Intelligent monitoring method and system for multi-modal archive data sharing

Abstract

The invention discloses an intelligent monitoring method and system for multi-modal archive data sharing, and relates to the technical field of archive management, the intelligent monitoring method for multi-modal archive data sharing comprises the following steps: step 1, data collection and preprocessing; 2, feature extraction and representation; step 3, feature fusion and data integration; step 4, intelligent monitoring and analysis; and 5, data sharing and access control. According to the method, the requirement for manually processing data is reduced, the working efficiency is improved, the cost is reduced, resource allocation is optimized through an intelligent algorithm, and the overall performance of the system is improved; according to the multi-modal file data sharing intelligent monitoring method and system, the aspects of data processing capacity, intelligent monitoring, data sharing, safety, maintainability and the like are remarkably improved, the requirements of modern file management can be better met, and more efficient, safer and more convenient services are provided for users.

Description

Technical Field

[0001] The present invention relates to the technical field of archive management, and in particular to a method and system for intelligent monitoring of multi-modal archive data sharing. Background Art

[0002] With the development of information technology, archive management has shifted from traditional paper archive management to digital archive management. Digital archive management needs to process a large amount of electronic files and multimedia materials. These archive data usually exist in different formats and modalities, such as text, images, audio and video. Therefore, how to effectively manage these multimodal archive data and improve the utilization and retrieval efficiency of archives has become an important issue facing the field of archive management.

[0003] The existing technology has the following problems: Traditional systems often lack efficient data processing capabilities and have difficulty processing large-scale or complex data sets. In the process of data sharing, traditional systems may not provide sufficient data encryption and protection measures, which may easily lead to data leakage or unauthorized access. The user interface design may not be friendly enough and the operation is complicated, resulting in poor user experience. Traditional systems may not provide flexible data sharing options, which limits data sharing and makes it difficult to meet the needs of different users. Traditional systems may not be able to achieve real-time data monitoring and updating, resulting in delays in data analysis and decision-making. When the system needs to integrate new data sources or add new functions, traditional systems may lack sufficient scalability and find it difficult to adapt to changes. Many traditional systems require a lot of manual intervention for data monitoring and exception handling, which is inefficient and error-prone. Traditional systems may not integrate advanced data analysis tools, which limits the ability of data mining and trend prediction. Due to the complex system architecture, maintaining and upgrading existing systems may require a lot of human and material resources. Different archival institutions may use different systems and standards, resulting in difficulties in data sharing and exchange. Traditional systems may only support specific types of data, such as text or images, but not multimodal data such as audio and video. Traditional systems may not meet increasingly stringent audit and compliance requirements, especially in terms of data privacy and security. Summary of the invention

[0004] The present invention provides a method and system for intelligent monitoring of multi-modal archive data sharing to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a method for intelligent monitoring of multimodal archival data sharing, the method for intelligent monitoring of multimodal archival data sharing comprising the following steps: Step 1: Data collection and preprocessing; Step 2: Feature extraction and representation; Step 3: Feature fusion and data integration; Step 4: Intelligent monitoring and analysis; Step 5: Data sharing and access control.

[0006] A further improvement of the technical solution of the present invention is that the step 1 further includes the following steps: A1: Needs analysis: determine the type, source and purpose of archival data collection in order to develop an appropriate collection strategy; A2: Data source identification: Identify and confirm data sources, including internal databases, external APIs, websites, sensors, file systems, etc. A3: Technology selection: According to the type and characteristics of the data source, select appropriate collection technology, such as database connection, web crawler, API call and other data collection, implement collection technology, and collect raw data from various data sources; A4: Data storage: Store the collected raw data in temporary storage or data lake for subsequent preprocessing and analysis; A5: Data cleaning: Identify and delete duplicate records, ensure data uniqueness, identify and process outliers or erroneous data, improve data quality, fill in missing data or delete records containing missing values; A6: Data standardization: converting data from different sources into a unified format for subsequent processing, and converting data in different units into a unified standard unit; A7: Data conversion: Convert unstructured data such as text, images, and videos into a structured format for easy analysis and storage. Convert data types into appropriate formats such as dates, numbers, and strings as needed.

