Digital twinning-based multi-modal anomaly detection system

Abstract

The invention provides a multi-mode anomaly detection system based on digital twinning, and relates to the technical field of information technologies. Comprising seven main subsystems including a digital twin modeling system, a real-time data acquisition and sensor network, a multi-modal data fusion and processing system, a real-time data analysis and anomaly detection algorithm, a decision support and feedback system, a virtual simulation and visualization system and a safety and compliance management system. The multi-mode anomaly detection system based on digital twinning further comprises six additional subsystems, namely an automatic response and adjustment system, an extensible model updating and management system, an intelligent prediction and early warning system, a deep learning and mode recognition system, a real-time optimization and resource allocation system and an intelligent feedback and user interface optimization system. Through application of a real-time data analysis and anomaly detection algorithm and in combination with a decision support and feedback system, the system can quickly generate decision suggestions or responses, and a decision result is visually displayed through a virtual simulation and visualization system.

Description

Technical Field

[0001] The present invention belongs to the field of information technology technology, and more specifically, particularly relates to a multimodal anomaly detection system based on digital twins. Background Art

[0002] The multimodal anomaly detection system based on digital twins is mainly used to monitor and manage the operating status of complex systems or equipment in order to detect and respond to possible anomalies in a timely manner. Specifically, this system combines digital twin modeling technology and multimodal data fusion processing technology to collect, integrate and analyze information from multiple sensors and data sources in real time.

[0003] The multimodal anomaly detection system based on digital twins has broad application prospects, which are mainly reflected in the following aspects:

[0004] 1. Industrial manufacturing and operation management: In the manufacturing industry, it can be applied to real-time monitoring and maintenance of equipment, helping to predict and prevent equipment failures and improve production efficiency and safety. Through digital twin technology, virtual modeling of equipment operation status can be achieved, and multimodal data can be combined for anomaly detection and early warning;

[0005] 2. Intelligent transportation and urban management: In smart cities, it can be used to manage and optimize the transportation system, monitor traffic flow, road conditions and facility status to improve the efficiency and safety of urban operations. The fusion analysis of multimodal data can more accurately predict traffic congestion and accident risks;

[0006] 3. Energy and Utility Management: In the energy industry and utilities, it can be used to monitor the operating status of power grids, water networks and natural gas pipelines, improve the stability and safety of facilities, and optimize the use of energy resources;

[0007] 4. Medical and health monitoring: In the medical field, it can be applied to remote monitoring and maintenance of medical equipment to improve the efficiency and quality of medical services. Combined with multimodal data analysis, it can achieve real-time monitoring and early warning of patients' health status;

[0008] 5. Environmental monitoring and natural disaster warning: In the field of environmental protection and disaster warning, it can be used to monitor environmental indicators such as air quality, water quality, and geological changes, to detect and respond to natural disaster risks early, and to protect people’s lives and property.

[0009] In summary, the multimodal anomaly detection system based on digital twins has a wide range of cross-industry and cross-field application prospects, which can improve management efficiency, reduce operational risks, enhance safety, and promote intelligent and sustainable development. For this reason, a multimodal anomaly detection system based on digital twins is proposed. The digital twin modeling system and the multimodal data fusion and processing system are combined to collect, integrate and process multi-source data in real time. This integration helps to improve the accuracy and real-time performance of the anomaly detection algorithm, so that potential anomalies or problems can be discovered earlier and more accurately. Summary of the invention

[0010] In order to solve the above technical problems, the present invention provides a multimodal anomaly detection system based on digital twins to solve the above problems.

[0011] The multimodal anomaly detection system based on digital twins includes seven main subsystems: digital twin modeling system, real-time data acquisition and sensor network, multimodal data fusion and processing system, real-time data analysis and anomaly detection algorithm, decision support and feedback system, virtual simulation and visualization system, and safety and compliance management system. The multimodal anomaly detection system based on digital twins also includes six additional subsystems: automated response and adjustment system, scalable model update and management system, intelligent prediction and early warning system, deep learning and pattern recognition system, real-time optimization and resource allocation system, and intelligent feedback and user interface optimization system.

