Digital human intelligent recommendation and decision-making system based on user behavior and context awareness

Abstract

The invention discloses a digital human intelligent recommendation and decision-making auxiliary system which provides personalized suggestions and solutions for historical behaviors, preferences and current situations of a user by using big data analysis and an artificial intelligence algorithm. The system comprises a user behavior analysis module which is used for deeply mining user behavior data and constructing a preference model; the situation awareness module monitors the environment where the user is located in real time and obtains related information; the personalized recommendation engine is used for generating a recommendation list in combination with user preferences and real-time situations; the decision-making auxiliary tool provides decision-making support to help the user to evaluate different selections; and a feedback learning mechanism is adopted, so that the system can perform self-optimization according to user feedback. In addition, the system particularly pays attention to user privacy protection and ensures data security. The system has wide application value in multiple fields such as shopping, travel, occupational planning and the like, can significantly improve the decision-making quality and life experience of the user, and has huge market potential and practical application significance.

Description

Technical field:

[0001] The present invention relates to the technical fields of artificial intelligence, big data analysis and personalized recommendation, and in particular to a digital human intelligent recommendation and decision-making support system. Background technology:

[0002] In the context of the digital age, with the rapid development of Internet technology, the amount of user behavior data on various online platforms has expanded dramatically, providing a rich data foundation for personalized services. However, traditional recommendation systems mostly rely on single user historical behavior analysis, such as content-based recommendation or collaborative filtering technology, which have limitations in processing user personalized needs and real-time contextual information. For example, they may not be able to take into account real-time contextual factors such as the user's current geographic location, time, social activities, etc., which are crucial for providing more accurate and timely recommendations. In addition, existing systems also have defects in user privacy protection and fail to fully consider the legal and regulatory requirements for data security and user privacy, which is particularly prominent at a time when data leaks are frequent.

[0003] With the advancement of artificial intelligence and machine learning technology, there is now an opportunity to develop more advanced recommendation systems that can not only process huge amounts of user data, but also respond to users' dynamic needs and preference changes in real time. At the same time, as society's awareness of personal privacy protection increases, how to protect user privacy while providing personalized services has become an urgent problem to be solved. The present invention is proposed in this context, aiming to create an intelligent system that can provide personalized recommendations and decision-making assistance by combining big data analysis and artificial intelligence algorithms, while taking into account the needs of user privacy protection. By deeply analyzing users' behavioral data and real-time contextual information, the system can provide users with more accurate and personalized service experiences in multiple fields, such as e-commerce, online content recommendations, travel planning, career development, etc. In addition, the design of the system fully considers the requirements of privacy protection to ensure that the security and privacy of user data are properly protected. Through such technological innovations, the present invention can not only improve user satisfaction and decision-making quality, but also promote the further development of personalized recommendation services and privacy protection technologies. Summary of the invention:

[0004] The digital human intelligent recommendation and decision support system provides users with personalized recommendations and decision support through the coordinated work of multiple carefully designed modules. First, the user behavior analysis module is responsible for collecting historical behavior data of users on different platforms, and constructs a model that reflects user preferences through data cleaning, feature extraction and machine learning algorithm training. Next, the context perception module uses integrated sensors or third-party services to monitor and obtain user's geographic location, time, environment and other information in real time to capture the user's current context. Then, the personalized recommendation engine combines the user preference model and real-time context information, uses the recommendation algorithm to generate a personalized recommendation list, and optimizes the relevance of the recommendation through the result sorting unit, and finally displays it to the user through the user interface. At the same time, the decision support tool provides tools such as decision trees and probability models to help users evaluate the possible results of different choices. The system also includes a feedback learning mechanism, which automatically adjusts the recommendation algorithm by collecting user feedback on the recommendation results to achieve self-optimization. Finally, the privacy protection mechanism ensures the security and privacy of user data throughout the process, and protects the security of user information and complies with relevant laws and regulations through measures such as data encryption, user authentication and compliance checks. The entire system is designed to provide users with a comprehensive, intelligent, secure and efficient personalized recommendation and decision support platform.