[0007] A further improvement of the technical solution of the present invention is that the step 2 further includes the following steps: B1: Feature selection: Analyze the correlation between different features and the target variable, select features relevant to the task, use principal component analysis, factor analysis and other methods to reduce data dimensions, evaluate the importance of features through statistical methods or machine learning models, and select important features; B2: Feature extraction: Use bag-of-words model, TF-IDF, Word2Vec, BERT and other methods to extract text features; use color histogram, edge detection, deep learning model to extract image features; use spectrum analysis, Mel frequency cepstral coefficient, recurrent neural network and other methods to extract audio features; combine image and audio feature extraction methods to extract key frame features, motion trajectory features, etc.; B3: Feature representation: Convert the extracted features into vectors of fixed length to facilitate input into the machine learning model. Design new features or feature combinations based on task requirements to enhance the model’s ability to distinguish. Use deep learning models to learn deep representations of features. B4: Feature Validation: Use the extracted features to train the machine learning model and evaluate the performance of the model. The quality and effectiveness of the features are evaluated through cross-validation, confusion matrix, ROC curve, etc. B5: Iterative optimization: Based on the model performance feedback, further optimize the feature extraction and fusion strategies and adjust the model parameters to obtain better performance.

[0008] A further improvement of the technical solution of the present invention is that the step three further includes the following steps: C1: Early fusion: Fusion is performed at the feature level, such as concatenating feature vectors of different modalities; C2: Late fusion: Fusion is performed at the decision level, that is, each modality makes a decision separately and then the decision results are merged; C3: Intermediate fusion: Fusion is performed at the model level, such as using multi-task learning or end-to-end multimodal models; C4: Fusion effect evaluation: Evaluate the effect of feature fusion through cross-validation or practical application, and make necessary adjustments; C5: Data parsing: Parsing data of different modalities, identifying data structure and content, and preparing for subsequent processing; C6: Data association: Finding association points between different data modalities, such as timestamps, IDs, or other identifiers; C7: Data mapping: Define the mapping rules between different modal data in order to integrate them into a unified framework; C8: Data storage: Store the integrated data in a database or data lake to ensure data security and accessibility; C9: Data Index: Create indexes to optimize data retrieval performance; C10: Data quality check: Perform quality check on the integrated data to ensure data integrity and consistency; C11: Data Sharing Preparation: Prepare for data sharing, which may include setting access permissions, creating user interfaces, etc.

[0009] A further improvement of the technical solution of the present invention is that the step 4 further includes the following steps: D1: Model training: Select a suitable machine learning or deep learning model based on the characteristics of the data and task requirements, train the model using a labeled training data set, and optimize the model parameters; D2: Intelligent monitoring: The data collected in real time or that needs to be monitored is input into the trained model. The model analyzes the input data and identifies patterns, trends and anomalies in the data. The system provides real-time feedback of the monitored information based on the output of the model. D3: Anomaly detection and warning: Identify anomalies in data based on preset rules or learned patterns. When an anomaly is detected, the system triggers an early warning mechanism, such as sending a notification or report. Based on the early warning results, take appropriate response measures, such as manual review or automatic correction. D4: Analysis and Reporting: Analyze the monitored data, extract valuable information, and generate reports containing key findings, trend analysis, and recommendations for reference by decision makers; D5: Optimization and iteration: Adjust and optimize model parameters based on monitoring results and feedback, and evaluate and adjust the entire monitoring process to improve efficiency and accuracy; D6: Continuous Monitoring: Continuously monitor the operating status of the system to ensure the stability and reliability of the system, and continuously update the monitoring rules and models as data changes and business needs develop.