[0012] Preferably, the scalable model update and management system includes a data processing module for updating the digital twin model in real time, and a management module for managing and optimizing the digital twin model. The data processing module and the management module work together to respond to abnormal conditions detected in the system and improve the accuracy and real-time performance of the model.

[0013] Preferably, the intelligent prediction and early warning system includes an algorithm module that uses deep learning technology or other advanced pattern recognition technology to identify abnormal patterns. The intelligent prediction and early warning system also includes a notification module that generates an early warning based on the recognition results and notifies the operator in real time, which is used to predict possible abnormal situations in advance and support timely preventive measures.

[0014] Preferably, the deep learning and pattern recognition system includes a deep learning algorithm module capable of identifying and analyzing complex abnormal patterns, and a processing module for real-time analysis and response to detected abnormal patterns, so as to improve the accuracy and sensitivity of the system for abnormality detection.

[0015] Preferably, the real-time optimization and resource allocation system includes an optimization module that uses a digital twin model and real-time data to optimize and allocate resources. The real-time optimization and resource allocation system also includes a scheduling algorithm and a real-time monitoring module for improving system operating efficiency and reliability.

[0016] Preferably, the intelligent feedback and user interface optimization system includes a user interface module that provides an intuitive user interface and an intelligent feedback mechanism. The intelligent feedback and user interface optimization system also includes an intelligent feedback and suggestion module for helping operators understand system status and abnormal situations and take appropriate measures.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] In the present invention, the digital twin modeling system is combined with the multimodal data fusion and processing system to collect, integrate and process multi-source data in real time. This integration helps to improve the accuracy and real-time performance of the anomaly detection algorithm, thereby enabling the early and more accurate detection of potential anomalies or problems.

[0019] In this invention, the application of real-time data analysis and anomaly detection algorithms, combined with decision support and feedback systems, enables the system to quickly generate decision suggestions or responses, and intuitively display decision results through virtual simulation and visualization systems. These functions greatly improve the system's real-time decision-making capabilities and efficiency in responding to emergencies.

[0020] In the present invention, the introduction of real-time optimization and resource allocation system can improve the effective utilization of resources through continuous optimization and analysis of system data. This includes human resources, equipment resources and other aspects, which helps to reduce costs and improve production efficiency.

[0021] In the present invention, the implementation of the safety and compliance management system ensures that the system operation complies with relevant laws and regulations and standards. At the same time, through the monitoring and feedback system, potential safety risks or compliance issues are discovered in a timely manner, thereby effectively preventing accidents and legal problems.

[0022] In the present invention, the application of intelligent feedback and user interface optimization system improves the user operation experience, reduces the learning curve and operation errors, and thus improves the overall system usability and user satisfaction by optimizing the user interface design and providing instant feedback.

[0023] In the present invention, the integration of the scalable model update and management system and the intelligent prediction and early warning system enables the system to continuously update and optimize its models, algorithms and prediction capabilities, thereby maintaining the competitiveness and continuous innovation capabilities of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0025] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0026] See also Figure 1 The present invention provides a multimodal anomaly detection system based on digital twins, including seven main subsystems: a digital twin modeling system, a real-time data acquisition and sensor network, a multimodal data fusion and processing system, a real-time data analysis and anomaly detection algorithm, a decision support and feedback system, a virtual simulation and visualization system, and a safety and compliance management system. The multimodal anomaly detection system based on digital twins also includes six additional subsystems: an automated response and adjustment system, an extensible model update and management system, an intelligent prediction and early warning system, a deep learning and pattern recognition system, a real-time optimization and resource allocation system, and an intelligent feedback and user interface optimization system.

