An intelligent prediction system for social public opinion risk

Abstract

This application relates to the field of home kitchens, and discloses a social public opinion risk intelligent prediction system, including: a data collection module, which is used to collect text, image and video data from multi-source heterogeneous data sources in real time, including social media, news websites, forums, blogs and self-media; a data preprocessing module, which is used to clean, normalize, segment and tag the collected data to form a structured data set; an advanced natural language processing module, which is used to use the pre-trained Transformer model and graph neural network to perform deep semantic analysis on the pre-processed text data, and extract entity, relationship and context semantic features. By integrating advanced natural language processing, deep learning and multimodal data fusion technology, the system can monitor and accurately predict social public opinion in real time, quickly capture emergencies and public opinion changes, and deeply analyze complex semantics and emotional tendencies.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing, and in particular to a social public opinion risk intelligent prediction system. Background Art

[0002] With the rapid development of the Internet and social media, the Internet has become the main platform for the public to express their opinions, emotions and views. Channels in social media platforms, news websites, forums and blogs generate massive amounts of user-generated content every day. These contents reflect the dynamic changes in public opinion. Effective monitoring and analysis of these data is of great significance for government departments, enterprises and organizations in the media to timely understand public sentiment, predict potential risks and formulate response strategies.

[0003] Traditional public opinion monitoring systems mainly rely on manual collection or a single data source, which makes it difficult to fully cover multiple data types and platforms. In addition, the delay in data collection leads to the inability to capture emergencies and public opinion hotspots in a timely manner, missing the best time to respond.

[0004] Existing systems mostly use keyword retrieval and simple statistical analysis methods, which are unable to deeply understand the semantics and emotions of the text. They lack effective recognition and processing capabilities for complex language phenomena such as sarcasm, metaphor, and Internet slang, resulting in insufficient accuracy and reliability of analysis results.

[0005] In view of the above problems, a social public opinion risk intelligent prediction system is provided. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a social public opinion risk intelligent prediction system, which overcomes the deficiencies of the existing public opinion monitoring system in terms of data real-time, intelligent analysis and early warning accuracy.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a social public opinion risk intelligent prediction system, comprising:

[0008] Data collection module, used to collect text, image and video data from multiple heterogeneous data sources in real time, including social media, news websites, forums, blogs and self-media;

[0009] The data preprocessing module is used to clean, normalize, segment and tag the collected data to form a structured data set;

[0010] Advanced natural language processing module, which uses pre-trained Transformer models and graph neural networks to perform deep semantic analysis on pre-processed text data and extract entity, relationship, and contextual semantic features;

[0011] Multimodal data fusion module, used to align and fuse the features of multiple data in text, images and videos, and build a unified sentiment analysis and public opinion assessment model;

[0012] The time series analysis and prediction module is used to analyze and predict the time series of public opinion data based on the recurrent neural network model to capture the trend and periodicity of public opinion development;

[0013] Early warning mechanism optimization module, which uses reinforcement learning algorithm to dynamically adjust the early warning threshold and optimize the accuracy and timeliness of the early warning mechanism;

[0014] The early warning and response module is used to trigger the corresponding level of early warning according to the prediction results, notify relevant personnel through multiple channels, and provide decision support;

[0015] High-availability distributed system architecture module, which is used to ensure the scalability and stability of the system through microservice architecture, containerized deployment, and distributed storage and computing;

[0016] The system security and privacy protection module is used to encrypt and control access to data, protect user privacy, and ensure system security.

[0017] Preferably, the data acquisition module includes:

[0018] The real-time data stream platform is built using Apache Kafka to achieve high-throughput real-time data collection and transmission, support horizontal expansion and load balancing of data, and meet the real-time processing requirements of large-scale data;

[0019] Distributed web crawler, based on the Scrapy-Redis framework, realizes efficient and distributed data crawling of target websites, and has multiple functions such as task scheduling, proxy IP pool management, and anti-crawler strategy response.

