strong support for tasks such as audio recognition, classification and retrieval; select the feature fusion strategy according to business needs and the characteristics of different modal data; common feature fusion strategies include early fusion and late fusion. Early fusion is to fuse data of different modalities in the feature extraction stage; late fusion is to fuse feature vectors of different modalities in the decision stage; according to the selected feature fusion strategy, the fusion of feature vectors of different modalities is realized; the fused feature vector is evaluated to ensure that it can accurately reflect the multimodal information input by the user; if the fusion effect is not good, the feature extraction algorithm, feature fusion strategy or fusion algorithm is adjusted and optimized to improve the fusion effect and accuracy; data of different modalities such as text, pictures and audio are preprocessed, including denoising, scaling, color correction, word segmentation, stop word removal and other processing steps to improve data quality and readability; according to the characteristics of the data, a suitable deep learning algorithm is selected and trained using a large-scale annotated data set. During the training process, the generalization ability and robustness of the model are improved through technical means such as transfer learning; the preprocessed data is input into the trained deep learning model to extract feature vectors of different modalities; according to the selected feature fusion strategy, the fusion of feature vectors of different modalities is realized, and the fused feature vector is evaluated. If the fusion effect is not good, it is adjusted and optimized.

[0097] The effect of the above technical solution is as follows: the solution selects a suitable deep learning algorithm for feature extraction according to the characteristics of different modal data such as text, pictures and audio. This selection ensures that the algorithm can fully capture and utilize the unique information of each modal data, and improves the pertinence and accuracy of data processing; by using a large-scale annotated data set to train the selected deep learning algorithm and applying transfer learning technology during the training process, the solution significantly improves the generalization ability and robustness of the model. This enables the model to better adapt to different scenarios and tasks, and improves the flexibility and adaptability of data processing; the solution can efficiently extract feature vectors of data such as text, pictures and audio through a trained deep learning model. These feature vectors can accurately reflect the key information in the data and provide strong support for subsequent information processing and analysis; according to business needs and the characteristics of different modal data, the solution selects a suitable feature fusion strategy (such as early fusion or late fusion) to achieve effective fusion of different modal feature vectors. This fusion can comprehensively consider the information of multiple modalities and improve the comprehensiveness and accuracy of information processing; the solution forms a complete set of data processing procedures by integrating steps such as feature extraction, fusion and evaluation. This process-based processing method makes data processing more standardized and efficient, and improves overall work efficiency; by adopting deep learning algorithms and efficient feature extraction and fusion technology, the solution significantly improves the efficiency of data processing. This enables the system to respond to user input more quickly and provide more timely and accurate information services; the solution can accurately reflect the multimodal information input by users, and provide rich data support for subsequent recommendations, searches and other applications. This enables the system to provide users with more personalized and accurate services, improving user satisfaction and experience; by processing user input information in multiple modes, the solution enables users to interact with the system in a more natural and convenient way. The improvement of this interactive method enhances the user's sense of participation and experience, and further improves user satisfaction and loyalty.

[0098] In one embodiment of the present invention, S5 includes:

[0099] S51, obtaining market data, wherein the market data includes product sales, user evaluations, industry reports, and competitor dynamics; and cleaning, standardizing, and normalizing the obtained market data;

[0100] S52. Select prediction models based on the characteristics of market data and prediction requirements, such as time series analysis, machine learning algorithms (such as SVM, random forest, etc.), deep learning algorithms (such as LSTM, GRU, etc.), and use historical market data to train and optimize the prediction models;

[0101] S53. Use the trained prediction model to predict market trends, including popular products, popular trends, user preferences, etc., and update product information in real time based on the market trend prediction results, including product features, prices, inventory, etc.;

[0102] S54. Adjust the matching strategy according to the updated product information.

[0103] The working principle of the above technical solution is: obtain market data such as product sales, user reviews, industry reports and competitor dynamics from multiple channels (such as e-commerce platforms, social media, industry research institutions, etc.); clean the acquired market data to remove duplicate, invalid or abnormal data to ensure the accuracy and reliability of the data; standardize and normalize the cleaned data to eliminate dimensional differences between different data and improve data consistency and comparability; select a suitable prediction model based on the characteristics of market data and prediction needs. For example, for time series data, you can choose a time series analysis model; for classification or regression problems, you can choose a machine learning algorithm (such as SVM, random forest, etc.) or a deep learning algorithm (such as LSTM, GRU, etc.); use historical market data to train the selected prediction model, and improve the prediction accuracy and generalization ability of the model by adjusting model parameters, optimizing algorithms, etc.; use the trained prediction model to predict market trends, including popular products, popular trends, user preferences, etc.; based on the market trend prediction results, update product information in real time, including product features, prices, inventory, etc., to ensure that product information is consistent with market trends; based on the updated product information, adjust the product information matching strategy of the AI ​​virtual person to ensure that the recommended products meet market trends and user needs; by analyzing user behavior , historical purchase records and other information to further optimize the recommendation algorithm and improve the accuracy and personalization of recommendations; first, obtain market data from multiple channels, and clean, standardize and normalize them to provide a high-quality data basis for subsequent analysis; select appropriate prediction models based on data characteristics and prediction needs, and use historical data for training and optimization to improve the prediction ability of the model; use the trained model to predict market trends, including hot products, popular trends, etc., to provide a basis for updating product information; update product information according to the results of market trend predictions, and adjust the product information matching strategy of AI virtual people to ensure that the recommended products are in line with market trends and user needs; continuously optimize the recommendation algorithm and matching strategy by continuously collecting user feedback, analyzing user behavior, etc., and improve the accuracy and personalization of recommendations.