[0027] The digital twin modeling system creates and maintains digital models of real-world entities, including detailed descriptions of physical properties, structures, operating behaviors, etc., and uses CAD software, 3D modeling tools, or other professional software to build accurate digital models based on real-time data and physical properties. These models provide the basis for the system to simulate, analyze, and predict the behavior of entities; real-time data acquisition and sensor networks collect multimodal data of field entities, such as temperature, pressure, sound, video images, etc., and use various sensors (such as temperature sensors, pressure sensors, cameras, microphones, etc.) to collect data in real time and transmit it to the central processing system through the network. Timely data collection and transmission are key to ensuring the real-time and accuracy of the system; multimodal data fusion and processing systems fuse and process multimodal data from different sensors and data sources to provide a comprehensive real-time data perspective, synchronize, align, and standardize various data so that they can be displayed and analyzed uniformly on the same platform. This process may involve technologies such as data fusion algorithms and time series analysis; real-time data analysis and anomaly detection algorithms use advanced data analysis and machine learning algorithms to analyze multimodal data in real time and detect anomalies. For different types of data, such as time series data and image data, specific anomaly detection algorithms are applied, such as statistical methods, pattern recognition-based methods, deep learning, etc., to identify abnormal patterns and generate alarms or notifications; when an abnormal situation is detected, the decision support and feedback system provides real-time decision support and feedback to help operators take appropriate measures, and combines anomaly detection results with preset decision rules to generate decision suggestions or automated control instructions, which are then communicated to operators or automated systems through interfaces or system integration; virtual simulation and visualization systems provide It provides a virtual simulation environment and an intuitive visualization interface to help users understand the system status and anomaly detection results. It uses 3D visualization technology or virtual reality (VR) technology to present the digital twin model and real-time data in a graphical form, enabling users to monitor and analyze system operation in real time. The security and compliance management system ensures that system operations comply with safety standards and regulatory requirements, including data privacy protection, access control, etc. It implements security policies and control measures such as encrypted communications, identity authentication, security audits, etc. to ensure the security and compliance of system data and operations. These major subsystems work together to form a complete multimodal anomaly detection system based on digital twins, which can provide real-time monitoring, analysis, prediction and response capabilities for the management and optimization of various complex systems.

[0028] The purpose of the automated response and adjustment system is to realize the automated processing and adjustment of the system when abnormal situations are detected. It includes the following parts:

[0029] 1. Anomaly Identifier: Uses real-time data analysis and anomaly detection algorithms to identify anomalies in the system, such as equipment failure, performance degradation, or irregular operating behavior;

[0030] 2. Preset decision rule library: contains predefined rules and strategies for formulating emergency response and adjustment measures in different types of abnormal situations;

[0031] 3. Automation controller: responsible for executing automated response actions based on abnormal situations, such as starting backup equipment, adjusting production parameters, and issuing alarms;

[0032] 4. Real-time communication and coordination module: responsible for real-time communication with other system modules and operators to convey abnormal situations and automated response measures;

[0033] Real-time data acquisition and multimodal data fusion systems transmit data to real-time data analysis and anomaly detection algorithms, which identify anomalies by comparing actual data with expected behavior or historical data models. Once an anomaly is identified, the system will evaluate the severity and urgency of the anomaly based on a preset decision rule library. These rules can be formulated based on the specific situation of real-time data and the operating requirements of the system. After confirming the anomaly and determining the corresponding measures, the automation controller will execute the preset response action. This may include automatically starting backup equipment, adjusting production parameters, stopping or adjusting specific operations, etc. At the same time, the system will send notifications or alarms to relevant operators or other systems to ensure that all relevant parties are aware of the abnormal situation and take appropriate cooperative measures. The automated response system can take quick action when an abnormality occurs, avoid or reduce the need for human intervention, and improve the response speed and overall efficiency of the system. Timely automated response can help the system recover quickly from abnormal conditions, minimize the risk of production interruptions and equipment damage, and thus reduce production losses. The automated response system improves the stability and reliability of the system by reducing the impact of abnormal conditions on system performance, thereby improving overall operating efficiency and quality. The automated response and adjustment system is a vital part of the multimodal anomaly detection system based on digital twins. By integrating real-time data analysis, preset decision rules and automated control technology, it realizes timely identification, rapid response and effective adjustment of system abnormalities to ensure the stability and efficiency of system operation.