[0020] Preferably, the data preprocessing module includes:

[0021] Data cleaning unit, used to unify character encoding, filter noise and normalize text of collected data, and eliminate garbled codes, special characters and redundant information;

[0022] The word segmentation and part-of-speech tagging unit adopts the parallel mode of Jieba word segmentation and a custom dictionary, combined with the CRF model of HanLP, to perform efficient and accurate Chinese word segmentation and part-of-speech tagging, providing a foundation for subsequent natural language processing.

[0023] Preferably, the advanced natural language processing module uses a pre-trained BERT or ERNIE model for fine-tuning to meet the semantic analysis requirements of a specific field, including:

[0024] Semantic representation unit, used to obtain the contextual semantic representation of the text, generate sentence or paragraph level vectors, and capture long-distance dependencies and complex semantic relationships;

[0025] The knowledge graph construction unit is used to construct a domain knowledge graph through entity recognition and relationship extraction, and uses the Neo4j graph database for storage and management;

[0026] The graph neural network unit uses graph convolutional network GCN or graph attention network GAT to perform deep semantic representation learning on the knowledge graph and improve the ability to understand complex semantics and entity relationships.

[0027] Preferably, the multimodal data fusion module uses deep Canonical Correlation Analysis to align features of different modalities, including:

[0028] Image information processing unit, which uses the pre-trained ResNet-50 model to perform feature extraction and sentiment classification on image data, supporting recognition and analysis of image sentiment;

[0029] The video information processing unit uses the C3D model and LSTM model to extract key frames, extract spatiotemporal features, and perform temporal dependency modeling on video data to capture emotional changes in video content;

[0030] The multimodal fusion unit fuses the features of text, image and video through early fusion or late fusion strategy, builds a unified sentiment analysis model, and adopts multi-task learning strategy for joint training.

[0031] Preferably, the time series analysis and prediction module uses a long short-term memory network (LSTM) and a gated recurrent unit (GRU) model to analyze and predict the time series data in the public opinion index and the sentiment index, including:

[0032] Time series data preprocessing unit, used to organize and standardize public opinion time series data, process missing values ​​and outliers, and ensure data quality;

[0033] Time series prediction unit, build a multi-layer LSTM or GRU network model, set the appropriate hidden layer size and time step, use mean square error MSE as the loss function, and use Adam optimizer for model training and optimization;

[0034] The anomaly detection unit uses the ARIMA model or the time series Autoencoder model to perform anomaly detection on time series data and identify abnormal points and mutation trends in public opinion data.

[0035] Preferably, the early warning mechanism optimization module uses a deep reinforcement learning algorithm to construct a reinforcement learning model with public opinion data and early warning feedback as the environment and early warning threshold adjustment strategy as the intelligent agent, including:

[0036] Strengthen the learning environment construction unit, define public opinion data, warning effects and system status as the environment, and design a reasonable state space and action space;

[0037] The reinforcement learning model training unit uses the deep Q network DQN or deep reinforcement learning DRL algorithm, and uses experience replay and fixed target network technology to train the warning threshold adjustment strategy;

[0038] The reward mechanism design unit designs a comprehensive reward function based on the accuracy, timeliness and resource consumption of the warning, balances exploration and utilization, and improves the overall performance of the warning mechanism.

[0039] Preferably, the high-availability distributed system architecture module uses Docker for containerized deployment and uses Kubernetes for container orchestration and management to achieve automatic scaling and fault recovery of services, including:

[0040] The microservice management unit splits each functional module of the system into independent microservices, communicates through gRPC or RESTful API, and realizes the decoupling and independent deployment of services;

[0041] Containerized deployment unit, using Docker to package microservices into container images to ensure the consistency and portability of the operating environment;

[0042] The container orchestration and scheduling unit uses Kubernetes to orchestrate, schedule, and manage containers to implement functions such as load balancing, automatic capacity expansion, and automatic failover to ensure high availability and scalability of the system.

[0043] Preferably, the system security and privacy protection module encrypts data transmission through TLS / SSL protocol, encrypts stored data using AES algorithm, and uses role-based access control RBAC and OAuth 2.0 protocol for identity authentication and authority management, including:

[0044] Data encryption unit, which performs symmetric and asymmetric encryption on sensitive data to prevent data leakage and tampering;

[0045] Access control unit, establishes fine-grained permission control strategies, and supports flexible configuration of users, roles, and permissions;

[0046] The privacy protection unit uses differential privacy technology and data anonymization to desensitize personal identity information (PII) to ensure compliance with relevant laws and regulations.