[0104] The effects of the above technical solutions are: by obtaining multi-dimensional market data such as product sales, user reviews, industry reports and competitor dynamics, we can fully understand the market dynamics and competitive situation; clean, standardize and normalize market data to improve the accuracy and comparability of the data, and provide a reliable data basis for subsequent analysis; use advanced prediction models (such as time series analysis, machine learning algorithms, deep learning algorithms, etc.) to predict market trends, and accurately capture information such as popular products, popular trends and user preferences; based on the market trend prediction results, we can update product information in real time, including key elements such as product features, prices, and inventory; real-time updated product information helps companies respond quickly to market changes, optimize product strategies, and improve market competitiveness; accurate product information can ensure that AI virtual people recommend products that meet user needs and expectations, thereby improving user satisfaction and loyalty. According to the updated product information, the matching strategy is adjusted to achieve personalized recommendations to meet the different needs and preferences of users; accurate recommendation strategies can increase user purchase willingness, improve product conversion rate, and bring higher economic benefits to the enterprise; personalized and accurate recommendations can enhance users' trust and dependence on AI virtual people, improve user stickiness, and promote long-term cooperation; by predicting market trends, it can guide enterprises to rationally allocate production resources, avoid resource waste and overcapacity; real-time updated product information helps enterprises optimize inventory management strategies, reduce inventory backlogs and out-of-stock risks, and improve inventory turnover; the data analysis and prediction results provided by this technical solution can provide strong support for enterprise decision-making and realize data-driven decision-making; through automated and intelligent data analysis processes, it can significantly improve enterprise decision-making efficiency and shorten decision-making cycles; prediction results based on big data and advanced algorithms can provide enterprises with more accurate and reliable decision-making basis and reduce decision-making risks.

[0105] One embodiment of the present invention, as Figure 2 As shown, an AI virtual human product matching optimization system comprises:

[0106] Data acquisition module: obtains multi-dimensional data of users, including basic information, historical behavior, preferences and emotional state of the multi-dimensional data; uses deep learning algorithms to perform feature extraction and cluster analysis on user data to build a detailed user portrait;

[0107] Strategy matching module: Based on the adaptive deep reinforcement learning model, the matching strategy is dynamically adjusted according to changes in user profiles and market environment; and the optimal matching strategy is learned through continuous trial and error and reward mechanisms;

[0108] Precision recommendation module: collects user feedback and satisfaction data in real time based on the interaction process between users and AI virtual people; uses adaptive deep reinforcement learning models to analyze user feedback, dynamically adjust matching strategies, and make precise personalized recommendations;

[0109] Information integration module: Based on cross-modal information fusion technology, it effectively integrates user input information in multiple modes (such as text, pictures, and audio);

[0110] Trend forecasting module: predict market trends based on big data analysis technology and identify potential product demand in advance; update product information and matching strategies in real time based on market trend forecast results.

[0111] The working principle of the above technical solution is as follows: obtaining multi-dimensional data of users from multiple sources (such as user registration information, historical behavior records, social media activities, sensor data, etc.); these data include basic information (such as age, gender, geographic location), historical behavior (such as purchase records, browsing history), preferences (such as brand preferences, color preferences) and emotional states (such as current mood, emotional changes); using deep learning algorithms (such as convolutional neural networks, recurrent neural networks, autoencoders, etc.) to extract features from user data and extract features that have a significant impact on user behavior, preferences and emotional states; applying clustering analysis techniques (such as K-means, hierarchical clustering, DBSCAN, etc.) to group users to form different user groups in order to more finely construct user portraits; based on the extracted features and clustering results, constructing a detailed user portrait, including user interests, needs, behavior patterns, etc.; setting an initial matching strategy based on the preliminary user portrait and market environment; based on the adaptive deep reinforcement learning model, according to changes in user portraits (such as changes in user preferences) and changes in the market environment (such as new product launches, seasonal demand The AI ​​​​virtual person can dynamically adjust the matching strategy according to the changes in the recommended products; learn the optimal matching strategy through continuous trial and error and reward mechanisms (such as improved user satisfaction and purchase conversion rate as positive feedback); collect user feedback and satisfaction data in real time during the interaction between users and AI virtual people; use adaptive deep reinforcement learning models to analyze user feedback and identify information such as user satisfaction with recommended products and changes in preferences; dynamically adjust matching strategies based on the analysis results to make more accurate personalized recommendations; based on cross-modal information fusion technology, effectively integrate user input information in multiple modes such as text, pictures, and audio; this helps to understand the user's intentions and needs more comprehensively; by integrating information from different modes, improve the comprehensiveness and accuracy of product matching, and ensure that recommended products are more in line with user expectations; based on big data analysis technology, predict market trends and identify potential product needs; this includes analyzing historical sales data, user behavior data, social media trends, etc.; based on market trend prediction results, update product information and matching strategies in real time; this ensures that AI virtual people can recommend products that meet market trends and improve the relevance and timeliness of recommendations.