[0034] The scalable model update and management system is an important additional subsystem in the multimodal anomaly detection system based on digital twins. Its main function is to ensure the continuous accuracy and adaptability of the digital twin model, including the following parts:

[0035] 1. Model update strategy management: including defining and managing the update strategy of the digital twin model, determining when and how to update the model to reflect changes in system operation and environment;

[0036] 2. Data integration and cleaning: Responsible for collecting data from real-time data acquisition systems and other data sources, and cleaning and preprocessing to ensure data quality and integrity;

[0037] 3. Model training and updating algorithms: Implement the training and updating process of the digital twin model, which may include machine learning algorithms, deep learning models, or incremental learning techniques to improve model accuracy based on new data and feedback;

[0038] 4. Version control and management: Manage different versions of digital twin models, track model update history, and support rollback to previous versions to deal with adverse effects or errors;

[0039] 5. Performance evaluation and validation tools: Provide tools and techniques to evaluate the performance, accuracy and adaptability of models to ensure that updated models can operate effectively under various operating conditions;

[0040] 6. Integration and deployment pipeline: Ensure that the updated digital twin model can be smoothly deployed into the actual system and integrated with other subsystems to achieve real-time monitoring and anomaly detection;

[0041] The real-time data acquisition system collects data when the system is running, including sensor data, equipment status, etc. This data is cleaned and preprocessed for model training and updating. According to the preset strategies and algorithms, new data and feedback are used to train or update the digital twin model. This may involve online learning technology, allowing the model to continuously improve its accuracy and predictive ability over time, managing different versions of the model, and ensuring that the updated model can be effectively deployed to the actual system. Version control also helps track changes in model performance and effectiveness, and uses various evaluation tools and techniques to verify updated models to ensure their effectiveness and reliability under different operating conditions. The scalable model update and management system enables the system to continuously optimize and improve the digital twin model to cope with the ever-changing operating environment and data conditions. The updated model can provide more accurate and real-time anomaly detection and prediction capabilities, helping the system to detect potential problems earlier and take measures. Through automated model updates and management, the system can reduce the need for manual intervention, reduce maintenance costs, and minimize system downtime caused by outdated models. The scalable model update and management system plays a key role in the digital twin's multimodal anomaly detection system. By ensuring the timely update and continuous optimization of the digital twin model, the system's intelligence level and operating efficiency are improved.

[0042] The main purpose of the intelligent prediction and early warning system is to predict possible anomalies or problems in the system by using deep learning and pattern recognition technology, and issue an alarm in advance so that appropriate response measures can be taken, including the following:

[0043] 1. Data collection and real-time monitoring: This part is responsible for collecting data from real-time data acquisition and sensor networks, and monitoring the system operation status in real time. The data can cover various types of information such as physical data collected from device sensors, system logs, operating parameters, etc.

[0044] 2. Data preprocessing and feature extraction: The collected raw data is preprocessed and feature extracted to prepare for input into the prediction model. Preprocessing may include data cleaning, missing value processing, standardization, etc. Feature extraction helps capture key patterns and features in the data;

[0045] 3. Deep learning models and pattern recognition algorithms: Use deep learning techniques (such as neural networks) or other pattern recognition algorithms to analyze and model preprocessed data. These models can learn and identify complex patterns and abnormal signals in the data in order to predict possible system anomalies;

[0046] 4. Anomaly detection and prediction module: Combined with the output of the deep learning model, this module is responsible for real-time detection and prediction of anomalies in the system. It identifies potential problems or risks by comparing the differences or trend changes between actual data and model predictions;