[0005] In the digital human intelligent recommendation and decision support system, the user behavior analysis module plays a vital role. First, the data collection unit is responsible for collecting user browsing history, purchase history, search query and other behavioral data from multiple online platforms. These data are captured through API calls or directly from the platform database to ensure the comprehensiveness and diversity of the data. Subsequently, the data processing unit thoroughly cleans the collected raw data, removes invalid or incomplete records, and converts unstructured data into a structured format through data conversion for further analysis. In the feature extraction stage, natural language processing and data mining techniques are used to extract key features reflecting user preferences from the data, such as the user's interest intensity and purchase frequency in a specific commodity category. Finally, the model training unit uses advanced machine learning algorithms, such as random forests, gradient boosting machines or deep learning models, to train the extracted features and build a user preference model that can accurately predict user preferences. During the training process, the system will continuously adjust model parameters, such as learning rate, regularization coefficient, etc., and evaluate the generalization ability of the model through cross-validation and other techniques to ensure the accuracy and robustness of the model. Through this series of sophisticated operations, the user behavior analysis module can provide the system with accurate and real-time user preference information, providing a solid data foundation for subsequent personalized recommendations and decision-making assistance.

[0006] The context-aware module is a key component in achieving personalized recommendations. The location-aware unit uses integrated GPS or other high-precision positioning technologies to set the positioning accuracy to 5 meters, and tracks the user's specific location in real time, whether in indoor or outdoor environments, to ensure the accuracy and real-time nature of the location information. The time-aware unit is responsible for obtaining the current date and time information, including hours, minutes, and days of the week, so that the system can provide corresponding recommendations based on different times of the day, such as recommending nearby breakfast shops on weekday mornings or recommending leisure activities on weekends. The environmental awareness unit uses sensors or third-party services, such as weather APIs, to obtain environmental information around the user, including temperature, humidity, air quality index, etc. This information helps the system understand the user's current external environment and adjust the recommendation strategy accordingly. For example, it recommends nearby indoor activity venues on rainy days. Through the collaborative work of these three units, the context-aware module can fully capture the user's real-time context and provide the necessary context information for the personalized recommendation engine, thereby generating a more accurate and timely recommendation list.

[0007] The personalized recommendation engine is the core component, which provides users with a customized recommendation experience through a carefully designed algorithm and user interface. The recommendation algorithm unit uses a combination of collaborative filtering and content recommendation algorithms, setting algorithm weights of 0.6 and 0.4 respectively, to integrate the user's historical behavior and current context information and perform complex recommendation logic. This unit screens out the goods or services that best meet the user's preferences by setting thresholds, such as user preferences with a similarity greater than 0.8. The result sorting unit further sorts the recommendation results, and sets the parameters of the sorting algorithm based on the strength of the user's preference and the relevance of the context, such as a relevance weight of 0.7 and a novelty weight of 0.3, to ensure that the top of the recommendation list is the option that the user is most likely to be interested in. The user interface unit is responsible for presenting the recommendation list to the user in an intuitive and friendly way, including using a card layout to display the recommended item's picture, description, and rating, as well as providing filtering and sorting functions so that users can adjust the recommendation results according to their needs. In addition, the user interface unit also integrates a feedback mechanism that allows users to evaluate the recommendation results. These feedback data will be used to adjust the recommendation algorithm to improve the accuracy of the recommendation and user satisfaction. Through this series of meticulous steps and parameter settings, the personalized recommendation engine ensures the relevance and personalization of recommended content while providing a user-friendly interactive experience.

