A social stability risk assessment and prediction software platform

Abstract

The present application relates to the field of social management technology, and discloses a social stability risk assessment and prediction software platform, including: a data acquisition and preprocessing module, which is used to collect multimodal data from text, images, videos, audio and structured data, and to normalize, clean and extract features from the data; a feature extraction and multimodal fusion module, which is used to extract features from each modal data and form a unified risk feature representation through multimodal fusion technology; a risk prediction and real-time dynamic adjustment module, which is used to predict social risks based on the fused features and dynamically adjust model parameters in real time. By extracting information from multi-source data, while ensuring that data of different modalities are consistent in a unified embedding space, efficient complementarity between features is achieved, and in conjunction with the self-attention mechanism and comparative learning, the comprehensiveness and accuracy of the platform's risk assessment in complex social situations are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of social management, and in particular to a social stability risk assessment and prediction software platform. Background Art

[0002] With the rapid development of society and the popularization of information technology, the risk factors that affect social stability are increasing, and they are becoming more complex and diverse. The sources of social risks include economic fluctuations, public health crises, and changes in natural disasters, and risk events are often intertwined to form a chain reaction. Traditional social risk management relies on manual monitoring and analysis methods based on a single data source, which makes it difficult to grasp the dynamic changes of social risks in real time and comprehensively. This limitation makes it difficult to issue early warnings and respond effectively in a timely manner when emergencies occur, affecting social stability and public safety.

[0003] The current risk assessment system mainly relies on structured data and simple statistical analysis methods, and lacks the ability to integrate complex and diverse social dynamic information. In addition, single-modal data has limitations in capturing the breadth and depth of social risks, and it is difficult to cover complete social dynamic information. Especially in the face of emergencies, unstructured data such as social media, news and surveillance data contain a large amount of valuable real-time information, and existing technologies make it difficult to integrate and deeply analyze multimodal data.

[0004] In terms of technological development, in recent years, technologies such as deep learning, natural language processing, computer vision, and reinforcement learning have made great progress in multimodal data analysis, real-time processing, and adaptive adjustment. The maturity of these technologies makes it possible to build a social stability risk assessment platform based on multimodal data fusion, real-time dynamic prediction, and decision support. However, how to ensure the consistency of features of different modalities in multimodal data fusion, how to cope with the rapid changes of social risks through dynamic learning, and how to improve the transparency of prediction results and the credibility of decision support through interpretable analysis are still the main technical challenges facing the current social risk assessment system.

[0005] Based on the above background, the present invention proposes a social stability risk assessment and prediction software platform, aiming to solve the above problems. Summary of the invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a social stability risk assessment and prediction software platform, which solves the problem of relying on a single data source and static analysis methods, making it difficult to integrate multimodal data, and resulting in the inability to fully capture complex social dynamics.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a social stability risk assessment and prediction software platform, comprising:

[0008] Data collection and preprocessing module, used to collect multimodal data from text, images, videos, audio and structured data, and normalize, clean and extract features from the data;

[0009] Feature extraction and multimodal fusion module, used to extract features from each modality data and form a unified risk feature representation through multimodal fusion technology;

[0010] The risk prediction and real-time dynamic adjustment module is used to predict social risks based on the fused features and adjust the model parameters in real time;

[0011] Decision support and visualization module, which is used to display risk assessment results and forecast data, and provide users with decision support suggestions and real-time visualization information;

[0012] The data acquisition and preprocessing module further includes:

[0013] A text data processing unit, used to collect text data from social media and news, and perform word segmentation, denoising and sentiment analysis on the text data;

[0014] An image data processing unit, used for collecting image data and extracting image features through a convolutional neural network;

[0015] A video data processing unit, used to collect video data and extract the temporal features of the video through a 3D convolutional network and a long short-term memory network;

[0016] An audio data processing unit, used for collecting audio data and extracting audio emotional features through Mel frequency cepstral coefficients and convolutional networks;

[0017] A structured data processing unit, used to collect structured data and extract time features using a time series model;

[0018] The text data processing unit performs quantitative analysis on the text sentiment using the following formula:

[0019]

[0020] Among them, S sent represents the text sentiment score, w i is the weight of sentiment words, s i The emotional intensity of each sentiment word.