[0047] Preferably, the early warning and response module is provided with a multi-level early warning mechanism, including level one early warning, level two early warning and level three early warning, and notifies relevant personnel in a variety of ways, such as SMS, email, and mobile application push, including:

[0048] The warning level setting unit dynamically adjusts the warning level and sets the corresponding response strategy based on the public opinion risk score and prediction results;

[0049] Multi-channel notification unit, integrating SMS gateway, mail server and push service, to achieve instant and reliable transmission of warning information;

[0050] The decision support unit provides detailed public opinion analysis reports and response strategy recommendations to assist managers in making scientific decisions and formulating effective public opinion handling plans.

[0051] The present invention provides a social public opinion risk intelligent prediction system. It has the following beneficial effects:

[0052] 1. By integrating advanced natural language processing, deep learning and multimodal data fusion technologies, the system can monitor and accurately predict social public opinion in real time, quickly capture emergencies and changes in public opinion, deeply analyze complex semantics and emotional tendencies, provide highly accurate analysis results, and help decision makers take effective measures in a timely manner to reduce public opinion risks.

[0053] 2. The present invention introduces a reinforcement learning algorithm. The system can autonomously learn and dynamically adjust the warning threshold, optimize the accuracy and timeliness of the warning mechanism, and through learning from historical warning effects and public opinion development trends, the system reduces false alarms and missed alarms, improves the effectiveness of warnings, and provides managers with intelligent auxiliary decision-making support.

[0054] 3. The present invention adopts microservice architecture, containerized deployment and distributed storage and computing technology. The system has good scalability and stability. It can cope with the needs of large-scale data processing and high concurrent access, support smooth capacity expansion and automatic fault recovery, ensure continuous and stable operation in complex and changeable application environments, and meet the long-term needs of practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a system framework diagram of the present invention;

[0056] Figure 2 It is a schematic diagram of the data preprocessing module system of the present invention;

[0057] Figure 3 It is a schematic diagram of the high-level natural language processing module system of the present invention;

[0058] Figure 4It is a schematic diagram of the multimodal data fusion module system of the present invention;

[0059] Figure 5 It is a schematic diagram of the timing analysis and prediction module system of the present invention;

[0060] Figure 6 It is a schematic diagram of the early warning mechanism module system of the present invention;

[0061] Figure 7 It is a schematic diagram of the system security and privacy protection module system of the present invention;

[0062] Figure 8 It is a schematic diagram of the system early warning and response module of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Please see attached Figure 1 - Attachment Figure 8 The embodiment of the present invention provides a social public opinion risk intelligent prediction system, including:

[0065] Data collection module, used to collect text, image and video data from multiple heterogeneous data sources in real time, including social media, news websites, forums, blogs and self-media;

[0066] The data preprocessing module is used to clean, normalize, segment and tag the collected data to form a structured data set;

[0067] Advanced natural language processing module, which uses pre-trained Transformer models and graph neural networks to perform deep semantic analysis on pre-processed text data and extract entity, relationship, and contextual semantic features;

[0068] Multimodal data fusion module, used to align and fuse the features of multiple data in text, images and videos, and build a unified sentiment analysis and public opinion assessment model;

[0069] The time series analysis and prediction module is used to analyze and predict the time series of public opinion data based on the recurrent neural network model to capture the trend and periodicity of public opinion development;

[0070] Early warning mechanism optimization module, which uses reinforcement learning algorithm to dynamically adjust the early warning threshold and optimize the accuracy and timeliness of the early warning mechanism;

[0071] The early warning and response module is used to trigger the corresponding level of early warning according to the prediction results, notify relevant personnel through multiple channels, and provide decision support;

[0072] High-availability distributed system architecture module, which is used to ensure the scalability and stability of the system through microservice architecture, containerized deployment, and distributed storage and computing;

[0073] The system security and privacy protection module is used to encrypt and control access to data, protect user privacy, and ensure system security.

