Multimodal risk content identification system based on Markov network

Abstract

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal risk content identification system based on a Markov network, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting and integrating multi-modal data in real time through a multi-source data fusion system; preprocessing the data to generate a feature vector; according to the method, advanced technologies such as the convolutional neural network (CNN), the long short-term memory network (LSTM) and the Markov network are introduced, so that the system can dynamically detect and respond to potential risk contents, and the accuracy, the flexibility and the adaptability of identification are greatly improved. The system is characterized in that feature generation is optimized through density peak clustering (DPC) and an adaptive auto-encoder (AAE), model adaptability is enhanced through a continuous learning mechanism, and efficient application of the system in a complex multi-mode environment is ensured. According to the method, the multi-modal risk content can be identified and managed more accurately, and the overall safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multimodal data processing, and in particular to a multimodal risk content identification system based on a Markov network. Background Art

[0002] With the rapid development and popularization of communication technology and digital technology, risky content on the Internet, such as fraud and rumors, has continued to increase, becoming an important challenge for social management and cyberspace governance. Multimodal risk content, including fraud, false information and other harmful content in the form of text, images, audio, video, etc., poses a huge hidden danger to national security, economic development and social stability. Existing content recognition technology seems to be unable to cope with these increasingly complex and changeable risk contents. Therefore, it is particularly important to develop a more advanced and sensitive detection method.

[0003] The multimodal risk content in cyberspace is complex and changeable, which is mainly reflected in the following aspects: First, the data on the Internet is in various forms, including text, pictures, audio, video and other different modes. These data have significant differences in representation and processing, which brings challenges to the unified identification of risk content. For example, text data can be processed by natural language processing technology, while image data requires computer vision technology.

[0004] Secondly, malicious content is often hidden in normal content to evade traditional detection methods. For example, fraudulent information may be in the form of text in an image, voice in a video, or hidden in normal text, which increases the difficulty of detection. In addition, the form and distribution of malicious content are constantly evolving, and traditional rule engines and signature-based detection methods have difficulty in responding to these changes. A detection system with dynamic adaptability is needed, which can be adjusted and updated as risky content changes.

[0005] Multimodal data is usually high-dimensional data, and processing and analyzing this data requires powerful computing power and efficient algorithms. In addition, real-time detection and response is also an important requirement, especially in areas such as social media and financial transactions, where timely identification and blocking of risky content is crucial.

[0006] At present, mainstream content recognition technologies mainly include methods based on rule engines, machine learning, and deep learning. These methods have improved the detection capabilities of risky content to a certain extent, but they also have obvious limitations. Detection is based on predefined rules, such as keyword matching, regular expressions, etc. Although the rule engine can quickly detect known risk patterns, it is often powerless in the face of new and changing attack methods, lacking flexibility and scalability. Feature engineering and classification algorithms (such as support vector machines, random forests, etc.) are used for detection. Although machine learning methods have improved the intelligence level of detection, feature engineering relies on expert experience and cannot automatically adapt to data changes. In addition, traditional machine learning methods have limited effects when processing high-dimensional and multimodal data. Using deep learning models such as convolutional neural networks (CNN) and long short-term memory networks (LSTM) can automatically extract features and improve detection accuracy. However, deep learning models require a large amount of labeled data for training, and the model is highly complex, with long training and inference time, making it difficult to achieve real-time detection. In addition, the black box nature of deep learning models also limits their application in sensitive areas. Summary of the invention

[0007] A multimodal risk content identification system based on Markov network achieves efficient and accurate identification and management of multimodal risk content by integrating multimodal data fusion, self-supervised learning, deep reinforcement learning and real-time stream processing technology.

[0008] To achieve the above object, the present invention provides a system, comprising the following modules:

[0009] Data collection and preprocessing module: This module collects and integrates multimodal data such as text, images, audio, video, etc. in real time through a multi-source data fusion system, and processes missing values ​​and outliers through an automatic data cleaning module to generate a standardized data set. The deep learning model is then used to preprocess the data and generate feature vectors. The data fusion algorithm fuses data from different sources through an adaptive learning method.

