Dynamic information fusion and memory enhancement system in multi-view continuous learning

Abstract

The invention discloses a dynamic information fusion and memory enhancement system in multi-view continuous learning, and aims to realize efficient and intelligent real-time data processing and learning ability improvement. The system is composed of four main modules: a data acquisition module which integrates environment, motion and biological characteristic sensors, a high-resolution camera and a depth camera and cooperates with an interaction interface to realize comprehensive real-time data acquisition; and the information fusion module adopts a dynamic weighting algorithm, combines an online learning mechanism to reduce redundancy and extract key information, and ensures timeliness and accuracy of data. And the memory enhancement module is used for constructing a long-term memory library based on a deep learning model (such as LSTM and GRU), dynamically updating memory content in cooperation with a forgetting mechanism, and keeping the freshness and correlation of memory. And the feedback and optimization module continuously optimizes information fusion and memory management strategies through performance monitoring and user feedback, supports online learning and adaptive adjustment, and improves system performance and user experience.

Description

Technical Field

[0001] The present invention belongs to the technical field related to dynamic information fusion and memory enhancement systems in multi-perspective continuous learning, and specifically relates to a dynamic information fusion and memory enhancement system in multi-perspective continuous learning. Background Art

[0002] With the development of big data and artificial intelligence technology, traditional machine learning models often face problems such as information redundancy, information loss, and low learning efficiency when processing multi-view information. Especially in the continuous learning scenario, the model needs to constantly adapt to new information while retaining old information to avoid catastrophic forgetting. Therefore, designing a system for dynamic information fusion and memory enhancement has become the key to solving the current problem. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic information fusion and memory enhancement system in multi-perspective continuous learning to solve the problems of low information processing efficiency and knowledge forgetting raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A dynamic information fusion and memory enhancement system in multi-perspective continuous learning, the system comprising:

[0006] Data acquisition module: responsible for acquiring real-time data from multiple perspectives, including sensors, cameras, and user input;

[0007] Information fusion module: uses dynamic weighting algorithm to fuse multi-source data, reduce redundant information and effectively extract key information;

[0008] Memory enhancement module: Based on deep learning algorithms, a long-term memory bank is established, and the forgetting mechanism is used to dynamically update the memory content to maintain the timeliness and relevance of information;

[0009] Feedback and optimization module: Optimize information fusion and memory management strategies through continuous learning and feedback mechanisms.

[0010] Preferably, the data acquisition module mainly includes:

[0011] Sensor data:

[0012] Environmental sensors: including temperature, humidity, light and other sensors, used to monitor environmental conditions in real time.

[0013] Motion sensors: such as accelerometers and gyroscopes, used to capture the motion state and position changes of objects.

[0014] Biometric sensors: Collection of biometric data of the user, such as heart rate monitors, gait sensors, etc.

[0015] Visual data:

[0016] Camera: Use a high-resolution camera to capture images and video streams of your surroundings in real time.

[0017] Depth camera: collects depth images and calculates the three-dimensional information of objects to enhance the system's understanding of the environment.

[0018] User input:

[0019] Interactive interface: provides channels for user input, including touch screen, voice recognition, gesture recognition, etc., allowing users to input data and feedback information in real time.

[0020] Historical data recording: Collect users’ historical interaction data for reference in subsequent learning and decision-making processes.

[0021] Module implementation:

[0022] Data interface and protocol:

[0023] The module uses standardized data interfaces and transmission protocols to ensure that different types of data can be seamlessly integrated, such as using communication protocols such as MQTT, HTTP / HTTPS, to facilitate real-time data upload and processing.

[0024] Real-time data stream processing:

[0025] Stream processing technology is used to process and analyze data streams from multiple data sources in real time, ensuring that data can be transmitted to the information fusion module in a timely and effective manner.

[0026] Data preprocessing:

[0027] During the data collection process, the raw data is subjected to noise elimination, data cleaning and formatting to improve the accuracy of subsequent processing. At the same time, the data is time-stamped to track the timing of data collection.

[0028] Interaction with other modules: The data acquisition module works closely with the information fusion module and regularly sends the collected data to the information fusion module. In real-time application scenarios, the module should have an automatic trigger mechanism to prioritize uploading key data when important environmental changes are detected to ensure the system's response speed.