[0074] The data acquisition module comprises:

[0075] The real-time data stream platform is built using Apache Kafka to achieve high-throughput real-time data collection and transmission, support horizontal expansion and load balancing of data, and meet the real-time processing requirements of large-scale data;

[0076] Distributed web crawler, based on the Scrapy-Redis framework, realizes efficient and distributed data crawling of target websites, and has multiple functions such as task scheduling, proxy IP pool management, and anti-crawler strategy response.

[0077] The data preprocessing module comprises:

[0078] Data cleaning unit, used to unify character encoding, filter noise and normalize text of collected data, and eliminate garbled codes, special characters and redundant information;

[0079] The word segmentation and part-of-speech tagging unit adopts the parallel mode of Jieba word segmentation and a custom dictionary, combined with the CRF model of HanLP, to perform efficient and accurate Chinese word segmentation and part-of-speech tagging, providing a foundation for subsequent natural language processing.

[0080] Specifically, the data collection module includes: a real-time data stream platform, built with Apache Kafka, to achieve high-throughput data collection and transmission, support horizontal expansion and load balancing, and meet large-scale real-time processing needs; a distributed web crawler, based on the Scrapy-Redis framework, to achieve efficient distributed data crawling, with the functions of task scheduling, proxy IP pool management and anti-crawler strategy. The data preprocessing module includes: a data cleaning unit, which is used to unify character encoding, filter noise and normalize text; a word segmentation and part-of-speech tagging unit, which combines the Jieba word segmentation and HanLP's CRF model to provide accurate Chinese word segmentation and part-of-speech tagging, providing a basis for subsequent processing.

[0081] The advanced natural language processing module uses the pre-trained BERT or ERNIE model for fine-tuning to meet the semantic analysis requirements of specific fields, including:

[0082] Semantic representation unit, used to obtain the contextual semantic representation of the text, generate sentence or paragraph level vectors, and capture long-distance dependencies and complex semantic relationships;

[0083] The knowledge graph construction unit is used to construct a domain knowledge graph through entity recognition and relationship extraction, and uses the Neo4j graph database for storage and management;

[0084] The graph neural network unit uses graph convolutional network GCN or graph attention network GAT to perform deep semantic representation learning on the knowledge graph and improve the ability to understand complex semantics and entity relationships.

[0085] Specifically, the advanced natural language processing module uses the BERT or ERNIE pre-trained model for fine-tuning to adapt to the semantic analysis needs of specific fields, including: a semantic representation unit that generates sentence or paragraph-level semantic vectors to capture long-distance dependencies and complex semantics; a knowledge graph construction unit that generates a domain knowledge graph through entity recognition and relationship extraction and stores it in the Neo4j graph database; a graph neural network unit that uses the GCN or GAT model to perform deep semantic learning on the knowledge graph to enhance the understanding of complex semantics and entity relationships.

[0086] The multimodal data fusion module uses deep Canonical Correlation Analysis to align features of different modalities, including:

[0087] Image information processing unit, which uses the pre-trained ResNet-50 model to perform feature extraction and sentiment classification on image data, supporting recognition and analysis of image sentiment;

[0088] The video information processing unit uses the C3D model and LSTM model to extract key frames, extract spatiotemporal features, and perform temporal dependency modeling on video data to capture emotional changes in video content;

[0089] The multimodal fusion unit fuses the features of text, image and video through early fusion or late fusion strategy, builds a unified sentiment analysis model, and adopts multi-task learning strategy for joint training.

[0090] Specifically, the multimodal data fusion module uses deep canonical correlation analysis (DCCA) to align features of different modalities, including: an image information processing unit that uses a pre-trained ResNet-50 to extract image features and perform sentiment classification; a video information processing unit that extracts the spatiotemporal features of the video and the key frame sentiment changes through the C3D and LSTM models; a multimodal fusion unit that fuses text, image, and video features into a unified sentiment analysis model through early or late fusion strategies, and uses multi-task learning for joint training.