[0112] The effects of the above technical solutions are as follows: through the collection and analysis of multi-dimensional data, a detailed user portrait is constructed, which can more deeply understand the user's preferences, needs and behavior patterns; the application of adaptive deep reinforcement learning models enables the matching strategy to be dynamically adjusted according to changes in user portraits and market environment, thereby providing more personalized recommendations and improving user satisfaction; real-time collection of feedback and satisfaction data during the interaction between users and AI virtual people, and dynamic adjustment of matching strategies based on these data, can ensure that the recommended products are more in line with user expectations; the application of cross-modal information fusion technology enables AI virtual people to better understand the user's input information, whether it is text, pictures or audio, and can be effectively integrated, thereby improving the comprehensiveness and accuracy of matching and enhancing user experience; based on big data analysis technology, market trends are predicted, which can identify potential product needs in advance, so that AI virtual people can Anthropomorphism can recommend products that are in line with market trends; this not only helps to improve the market competitiveness of products, but also ensures that AI virtual people always recommend the latest and most popular products, thereby enhancing users' trust in and willingness to buy products; the adaptive deep reinforcement learning model learns the optimal matching strategy through continuous trial and error and reward mechanisms, which means that AI virtual people can continuously learn and grow from user feedback, improve their matching capabilities and personalized recommendation levels; this ability to continuously learn and optimize enables AI virtual people to always stay at the forefront of the industry and provide users with better and more efficient services; accurate personalized recommendations can reduce users' screening costs and increase purchase conversion rates, thereby bringing higher marketing efficiency to companies; at the same time, by providing product recommendations that meet user expectations and market demand, it can enhance user satisfaction and loyalty, improve user stickiness, and lay the foundation for the long-term development of the company.

[0113] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An AI virtual human product matching optimization method, characterized in that: The method comprises: S1. Obtain multi-dimensional data of users, use deep learning algorithms to extract features and perform cluster analysis on user data, and build user portraits; S2, based on the adaptive deep reinforcement learning model, dynamically adjust the matching strategy according to changes in user profiles and market environment; S3. Collect user feedback and satisfaction data in real time based on the interaction process between users and AI virtual people; use adaptive deep reinforcement learning models to analyze user feedback, dynamically adjust matching strategies, and make accurate personalized recommendations; S4. Based on cross-modal information fusion technology, user input information of multiple modalities is effectively integrated; S5. Predict market trends based on big data analysis technology and identify potential product demand in advance; update product information and matching strategies in real time based on market trend forecast results.

2. According to claim 1, an AI virtual human product matching optimization method is characterized in that: Said S1 comprises: S11. Collect multi-dimensional data of users from multiple channels, and pre-process the collected multi-dimensional data; S12, extracting features from the preprocessed multi-dimensional data through a deep learning algorithm; S13. Use clustering algorithms to group users to form user groups with similar characteristics, build detailed portraits for each user group, and generate personalized portraits for each individual user.

3. According to claim 2, an AI virtual human product matching optimization method is characterized in that: The S13 comprises: A clustering algorithm is used to preliminarily group the preprocessed multi-dimensional user data, and users are divided into several groups with similar characteristics based on key indicators; Conduct in-depth analysis on each initially divided user group, extract the common characteristics of the group, and further use deep learning algorithms to subdivide the group based on the initial division to form more refined user subgroups; Combine group characteristics and individual user data to build a detailed portrait for each user subgroup. Based on the group portrait, generate a personalized portrait for each user according to the specific behavior and preference data of individual users; Based on the real-time data monitoring mechanism, we continuously collect new user behaviors and preference data, and update user portraits regularly or on demand based on the real-time data monitoring results; and continuously optimize user portraits through machine learning algorithms.