[0029] Preferably, the information fusion module mainly includes:

[0030] Dynamic weighting algorithm:

[0031] Using dynamic weighting algorithms, the information weight of each data source is automatically adjusted according to the credibility, importance and timeliness of different data sources. For example, in traffic monitoring applications, sensor data with high credibility will be given a greater weight to ensure the accuracy of data fusion;

[0032] Adopting online learning mechanism, the weights are adjusted in real time according to the historical performance of data input, which can adapt to environmental changes and data quality fluctuations, and ensure the real-time and reliability of fusion results;

[0033] Redundant information reduction:

[0034] Through data deduplication and feature selection technology, redundant data is identified and eliminated to avoid information overload. Clustering algorithms are used to screen sensor data with high similarity and only representative samples are retained.

[0035] Realize information compression and reduce the pressure of data processing and storage by extracting the main feature vectors;

[0036] Key information extraction:

[0037] Implement model-based feature selection methods, using machine learning algorithms to extract the most informative features from fused data and improve the efficiency of subsequent analysis;

[0038] Combined with contextual information, dynamically judge the importance and relevance of data to ensure that the system can quickly extract key information in specific application scenarios. In health monitoring scenarios, it prioritizes the extraction of indicators that have a significant impact on changes in health status.

[0039] Module implementation:

[0040] Data fusion algorithm:

[0041] Adopt a variety of data fusion technologies, including weighted average, Kalman filtering, Dempster-Shafer theory, etc., and select the appropriate fusion method according to the specific application to ensure the best fusion effect;

[0042] Real-time processing engine:

[0043] Build an efficient real-time processing engine that supports high-throughput data processing, ensure that information from different data sources can be integrated and analyzed with nanosecond latency, and use a parallel processing framework to significantly increase data processing speed.

[0044] Interaction with other modules:

[0045] The information fusion module continuously receives the real-time data stream from the data acquisition module and transmits the processing results to the memory enhancement module after fusion. Through such interaction, the system can achieve rapid response to environmental changes and user activities.

[0046] Preferably, the memory enhancement module mainly includes:

[0047] Long-term memory building:

[0048] Data storage: Based on deep learning models (such as long short-term memory networks or transformer models), an efficient long-term memory library is built to store key user interaction information and environmental data;

[0049] Feature representation learning: Use deep learning algorithms to automatically extract the potential features of data and improve the expressiveness of the memory bank, so that the system can better understand and use historical information;

[0050] Forgetting Mechanism:

[0051] Dynamic update: By adopting the forgetting mechanism, the system can update the memory content according to the importance, frequency and timeliness of the information, including setting the timeliness threshold of certain information. When new information updates arrive, the old information that exceeds the threshold will be marked as outdated and removed from the memory bank;

[0052] Information decay strategy: Based on the time decay model, information is scored and its importance is gradually reduced over time to ensure that the latest and most relevant information is retained;

[0053] Dependency maintenance:

[0054] Contextual understanding: Combine environmental context with user behavior data to dynamically determine which information is still relevant in the current situation, ensuring that the information in the memory bank has practical application value;

[0055] Multimodal learning: By integrating features from different data sources, the expressive power of the memory bank is enhanced to improve the system's intelligent decision-making level;

[0056] Module implementation:

[0057] Deep Learning Models:

[0058] Choose appropriate deep learning architectures, including LSTM, GRU, or transformers, to model and update long-term memory, train on historical data, and ensure that the memory library can accurately capture user behavior patterns and key events;

[0059] Online learning mechanism:

[0060] Implement online learning algorithms so that the module can continuously update the model as new data arrives, keep the memory bank real-time, and at the same time, use previous knowledge through methods such as transfer learning to accelerate the learning process of new information;

[0061] Interaction with other modules:

[0062] The memory enhancement module is closely related to the information fusion module, receiving the fused key information in real time and returning the updated memory content to the decision-making layer of the system, thus ensuring that the system can use the latest knowledge background when making decisions;

[0063] Preferably, the feedback and optimization module mainly includes:

[0064] Performance Monitoring and Analysis:

[0065] Key indicator tracking: Continuously monitor the main performance indicators of the system, such as the accuracy of fused data, the response speed of the memory library, and the satisfaction of user interaction. By regularly evaluating these indicators, possible areas for improvement can be identified;

[0066] User feedback collection: Collect users’ opinions and suggestions on system output through user interaction records and active feedback mechanisms. These feedbacks provide basic data for subsequent optimization.