[0091] The time series analysis and prediction module uses the long short-term memory network LSTM and the gated recurrent unit GRU model to analyze and predict the time series data in the public opinion index and the sentiment index, including:

[0092] Time series data preprocessing unit, used to organize and standardize public opinion time series data, process missing values ​​and outliers, and ensure data quality;

[0093] Time series prediction unit, build a multi-layer LSTM or GRU network model, set the appropriate hidden layer size and time step, use mean square error MSE as the loss function, and use Adam optimizer for model training and optimization;

[0094] The anomaly detection unit uses the ARIMA model or the time series Autoencoder model to perform anomaly detection on time series data and identify abnormal points and mutation trends in public opinion data.

[0095] Specifically, the time series analysis and prediction module uses LSTM and GRU models to predict the time series data in the public opinion index and sentiment index, including: a time series data preprocessing unit, which is used to standardize time series data, handle missing and outliers, and ensure data quality; a time series prediction unit, which builds a multi-layer LSTM or GRU network, uses the mean square error (MSE) as the loss function, and uses the Adam optimizer for training and optimization; an anomaly detection unit, which detects anomalies through the ARIMA or time series Autoencoder model and identifies mutation points in public opinion.

[0096] The early warning mechanism optimization module uses a deep reinforcement learning algorithm to construct a reinforcement learning model with public opinion data and early warning feedback as the environment and early warning threshold adjustment strategy as the intelligent agent, including:

[0097] Strengthen the learning environment construction unit, define public opinion data, warning effects and system status as the environment, and design a reasonable state space and action space;

[0098] The reinforcement learning model training unit uses the deep Q network DQN or deep reinforcement learning DRL algorithm, and uses experience replay and fixed target network technology to train the warning threshold adjustment strategy;

[0099] The reward mechanism design unit designs a comprehensive reward function based on the accuracy, timeliness and resource consumption of the warning, balances exploration and utilization, and improves the overall performance of the warning mechanism.

[0100] Specifically, the early warning mechanism optimization module uses deep reinforcement learning algorithms to build a reinforcement learning model based on public opinion data and early warning feedback, with the early warning threshold adjustment strategy as the intelligent body, including: a reinforcement learning environment construction unit, which sets public opinion data, early warning effects and system states as the environment, and designs state space and action space; a reinforcement learning model training unit, which uses deep Q networks (DQN) or deep reinforcement learning (DRL), combined with experience replay and fixed target network technology to train early warning threshold strategies; a reward mechanism design unit, which designs reward functions through the accuracy, timeliness and resource consumption of early warnings, achieves a balance between exploration and utilization, and optimizes early warning effects. The Q value update formula of the model is:

[0101]

[0102] Among them, α is the learning rate and γ is the discount factor. The reward function is defined as:

[0103] R = ω1·Accuracy+ω2·Timeliness-ω3·Resource Consumption

[0104] Among them, ω1, ω2, and ω3 are weight parameters used to balance various indicators.

[0105] The high-availability distributed system architecture module uses Docker for containerized deployment and Kubernetes for container orchestration and management to achieve automatic scaling and fault recovery of services, including:

[0106] The microservice management unit splits each functional module of the system into independent microservices, communicates through gRPC or RESTful API, and realizes the decoupling and independent deployment of services;

[0107] Containerized deployment unit, using Docker to package microservices into container images to ensure the consistency and portability of the operating environment;

[0108] The container orchestration and scheduling unit uses Kubernetes to orchestrate, schedule, and manage containers to implement functions such as load balancing, automatic capacity expansion, and automatic failover to ensure high availability and scalability of the system.

[0109] The system security and privacy protection module encrypts data transmission through TLS / SSL protocol, encrypts stored data using AES algorithm, and uses role-based access control RBAC and OAuth 2.0 protocol for identity authentication and permission management, including:

[0110] Data encryption unit, which performs symmetric and asymmetric encryption on sensitive data to prevent data leakage and tampering;

[0111] Access control unit, establishes fine-grained permission control strategies, and supports flexible configuration of users, roles, and permissions;

[0112] The privacy protection unit uses differential privacy technology and data anonymization to desensitize personal identity information (PII) to ensure compliance with relevant laws and regulations.

[0113] Specifically, the security protection module includes: a data encryption unit, which uses symmetric and asymmetric encryption for sensitive data to prevent leakage and tampering; an access control unit, which flexibly configures users, roles and permissions through fine-grained permission policies; and a privacy protection unit, which uses differential privacy and data anonymization technology to desensitize personal identity information (PII) to ensure compliance.