[0008] Decision-making assistance tools are important components that help users make more informed choices. The decision tree construction unit analyzes the user's historical decision data and current situation, sets the threshold of the decision node to 0.5, and constructs a decision tree model that reflects the user's possible choices. This model can show different decision paths and their corresponding results, helping users understand the potential impact of each choice. The probability analysis unit further evaluates the probability of each decision node, sets the confidence threshold to 95%, analyzes the probability of different choices and the possible results, uses Bayesian theorem and historical data to calculate the probability, and provides data-based decision support for users. The user interaction unit is responsible for collecting users' decision preferences and feedback, and allows users to easily express their preferences by designing intuitive interfaces and interactive processes, such as sliders and drop-down menus. In addition, the unit also ensures that the system can quickly respond to user input and provide real-time decision support by setting the feedback response time to within 2 seconds. Through these carefully designed units and parameters, the decision-making assistance tool can provide users with a comprehensive, intuitive and interactive decision-making assistance platform to help them make more informed choices in complex decision-making processes.

[0009] The feedback learning mechanism is a key link to ensure that the system continues to improve and adapt to user needs. The feedback collection unit collects user satisfaction feedback on the recommendation results in real time through the rating system and comment function in the user interface, and sets the feedback validity threshold to 80% to ensure that the collected data is representative and accurate. The algorithm optimization unit uses the gradient descent algorithm to adjust the parameters of the recommendation algorithm based on the collected feedback, setting the learning rate to 0.01 and the number of iterations to 500 to optimize the performance of the algorithm and improve the accuracy and relevance of the recommendation. The model update unit is responsible for regularly updating the user preference model, setting the update cycle to once every 7 days to reflect the latest user behavior and preference changes. During the update process, the model is retrained using the latest user behavior data, and the model performance degradation threshold is set to 5%. Once the model performance drops beyond this threshold, the system will automatically trigger the retraining process. Through this series of meticulous steps and parameter settings, the feedback learning mechanism ensures that the system can continuously learn and adapt to provide more personalized and accurate recommendation services.

[0010] The privacy protection mechanism is the core part of maintaining user data security and privacy rights. The data encryption unit uses the Advanced Encryption Standard (AES) algorithm and sets the key length to 256 bits to encrypt stored and transmitted user data to ensure that the data is secure at all times and prevent unauthorized access and data leakage. The user authentication unit verifies the user's identity and authorizes data access through a multi-factor authentication mechanism, including passwords and mobile phone SMS verification codes, to ensure that only verified users can access their personal data. The unit also sets an automatic compliance check cycle and conducts a comprehensive compliance assessment every quarter to ensure that the system continues to comply with the latest legal and regulatory changes. Through these strict privacy protection measures, the system not only protects the user's privacy, but also builds the user's trust in the system, creating a safe and reliable usage environment for users.

[0011] Using the method of digital human intelligent recommendation and decision-making support system, efficient and personalized services can be achieved through the following detailed steps:

[0012] S1: Collecting user behavior data and analyzing it to build a user preference model

[0013] The system uses an integrated data collection tool that is set to automatically collect data every minute from users' online behavior, including web browsing history, purchase history, and user interactions on social media.

[0014] The data is preprocessed, including cleaning invalid data and formatting, and then feature extraction techniques, such as the TF-IDF algorithm, are used to extract key features that reflect user preferences.

[0015] Use machine learning algorithms, such as random forest, to train the user preference model. Set the model iteration number to 1000 times and the learning rate to 0.01, and evaluate the accuracy of the model through the cross-validation method.

[0016] S2: Real-time monitoring of user context information invention

[0017] Using integrated GPS and other sensors, the system monitors the user's location information in real time, setting the positioning accuracy to 5 meters and the update frequency to once per second.

[0018] The time-aware unit obtains the current date and time information to identify possible changes in user needs, such as the difference between weekdays and weekends.

[0019] The environmental perception unit obtains information about the user's surrounding environment, such as weather and traffic conditions, through a third-party API, and sets the update frequency to once every 5 minutes.