[0010] Feature Engineering Module: This module uses deep learning and reinforcement learning techniques to automatically extract multimodal features, applies feature generation algorithms to extract feature vectors and combines convolutional neural networks (CNNs) to extract time series features. It also uses time series analysis methods such as Markov chains to model time series data. It builds a dynamic feature update unit to automatically adjust and update features according to real-time data changes, and optimizes the model in combination with the policy gradient method in deep reinforcement learning.

[0011] Markov network construction module: This module constructs a Markov network model through the collected multimodal data to describe and predict the dynamic behavior of the data. Define the state space S of the multimodal data, which contains different types of content and behavior patterns; construct a state transition probability matrix P based on the multimodal data to describe the transition probability between different states; use the observed multimodal data sequence to calculate the transition probability and construct a Markov chain through the maximum likelihood estimation method; for some implicit behavior patterns, use the hidden Markov model (HMM) for modeling, including state transition probability and observation probability.

[0012] Model selection and training module: This module adopts a multi-level architecture, including pre-trained models and domain-specific deep learning models; uses convolutional neural networks (CNNs) for preliminary feature learning and generates intermediate representations; uses domain-specific LSTM models for fine-tuning; uses gradient descent algorithms to optimize model parameters and trains based on loss functions; builds a self-supervised learning and fine-tuning unit, uses unlabeled data to generate pseudo labels for model pre-training, and then fine-tunes with a small amount of labeled data; builds a continuous learning unit to continuously update the model based on real-time feedback data to ensure the adaptability and generalization ability of the model.

[0013] Model deployment and application module: The efficient real-time detection system combines stream processing technology and sliding window algorithm, performs real-time data processing and risk assessment through the Apache Flink framework, and realizes real-time monitoring of multimodal data; the intelligent alarm and response mechanism formulates rules based on the risk score output by the model, and automatically generates response plans according to different types of risk content, including sending early warning notifications, restricting account operations and further monitoring.

[0014] The beneficial effects of the present invention are:

[0015] 1. Efficient data fusion and processing: Realize efficient fusion and processing of multi-source data.

[0016] 2. Accurate feature extraction and updating: Use deep learning and reinforcement learning techniques to automatically extract and dynamically update features to improve the accuracy of feature generation.

[0017] 3. Advanced Markov Network Models: Construct Markov networks and hidden Markov models to describe and predict the dynamic behavior of multimodal data.

[0018] 4. Real-time monitoring and response: Combined with stream processing technology, the system can monitor multimodal data in real time, dynamically identify and respond to risk content, and improve the efficiency and accuracy of risk management.

[0019] 5. Multi-level architecture: Combine pre-trained models and domain-specific deep learning models (such as LSTM) to achieve efficient feature learning and model optimization.

[0020] 6. Self-supervised learning and continuous learning: Use unlabeled data to generate pseudo labels for model pre-training, and continuously optimize the model through continuous learning technology to improve adaptability and generalization capabilities.

[0021] 7. Dynamic feature update: Combined with reinforcement learning technology, features are automatically adjusted and updated to ensure the timeliness and accuracy of feature generation.

[0022] 8. Intelligent alarm and response mechanism: Based on graph neural network (GNN), complex correlation analysis is carried out to automatically trigger response measures and improve the efficiency and accuracy of risk management.

[0023] Through the present invention, multimodal risk content can be more accurately identified and managed, overall security can be improved, economic losses can be reduced, and user experience can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the specific workflow of the data collection and preprocessing module;

[0025] Figure 2 This is a schematic diagram of the specific workflow of the feature engineering module;

[0026] Figure 3 Schematic diagram of the detailed workflow for building modules for Markov networks;

[0027] Figure 4 Schematic diagram of the specific workflow of the model selection and training module;

[0028] Figure 5 This is a schematic diagram of the specific workflow of the model deployment and application module. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0030] Current social media platforms are facing increasingly complex risk content management issues. User content on the platform is in various forms, including text, images, videos, audio, etc. These contents may contain harmful content such as false information, hate speech, fraud information, etc. Once these contents are spread, they may have an adverse impact on the platform's reputation, user safety, and social stability.