[0114] The warning and response module is equipped with a multi-level warning mechanism, including level 1 warning, level 2 warning and level 3 warning, and notifies relevant personnel through SMS, email, mobile application push, including:

[0115] The warning level setting unit dynamically adjusts the warning level and sets the corresponding response strategy based on the public opinion risk score and prediction results;

[0116] Multi-channel notification unit, integrating SMS gateway, mail server and push service, to achieve instant and reliable transmission of warning information;

[0117] The decision support unit provides detailed public opinion analysis reports and response strategy recommendations to assist managers in making scientific decisions and formulating effective public opinion handling plans.

[0118] Specifically, the early warning and response module adopts a multi-level early warning mechanism, including level one, level two and level three warnings, and notifies relevant personnel through SMS, email, and mobile application push, including: an early warning level setting unit, which dynamically adjusts the early warning level and sets the response strategy according to the public opinion risk score and prediction results; a multi-channel notification unit, which integrates SMS gateways, email servers and push services to ensure timely communication of early warning information; a decision support unit, which provides public opinion analysis reports and response suggestions to assist managers in formulating effective response plans.

[0119] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A social public opinion risk intelligent prediction system, characterized in that: include: Data collection module, used to collect text, image and video data from multiple heterogeneous data sources in real time, including social media, news websites, forums, blogs and self-media; The data preprocessing module is used to clean, normalize, segment and tag the collected data to form a structured data set; Advanced natural language processing module, which uses pre-trained Transformer models and graph neural networks to perform deep semantic analysis on pre-processed text data and extract entity, relationship, and contextual semantic features; Multimodal data fusion module, used to align and fuse the features of multiple data in text, images and videos, and build a unified sentiment analysis and public opinion assessment model; The time series analysis and prediction module is used to analyze and predict the time series of public opinion data based on the recurrent neural network model to capture the trend and periodicity of public opinion development; Early warning mechanism optimization module, which uses reinforcement learning algorithm to dynamically adjust the early warning threshold and optimize the accuracy and timeliness of the early warning mechanism; The early warning and response module is used to trigger the corresponding level of early warning according to the prediction results, notify relevant personnel through multiple channels, and provide decision support; High-availability distributed system architecture module, which is used to ensure the scalability and stability of the system through microservice architecture, containerized deployment, and distributed storage and computing; The system security and privacy protection module is used to encrypt and control access to data, protect user privacy, and ensure system security.

2. According to claim 1, a social public opinion risk intelligent prediction system is characterized in that: The data acquisition module comprises: The real-time data stream platform is built using Apache Kafka to achieve high-throughput real-time data collection and transmission, support horizontal expansion and load balancing of data, and meet the real-time processing requirements of large-scale data; Distributed web crawler, based on the Scrapy-Redis framework, realizes efficient and distributed data crawling of target websites, and has multiple functions such as task scheduling, proxy IP pool management, and anti-crawler strategy response.

3. According to claim 1, a social public opinion risk intelligent prediction system is characterized in that: The data preprocessing module comprises: Data cleaning unit, used to unify character encoding, filter noise and normalize text of collected data, and eliminate garbled codes, special characters and redundant information; The word segmentation and part-of-speech tagging unit adopts the parallel mode of Jieba word segmentation and a custom dictionary, combined with the CRF model of HanLP, to perform efficient and accurate Chinese word segmentation and part-of-speech tagging, providing a foundation for subsequent natural language processing.

4. According to claim 1, a social public opinion risk intelligent prediction system is characterized in that: The advanced natural language processing module uses the pre-trained BERT or ERNIE model for fine-tuning to meet the semantic analysis requirements of specific fields, including: Semantic representation unit, used to obtain the contextual semantic representation of the text, generate sentence or paragraph level vectors, and capture long-distance dependencies and complex semantic relationships; The knowledge graph construction unit is used to construct a domain knowledge graph through entity recognition and relationship extraction, and uses the Neo4j graph database for storage and management; The graph neural network unit uses graph convolutional network GCN or graph attention network GAT to perform deep semantic representation learning on the knowledge graph and improve the ability to understand complex semantics and entity relationships.