4. The AI ​​virtual human product matching optimization method according to claim 1, characterized in that: The S2 comprises: S21. Use user profile and market environment data as input of the state space and define a set of matching strategies; S22. Set a reward function based on user feedback, evaluate the effect of the matching strategy, and build a training set using historical user data and matching strategy data; S23. Train the model based on the deep reinforcement learning algorithm, evaluate the effect of the matching strategy through simulation experiments or A / B testing, and adjust the model parameters and strategies according to the evaluation results; S24. Update the state space of the model in real time according to changes in user profiles and market environment, and use the trained model to generate the optimal matching strategy according to the real-time status; S25. Continuously monitor the effect of the matching strategy and adjust the matching strategy based on the monitoring results.

5. The AI ​​virtual human product matching optimization method according to claim 4 is characterized in that: The S24 comprises: S241. Based on the real-time data stream access mechanism, the latest data is continuously obtained from multiple data sources; and the data is cleaned, converted and aggregated in real time through stream processing technology; S242, dynamically adjusting the state space of the model according to the real-time data monitoring results; using the trained deep reinforcement learning model to generate an initial matching strategy according to the real-time updated state space; S243. Before implementing the matching strategy, use the simulation environment or historical data to conduct a preliminary assessment; by comparing the expected returns under different strategies, screen out the potential optimal strategy; S244. Fine-tune the initially generated matching strategy based on real-time user feedback and market changes; deploy the optimized matching strategy to actual business scenarios, and collect user feedback and business data in real time; S245. Based on the effect monitoring system, the implementation effect of the matching strategy is tracked in real time, and the matching strategy is iteratively optimized according to the real-time monitoring results.

6. The AI ​​virtual human product matching optimization method according to claim 5, characterized in that: The S242 includes: Conduct in-depth analysis of real-time data to uncover the latest trends in user behavior, key signals of market changes, and competitor movements; and use machine learning algorithms to obtain hidden patterns and correlations in the data; Dynamically update the dimensions and weights of user portraits based on real-time data analysis results; and use deep learning algorithms to continuously optimize user portraits; Dynamically adjust the input of market environment data based on market trend reports and competitor analysis; define the space for matching strategies based on the dynamically adjusted state space; Using the trained deep reinforcement learning model, the initial matching strategy is generated according to the real-time updated state space.

7. The AI ​​virtual human product matching optimization method according to claim 1, characterized in that: The S3 includes: S31. Collect the user's behavior data in the process of AI virtual human interaction in real time, and collect the user's satisfaction evaluation data on the recommendation results through multiple methods; S32. Analyze user behavior patterns using association rule mining algorithms to identify user preferences and purchasing habits, and use sentiment analysis algorithms to conduct in-depth analysis of user satisfaction evaluation data to extract key indicators of user recommendations; S33. Evaluate the effectiveness of the current matching strategy based on the user feedback analysis results, strengthen the products and strategies that have received positive user feedback, and weaken or adjust the products and strategies that have received negative user feedback; S34. Combine market trends and competitor dynamics to continuously optimize matching strategies.

8. The AI ​​virtual human product matching optimization method according to claim 1, characterized in that: The S4 comprises: S41, collecting information of multiple modes input by the user, and performing corresponding preprocessing on the information of multiple modes; S42, extracting feature vectors of different modal data based on a deep learning algorithm, and fusing the feature vectors of different modalities based on a feature fusion algorithm; S43. Use natural language processing and computer vision technology to deeply understand the fused information and extract user intentions and needs; S44. According to user intentions and needs, the integrated information is matched with the product library to screen out products that meet user needs. Combined with user portraits and market trends, the recommendation strategy is optimized.

9. The AI ​​virtual human product matching optimization method according to claim 1, characterized in that: The S5 comprises: S51. Obtain market data, and clean, standardize and normalize the acquired market data; S52. Select a forecasting model based on the characteristics of market data and forecasting requirements, and train and optimize the forecasting model using historical market data; S53, using the trained prediction model to predict the market trend, and updating the product information in real time according to the market trend prediction results; S54. Adjust the matching strategy according to the updated product information.

10. An AI virtual human product matching optimization system, characterized in that: The system comprises: Data acquisition module: obtain multi-dimensional data of users; use deep learning algorithms to extract features and perform cluster analysis on user data to build user portraits; Strategy matching module: Based on the adaptive deep reinforcement learning model, the matching strategy is dynamically adjusted according to changes in user profiles and market environment; and the optimal matching strategy is learned through continuous trial and error and reward mechanisms; Precision recommendation module: collects user feedback and satisfaction data in real time based on the interaction process between users and AI virtual people; uses adaptive deep reinforcement learning models to analyze user feedback, dynamically adjust matching strategies, and make precise personalized recommendations; Information integration module: Based on cross-modal information fusion technology, it effectively integrates user input information of multiple modalities; Trend forecasting module: predict market trends based on big data analysis technology and identify potential product demand in advance; update product information and matching strategies in real time based on market trend forecast results.

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