[0010] A further improvement of the technical solution of the present invention is that the step five further includes the following steps: E1: User registration and authentication: Users need to provide necessary identity information to register, and their identities are verified through passwords, SMS verification codes, two-factor authentication, etc. E2: Permission allocation: define different user roles, such as administrator, ordinary user, guest, etc., and assign different access rights to each role, including read, edit, delete, etc.; E3: Data classification and labeling: Classify data according to factors such as its importance and sensitivity, and assign labels to the data to facilitate subsequent data retrieval and permission control; E4: Access control policy formulation: Define data access control policies based on business needs and security requirements, and implement access control policies through the system, such as based on user roles, data classification, time and other factors; E5: User request processing: The user initiates a data access request to the system, and the system verifies the validity of the request based on the user's identity and access control policy; E6: Data access and sharing: Based on the user's request, the system retrieves eligible data and shares the retrieved data with the user in an appropriate manner, which may include downloading, previewing, online viewing, etc. E7: Audit and monitoring: Record user access behavior, including access time, accessed data, operation type, etc., monitor abnormal behavior in the system, and respond to potential security threats in a timely manner; E8: Implementation of security measures: Encrypt sensitive data to ensure the security of transmission and storage, and implement strict access control measures to prevent unauthorized access; E9: Feedback and optimization: Collect user feedback during use, and continuously optimize data sharing and access control strategies based on user feedback and system operation; E10: Legal compliance check: Ensure that data sharing and access control processes comply with relevant laws, regulations and industry standards, and regularly check the processes to ensure continuous compliance.

[0011] In the second aspect, the present invention also provides an intelligent monitoring system for multi-modal archival data sharing, including a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a model training and monitoring module, a data sharing platform, a security and permission management module, and an alarm and notification module. The data acquisition module is responsible for automatically collecting archival data of multiple modalities from different data sources. The data preprocessing module preprocesses the collected data, including cleaning, standardization and indexing. The feature extraction and fusion module extracts features from the data of each modality and then fuses these features. The model training and monitoring module uses historical data to train a machine learning model and monitors data streams in real time to identify anomalies or important patterns. The data sharing platform provides a user-friendly interface for retrieval, sharing and access to data. The security and permission management module can ensure the security of data and manage user access to data through identity authentication and permission control. When an abnormality or key event is detected in the alarm and notification module, the system will automatically trigger an alarm and notify the relevant users.

[0012] A further improvement of the technical solution of the present invention is that the data collected by the data acquisition module is directly input into the data preprocessing module, and the data preprocessing module cleans and formats the collected data to ensure data quality, laying a foundation for subsequent feature extraction and model training; The preprocessed data is sent to the feature extraction and fusion module for feature extraction and fusion. The feature extraction and fusion module needs to select the appropriate feature extraction algorithm according to the data format and type provided by the preprocessing module; The feature vectors extracted by the feature extraction and fusion module are used in the model training and monitoring module, which uses these feature vectors to train the machine learning model and perform real-time monitoring; The results of the model training and monitoring modules can be stored on the data sharing platform, which provides access and sharing functions for these results. The data on the data sharing platform needs to be protected by the security and permission management module to ensure data security. The security and permission management module controls the user's access rights to the data on the data sharing platform. When the security and permission management module detects security threats or illegal operations, it will trigger the alarm and notification module; The alarm and notification module notifies the corresponding managers or users of security events. When the model training and monitoring module detects an abnormality or a critical event, it triggers the alarm and notification module, which notifies the relevant personnel of the abnormal information for timely response and processing. The model training and monitoring module will adjust the data collection strategy based on the monitoring results and update the preprocessing process based on the results of anomaly detection.

[0013] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides a method and system for intelligent monitoring of multimodal archival data sharing, which can process and integrate data in multiple modes such as text, image, audio, video, etc., while traditional systems can usually only process data in a single mode. Advanced algorithms are used to automatically clean, standardize and format data, thereby improving the efficiency and accuracy of data processing. Deep learning and other technologies are used to extract more complex features, thereby improving the expressiveness of data. Multimodal feature fusion technology can represent data more comprehensively, allowing the model to capture richer information.

[0014] 2. The present invention provides a method and system for intelligent monitoring of multimodal archival data sharing. The system can monitor data flows in real time, respond quickly to abnormal situations, and automatically identify abnormal patterns and trends through machine learning models, thereby reducing the need for manual intervention and providing a more flexible data sharing method. Users can easily share and access data as needed. The data sharing platform provides an intuitive and easy-to-use interface, making it easy for non-technical users to use.

[0015] 3. The present invention provides a method and system for intelligent monitoring of multimodal archival data sharing, which integrates more advanced security and authority management modules to ensure the security of data during the sharing process, and realizes refined management of data access through identity authentication and authority control. The system adopts a modular design and is easy to expand and maintain. The system can automatically upgrade and repair faults, reducing maintenance costs.