[0067] Adjustment and optimization strategies:

[0068] Dynamic strategy adjustment: Based on performance monitoring and user feedback, automatically adjust the parameters of the information fusion algorithm and the memory management strategy, including modifying the fusion weight, optimizing the feature extraction method, and updating the forgetting mechanism;

[0069] Deep learning optimization: Combined with technologies such as reinforcement learning, it dynamically learns and optimizes information processing and memory updating strategies to better meet user needs and environmental changes;

[0070] Continuous learning mechanism:

[0071] Online learning model: Implement online learning mechanisms so that the system can learn new knowledge in real time and continuously improve memory and fusion strategies, including adjusting model weights through new data and feedback to maintain the timeliness and accuracy of the model;

[0072] Adaptive adjustment: Adopt adaptive algorithms to dynamically optimize the processing flow according to changes in the environment and users, ensuring that the system can always meet user needs;

[0073] Module implementation:

[0074] Feedback collection tools:

[0075] Design user interfaces and backend mechanisms to facilitate user feedback and automatically record user behavior data for subsequent analysis;

[0076] Data analysis and processing framework:

[0077] Use data analysis tools (such as Pandas, NumPy, etc.) and machine learning libraries (such as TensorFlow, PyTorch, etc.) to clean, analyze, and train models on the collected data to form a closed loop of feedback optimization;

[0078] Decision Support Systems:

[0079] Build rule-based or machine learning decision support systems to help identify the most effective optimization strategies and implement them to continuously improve fusion and memory management;

[0080] Interaction with other modules:

[0081] The feedback and optimization module is tightly integrated with the information fusion and memory enhancement module, receiving their output results and performance indicators, while feeding back optimization suggestions and adjustment strategies to these two modules, enabling them to make real-time improvements.

[0082] Compared with the prior art, the present invention provides a dynamic information fusion and memory enhancement system in multi-perspective continuous learning, which has the following beneficial effects:

[0083] Comprehensive environmental perception and user interaction improvement

[0084] The system can collect data in real time from multiple angles, not only limited to environmental factors and physical movements, but also including user biometrics and interactive behaviors, which greatly improves the system's perception of the external environment and internal user status.

[0085] Enhanced information processing efficiency and quality

[0086] The information fusion module ensures the accuracy and timeliness of information through dynamic weighting and redundancy reduction, while the extraction of key information further improves the reliability and efficiency of decision-making.

[0087] Intelligent memory management and learning capabilities

[0088] The memory enhancement module builds a long-term memory bank through deep learning and dynamically updates information in combination with the forgetting mechanism, which ensures the freshness and relevance of the system's memory and enhances the system's learning and adaptability.

[0089] System performance and user experience optimization

[0090] The feedback and optimization module continuously monitors system performance and fine-tunes strategies based on user feedback, ensuring the stability of system operation and improving user satisfaction.

[0091] Flexibility and scalability

[0092] The system is flexible in design, easy to integrate different types of data sources, and supports multiple communication protocols, leaving ample room for future functional expansion and technological upgrades.

[0093] Real-time and decision-making speed

[0094] The entire system design emphasizes real-time processing and rapid response. Whether it is data collection or information fusion, it can achieve a response speed of milliseconds or even nanoseconds, ensuring the immediate effectiveness of decision-making.