[0031] The main challenges currently facing the platform include:

[0032] 1. Complexity of multimodal data: Different forms of content (such as text, images, videos, and audio) have significant differences in representation and processing. For example, hate speech in text can be identified through natural language processing (NLP) technology, while negative information in images requires computer vision technology (CV) for analysis. This multimodality increases the difficulty of unified detection.

[0033] 2. Dynamically changing risky content: Malicious content is often hidden in normal content, and the content form and dissemination method evolve over time. For example, fraudulent information may be presented through text in pictures and voice in videos. Traditional rule-based detection methods are difficult to adapt to such dynamic changes.

[0034] 3. High-dimensional data processing requirements: The platform generates and processes a large amount of data every day, especially multimodal data, which is high-dimensional and highly unstructured. Effectively processing this data requires powerful computing power and efficient algorithms.

[0035] Based on this, the present invention proposes a multimodal risk content identification system based on Markov network. Figure 1-5 ,include:

[0036] Data collection and preprocessing module:

[0037] Data fusion unit: collects and integrates multimodal data in real time through a multi-source data fusion system. The data fusion algorithm uses an adaptive learning method to ensure that information from different data sources is fully integrated.

[0038] Automatic data cleaning module: handles missing values ​​and outliers and generates standardized data sets. Adaptive data cleaning technology automatically adjusts the cleaning strategy based on data quality.

[0039] Feature Engineering Module:

[0040] Feature generation unit: Use deep learning and reinforcement learning techniques to automatically extract multimodal features and generate feature vectors.

[0041] Convolutional neural network (CNN) is used to extract time series features, and Markov chain is used to model time series data and identify behavior patterns.

[0042] Dynamic feature update unit: automatically adjusts and updates features according to real-time data changes to ensure the accuracy and timeliness of feature generation.

[0043] Dynamic optimization using policy gradient methods in deep reinforcement learning.

[0044] Markov Network Building Blocks:

[0045] State space definition: Define the state space S of multimodal data, which contains different types of content and behavior patterns.

[0046] State transition probability matrix: Construct the state transition probability matrix P based on multimodal data to describe the transition probability between different states.

[0047] Markov chain generation: Using the observed multimodal data sequence, the transition probabilities are calculated by maximum likelihood estimation and a Markov chain is constructed.

[0048] Hidden Markov Model (HMM): For implicit behavior patterns, the hidden Markov model is used for modeling. The formula is:

[0049] π i =P(s1=i),a ij =P(s t+1 =j|s t =i),b i (o t )=P(o t |s t =i)

[0050] Among them, π i is the initial state probability, a ij is the state transition probability, b i (o t ) is in state s t = i when o is observed t probability.

[0051] Model selection and training module:

[0052] Combining pre-trained models with domain-specific deep learning models (such as LSTM) for feature learning and fine-tuning.

[0053] Self-supervised learning technology is used to generate pseudo labels for pre-training, and fine-tuning is performed using a small amount of labeled data.

[0054] Continue to conduct online learning and model optimization to improve the adaptability and generalization ability of the model.

[0055] Model deployment and application module:

[0056] Combined with Apache Flink, real-time monitoring and risk assessment of multimodal data can be achieved.

[0057] According to different types of risk content, the intelligent alarm and response mechanism is automatically triggered to generate a response plan.

[0058] Markov networks, through their state transition characteristics, can effectively model the dynamic behavior of time series data, providing a powerful tool to address the above challenges. Combining deep learning and reinforcement learning techniques, the multimodal risk content identification system based on Markov networks has shown obvious advantages in the following aspects. Through Markov networks, data of different modalities can be naturally fused and jointly modeled to more comprehensively capture the characteristics of risk content. For example, information from multiple dimensions such as text content, text in images, and voice in videos can be considered simultaneously to improve the overall detection effect.