[0020] S3: Invention for generating personalized recommendations based on user preferences and contextual information

[0021] The personalized recommendation engine combines the user preference model and real-time context information, uses a collaborative filtering algorithm, sets the similarity threshold to 0.8, and generates a personalized recommendation list.

[0022] The result ranking unit ranks the recommendation results, considering relevance and novelty, setting the relevance weight to 0.7 and the novelty weight to 0.3.

[0023] S4: Provide decision-making tools to help users evaluate and select inventions

[0024] The decision tree building unit builds a decision tree based on the user's choices to help the user understand the consequences of different choices.

[0025] The probability analysis unit analyzes the probability and potential outcomes of each selection, setting a confidence threshold of 95%.

[0026] S5: Collect user feedback and optimize recommendation algorithm invention

[0027] The system collects user feedback on recommendation results through the user interface, including ratings and comments.

[0028] The algorithm optimization unit adjusts the parameters of the recommendation algorithm based on the feedback, setting the learning rate to 0.01 and the number of iterations to 500.

[0029] S6: Inventions to ensure the security and privacy of user data

[0030] The data encryption unit uses the AES-256 algorithm to encrypt user data to ensure the security of data during storage and transmission.

[0031] The User Authentication Unit implements a multi-factor authentication process, including a password and a one-time SMS verification code, to ensure that only verified users can access their personal data.

[0032] The compliance check unit regularly checks whether system operations comply with data protection regulations, such as GDPR, and sets the automatic compliance check cycle to once a quarter. Description of the drawings:

[0033] Figure 1 The system operation flow chart of the present invention is Specific implementation method:

[0034] The following will be combined with the attached embodiment of the present invention Figure 1 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The digital human intelligent recommendation and decision support system achieves personalized services through a series of precisely set parameters and steps. The system first sets the data collection parameters, including the type of user behavior data such as browsing history and purchase history, the frequency of data collection such as every minute and every hour, and the data source such as internal logs and third-party APIs. The parameter settings of the user preference model include setting the feature dimension in the factorization machine to 100 and the regularization coefficient to 0.01. In the parameter setting of the real-time situational awareness module, the GPS positioning accuracy is set to 5 meters, and the environmental monitoring frequency is set to update every 5 minutes. In the data collection and preprocessing stage, the system collects user behavior data such as web browsing records and purchase records, and obtains user social activity data through APIs, then cleans the data, excludes invalid records, and performs feature extraction to convert user behavior into vector representation. In the model training stage, the user preference model is trained using user behavior data and project features. The optimization goal is to maximize the consistency between the predicted score and the actual score, such as minimizing the mean square error. The gradient descent method is used to adjust the model parameters, the learning rate is set to 0.01, the number of iterations is set to 1000 times, and the model performance is evaluated using cross-validation to select the best model parameters. In the real-time context perception and recommendation generation phase, the user's current context is monitored in real time, including location, time, and environmental information, and a personalized recommendation list is generated by combining the user preference model and real-time context information. The diversity and novelty of the recommendations are considered, and the diversity weight is set to 0.5 and the novelty weight is set to 0.3. In the system testing phase, comprehensive testing is carried out, including unit testing, integration testing, and user testing, to ensure that each module functions properly and the user interface is user-friendly. In the maintenance and update phase, user feedback is collected regularly to evaluate system performance, such as recommendation accuracy and user satisfaction. Based on feedback and market changes, the recommendation model is updated, such as adjusting feature weights and introducing new data sources. Ultimately, through these meticulous parameter settings and steps, the system ensures that users are provided with an efficient, intelligent, secure, and highly personalized recommendation and decision-making assistance service.