[0095] In summary, the system has significantly improved the real-time perception, decision-making capabilities and user interaction experience of intelligent systems in big data environments through advanced information fusion and memory enhancement technologies, bringing revolutionary change potential to the fields of smart city management, personalized medical health, industrial Internet of Things, etc. DETAILED DESCRIPTION

[0096] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0097] The present invention provides a technical solution:

[0098] A dynamic information fusion and memory enhancement system in multi-perspective continuous learning, the system comprising:

[0099] Data acquisition module: responsible for acquiring real-time data from multiple perspectives, including sensors, cameras, and user input;

[0100] Information fusion module: uses dynamic weighting algorithm to fuse multi-source data, reduce redundant information and effectively extract key information;

[0101] Memory enhancement module: Based on deep learning algorithms, a long-term memory bank is established, and the forgetting mechanism is used to dynamically update the memory content to maintain the timeliness and relevance of information;

[0102] Feedback and optimization module: Optimize information fusion and memory management strategies through continuous learning and feedback mechanisms.

[0103] The data acquisition module mainly includes:

[0104] Sensor data:

[0105] Environmental sensors: including temperature, humidity, light and other sensors, used to monitor environmental conditions in real time.

[0106] Motion sensors: such as accelerometers and gyroscopes, used to capture the motion state and position changes of objects.

[0107] Biometric sensors: Collection of biometric data of the user, such as heart rate monitors, gait sensors, etc.

[0108] Visual data:

[0109] Camera: Use a high-resolution camera to capture images and video streams of your surroundings in real time.

[0110] Depth camera: collects depth images and calculates the three-dimensional information of objects to enhance the system's understanding of the environment.

[0111] User input:

[0112] Interactive interface: provides channels for user input, including touch screen, voice recognition, gesture recognition, etc., allowing users to input data and feedback information in real time.

[0113] Historical data recording: Collect users’ historical interaction data for reference in subsequent learning and decision-making processes.

[0114] Module implementation:

[0115] Data interface and protocol:

[0116] The module uses standardized data interfaces and transmission protocols to ensure that different types of data can be seamlessly integrated, such as using communication protocols such as MQTT, HTTP / HTTPS, to facilitate real-time data upload and processing.

[0117] Real-time data stream processing:

[0118] Stream processing technology is used to process and analyze data streams from multiple data sources in real time, ensuring that data can be transmitted to the information fusion module in a timely and effective manner.

[0119] Data preprocessing:

[0120] During the data collection process, the raw data is subjected to noise elimination, data cleaning and formatting to improve the accuracy of subsequent processing. At the same time, the data is time-stamped to track the timing of data collection.

[0121] Interaction with other modules: The data acquisition module works closely with the information fusion module and regularly sends the collected data to the information fusion module. In real-time application scenarios, the module should have an automatic trigger mechanism to prioritize uploading key data when important environmental changes are detected to ensure the system's response speed.

[0122] The information fusion module mainly includes:

[0123] Dynamic weighting algorithm:

[0124] Using dynamic weighting algorithms, the information weight of each data source is automatically adjusted according to the credibility, importance and timeliness of different data sources. For example, in traffic monitoring applications, sensor data with high credibility will be given a greater weight to ensure the accuracy of data fusion;

[0125] Adopting online learning mechanism, the weights are adjusted in real time according to the historical performance of data input, which can adapt to environmental changes and data quality fluctuations, and ensure the real-time and reliability of fusion results;

[0126] Redundant information reduction:

[0127] Through data deduplication and feature selection technology, redundant data is identified and eliminated to avoid information overload. Clustering algorithms are used to screen sensor data with high similarity and only representative samples are retained.

[0128] Realize information compression and reduce the pressure of data processing and storage by extracting the main feature vectors;

[0129] Key information extraction:

[0130] Implement model-based feature selection methods, using machine learning algorithms to extract the most informative features from fused data and improve the efficiency of subsequent analysis;

[0131] Combined with contextual information, dynamically judge the importance and relevance of data to ensure that the system can quickly extract key information in specific application scenarios. In health monitoring scenarios, it prioritizes the extraction of indicators that have a significant impact on changes in health status.

[0132] Module implementation:

[0133] Data fusion algorithm:

[0134] Adopt a variety of data fusion technologies, including weighted average, Kalman filtering, Dempster-Shafer theory, etc., and select the appropriate fusion method according to the specific application to ensure the best fusion effect;

[0135] Real-time processing engine:

[0136] Build an efficient real-time processing engine that supports high-throughput data processing, ensure that information from different data sources can be integrated and analyzed with nanosecond latency, and use a parallel processing framework to significantly increase data processing speed.