[0016] 4. The present invention provides a method and system for intelligent monitoring of multimodal archival data sharing, which reduces the need for manual data processing, improves work efficiency, reduces costs, optimizes resource allocation through intelligent algorithms, and improves the overall performance of the system. In short, the method and system for intelligent monitoring of multimodal archival data sharing have significant improvements in data processing capabilities, intelligent monitoring, data sharing, security and maintainability, and can better meet the requirements of modern archive management, providing users with more efficient, secure and convenient services. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a multi-modal intelligent monitoring method for archival data sharing according to the present invention; Figure 2 This is a system architecture diagram of a multi-modal archive data sharing intelligent monitoring system according to the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with embodiments: Example 1 like Figure 1 As shown, the present invention provides a method and system for intelligent monitoring of multimodal archive data sharing, and the method for intelligent monitoring of multimodal archive data sharing includes the following steps: Step 1: Data collection and preprocessing; Step 2: Feature extraction and representation; Step 3: Feature fusion and data integration; Step 4: Intelligent monitoring and analysis; Step 5: Data sharing and access control.

[0019] In this embodiment, the multimodal archive data sharing intelligent monitoring method and system have significant improvements in data processing capabilities, intelligent monitoring, data sharing, security and maintainability, which can better meet the requirements of modern archive management and provide users with more efficient, safe and convenient services.

[0020] Example 2 Based on Example 1, the present invention provides a technical solution: preferably, step 1 further includes the following steps: A1: Needs analysis: determine the type, source and purpose of archival data collection in order to develop an appropriate collection strategy; A2: Data source identification: Identify and confirm data sources, including internal databases, external APIs, websites, sensors, file systems, etc. A3: Technology selection: According to the type and characteristics of the data source, select appropriate collection technology, such as database connection, web crawler, API call and other data collection, implement collection technology, and collect raw data from various data sources; A4: Data storage: Store the collected raw data in temporary storage or data lake for subsequent preprocessing and analysis; A5: Data cleaning: Identify and delete duplicate records, ensure data uniqueness, identify and process outliers or erroneous data, improve data quality, fill in missing data or delete records containing missing values; A6: Data standardization: converting data from different sources into a unified format for subsequent processing, and converting data in different units into a unified standard unit; A7: Data conversion: Convert unstructured data such as text, images, and videos into a structured format for easy analysis and storage. Convert data types into appropriate formats such as dates, numbers, and strings as needed.

[0021] Data collection and preprocessing are crucial steps that ensure the accuracy and effectiveness of subsequent analysis. Through the above steps, the data collection and preprocessing process can not only ensure the integrity and accuracy of the data, but also provide a high-quality data foundation for subsequent intelligent monitoring and data analysis.

[0022] Step 2 also includes the following steps: B1: Feature selection: Analyze the correlation between different features and the target variable, select features relevant to the task, use principal component analysis, factor analysis and other methods to reduce data dimensions, evaluate the importance of features through statistical methods or machine learning models, and select important features; B2: Feature extraction: Use bag-of-words model, TF-IDF, Word2Vec, BERT and other methods to extract text features; use color histogram, edge detection, deep learning model to extract image features; use spectrum analysis, Mel frequency cepstral coefficient, recurrent neural network and other methods to extract audio features; combine image and audio feature extraction methods to extract key frame features, motion trajectory features, etc.; B3: Feature representation: Convert the extracted features into vectors of fixed length to facilitate input into the machine learning model. Design new features or feature combinations based on task requirements to enhance the model’s ability to distinguish. Use deep learning models to learn deep representations of features. B4: Feature Validation: Use the extracted features to train the machine learning model and evaluate the performance of the model. The quality and effectiveness of the features are evaluated through cross-validation, confusion matrix, ROC curve, etc. B5: Iterative optimization: Based on the model performance feedback, further optimize the feature extraction and fusion strategies and adjust the model parameters to obtain better performance.

[0023] Feature extraction and representation is a key step, which involves extracting meaningful features from data of different modalities and converting these features into representations that can be understood and processed by machine learning models. Feature extraction and representation is an iterative and interactive process that requires continuous evaluation and adjustment until the most effective feature representation is found, thereby improving the performance and prediction accuracy of the model.