[0047] 5. Alert and notification system: Once an anomaly is detected or a potential problem is predicted, the system will generate an alert and send a notification to relevant personnel. This can be done through instant messaging, email, SMS, etc., to ensure that key stakeholders can respond in a timely manner;

[0048] The system regularly collects data from various data sources and continuously monitors the system's operating status. The collected data is first cleaned and processed, and then key features that help predict anomalies are extracted from it. Historical data and labeled abnormal data are used to train deep learning models so that the models can learn to identify different types of abnormal patterns. The trained models are applied to real-time data streams to detect possible abnormal situations and make predictions. The model can automatically adapt to new data and changing working conditions. Once an anomaly is detected, the system will generate an alert and send notifications to relevant personnel based on pre-set policies. These notifications can include detailed abnormality information and suggested response measures, helping the system predict possible abnormalities before problems occur, providing sufficient time to take preventive or repair measures, thereby reducing the possibility of losses and production interruptions. Through continuous monitoring and prediction, the system can improve the stability of operating equipment or processes and reduce the occurrence of unexpected downtime and failures. Timely warning and response can help optimize resource allocation, reduce unnecessary maintenance costs and waste of resources, and provide real-time and accurate abnormal situation alerts, which helps managers make decisions quickly and take action to ensure the normal operation and production efficiency of the system. As part of the digital twin multimodal detection anomaly system, the intelligent prediction and warning system can effectively improve the intelligence level and operational efficiency of the system and ensure the stability and reliability of equipment and processes.

[0049] The main purpose of the deep learning and pattern recognition system is to use deep learning algorithms and pattern recognition technology to process complex multimodal data and extract key patterns and abnormal signals from it, including the following parts:

[0050] Data preparation and preprocessing: First, various types of data collected from real-time data acquisition and sensor networks are preprocessed, including data cleaning, standardization, and normalization. These steps are designed to ensure the quality and consistency of the data so that it is suitable for the input requirements of the deep learning model;

[0051] Feature extraction and selection: After data preprocessing, the feature extraction process extracts meaningful features for anomaly detection and pattern recognition tasks from the raw data. Features can be statistical features in time series data, spectral features obtained from frequency domain analysis, or visual features in image and video data;

[0052] Deep learning model selection and training: According to the specific needs of the system, select appropriate deep learning models, such as convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), variational autoencoder (VAE), etc. These models are trained to learn complex patterns and abnormal signals in the data;

[0053] Model optimization and adjustment: During the training process, the model is optimized and adjusted to improve the performance and generalization ability of the model on specific tasks. The optimization process may involve hyperparameter adjustment, regularization, data enhancement and other techniques;

[0054] Real-time monitoring and anomaly detection: The trained deep learning model is deployed to the real-time data stream to continuously monitor the system operation status and detect anomalies in real time. The model will be compared with the actual data to find possible abnormal patterns or emergencies;

[0055] The system acquires data from real-time data sources and passes it to the preprocessing module. In the preprocessing stage, the data is transformed and prepared for input into the deep learning model. The preprocessed data undergoes feature extraction to extract key information that can help the system identify anomalies. This process uses deep learning technology for learning, and the trained deep learning model is used to identify and classify patterns and abnormal signals in the data. Anomalies are detected by comparing the differences between real-time data and the normal patterns learned by the model. Once an anomaly is detected, the system will trigger an alarm and start the corresponding automated response system, or provide it to the decision support system for further analysis and processing. The deep learning model can learn complex data patterns and changes, thereby improving the accuracy and sensitivity of anomaly detection. The system can process large amounts of data in real time and respond quickly, thereby reducing the system response time and enhancing the ability of automated response. The deep learning model has certain adaptability and generalization capabilities and can process data of different types and sources without manual adjustment. Automated anomaly detection and response reduces the need for manual intervention, thereby reducing maintenance and operating costs. Reliable anomaly detection and prediction capabilities help the system identify potential faults and problems in advance, thereby enhancing the security and stability of the system. As an important part of the digital twin multimodal anomaly detection system, the deep learning and pattern recognition system provides strong technical support for the operation and management of modern complex systems, effectively improving the reliability and operation efficiency of the system.