[0137] Interaction with other modules:

[0138] The information fusion module continuously receives the real-time data stream from the data acquisition module and transmits the processing results to the memory enhancement module after fusion. Through such interaction, the system can achieve rapid response to environmental changes and user activities.

[0139] The memory enhancement module mainly includes:

[0140] Long-term memory building:

[0141] Data storage: Based on deep learning models (such as long short-term memory networks or transformer models), an efficient long-term memory library is built to store key user interaction information and environmental data;

[0142] Feature representation learning: Use deep learning algorithms to automatically extract the potential features of data and improve the expressiveness of the memory bank, so that the system can better understand and use historical information;

[0143] Forgetting Mechanism:

[0144] Dynamic update: By adopting the forgetting mechanism, the system can update the memory content according to the importance, frequency and timeliness of the information, including setting the timeliness threshold of certain information. When new information updates arrive, the old information that exceeds the threshold will be marked as outdated and removed from the memory bank;

[0145] Information decay strategy: Based on the time decay model, information is scored and its importance is gradually reduced over time to ensure that the latest and most relevant information is retained;

[0146] Dependency maintenance:

[0147] Contextual understanding: Combine environmental context with user behavior data to dynamically determine which information is still relevant in the current situation, ensuring that the information in the memory bank has practical application value;

[0148] Multimodal learning: By integrating features from different data sources, the expressive power of the memory bank is enhanced to improve the system's intelligent decision-making level;

[0149] Module implementation:

[0150] Deep Learning Models:

[0151] Choose appropriate deep learning architectures, including LSTM, GRU, or transformers, to model and update long-term memory, train on historical data, and ensure that the memory library can accurately capture user behavior patterns and key events;

[0152] Online learning mechanism:

[0153] Implement online learning algorithms so that the module can continuously update the model as new data arrives, keep the memory bank real-time, and at the same time, use previous knowledge through methods such as transfer learning to accelerate the learning process of new information;

[0154] Interaction with other modules:

[0155] The memory enhancement module is closely related to the information fusion module, receiving the fused key information in real time and returning the updated memory content to the decision-making layer of the system, thus ensuring that the system can use the latest knowledge background when making decisions;

[0156] The feedback and optimization module mainly includes:

[0157] Performance Monitoring and Analysis:

[0158] Key indicator tracking: Continuously monitor the main performance indicators of the system, such as the accuracy of fused data, the response speed of the memory library, and the satisfaction of user interaction. By regularly evaluating these indicators, possible areas for improvement can be identified;

[0159] User feedback collection: Collect users’ opinions and suggestions on system output through user interaction records and active feedback mechanisms. These feedbacks provide basic data for subsequent optimization.

[0160] Adjustment and optimization strategies:

[0161] Dynamic strategy adjustment: Based on performance monitoring and user feedback, automatically adjust the parameters of the information fusion algorithm and the memory management strategy, including modifying the fusion weight, optimizing the feature extraction method, and updating the forgetting mechanism;

[0162] Deep learning optimization: Combined with technologies such as reinforcement learning, it dynamically learns and optimizes information processing and memory updating strategies to better meet user needs and environmental changes;

[0163] Continuous learning mechanism:

[0164] Online learning model: Implement online learning mechanisms so that the system can learn new knowledge in real time and continuously improve memory and fusion strategies, including adjusting model weights through new data and feedback to maintain the timeliness and accuracy of the model;

[0165] Adaptive adjustment: Adopt adaptive algorithms to dynamically optimize the processing flow according to changes in the environment and users, ensuring that the system can always meet user needs;

[0166] Module implementation:

[0167] Feedback collection tools:

[0168] Design user interfaces and backend mechanisms to facilitate user feedback and automatically record user behavior data for subsequent analysis;

[0169] Data analysis and processing framework:

[0170] Use data analysis tools (such as Pandas, NumPy, etc.) and machine learning libraries (such as TensorFlow, PyTorch, etc.) to clean, analyze, and train models on the collected data to form a closed loop of feedback optimization;

[0171] Decision Support Systems:

[0172] Build rule-based or machine learning decision support systems to help identify the most effective optimization strategies and implement them to continuously improve fusion and memory management;

[0173] Interaction with other modules:

[0174] The feedback and optimization module is tightly integrated with the information fusion and memory enhancement module, receiving their output results and performance indicators, while feeding back optimization suggestions and adjustment strategies to these two modules, enabling them to make real-time improvements.