[0059] Markov chains and hidden Markov models (HMMs) can dynamically model time series data and capture the time dependencies and behavior patterns in the data. This is very effective for identifying continuously changing or hidden risk content. For example, by analyzing user behavior sequences, potential risk activities can be predicted. Combined with deep reinforcement learning, systems based on Markov networks can achieve adaptive and continuous learning, continuously optimizing and adjusting models to respond to new risk content and attack methods. Policy gradient methods and other reinforcement learning techniques can help systems maintain efficient and accurate detection capabilities in complex environments.

[0060] By combining real-time stream processing technologies such as Apache Flink, the system can efficiently process and analyze real-time data streams, enabling instant detection and response to multimodal risk content. This is particularly important in application scenarios that require real-time response, such as social media monitoring and financial transaction protection.

[0061] In summary, the multimodal risk content identification technology based on Markov networks can significantly improve the detection capability of complex and changeable risk content by integrating advanced technologies such as multimodal data fusion, self-supervised learning, deep reinforcement learning and real-time stream processing. This technology not only has higher detection accuracy and real-time performance, but also has good adaptability and scalability, providing strong technical support for cyberspace governance and social management.

[0062] The working process of a multimodal risk content identification system based on Markov network is as follows:

[0063] M1, data collection and preprocessing module

[0064] 1.1 Data Collection

[0065] Multi-source data fusion system: deployed in the background system, responsible for real-time collection and integration of multimodal data from different sources, including text, images, audio, video, etc.

[0066] Text data: Collect text content from social media, news sites, forums, etc.

[0067] Image data: Collect images from social media, image sharing platforms, etc.

[0068] Audio data: Collect audio files from voice chats, podcasts, etc.

[0069] Video data: Collect video content from video websites, social media and other channels.

[0070] 1.2 Data Preprocessing

[0071] Automatic data cleaning module: handles missing values ​​and outliers and generates standardized data sets.

[0072] Missing value handling: Use methods such as mean filling, interpolation, or nearest neighbor filling to handle missing values.

[0073] Outlier detection and processing: Use statistical methods (such as standard deviation method, IQR method) to detect outliers and correct or eliminate them.

[0074] Data standardization: Standardization of numerical data, such as normalization (min-max scaling) or standardization (z-score).

[0075] Feature extraction: Use a deep learning model to preprocess the data and generate feature vectors.

[0076] Text data preprocessing: Use natural language processing techniques such as word segmentation, stop word removal, and stemming.

[0077] Image data preprocessing: Use image enhancement techniques (such as rotation, scaling, cropping) and convolutional neural networks (CNN) to extract image features.

[0078] Audio data preprocessing: Mel-frequency cepstral coefficients (MFCC) are used to extract audio features.

[0079] Video data preprocessing: Use frame extraction and CNN combined with long short-term memory network (LSTM) to extract video features.

[0080] M2, feature engineering module

[0081] 2.1 Feature Generation

[0082] Automatic feature extraction: Use deep learning and reinforcement learning techniques to automatically extract multimodal features.

[0083] Convolutional Neural Network (CNN): Extract temporal features from images and videos.

[0084] Long Short-Term Memory (LSTM): Extracts temporal features from text and audio.

[0085] Feature generation algorithm: vectorize the extracted features.

[0086] Time Series Analysis: Modeling time series data using Markov chains.

[0087] State space definition: Define the state space S of multimodal data, which contains different types of content and behavior patterns.

[0088] State transition probability matrix construction: Construct the state transition probability matrix P based on multimodal data to describe the transition probability between different states.

[0089] 2.2 Dynamic Feature Update

[0090] Dynamic feature update algorithm: automatically adjusts and updates features according to real-time data changes.