[0021] Preferably, the feature extraction and multimodal fusion module includes:

[0022] Feature extraction unit, used for deep learning feature extraction of text, images, videos, audio and structured data;

[0023] The multimodal fusion unit is used to fuse the multimodal features obtained by the feature extraction unit and map the features of each modality into a unified embedding space to form a comprehensive feature representation;

[0024] The multimodal fusion unit calculates the interaction between multimodal features based on the self-attention mechanism. The calculation formula of the self-attention mechanism is:

[0025]

[0026] Where Q, K, and V are query, key, and value matrices, respectively, and d k is the dimension of the matrix.

[0027] Preferably, the multimodal fusion unit dynamically weights the contribution of each modal feature through a gating mechanism to form a fusion feature, and the calculation formula is:

[0028] F fused =g text F text +g img F img +g vid F vid +g aud F aud +g struc F struc

[0029] where F fused is the fusion feature representation, g text F text , g img F img , g vid F vid , g aud F aud , g struc F struc is the dynamic weight of each mode.

[0030] Preferably, the multimodal fusion unit performs feature normalization on data of different modalities in the embedding space, and the specific steps are as follows:

[0031] S1. Feature normalization: Normalize the feature vectors of each mode to ensure that the value range of each mode feature is consistent;

[0032] S2, feature scale adjustment: adjust the normalized feature vectors to the same scale range to reduce the difference in feature values ​​between modes;

[0033] S3, Feature Embedding: Map the standardized features of each modality into a shared embedding space through a pre-trained embedding model to generate a unified feature embedding;

[0034] S4. Feature distance alignment: Through contrastive learning or distance alignment methods, the feature distances embedded in the same type of data are closer, and the feature distances embedded in different types of data are farther.

[0035] Preferably, the risk prediction and real-time dynamic adjustment module includes:

[0036] Static risk prediction unit, used to make preliminary predictions of social risks based on fused feature representation;

[0037] A real-time data stream processing unit, used to receive the real-time data stream and update the prediction model;

[0038] An online learning unit for dynamically updating model parameters through incremental learning;

[0039] Reinforcement learning unit for optimizing risk prediction strategies based on real-time feedback.

[0040] Preferably, the online learning unit updates the model parameters in real time using the following formula:

[0041]

[0042] Among them, θ t represents the weight of the model at the tth moment, α is the learning rate, and L is the loss function.

[0043] Preferably, the reinforcement learning unit dynamically optimizes the risk warning strategy based on the Q-learning algorithm, and its update formula is:

[0044]

[0045] Among them, Q(s,a) is the reward for selecting action a in state s, α is the learning rate, and γ is the discount factor.

[0046] Preferably, the decision support and visualization module includes:

[0047] A risk scoring and grading unit, used to grade the predicted risk scores;

[0048] A decision support unit, which is used to provide intervention and response suggestions to users based on risk levels;

[0049] Visual display unit, used to display risk assessment results in real time, including risk distribution heat map, trend analysis chart and explanatory analysis results.

[0050] Preferably, the risk scoring and grading unit uses SHAP and LIME methods to display the impact of features on risk scores, and the specific steps include:

[0051] S1. Feature sensitivity calculation: Based on the SHAP method, the Shapley value of each feature to the risk score is calculated to represent the marginal contribution of the feature to the prediction result.

[0052] S2. LIME local explanation generation: Using the LIME method, a local linear model is constructed to analyze the impact of each feature in specific input data on the risk score;

[0053] S3. Explanatory chart generation: The analysis results of SHAP and LIME are visualized through charts to generate feature contribution charts, waterfall charts, and feature importance ranking charts;

[0054] S4. Visual interaction: Allows decision makers to select different time periods or regions on the interactive dashboard and dynamically view the sensitivity analysis results of each feature on the risk score.

[0055] Preferably, the risk scoring and grading unit grades the risk scores according to the following formula:

[0056]

[0057] Among them, R is the risk score and τ1 is the risk level threshold.

[0058] The present invention provides a social stability risk assessment and prediction software platform. It has the following beneficial effects:

[0059] 1. The present invention can extract information from multi-source data such as text, images, videos, audio and structured data through a multimodal data acquisition and preprocessing module. It ensures that data of different modalities behave consistently in a unified embedding space through feature standardization, multimodal embedding and fusion mechanisms, and achieves efficient complementarity between features. In combination with the self-attention mechanism and comparative learning, multimodal features can work together in the same space, greatly improving the comprehensiveness and accuracy of the platform's risk assessment in complex social situations.