[0175] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic information fusion and memory enhancement system in multi-perspective continuous learning, characterized by: The system includes: Data acquisition module: responsible for acquiring real-time data from multiple perspectives, including sensors, cameras, and user input; Information fusion module: uses dynamic weighting algorithm to fuse multi-source data, reduce redundant information and effectively extract key information; Memory enhancement module: Based on deep learning algorithms, a long-term memory bank is established, and the forgetting mechanism is used to dynamically update the memory content to maintain the timeliness and relevance of information; Feedback and optimization module: Optimize information fusion and memory management strategies through continuous learning and feedback mechanisms.

2. The dynamic information fusion and memory enhancement system in multi-perspective continuous learning according to claim 1, characterized in that: The data acquisition module mainly includes: Sensor data: Environmental sensors: including temperature, humidity, light and other sensors, used to monitor environmental conditions in real time. Motion sensors: such as accelerometers and gyroscopes, used to capture the motion state and position changes of objects. Biometric sensors: Collection of biometric data of the user, such as heart rate monitors, gait sensors, etc. Visual data: Camera: Use a high-resolution camera to capture images and video streams of your surroundings in real time. Depth camera: collects depth images and calculates the three-dimensional information of objects to enhance the system's understanding of the environment. User input: Interactive interface: provides channels for user input, including touch screen, voice recognition, gesture recognition, etc., allowing users to input data and feedback information in real time. Historical data recording: Collect users’ historical interaction data for reference in subsequent learning and decision-making processes. Module implementation: Data interface and protocol: The module uses standardized data interfaces and transmission protocols to ensure that different types of data can be seamlessly integrated, such as using communication protocols such as MQTT, HTTP / HTTPS, to facilitate real-time data upload and processing. Real-time data stream processing: Stream processing technology is used to process and analyze data streams from multiple data sources in real time, ensuring that data can be transmitted to the information fusion module in a timely and effective manner. Data preprocessing: During the data collection process, the raw data is subjected to noise elimination, data cleaning and formatting to improve the accuracy of subsequent processing. At the same time, the data is time-stamped to track the timing of data collection. Interaction with other modules: The data acquisition module works closely with the information fusion module and regularly sends the collected data to the information fusion module. In real-time application scenarios, the module should have an automatic trigger mechanism to prioritize uploading key data when important environmental changes are detected to ensure the system's response speed.

3. The dynamic information fusion and memory enhancement system in multi-perspective continuous learning according to claim 1, characterized in that: The information fusion module mainly includes: Dynamic weighting algorithm: Using dynamic weighting algorithms, the information weight of each data source is automatically adjusted according to the credibility, importance and timeliness of different data sources. For example, in traffic monitoring applications, sensor data with high credibility will be given a greater weight to ensure the accuracy of data fusion; Adopting online learning mechanism, the weights are adjusted in real time according to the historical performance of data input, which can adapt to environmental changes and data quality fluctuations, and ensure the real-time and reliability of fusion results; Redundant information reduction: Through data deduplication and feature selection technology, redundant data is identified and eliminated to avoid information overload. Clustering algorithms are used to screen sensor data with high similarity and only representative samples are retained. Realize information compression and reduce the pressure of data processing and storage by extracting the main feature vectors; Key information extraction: Implement model-based feature selection methods, using machine learning algorithms to extract the most informative features from fused data and improve the efficiency of subsequent analysis; Combined with contextual information, dynamically judge the importance and relevance of data to ensure that the system can quickly extract key information in specific application scenarios. In health monitoring scenarios, it prioritizes the extraction of indicators that have a significant impact on changes in health status. Module implementation: Data fusion algorithm: Adopt a variety of data fusion technologies, including weighted average, Kalman filtering, Dempster-Shafer theory, etc., and select the appropriate fusion method according to the specific application to ensure the best fusion effect; Real-time processing engine: Build an efficient real-time processing engine that supports high-throughput data processing, ensure that information from different data sources can be integrated and analyzed with nanosecond latency, and use a parallel processing framework to significantly increase data processing speed. Interaction with other modules: The information fusion module continuously receives the real-time data stream from the data acquisition module and transmits the processing results to the memory enhancement module after fusion. Through such interaction, the system can achieve rapid response to environmental changes and user activities.