5. According to claim 1, a social public opinion risk intelligent prediction system is characterized in that: The multimodal data fusion module uses deep Canonical Correlation Analysis to align features of different modalities, including: Image information processing unit, which uses the pre-trained ResNet-50 model to perform feature extraction and sentiment classification on image data, supporting recognition and analysis of image sentiment; The video information processing unit uses the C3D model and LSTM model to extract key frames, extract spatiotemporal features, and perform temporal dependency modeling on video data to capture emotional changes in video content; The multimodal fusion unit fuses the features of text, image and video through early fusion or late fusion strategy, builds a unified sentiment analysis model, and adopts multi-task learning strategy for joint training.

6. According to claim 1, a social public opinion risk intelligent prediction system is characterized in that: The time series analysis and prediction module uses the long short-term memory network LSTM and the gated recurrent unit GRU model to analyze and predict the time series data in the public opinion index and the sentiment index, including: Time series data preprocessing unit, used to organize and standardize public opinion time series data, process missing values ​​and outliers, and ensure data quality; Time series prediction unit, build a multi-layer LSTM or GRU network model, set the appropriate hidden layer size and time step, use mean square error MSE as the loss function, and use Adam optimizer for model training and optimization; The anomaly detection unit uses the ARIMA model or the time series Autoencoder model to perform anomaly detection on time series data and identify abnormal points and mutation trends in public opinion data.

7. According to claim 1, a social public opinion risk intelligent prediction system is characterized in that: The early warning mechanism optimization module uses a deep reinforcement learning algorithm to construct a reinforcement learning model with public opinion data and early warning feedback as the environment and early warning threshold adjustment strategy as the intelligent agent, including: Strengthen the learning environment construction unit, define public opinion data, warning effects and system status as the environment, and design a reasonable state space and action space; The reinforcement learning model training unit uses the deep Q network DQN or deep reinforcement learning DRL algorithm, and uses experience replay and fixed target network technology to train the warning threshold adjustment strategy; The reward mechanism design unit designs a comprehensive reward function based on the accuracy, timeliness and resource consumption of the warning, balances exploration and utilization, and improves the overall performance of the warning mechanism.

8. The social public opinion risk intelligent prediction system according to claim 1 is characterized in that: The high-availability distributed system architecture module uses Docker for containerized deployment and Kubernetes for container orchestration and management to achieve automatic scaling and fault recovery of services, including: The microservice management unit splits each functional module of the system into independent microservices, communicates through gRPC or RESTful API, and realizes the decoupling and independent deployment of services; Containerized deployment unit, using Docker to package microservices into container images to ensure the consistency and portability of the operating environment; The container orchestration and scheduling unit uses Kubernetes to orchestrate, schedule, and manage containers to implement functions such as load balancing, automatic capacity expansion, and automatic failover to ensure high availability and scalability of the system.

9. The social public opinion risk intelligent prediction system according to claim 1 is characterized in that: The system security and privacy protection module encrypts data transmission through TLS / SSL protocol, encrypts stored data using AES algorithm, and uses role-based access control RBAC and OAuth 2.0 protocol for identity authentication and authority management, including: Data encryption unit, which performs symmetric and asymmetric encryption on sensitive data to prevent data leakage and tampering; Access control unit, establishes fine-grained permission control strategies, and supports flexible configuration of users, roles, and permissions; The privacy protection unit uses differential privacy technology and data anonymization to desensitize personal identity information (PII) to ensure compliance with relevant laws and regulations.

10. The social public opinion risk intelligent prediction system according to claim 1, characterized in that: The warning and response module is equipped with a multi-level warning mechanism, including level 1 warning, level 2 warning and level 3 warning, and notifies relevant personnel through SMS, email, mobile application push, including: The warning level setting unit dynamically adjusts the warning level and sets the corresponding response strategy based on the public opinion risk score and prediction results; Multi-channel notification unit, integrating SMS gateway, mail server and push service, to achieve instant and reliable transmission of warning information; The decision support unit provides detailed public opinion analysis reports and response strategy recommendations to assist managers in making scientific decisions and formulating effective public opinion handling plans.

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