[0056] The main purpose of the real-time optimization and resource allocation system is to optimize the operation of the system through real-time analysis and decision-making, and effectively allocate resources to cope with changing conditions and needs, including the following:

[0057] 1. Real-time data collection and monitoring: The system obtains real-time information from various data sources, such as equipment operating status, production data, environmental parameters, etc., through real-time data collection and sensor networks;

[0058] 2. Real-time data processing and analysis: The collected data is processed and analyzed in real time, including data cleaning, feature extraction, pattern recognition and other technologies, to obtain key operating trends and abnormal signals;

[0059] 3. Optimization algorithm and decision support: Based on the results of real-time data analysis, the system uses optimization algorithms (such as dynamic programming, genetic algorithm, simulated annealing, etc.) to generate the best resource allocation plan. These plans can involve equipment scheduling, energy utilization, human resource allocation, etc.;

[0060] 4. Resource allocation execution and supervision: The system implements the optimized resource allocation plan into actual operation and continuously monitors the execution effect. This may involve the operation of the automated control system to ensure efficient use of resources and stable operation of the system;

[0061] The system continuously collects and analyzes real-time data from multiple data sources. This data may include sensor data of equipment, operating conditions of production lines, environmental monitoring, etc. Based on the collected data, the system uses pre-defined optimization algorithms to generate the best resource allocation plan. These algorithms take into account the system's operating goals (such as maximizing production efficiency, minimizing energy consumption, etc.) and constraints (such as equipment capacity, human resource limitations, etc.). The generated optimization plan is implemented into the system, which may realize equipment scheduling, energy management and other related operations through automated control systems. At the same time, the system continuously monitors the implementation effect to ensure the effectiveness of real-time resource allocation and the stability of system operation. By optimizing resource allocation, the system can achieve higher production efficiency or service quality while reducing energy consumption and waste of other resources. The system can respond quickly to changing conditions and needs, and quickly adjust resource allocation plans through real-time analysis and optimization algorithms. Optimizing resource allocation not only improves efficiency, but also helps to reduce operating costs, such as reducing equipment maintenance costs or energy usage costs, providing real-time optimization results and decision support to help management make more accurate and data-based decisions, thereby improving the overall operation management level. The system can flexibly respond to different operating scenarios and changing conditions to ensure the stability and reliability of the system in complex environments. As a key component of the digital twin multimodal anomaly detection system, the real-time optimization and resource allocation system significantly improves the system's operating efficiency and responsiveness through effective data-driven and optimization algorithm applications, thereby providing important support for the management of modern complex systems.

[0062] The intelligent feedback and user interface optimization system is an important additional subsystem in the multimodal anomaly detection system based on digital twins. Its main function is to improve the user experience and operation efficiency of the system through effective feedback mechanism and user interface design optimization, including the following:

[0063] 1. Real-time feedback generator: This component is responsible for generating real-time feedback based on system analysis results and user operations. Feedback can be in the form of text prompts, graphical instructions, sound signals, etc., so that users can understand the system status and action suggestions;

[0064] 2. User interface design tools: Provide tools and technologies for configuring and optimizing user interfaces. These tools may include drag-and-drop interface builders, custom layout options, color and font adjustments, etc. to ensure that the user interface meets user needs and operating habits;

[0065] 3. Multimodal feedback integration: Integrate multiple feedback methods, such as visual, auditory and tactile feedback, to meet the needs of different users and environmental conditions;

[0066] 4. User behavior analysis module: analyzes the user's operation mode and behavior in order to dynamically adjust the feedback content and interface layout to improve user operation efficiency and satisfaction;