[0024] Step three also includes the following steps: C1: Early fusion: Fusion is performed at the feature level, such as concatenating feature vectors of different modalities; C2: Late fusion: Fusion is performed at the decision level, that is, each modality makes a decision separately and then the decision results are merged; C3: Intermediate fusion: Fusion is performed at the model level, such as using multi-task learning or end-to-end multimodal models; C4: Fusion effect evaluation: Evaluate the effect of feature fusion through cross-validation or practical application, and make necessary adjustments; C5: Data parsing: Parsing data of different modalities, identifying data structure and content, and preparing for subsequent processing; C6: Data association: Finding association points between different data modalities, such as timestamps, IDs, or other identifiers; C7: Data mapping: Define the mapping rules between different modal data in order to integrate them into a unified framework; C8: Data storage: Store the integrated data in a database or data lake to ensure data security and accessibility; C9: Data Index: Create indexes to optimize data retrieval performance; C10: Data quality check: Perform quality check on the integrated data to ensure data integrity and consistency; C11: Data Sharing Preparation: Prepare for data sharing, which may include setting access permissions, creating user interfaces, etc.

[0025] Feature fusion and data integration are key steps that ensure that data from different modalities can be effectively combined and utilized. Through these process steps, feature fusion and data integration can ensure the effective integration and utilization of multimodal archival data, thereby supporting higher-level analysis and applications.

[0026] Step 4 also includes the following steps: D1: Model training: Select a suitable machine learning or deep learning model based on the characteristics of the data and task requirements, train the model using a labeled training data set, and optimize the model parameters; D2: Intelligent monitoring: The data collected in real time or that needs to be monitored is input into the trained model. The model analyzes the input data and identifies patterns, trends and anomalies in the data. The system provides real-time feedback of the monitored information based on the output of the model. D3: Anomaly detection and warning: Identify anomalies in data based on preset rules or learned patterns. When an anomaly is detected, the system triggers an early warning mechanism, such as sending a notification or report. Based on the early warning results, take appropriate response measures, such as manual review or automatic correction. D4: Analysis and Reporting: Analyze the monitored data, extract valuable information, and generate reports containing key findings, trend analysis, and recommendations for reference by decision makers; D5: Optimization and iteration: Adjust and optimize model parameters based on monitoring results and feedback, and evaluate and adjust the entire monitoring process to improve efficiency and accuracy; D6: Continuous Monitoring: Continuously monitor the operating status of the system to ensure the stability and reliability of the system, and continuously update the monitoring rules and models as data changes and business needs develop.

[0027] These steps are interrelated, forming a closed-loop intelligent monitoring and analysis process, which aims to ensure that the multimodal archival data sharing system can monitor and analyze data efficiently and accurately, thereby providing valuable insights and timely warnings.

[0028] Step five also includes the following steps: E1: User registration and authentication: Users need to provide necessary identity information to register, and their identities are verified through passwords, SMS verification codes, two-factor authentication, etc. E2: Permission allocation: define different user roles, such as administrator, ordinary user, guest, etc., and assign different access rights to each role, including read, edit, delete, etc.; E3: Data classification and labeling: Classify data according to factors such as its importance and sensitivity, and assign labels to the data to facilitate subsequent data retrieval and permission control; E4: Access control policy formulation: Define data access control policies based on business needs and security requirements, and implement access control policies through the system, such as based on user roles, data classification, time and other factors; E5: User request processing: The user initiates a data access request to the system, and the system verifies the validity of the request based on the user's identity and access control policy; E6: Data access and sharing: Based on the user's request, the system retrieves eligible data and shares the retrieved data with the user in an appropriate manner, which may include downloading, previewing, online viewing, etc. E7: Audit and monitoring: Record user access behavior, including access time, accessed data, operation type, etc., monitor abnormal behavior in the system, and respond to potential security threats in a timely manner; E8: Implementation of security measures: Encrypt sensitive data to ensure the security of transmission and storage, and implement strict access control measures to prevent unauthorized access; E9: Feedback and optimization: Collect user feedback during use, and continuously optimize data sharing and access control strategies based on user feedback and system operation; E10: Legal compliance check: Ensure that data sharing and access control processes comply with relevant laws, regulations and industry standards, and regularly check the processes to ensure continuous compliance.