[0091] Policy gradient method: Combine the policy gradient method in deep reinforcement learning to optimize the model.

[0092] Online learning: Continuously learn from new data and continuously update feature vectors.

[0093] M3, Markov network building module

[0094] 3.1 Markov Network Generation of Multimodal Data

[0095] Markov chain construction: Construct a Markov network model through the collected multimodal data.

[0096] State space definition: Define the state space S of multimodal data, including different types of content and behavior patterns.

[0097] State transition probability matrix construction: Based on the observation sequence, estimate the transition probability and calculate the transition probability using the maximum likelihood estimation method.

[0098] Hidden Markov Model (HMM): For implicit behavior patterns, a hidden Markov model is used to model the behavior, which includes state transition probability and observation probability.

[0099] 3.2 Formula Implementation

[0100] The formula of the state transition probability matrix P is:

[0101]

[0102] Among them, N ij From state s i Transfer to j The number of observations, N i For state s i The total number of observations.

[0103] The key formula of Hidden Markov Model is:

[0104] Initial state probability π:

[0105] π i =P(s1=i)

[0106] State transition probability a ij :

[0107] a ij =P(s t+1 =j|s t =i)

[0108] Observation probability b i (o t ):

[0109] b i (o t )=P(o t |s t =i)

[0110] M4, model selection and training module

[0111] 4.1 Pre-trained Models and Domain-specific Models

[0112] Pre-trained models (such as the GPT series): perform preliminary feature learning and generate intermediate representations.

[0113] Domain-specific models (such as LSTM): fine-tuned based on the intermediate representation to adapt to data in a specific domain.

[0114] Model fine-tuning: Use the gradient descent algorithm to optimize model parameters and perform training based on the loss function.

[0115] 4.2 Self-supervised learning and fine-tuning

[0116] Self-supervised learning: Use unlabeled data to generate pseudo labels for model pre-training.

[0117] Pseudo-label generation: Generate pseudo-labels through methods such as data augmentation and contrastive learning.

[0118] Model fine-tuning: Fine-tune the model through a small amount of labeled data to improve the generalization ability of the model.

[0119] 4.3 Continuous Learning

[0120] Real-time feedback learning: The model is continuously updated based on real-time feedback data to ensure the adaptability and generalization ability of the model.

[0121] M5, model deployment and application module

[0122] 5.1 Efficient real-time detection system

[0123] Stream processing technology: Combined with Apache Flink to achieve real-time monitoring and risk assessment of multimodal data.

[0124] Stream processing framework: Process and evaluate data in real time, using sliding window technology to process data in real time.

[0125] Real-time detection system structure: Based on the stream processing framework, efficient real-time detection is achieved.

[0126] 5.2 Intelligent alarm and response mechanism

[0127] Intelligent alarm rules: Automatically trigger response measures based on the risk score output by the model.

[0128] Risk scoring algorithm: Calculate the risk score based on the model prediction results.

[0129] Automatic response strategy: Generate corresponding response plans based on risk scores.

[0130] Response strategy: including sending early warning notifications, restricting account operations, further monitoring and other measures.

[0131] 1. The feature extraction technology details are as follows:

[0132] Text data: Use pre-trained models such as BERT or GPT for feature extraction and generate context-sensitive text representations.

[0133] Image data: Use pre-trained ResNet or Inception networks to extract high-level image features.

[0134] Audio data: Frequency domain features are extracted through MFCC, and LSTM is used to capture timing characteristics.

[0135] Video data: Use 3D-CNN or two-stream network to extract spatiotemporal features and combine LSTM to capture timing information.

[0136] 2. Dynamic feature update:

[0137] Policy gradient method: Use the REINFORCE algorithm for policy optimization and adjust the feature representation based on feedback data.

[0138] Online learning: Use streaming data processing frameworks such as Apache Kafka to update feature vectors and model parameters in real time.

[0139] 3. Parameter estimation of hidden Markov model:

[0140] Initial state probability: estimated by frequency statistics method.