[0060] 2. The present invention adopts real-time data stream processing and online learning technology, combined with reinforcement learning strategy optimization, so that the model can dynamically adjust parameters when the data is constantly updated, thereby achieving real-time and adaptability of risk prediction. Especially in sudden social events and rapidly changing dynamic environments, this mechanism ensures that the platform can respond in a timely manner and generate accurate risk assessment results, significantly improving the flexibility and prediction effect of the platform in practical applications.

[0061] 3. The present invention integrates interpretable analysis methods such as SHAP and LIME, analyzes the sensitivity and contribution of each feature, and displays the basis for risk prediction through visual methods such as feature contribution diagrams, waterfall diagrams, and risk level classification. With the interactive visualization interface, users can understand the reasons for risk prediction and the influence weights of various factors from multiple dimensions, so as to understand the prediction results more transparently, enhance the interpretability of decisions and user trust. This visualization and explanation function provides decision makers with intuitive risk analysis support and helps to quickly formulate effective response measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the platform module of the present invention;

[0063] Figure 2 It is a schematic diagram of the data acquisition and preprocessing module of the present invention;

[0064] Figure 3 It is a schematic diagram of the feature extraction and multimodal fusion module of the present invention;

[0065] Figure 4 A flow chart of standardized steps for the features of the present invention;

[0066] Figure 5 It is a schematic diagram of the risk prediction and real-time dynamic adjustment module of the present invention;

[0067] Figure 6 It is a schematic diagram of the decision support and visualization module of the present invention;

[0068] Figure 7 It is a flow chart showing characteristic steps of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the specification of the present invention 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.

[0070] Please see attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides a social stability risk assessment and prediction software platform, including:

[0071] Data collection and preprocessing module, which is used to collect multimodal data from text, images, videos, audio and structured data, and normalize, clean and extract features from the data;

[0072] Feature extraction and multimodal fusion module, used to extract features from each modality data and form a unified risk feature representation through multimodal fusion technology;

[0073] The risk prediction and real-time dynamic adjustment module is used to predict social risks based on the fused features and adjust the model parameters in real time;

[0074] Decision support and visualization module, which is used to display risk assessment results and forecast data, and provide users with decision support suggestions and real-time visualization information;

[0075] The data acquisition and preprocessing module further includes:

[0076] A text data processing unit, which is used to collect text data from social media and news, and perform word segmentation, denoising and sentiment analysis on the text data;

[0077] An image data processing unit, used for collecting image data and extracting image features through a convolutional neural network;

[0078] A video data processing unit, used to collect video data and extract the temporal features of the video through a 3D convolutional network and a long short-term memory network;

[0079] An audio data processing unit, used for collecting audio data and extracting audio emotional features through Mel frequency cepstral coefficients and convolutional networks;

[0080] A structured data processing unit, used to collect structured data and extract time features using a time series model;

[0081] The text data processing unit performs quantitative analysis on text sentiment using the following formula:

[0082]

[0083] Among them, S sent represents the text sentiment score, w i is the weight of sentiment words, s i The emotional intensity of each sentiment word.

[0084] Specifically, a social stability risk assessment and prediction software platform includes a data collection and preprocessing module, a feature extraction and multimodal fusion module, a risk prediction and real-time dynamic adjustment module, and a decision support and visualization module. The platform aims to collect, fuse and analyze social stability risk information from multiple data sources, and generate decision support in real time to help users grasp the social risk dynamics in a timely manner and predict possible social instability factors in advance. First, the data collection and preprocessing module is used to collect multimodal data from text, images, videos, audio and structured data, and normalize, clean and extract features of different types of data to ensure data consistency and high quality, laying the foundation for subsequent processing. The processing of multiple data sources by this module is the core prerequisite for achieving comprehensive social risk assessment, because multimodal information from social media, news, surveillance videos and public data can provide a broader and richer perspective on social dynamics. Through the normalization and cleaning steps, the data is guaranteed to be noise-free and in a unified format, thereby improving the availability of data and the accuracy of the model.