[0029] Data sharing and access control is a key link that ensures the security and compliance of data. Through the above process, the multimodal archival data sharing intelligent monitoring system can achieve efficient data sharing and access control while protecting data security.

[0030] Example 3 Second, as Figure 2 As shown, the present invention also provides an intelligent monitoring system for multi-modal archival data sharing, including a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a model training and monitoring module, a data sharing platform, a security and permission management module, and an alarm and notification module. The data acquisition module is responsible for automatically collecting archival data of multiple modes from different data sources. The data preprocessing module preprocesses the collected data, including cleaning, standardization and indexing. The feature extraction and fusion module extracts features from the data of each mode and then fuses these features. The model training and monitoring module uses historical data to train a machine learning model and monitors data streams in real time to identify abnormalities or important patterns. The data sharing platform provides a user-friendly interface for retrieval, sharing and access to data. The security and permission management module can ensure the security of data and manage user access to data through identity authentication and permission control. When an abnormality or key event is detected in the alarm and notification module, the system will automatically trigger an alarm and notify the relevant users.

[0031] The data collected by the data acquisition module will be directly input into the data preprocessing module, which will clean and format the collected data to ensure data quality and lay the foundation for subsequent feature extraction and model training; The preprocessed data is sent to the feature extraction and fusion module for feature extraction and fusion. The feature extraction and fusion module needs to select the appropriate feature extraction algorithm according to the data format and type provided by the preprocessing module; The feature vectors extracted by the feature extraction and fusion module are used in the model training and monitoring module, which uses these feature vectors to train the machine learning model and perform real-time monitoring; The results of the model training and monitoring modules can be stored on the data sharing platform, which provides access and sharing functions for these results. The data on the data sharing platform needs to be protected by the security and permission management module to ensure data security. The security and permission management module controls the user's access rights to the data on the data sharing platform. When the security and permission management module detects security threats or illegal operations, it will trigger the alarm and notification module; The alarm and notification module notifies the corresponding managers or users of security events. When the model training and monitoring module detects an abnormality or a critical event, it triggers the alarm and notification module, which notifies the relevant personnel of the abnormal information for timely response and processing. The model training and monitoring module will adjust the data collection strategy based on the monitoring results and update the preprocessing process based on the results of anomaly detection.

[0032] In this embodiment, by integrating modules such as data collection, preprocessing, feature extraction, and model training, comprehensive management of multimodal archival data is achieved, the automation level of data processing and analysis is improved, and data accuracy and availability are improved through data preprocessing and feature extraction, providing a high-quality data foundation for subsequent analysis and decision-making. By using the feature extraction and fusion module, useful information can be extracted and comprehensive analysis can be performed, thereby accelerating the decision-making process and improving work efficiency. The model training and monitoring module can monitor data changes in real time, detect abnormal situations in a timely manner, and issue early warnings through the alarm and notification module to help organizations respond quickly to potential risks. The data sharing platform provides a safe and convenient data sharing mechanism, promotes data exchange and collaboration across departments and teams, improves communication efficiency within the organization, and improves security and authority management. The management module ensures the security of data in the entire system, prevents unauthorized access and data leakage, and protects data privacy. The system provides decision makers with data-driven insights and predictions through intelligent analysis to help formulate more effective strategies and decisions. The system simplifies user workflows and improves user experience through automated data processing and analysis processes. The modular design of the system enables it to adapt to different business needs and data types, while being easy to expand and upgrade. Through automated data processing and monitoring, it reduces manual intervention, reduces management costs, and improves resource utilization efficiency. In summary, the multimodal archive data sharing intelligent monitoring system can improve the efficiency of an organization's business processes, enhance data security, improve decision-making quality, optimize user experience, and ultimately help organizations achieve better business results and strategic goals.

[0033] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for intelligent monitoring of multimodal archival data sharing, characterized in that: The multimodal archival data sharing intelligent monitoring method comprises the following steps: Step 1: Data collection and preprocessing; Step 2: Feature extraction and representation; Step 3: Feature fusion and data integration; Step 4: Intelligent monitoring and analysis; Step 5: Data sharing and access control.