[0141] State transition probability: calculated by maximum likelihood estimation method.

[0142] Observation probability: estimated using a Gaussian mixture model (GMM) or other probability distribution models.

[0143] 4. Stream processing system architecture:

[0144] Data input: Use message queue systems such as Kafka or RabbitMQ to achieve real-time input of data streams.

[0145] Data processing: Use Flink or Spark Streaming for real-time processing and analysis of data streams.

[0146] Alarm and response: Alarm information and response measures are sent to related systems or user terminals through REST API or WebSocket interface.

[0147] Through the above detailed steps and technical details, the present invention can achieve efficient and accurate identification and management of multimodal risk content, and improve the security and stability of cyberspace.

[0148] By implementing the above method, the present invention realizes efficient and accurate identification and management of multimodal risk content, and the main effects include:

[0149] 1. Improve identification accuracy: Through a multi-level architecture, combined with self-supervised learning and continuous learning technology, the accuracy of risk content identification is significantly improved.

[0150] 2. Real-time monitoring and response: Using stream processing technology, the system can monitor multimodal data in real time, respond to potential risks in a timely manner, and improve risk management efficiency.

[0151] 3. Dynamic feature update: Combined with deep reinforcement learning technology, features are automatically adjusted and updated to ensure the timeliness and accuracy of feature generation.

[0152] 4. Reduce operational risks: effectively identify and manage risk content, reduce safety hazards and economic losses, and improve overall safety.

[0153] Compared with the existing technology, the present invention introduces a multimodal risk content identification system based on Markov network, which utilizes multimodal data fusion, self-supervised learning, deep reinforcement learning and real-time stream processing technology to achieve efficient and accurate risk content identification and management.

[0154] 1. Data collection and preprocessing

[0155] Objective: To collect and integrate multimodal data from different sources in real time and perform preliminary preprocessing to form a standardized data set for subsequent analysis and modeling.

[0156] Implementation steps:

[0157] (1) Data collection: Use a multi-source data fusion system to collect multimodal data in real time, including text from social media and news websites, images from photo sharing platforms, audio from voice chat records, and relevant video content from video websites.

[0158] (2) Automatic data cleaning: Process the collected raw data to remove missing values ​​and outliers. For example, text data is preprocessed through word segmentation, stop word removal, and stemming. Image data is standardized through image enhancement techniques (such as rotation, scaling, and cropping). Audio data uses Mel-frequency cepstral coefficients (MFCC) to extract features. Video data is preprocessed through frame extraction combined with CNN and LSTM.

[0159] (3) Data fusion: Through adaptive learning methods, data from different sources are fused into a unified format to prepare for subsequent feature extraction and model training. For example, after converting speech into text, it is combined with chat records for unified analysis, and the text in the image is also extracted and fused with the text data.

[0160] 2. Feature Engineering

[0161] Objective: Automatically extract multimodal features and combine them with time series analysis to generate high-quality feature vectors for model training, providing reliable input for model training.

[0162] Implementation steps:

[0163] (1) Feature generation: Use deep learning techniques (such as convolutional neural networks (CNNs)) to extract temporal features from images and videos, and combine reinforcement learning techniques to extract useful features from text and audio. These feature vectors will represent the core information of different modal data.

[0164] (2) Time series analysis: The system converts multimodal data into time series, uses Markov chains to model the time series data, and identifies dynamic behavior patterns in the data. For example, by analyzing the user’s text communication patterns, potential malicious behavior patterns (such as fraud, false information dissemination, etc.) can be identified.

[0165] (3) Dynamic feature update: Using the policy gradient method in deep reinforcement learning, the system can automatically adjust and update features according to real-time data changes to maintain sensitivity and accuracy to risk content. For example, after discovering a new fraud pattern, the system will immediately update the relevant features and retrain the model.

[0166] 3. Markov network construction

[0167] Objective: To model and predict the dynamic behavior of multimodal data and capture hidden risk patterns by constructing Markov networks and hidden Markov models (HMMs).