[0067] The system collects and analyzes real-time data, including user operation data, system status data, etc., to understand the current working environment and user needs. Based on the data analysis results, the system generates corresponding feedback information, such as operation suggestions, warning prompts or system status updates. This information is intuitively conveyed to the operator through the user interface. According to the configuration options provided by the user interface design tool, the system can adjust the interface layout, feedback form and information display method to improve the intuitiveness and efficiency of user operations. Using the user behavior analysis module, the system can dynamically adjust the feedback and interface design to adapt to the operating habits and changes in needs of different users, thereby continuously optimizing the user experience. Through intuitive and accurate feedback information, it helps users better understand the system status and operating instructions, thereby reducing misoperation and learning costs. The optimized interface design and timely feedback mechanism help users complete operating tasks quickly and accurately and improve work efficiency. Through warnings and suggestions, the system can help users avoid potential operating errors or adverse consequences and improve the reliability and safety of the system. According to the dynamic adjustment of user feedback and operating habits, the system can adapt to the needs and environmental conditions of different users and enhance the flexibility and adaptability of the system. As part of the digital twin multimodal anomaly detection system, the intelligent feedback and user interface optimization system significantly improves the overall efficiency and user satisfaction of the system by optimizing the user interaction experience and operating interface design, and further supports the stable operation and effective management of complex systems.

[0068] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A multimodal anomaly detection system based on digital twins, characterized by: It includes seven major subsystems: digital twin modeling system, real-time data acquisition and sensor network, multimodal data fusion and processing system, real-time data analysis and anomaly detection algorithm, decision support and feedback system, virtual simulation and visualization system, and safety and compliance management system; The multimodal anomaly detection system based on digital twins also includes six additional subsystems: automated response and adjustment system, scalable model update and management system, intelligent prediction and early warning system, deep learning and pattern recognition system, real-time optimization and resource allocation system, and intelligent feedback and user interface optimization system.

2. The multimodal anomaly detection system based on digital twins as claimed in claim 1, characterized in that: A scalable model update and management system, including a data processing module for real-time updating of digital twin models, and a management module for managing and optimizing digital twin models.

3. The multimodal anomaly detection system based on digital twins as claimed in claim 2, characterized in that: The data processing module and the management module work together to respond to abnormal situations detected in the system and improve the accuracy and real-time performance of the model.

4. The multimodal anomaly detection system based on digital twins as claimed in claim 1, characterized in that: Intelligent prediction and early warning systems, including algorithm modules that use deep learning technology or other advanced pattern recognition technologies to identify abnormal patterns.

5. The multimodal anomaly detection system based on digital twins as claimed in claim 1, characterized in that: A deep learning and pattern recognition system includes a deep learning algorithm module capable of identifying and analyzing complex abnormal patterns, and a processing module for real-time analysis and response to detected abnormal patterns, to improve the accuracy and sensitivity of the system for abnormality detection.

6. The multimodal anomaly detection system based on digital twins as claimed in claim 1, characterized in that: Real-time optimization and resource allocation system, including an optimization module that uses digital twin models and real-time data for resource optimization and allocation.

7. The multimodal anomaly detection system based on digital twins as claimed in claim 6, characterized in that: The real-time optimization and resource allocation system also includes a scheduling algorithm and a real-time monitoring module for improving system operation efficiency and reliability.

8. The multimodal anomaly detection system based on digital twins as claimed in claim 1, characterized in that: The intelligent feedback and user interface optimization system includes a user interface module that provides an intuitive user interface and an intelligent feedback mechanism.

9. The multimodal anomaly detection system based on digital twins as claimed in claim 8, characterized in that: Intelligent Feedback and User Interface Optimization The system also includes an intelligent feedback and suggestion module to help operators understand system status and abnormal situations and take appropriate measures.

10. The multimodal anomaly detection system based on digital twins as claimed in claim 4, characterized in that: The intelligent prediction and early warning system also includes a notification module that generates early warnings based on the identification results and notifies operators in real time, which is used to predict possible abnormal situations in advance and support timely preventive measures.

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