2. The method for multimodal archival data sharing intelligent monitoring according to claim 1, characterized in that: The step 1 also includes the following steps: A1: Needs analysis: determine the type, source and purpose of archival data collection in order to develop an appropriate collection strategy; A2: Data source identification: Identify and confirm data sources, including internal databases, external APIs, websites, sensors, and file systems; A3: Technology selection: According to the type and characteristics of the data source, select appropriate collection technology, such as database connection, web crawler, API call data collection, implement collection technology, and collect raw data from various data sources; A4: Data storage: Store the collected raw data in temporary storage or data lake for subsequent preprocessing and analysis; A5: Data cleaning: Identify and delete duplicate records, ensure data uniqueness, identify and process outliers or erroneous data, improve data quality, fill in missing data or delete records containing missing values; A6: Data standardization: converting data from different sources into a unified format for subsequent processing, and converting data in different units into a unified standard unit; A7: Data conversion: Convert unstructured data such as text, images, and videos into a structured format for easy analysis and storage. Convert data types into appropriate formats such as dates, numbers, and strings as required.

3. The method for multimodal archival data sharing intelligent monitoring according to claim 1, characterized in that: The step 2 also includes the following steps: B1: Feature selection: Analyze the correlation between different features and the target variable, select features relevant to the task, use principal component analysis and factor analysis to reduce data dimensions, evaluate the importance of features through statistical methods or machine learning models, and select important features; B2: Feature extraction: Use the bag-of-words model, TF-IDF, Word2Vec, and BERT methods to extract text features; use color histograms, edge detection, and deep learning models to extract image features; use spectrum analysis, Mel-frequency cepstral coefficients, and recurrent neural network methods to extract audio features; combine image and audio feature extraction methods to extract key frame features and motion trajectory features; B3: Feature representation: Convert the extracted features into vectors of fixed length to facilitate input into the machine learning model. Design new features or feature combinations based on task requirements to enhance the model’s ability to distinguish. Use deep learning models to learn deep representations of features. B4: Feature Validation: Use the extracted features to train the machine learning model and evaluate the performance of the model. The quality and effectiveness of the features are evaluated through cross-validation, confusion matrix, and ROC curve methods. B5: Iterative optimization: Based on the model performance feedback, further optimize the feature extraction and fusion strategies and adjust the model parameters to obtain better performance.

4. The method for multimodal archival data sharing intelligent monitoring according to claim 1, characterized in that: The step three also includes the following steps: C1: Early fusion: Fusion is performed at the feature level, such as concatenating feature vectors of different modalities; C2: Late fusion: Fusion is performed at the decision level, that is, each modality makes a decision separately and then the decision results are merged; C3: Intermediate fusion: Fusion is performed at the model level, such as using multi-task learning or end-to-end multimodal models; C4: Fusion effect evaluation: Evaluate the effect of feature fusion through cross-validation or practical application, and make necessary adjustments; C5: Data parsing: Parsing data of different modalities, identifying data structure and content, and preparing for subsequent processing; C6: Data association: Finding association points between different data modalities, such as timestamps, IDs, or other identifiers; C7: Data mapping: Define the mapping rules between different modal data in order to integrate them into a unified framework; C8: Data storage: Store the integrated data in a database or data lake to ensure data security and accessibility; C9: Data Index: Create indexes to optimize data retrieval performance; C10: Data quality check: Perform quality check on the integrated data to ensure data integrity and consistency; C11: Data Sharing Preparation: Prepare for data sharing, which may include setting access permissions and creating user interfaces.

5. The method for multimodal archival data sharing intelligent monitoring according to claim 1, characterized in that: The step 4 also includes the following steps: D1: Model training: Select a suitable machine learning or deep learning model based on the characteristics of the data and task requirements, train the model using a labeled training data set, and optimize the model parameters; D2: Intelligent monitoring: The data collected in real time or that needs to be monitored is input into the trained model. The model analyzes the input data and identifies patterns, trends and anomalies in the data. The system provides real-time feedback of the monitored information based on the output of the model. D3: Anomaly detection and warning: Identify anomalies in data based on preset rules or learned patterns. When an anomaly is detected, the system triggers an early warning mechanism, such as sending a notification or report. Based on the early warning results, take corresponding response measures, such as manual review or automatic correction. D4: Analysis and Reporting: Analyze the monitored data, extract valuable information, and generate reports containing key findings, trend analysis, and recommendations for reference by decision makers; D5: Optimization and iteration: Adjust and optimize model parameters based on monitoring results and feedback, and evaluate and adjust the entire monitoring process to improve efficiency and accuracy; D6: Continuous Monitoring: Continuously monitor the operating status of the system to ensure the stability and reliability of the system, and continuously update the monitoring rules and models as data changes and business needs develop.