[0168] Implementation steps:

[0169] (1) State space definition: The system first defines the state space S of multimodal data, covering different types of content and behavior patterns. These states can include user behavior (such as normal communication, potential fraud) and content type (such as normal text, hate speech).

[0170] (2) Construction of state transition probability matrix: Based on the observed multimodal data sequence, the system calculates the state transition probability through the maximum likelihood estimation (MLE) method to form a state transition matrix P. This matrix can describe the transition probability between different states, thereby helping the system predict possible risk content in the future. For example, if a user tries to enter the wrong payment password multiple times, this may be a signal that the account has been stolen.

[0171] (3) Hidden Markov Model (HMM): For some hidden behavior patterns that are difficult to observe directly (such as hidden fraudulent behavior), the system uses hidden Markov models to build models and conduct comprehensive analysis by combining state transition probability and observation probability. This includes establishing the hidden state (such as fraudulent behavior) and observed state (such as the user's operation sequence) of user behavior, and inferring whether the user is in a high-risk state through HMM. For example, by analyzing the user's voice customer service records, HMM can identify the voice pattern of fake customer service, thereby marking the user as a potential fraud target.

[0172] 4. Model selection and training

[0173] Objective: To select and train the model that best suits the current risky content identification needs using a multi-level architecture and self-supervised learning techniques.

[0174] Implementation steps:

[0175] (1) Multi-level architecture: The system adopts a multi-level deep learning model architecture. In the initial stage, the system uses pre-trained models (such as GPT, BERT) to perform preliminary feature learning and generate intermediate representations. Subsequently, domain-specific deep learning models (such as LSTM) are used to fine-tune these representations to meet the risk content identification needs of specific domains.

[0176] (2) Self-supervised learning: Since real labeled data is very limited, the system uses self-supervised learning methods. Pseudo labels are generated using unlabeled data. Through techniques such as data enhancement and contrastive learning, the system is pre-trained on large amounts of unlabeled data, thereby reducing its reliance on large amounts of labeled data.

[0177] (3) Continuous learning: In order to maintain sensitivity to new types of fraud, the system adopts a continuous learning mechanism. Through real-time feedback (such as user reports, transaction anomalies, etc.), the model is continuously updated and optimized. This continuous learning mechanism ensures that the system can quickly adjust and adapt when faced with new risk content. For example, when the platform detects a new type of phishing attack, the system will automatically adjust the model parameters and retrain to deal with this threat.

[0178] 5. Model deployment and application

[0179] Objective: Deploy the trained model to the platform’s real-time stream processing system to perform real-time risk content detection and response.

[0180] Implementation steps:

[0181] (1) Real-time stream processing: The system combines the Apache Flink framework to monitor multimodal data in real time through stream processing technology. Using sliding window technology, the system can efficiently process and evaluate real-time data streams.

[0182] (2) Intelligent Alert and Response: Based on the risk score output by the model, the system automatically generates corresponding response measures. For example, when potential high-risk content is detected, the system will automatically send an early warning notification, restrict related account operations, or strengthen monitoring to prevent the further spread of harmful content. In addition, the system will also record these high-risk operations in the background for further analysis and improvement of the model in the future.

[0183] Through the implementation of the above systems, the platform has achieved remarkable results:

[0184] 1. Improved recognition accuracy: Since the system can comprehensively consider multimodal data and maintain the timeliness and accuracy of the model through dynamic feature updates, the recognition accuracy of risk content has been significantly improved, reducing false positives and missed negatives.

[0185] 2. Real-time monitoring and response: With the help of real-time stream processing technology, the system can monitor a large amount of user-generated content in real time, detect and respond to potential risk content in a timely manner, and prevent the spread of malicious information.

[0186] 3. Dynamic adaptation and optimization: Continuous learning and dynamic feature update mechanisms enable the system to quickly adapt to changing risk content and attack methods, ensuring that the platform can continue to effectively respond to new threats.