[0036] In the digital human intelligent recommendation and decision support system, the user behavior analysis module achieves an in-depth understanding of user preferences through a series of precisely set parameters and steps. The data collection unit is set to automatically capture the user's behavior data on different platforms every 5 minutes, including web browsing records, purchase history and search queries, to ensure the real-time and comprehensiveness of the data. Then, the data processing unit cleans the data, sets the threshold to 95% to ensure the accuracy of the data, and converts the unstructured data into a structured format through data conversion, such as converting text data into vector representation. In the feature extraction stage, natural language processing technology is used to set the word embedding dimension to 300 to extract key features reflecting user preferences from the data, such as the user's interest intensity and purchase frequency in a specific commodity category. Finally, the model training unit uses the random forest algorithm, sets the number of trees to 100, optimizes the model parameters through the gradient boosting method, sets the learning rate to 0.01, and the regularization coefficient to 0.05 to minimize the prediction error. Through cross-validation, the number of folds is set to 5 to evaluate the generalization ability of the model and ensure the accuracy and robustness of the model. This series of sophisticated operations enables the user behavior analysis module to provide the system with accurate and real-time user preference information, providing a solid data foundation for subsequent personalized recommendations and decision-making assistance.

[0037] The context-aware module achieves comprehensive awareness of the user's environment through precise parameter settings and advanced technologies. The location-aware unit uses GPS positioning technology, sets the positioning accuracy to 5 meters, and ensures real-time tracking of the user's location by updating the location information once a second. The time-aware unit is set to synchronize the server time every minute to obtain the current date, hour, minute and week so that the system can provide corresponding recommendations based on different time periods of the day. The environmental awareness unit obtains environmental information around the user through integrated sensors and third-party services, such as the weather API that updates every 15 minutes, including temperature (degrees Celsius), humidity (percentage), air quality index (AQI), and possible rainfall probability. These environmental data are enhanced by setting thresholds, such as recommending indoor activities when the temperature is below 10 degrees Celsius and recommending to bring rain gear when the humidity exceeds 80%, to enhance the relevance and practicality of the recommendations. Through the close collaboration of these three units, the context-aware module can capture and analyze the user's detailed context in real time, provide accurate context information for the personalized recommendation engine, and thus generate a more accurate and timely recommendation list to improve the user experience.

[0038] The personalized recommendation engine provides users with a highly customized recommendation experience through a series of precise parameter settings and algorithm logic. The recommendation algorithm unit adopts a hybrid recommendation strategy, in which the weight of the collaborative filtering algorithm is set to 0.6 and the weight of the content recommendation algorithm is set to 0.4, in order to comprehensively consider the user's historical behavior data and real-time context information. This unit sets the similarity threshold to 0.8 to screen out products or services that highly match the user's preferences. The result sorting unit further fine-sorts the recommendation results, in which the relevance weight is set to 0.7 and the novelty weight is set to 0.3, ensuring that the top of the recommendation list is the option that the user is most likely to be interested in. The user interface unit displays the recommended items through an intuitive card layout. Each card displays a high-definition picture, detailed description and user rating of the product or service, and provides filtering and sorting functions, allowing users to adjust the recommendation results according to their personal preferences. The user interface also integrates a feedback mechanism, where users can evaluate the recommendation results. These feedback data will be used to adjust the recommendation algorithm, and the learning rate is set to 0.01 to improve the accuracy of the recommendation and user satisfaction. Through this series of meticulous steps and parameter settings, the personalized recommendation engine ensures the relevance and personalization of the recommended content and provides a user-friendly interactive experience.

[0039] The decision support tool provides decision support to users through a series of precise parameters and algorithms. The decision tree construction unit adopts analysis based on the user's historical choices and current situation, sets the threshold of the decision node to 0.5, and constructs a decision tree model that reflects the user's possible choices. The model sets the branch probability threshold to 0.05 to ensure that each branch represents a reasonable choice path. The probability analysis unit further evaluates the probability of each decision node, sets the confidence threshold to 95%, and uses Bayes' theorem and historical data to calculate the probability of each choice and the possible results. The user interaction unit collects users' decision preferences and feedback by designing intuitive interfaces and interactive processes, such as sliders and drop-down menus. The unit also sets the feedback response time to within 2 seconds to ensure that the system can quickly respond to user input and provide real-time decision support. Through these carefully designed units and parameters, the decision support tool can provide users with a comprehensive, intuitive and interactive decision support platform to help them make more informed choices in complex decision-making processes.