6. The method for multimodal archival data sharing intelligent monitoring according to claim 1, characterized in that: The step five also includes the following steps: E1: User registration and authentication: Users need to provide necessary identity information to register, and their identities are verified through passwords, SMS verification codes, and two-factor authentication; E2: Permission allocation: define different user roles, such as administrator, ordinary user, and guest, and assign different access rights to each role, including read, edit, and delete; E3: Data classification and labeling: Classify data according to its importance and sensitivity, and assign labels to the data to facilitate subsequent data retrieval and permission control; E4: Access control policy formulation: Define data access control policies based on business needs and security requirements, and implement access control policies through the system, such as based on user roles, data classification, and time factors; E5: User request processing: The user initiates a data access request to the system, and the system verifies the validity of the request based on the user's identity and access control policy; E6: Data access and sharing: Based on the user's request, the system retrieves eligible data and shares the retrieved data with the user in an appropriate manner, which may include downloading, previewing, and online viewing; E7: Audit and monitoring: Record user access behavior, including access time, accessed data, and operation type, monitor abnormal behavior in the system, and respond to potential security threats in a timely manner; E8: Implementation of security measures: Encrypt sensitive data to ensure the security of transmission and storage, and implement strict access control measures to prevent unauthorized access; E9: Feedback and optimization: Collect user feedback during use, and continuously optimize data sharing and access control strategies based on user feedback and system operation; E10: Legal compliance check: Ensure that data sharing and access control processes comply with relevant laws, regulations and industry standards, and regularly check the processes to ensure continuous compliance.

7. An intelligent monitoring system for multimodal archival data sharing, characterized in that: It includes a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a model training and monitoring module, a data sharing platform, a security and permission management module, and an alarm and notification module. The data acquisition module is responsible for automatically collecting archival data of various modalities from different data sources. The data preprocessing module preprocesses the collected data, including cleaning, standardization and indexing. The feature extraction and fusion module extracts features from the data of each modality and then fuses these features. The model training and monitoring module uses historical data to train machine learning models and monitors data streams in real time to identify abnormalities or important patterns. The data sharing platform provides a user-friendly interface for data retrieval, sharing and access. The security and permission management module can ensure the security of data and manage user access to data through identity authentication and permission control. When an abnormality or key event is detected in the alarm and notification module, the system will automatically trigger an alarm and notify relevant users.

8. The multi-modal archival data sharing intelligent monitoring system according to claim 7, characterized in that: The data collected by the data acquisition module will be directly input into the data preprocessing module, which will clean and format the collected data to ensure data quality and lay the foundation for subsequent feature extraction and model training; The preprocessed data is sent to the feature extraction and fusion module for feature extraction and fusion. The feature extraction and fusion module needs to select the appropriate feature extraction algorithm according to the data format and type provided by the preprocessing module; The feature vectors extracted by the feature extraction and fusion module are used in the model training and monitoring module, which uses these feature vectors to train the machine learning model and perform real-time monitoring; The results of the model training and monitoring modules can be stored on the data sharing platform, which provides access and sharing functions for these results. The data on the data sharing platform needs to be protected by the security and permission management module to ensure data security. The security and permission management module controls the user's access rights to the data on the data sharing platform. When the security and permission management module detects security threats or illegal operations, it will trigger the alarm and notification module; The alarm and notification module notifies the corresponding managers or users of security events. When the model training and monitoring module detects an abnormality or a critical event, it triggers the alarm and notification module, which notifies the relevant personnel of the abnormal information for timely response and processing. The model training and monitoring module will adjust the data collection strategy based on the monitoring results and update the preprocessing process based on the results of anomaly detection.

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