[0187] 4. Reduce operational risks: By effectively identifying and managing multimodal risk content, the platform greatly reduces potential legal and social liability risks, while also protecting the safety of users and enhancing users' trust in the platform and user experience.

[0188] The multimodal risk content identification system based on Markov networks has demonstrated its excellent detection and dynamic adaptability when faced with complex and ever-changing risk content. Through the comprehensive application of multimodal data fusion, self-supervised learning, deep reinforcement learning, and real-time stream processing technology, the system not only improves the accuracy of risk content identification, but also significantly enhances the platform's real-time monitoring and response capabilities, providing a solid guarantee for the safe operation of social media platforms.

[0189] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multimodal risk content identification system based on Markov network, characterized by: Includes the following modules: Data collection and preprocessing module for: Collect and integrate multimodal data in real time through a multi-source data fusion system; Preprocess the data and generate feature vectors; Feature Engineering Module for: Extract multimodal features, apply feature generation algorithms, extract feature vectors, and combine convolutional neural networks (CNNs) to extract temporal features; Modeling time series data using Markov chains; Markov network building blocks for: Construct a Markov network model through the collected multimodal data; Construct a Markov chain, define the state space and state transition probability matrix; Based on the observed sequence, the transition probability is estimated using the maximum likelihood estimation method; The model selection and training module uses a multi-level architecture that includes pre-trained models and domain-specific deep learning models for: Combining pre-trained models with domain-specific deep learning models (such as LSTM) for feature learning and fine-tuning; Use the gradient descent algorithm to optimize model parameters and train based on the loss function; Use unlabeled data to generate pseudo labels for model pre-training, and then fine-tune with a small amount of labeled data; Continuously update the model based on real-time feedback data; Model deployment and application module, used for: Real-time data processing and risk assessment; Automatically trigger response measures based on different types of risk content; Output risk score based on the model; Automatic response strategy generates corresponding response plans based on risk scores.

2. The multimodal risk content identification system based on Markov network according to claim 1 is characterized in that: In the feature engineering module: Use convolutional neural networks (CNNs) to extract temporal features of multimodal data; CNN model structure, extracting features through convolution operations and activation functions.

3. The multimodal risk content identification system based on Markov network according to claim 1 is characterized in that: In the model selection and training module, the model is continuously updated according to the real-time feedback data, specifically including: Use the policy gradient method in deep reinforcement learning to continuously optimize the model through feedback mechanism; The policy gradient method optimizes model parameters to improve decision-making performance.

4. The multimodal risk content identification system based on Markov network according to claim 1 is characterized in that: In the model deployment and application module, according to different types of risk content, the response measures automatically triggered specifically include: Based on graph neural network (GNN), complex correlation analysis is performed to identify potential risk content; GNN model structure, node representation update and association relationship analysis are performed through message passing functions.

5. The multimodal risk content identification system based on Markov network according to claim 1, characterized in that: The feature engineering module also includes: Anomaly detection using density peak clustering (DPC); The DPC model performs anomaly detection by calculating local density and distance.

6. The multimodal risk content identification system based on Markov network according to claim 2 is characterized in that: The feature engineering module also includes: Use adaptive autoencoders (AAE) for feature generation and data dimension reduction; The AAE model optimizes feature representation through reconstruction loss and adversarial loss.

7. A multimodal risk content identification method based on a Markov network, using a multimodal risk content identification system based on a Markov network according to any one of claims 1 to 6, characterized in that: The following steps are involved: Collect and integrate multimodal data from different sources in real time; Handle missing values ​​and outliers to generate standardized data sets; Standardize numerical data; Use deep learning models to preprocess data and generate feature vectors; Automatically extract multimodal features using deep learning and reinforcement learning techniques; Markov network generation of multimodal data; Combined with Apache Flink, it realizes real-time monitoring and risk assessment of multimodal data and automatically triggers response measures.

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