[0040] The feedback learning mechanism ensures that the system can continuously optimize itself to meet user needs through carefully designed parameters and processes. The feedback collection unit collects user satisfaction feedback on the recommendation results in real time through the rating system and comment function in the user interface, and sets the feedback validity threshold to 80% to ensure that the collected data is representative and accurate. Based on these feedbacks, the algorithm optimization unit uses the gradient descent algorithm to adjust the parameters of the recommendation algorithm, setting the learning rate to 0.01 and the number of iterations to 500 to optimize the performance of the algorithm and improve the accuracy and relevance of the recommendation. The model update unit is responsible for regularly updating the user preference model, setting the update cycle to once every 7 days to reflect the latest user behavior and preference changes. During the update process, the model is retrained using the latest user behavior data, and the model performance degradation threshold is set to 5%. Once the model performance drops beyond this threshold, the system will automatically trigger the retraining process. In addition, in order to ensure the generalization ability of the model, a cross-validation method is used with a fold of 5 to evaluate the performance of the model on different data sets. Through this series of meticulous steps and parameter settings, the feedback learning mechanism ensures that the system can continuously learn and adapt to provide more personalized and accurate recommendation services.

[0041] The privacy protection mechanism ensures that the security and privacy of user data are fully protected through a series of strict parameter settings and technical means. The data encryption unit uses the AES-256 encryption algorithm with a key length set to 256 bits to perform end-to-end encryption on user data during storage and transmission to ensure the confidentiality and integrity of the data. The user authentication unit implements a multi-factor authentication process, including at least two authentication methods, such as passwords and one-time SMS verification codes, as well as regularly updated authentication tokens, which are replaced every 30 days to verify user identity and authorize data access. The compliance check unit is set to automatically perform a comprehensive compliance assessment every quarter to ensure that system operations comply with data protection regulations such as GDPR, and set up a real-time monitoring system to ensure that data processing activities comply with the principles of data minimization and purpose limitation. In addition, the system also sets a data retention period, and user data will be automatically deleted or anonymized within 180 days after collection, unless otherwise required by law. Through these meticulous privacy protection measures, the system not only protects the privacy rights and interests of users, but also enhances users' trust in the system, ensuring the security of user data and the compliance of the system.

[0042] Using the method of digital human intelligent recommendation and decision-making support system, efficient and personalized services can be achieved through the following detailed steps:

[0043] S1: Collecting user behavior data and analyzing it to build a user preference model

[0044] The system uses an integrated data collection tool that is set to automatically collect data every minute from users' online behavior, including web browsing history, purchase history, and user interactions on social media.

[0045] The data is preprocessed, including cleaning invalid data and formatting, and then feature extraction techniques, such as the TF-IDF algorithm, are used to extract key features that reflect user preferences.

[0046] Use machine learning algorithms, such as random forest, to train the user preference model. Set the model iteration number to 1000 times and the learning rate to 0.01, and evaluate the accuracy of the model through the cross-validation method.

[0047] S2: Real-time monitoring of user context information invention

[0048] Using integrated GPS and other sensors, the system monitors the user's location information in real time, setting the positioning accuracy to 5 meters and the update frequency to once per second.

[0049] The time-aware unit obtains the current date and time information to identify possible changes in user needs, such as the difference between weekdays and weekends.

[0050] The environmental perception unit obtains information about the user's surrounding environment, such as weather and traffic conditions, through a third-party API, and sets the update frequency to once every 5 minutes.

[0051] S3: Invention for generating personalized recommendations based on user preferences and contextual information

[0052] The personalized recommendation engine combines the user preference model and real-time context information, uses a collaborative filtering algorithm, sets the similarity threshold to 0.8, and generates a personalized recommendation list.