4. The dynamic information fusion and memory enhancement system in multi-perspective continuous learning according to claim 1, characterized in that: The memory enhancement module mainly includes: Long-term memory building: Data storage: Based on the deep learning model, an efficient long-term memory library is built to store key user interaction information and environmental data; Feature representation learning: Use deep learning algorithms to automatically extract the potential features of data and improve the expressiveness of the memory bank, so that the system can better understand and use historical information; Forgetting Mechanism: Dynamic update: By adopting the forgetting mechanism, the system can update the memory content according to the importance, frequency and timeliness of the information, including setting the timeliness threshold of certain information. When new information updates arrive, the old information that exceeds the threshold will be marked as outdated and removed from the memory bank; Information decay strategy: Based on the time decay model, information is scored and its importance is gradually reduced over time to ensure that the latest and most relevant information is retained; Dependency maintenance: Contextual understanding: Combine environmental context with user behavior data to dynamically determine which information is still relevant in the current situation, ensuring that the information in the memory bank has practical application value; Multimodal learning: By integrating features from different data sources, the expressive power of the memory bank is enhanced to improve the system's intelligent decision-making level; Module implementation: Deep Learning Models: Choose appropriate deep learning architectures, including LSTM, GRU, or transformers, to model and update long-term memory, train on historical data, and ensure that the memory library can accurately capture user behavior patterns and key events; Online learning mechanism: Implement online learning algorithms so that the module can continuously update the model as new data arrives, keep the memory bank real-time, and at the same time, use previous knowledge through methods such as transfer learning to accelerate the learning process of new information; Interaction with other modules: The memory enhancement module is closely related to the information fusion module. It receives the fused key information in real time and returns the updated memory content to the decision-making layer of the system, thus ensuring that the system can use the latest knowledge background when making decisions.

5. The dynamic information fusion and memory enhancement system in multi-perspective continuous learning according to claim 1, characterized in that: The feedback and optimization module mainly includes: Performance Monitoring and Analysis: Key indicator tracking: Continuously monitor the main performance indicators of the system, such as the accuracy of fused data, the response speed of the memory library, and the satisfaction of user interaction. By regularly evaluating these indicators, possible areas for improvement can be identified; User feedback collection: Collect users’ opinions and suggestions on system output through user interaction records and active feedback mechanisms. These feedbacks provide basic data for subsequent optimization. Adjustment and optimization strategies: Dynamic strategy adjustment: Based on performance monitoring and user feedback, automatically adjust the parameters of the information fusion algorithm and the memory management strategy, including modifying the fusion weight, optimizing the feature extraction method, and updating the forgetting mechanism; Deep learning optimization: Combined with technologies such as reinforcement learning, it dynamically learns and optimizes information processing and memory updating strategies to better meet user needs and environmental changes; Continuous learning mechanism: Online learning model: Implement online learning mechanisms so that the system can learn new knowledge in real time and continuously improve memory and fusion strategies, including adjusting model weights through new data and feedback to maintain the timeliness and accuracy of the model; Adaptive adjustment: Adopt adaptive algorithms to dynamically optimize the processing flow according to changes in the environment and users, ensuring that the system can always meet user needs; Module implementation: Feedback collection tools: Design user interfaces and backend mechanisms to facilitate user feedback and automatically record user behavior data for subsequent analysis; Data analysis and processing framework: Use data analysis tools and machine learning libraries to clean, analyze, and train models on the collected data, forming a closed loop of feedback optimization; Decision Support System: Build rule-based or machine learning decision support systems to help identify the most effective optimization strategies and implement them to continuously improve fusion and memory management; Interaction with other modules: The feedback and optimization module is tightly integrated with the information fusion and memory enhancement module, receiving their output results and performance indicators, while feeding back optimization suggestions and adjustment strategies to these two modules, enabling them to make real-time improvements.