[0053] The result ranking unit ranks the recommendation results, considering relevance and novelty, setting the relevance weight to 0.7 and the novelty weight to 0.3.

[0054] S4: Provide decision-making tools to help users evaluate and select inventions

[0055] The decision tree building unit builds a decision tree based on the user's choices to help the user understand the consequences of different choices.

[0056] The probability analysis unit analyzes the probability and potential outcomes of each selection, setting a confidence threshold of 95%.

[0057] S5: Collect user feedback and optimize recommendation algorithm invention

[0058] The system collects user feedback on recommendation results through the user interface, including ratings and comments.

[0059] The algorithm optimization unit adjusts the parameters of the recommendation algorithm based on the feedback, setting the learning rate to 0.01 and the number of iterations to 500.

[0060] S6: Inventions to ensure the security and privacy of user data

[0061] The data encryption unit uses the AES-256 algorithm to encrypt user data to ensure the security of data during storage and transmission.

[0062] The User Authentication Unit implements a multi-factor authentication process, including a password and a one-time SMS verification code, to ensure that only verified users can access their personal data.

[0063] The compliance check unit regularly checks whether system operations comply with data protection regulations, such as GDPR, and sets the automatic compliance check cycle to once a quarter.

[0064] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A digital human intelligent recommendation and decision support system, characterized in that: include: User behavior analysis module, used to collect and analyze users' historical behavior data to build a user preference model; A context-aware module is used to obtain real-time context information of users through integrated sensors or third-party services; a personalized recommendation engine is used to combine user preference models and real-time context information to generate a personalized recommendation list; a decision-making support tool is used to provide auxiliary tools such as decision trees and probability models to help users evaluate the potential results of different choices; Feedback learning mechanism, used to automatically adjust the recommendation algorithm based on user feedback to achieve system self-optimization; privacy protection mechanism, used to ensure the security and privacy of user data and comply with relevant laws and regulations.

2. The system according to claim 1, wherein the user behavior analysis module comprises: Data collection unit, used to collect user behavior data on different platforms; A data processing unit, used for cleaning, converting and feature extraction of the collected data; The model training unit is used to train the user preference model using a machine learning algorithm.

3. The system of claim 1, wherein the context awareness module comprises: The location sensing unit is used to obtain the user's location information through GPS or other positioning technologies; the time sensing unit is used to obtain the current date and time information; The environmental perception unit is used to obtain information about the user's surrounding environment through sensors or third-party services.

4. The system of claim 1, wherein the personalized recommendation engine comprises: A recommendation algorithm unit, used to execute recommendation logic based on user preferences and context information; A result ranking unit, used to rank the recommendation results to provide the most relevant recommendations; A user interface unit for displaying recommendation lists and related information to the user.

5. The system of claim 1, wherein the decision aid comprises: A decision tree construction unit, used to construct a decision tree according to a user's selection; Probability Analysis Unit, which is used to analyze the probabilities and potential outcomes of different options; User interaction unit, used to collect users' decision preferences and feedback.

6. The system of claim 1, wherein the feedback learning mechanism comprises: A feedback collection unit, used to collect user feedback on recommendation results; An algorithm optimization unit, used to adjust the parameters of the recommendation algorithm based on feedback; The model updating unit is used to periodically update the user preference model to reflect the latest user behavior.

7. The system of claim 1, wherein the privacy protection mechanism comprises: Data encryption unit, used to encrypt stored and transmitted user data; User authentication unit, used to verify user identity and authorize data access; A compliance checking unit to ensure that the system operates in compliance with data protection regulations.

8. A method for using the digital human intelligent recommendation and decision support system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Collect user behavior data and analyze it to build a user preference model; S2. Real-time monitoring of user context information; S3. Generate personalized recommendations based on user preferences and contextual information; S4. Provide decision-making tools to help users evaluate options; S5. Collect user feedback and optimize recommendation algorithms; S6. Ensure the security and privacy protection of user data.

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