[0085] Secondly, the feature extraction and multimodal fusion module is used to extract features from each modal data and form a unified risk feature representation through multimodal fusion technology. By combining deep learning and multimodal fusion technology, this module can extract key features from different modal data and achieve feature consistency in a shared embedding space to enhance the expressiveness of risk features. The fused features can better characterize complex social events, allowing the platform to consider information from multiple dimensions when evaluating and predicting social risks, thereby improving the comprehensiveness and accuracy of the model. For example, text data can provide social trends, image and video data reflect on-site conditions, and structured data provides macro-socioeconomic conditions. The features obtained after fusion are more comprehensive, thereby achieving a more accurate description of social risks.

[0086] The risk prediction and real-time dynamic adjustment module predicts social risks based on the fused features, and adjusts the model parameters in real time and dynamically through adaptive learning mechanisms and online learning technologies. This module updates the data stream in real time, allowing the model to maintain efficient prediction capabilities when facing new data. The online learning mechanism ensures that the model can quickly adapt to new risk dynamics, and the reinforcement learning optimization enables the platform to gradually improve prediction accuracy and response speed in the process of continuous feedback and optimization. When an emergency occurs, the risk prediction module can quickly identify risks and adjust strategies to ensure that the platform has stable and efficient prediction performance in a dynamic environment. The dynamic adjustment mechanism is particularly suitable for scenarios where social risks change rapidly, allowing the platform to provide timely warnings and response support in emergencies and social unrest.

[0087] Finally, the decision support and visualization module is used to display risk assessment results and forecast data, providing users with intuitive decision support suggestions and real-time visualization information. This module converts complex risk assessment results into easy-to-understand visualization content, including risk trends, risk distribution, and importance analysis of each feature, helping users to clearly understand the source, intensity, and possible impact of risks during the decision-making process. Through an interactive visualization dashboard, users can dynamically view changes in social risks based on risk levels, time periods, and regional characteristics. Compared with traditional risk assessment methods, this visualization module significantly improves the readability and ease of use of risk information, and the rationality of decision support enhances users' trust in the platform's prediction results.

[0088] Specifically, the data collection and preprocessing module further includes a text data processing unit, an image data processing unit, a video data processing unit, an audio data processing unit, and a structured data processing unit. The text data processing unit is used to collect text data from social media and news, and analyze, denoise, and perform sentiment analysis on the text data, and capture changes in public sentiment by quantifying the sentiment tendency of the text. The image data processing unit is used to collect image data and extract image features through a convolutional neural network, and extract the sentiment, event, and scene information of the image through a CNN model, thereby obtaining risk-related context information from the image. The video data processing unit is used to collect video data, and extract the temporal features of the video through a 3D convolutional network and a long short-term memory network, thereby capturing the temporal evolution information of events in the video, and providing support for real-time monitoring data in social event analysis. The audio data processing unit is used to collect audio data, and extract audio sentiment features through Mel frequency cepstral coefficients and convolutional networks, extract emotion-related features from sound information, and further enrich the modality of risk assessment. The structured data processing unit is used to collect structured data and use time series models to extract time features, incorporate macroeconomic and demographic data into risk prediction, and provide quantitative social background information.

[0089] Through the organic combination of these modules, the present invention realizes the deep integration, real-time prediction and adjustment of multimodal data, and provides users with clear interpretability and decision-making support, which comprehensively improves the accuracy and timeliness of social risk prediction.

[0090] Please see attached Figure 3 -Attached Figure 4 , the feature extraction and multimodal fusion modules include:

[0091] Feature extraction unit, used for deep learning feature extraction of text, images, videos, audio and structured data;

[0092] The multimodal fusion unit is used to fuse the multimodal features obtained by the feature extraction unit and map the features of each modality into a unified embedding space to form a comprehensive feature representation;

[0093] The multimodal fusion unit calculates the interaction between multimodal features based on the self-attention mechanism. The calculation formula of the self-attention mechanism is:

[0094]

[0095] Where Q, K, and V are query, key, and value matrices, respectively, and d k is the dimension of the matrix;

[0096] The multimodal fusion unit dynamically weights the contribution of each modal feature through a gating mechanism to form a fusion feature. The calculation formula is:

[0097] F fused =g text F text +g img F img +g vid F vid +g aud F aud +g struc F struc

[0098] Among them, F fused is the fusion feature representation, g text F text , g img F img , g vid F vid , g aud F aud , g struc F struc is the dynamic weight of each mode;

[0099] The multimodal fusion unit normalizes the features of data of different modalities in the embedding space. The specific steps are as follows:

[0100] S1. Feature normalization: Normalize the feature vectors of each mode to ensure that the value range of each mode feature is consistent;

[0101] S2, feature scale adjustment: adjust the normalized feature vectors to the same scale range to reduce the difference in feature values ​​between modes;

[0102] S3, Feature Embedding: Map the standardized features of each modality into a shared embedding space through a pre-trained embedding model to generate a unified feature embedding;

[0103] S4. Feature distance alignment: Through contrastive learning or distance alignment methods, the feature distances embedded in the same type of data are closer, and the feature distances embedded in different types of data are farther.

[0104] Specifically, the feature extraction and multimodal fusion module includes a feature extraction unit and a multimodal fusion unit, wherein the feature extraction unit is used to perform deep learning feature extraction on text, image, video, audio and structured data, and build high-quality feature representations for different modal data. By using deep learning models such as BERT, ResNet, 3D convolutional network and LSTM, text data can capture semantic information, image data can extract visual features, video data can obtain temporal information, audio data can identify emotional features, and structured data provides quantitative social background features. The multimodal fusion unit fuses the multimodal features obtained by the feature extraction unit, maps each modal feature into a unified embedding space to form a comprehensive feature representation, so as to ensure efficient integration and mutual collaboration of different modal features. The fusion unit uses the self-attention mechanism to calculate the interactive relationship between multimodal features. The self-attention mechanism dynamically captures the mutual influence between each feature through the interactive operation of query, key and value matrices.

[0105] At the same time, the multimodal fusion unit dynamically weights the contribution of each modal feature through the gating mechanism to form the final fusion feature. The gating mechanism allocates the importance of different modal features through dynamic weights according to the contribution of each modal feature to the prediction target. The dynamic weighting of the gating mechanism enables the platform to flexibly adjust the role of different modal features in the overall fusion, ensuring that the most representative modal features can get higher weights in different social situations, thereby improving the model's prediction accuracy for complex social risks.

[0106] In addition, the multimodal fusion unit performs feature normalization on the data of different modalities in the embedding space to ensure the consistency of the expression of multimodal features. The specific steps are as follows: First, the feature normalization step normalizes the feature vectors of each modality to ensure that the value range of each modality feature is consistent. This process eliminates the differences in the numerical range of different modal features and ensures that different modalities have a unified expression basis in the embedding space; then, the feature scale is adjusted to adjust the normalized feature vectors to the same scale range. This adjustment process makes the different modal features consistent in numerical scale and avoids the deviation caused by different scales during fusion. Then, through the feature embedding step, the pre-trained embedding model is used to map the standardized features of each modality to the shared embedding space to generate a unified feature embedding representation, ensuring that all modal features are represented in the same space and enhancing the semantic consistency between modalities. Finally, feature distance alignment is performed. Through contrast learning or distance alignment methods, the embedding distance of features in the same type of data is close, and the embedding distance of features in different types of data is far. This process can strengthen the aggregation of similar events in the embedding space and distinguish different types of risk features, thereby improving the discrimination and expressiveness of multimodal fusion features. The entire feature standardization process ensures the consistency and interoperability of multimodal data in a unified embedding space, enabling the platform to achieve higher fusion effects in social risk assessment and comprehensively improve the accuracy and robustness of social risk prediction.

[0107] Please see attached Figure 5 , the risk prediction and real-time dynamic adjustment module includes:

[0108] Static risk prediction unit, used to make preliminary predictions of social risks based on fused feature representation;

[0109] A real-time data stream processing unit, used to receive the real-time data stream and update the prediction model;

[0110] An online learning unit for dynamically updating model parameters through incremental learning;

[0111] A reinforcement learning unit to optimize risk prediction strategies based on real-time feedback;

[0112] The online learning unit updates the model parameters in real time using the following formula:

[0113]

[0114] Among them, θ t represents the weight of the model at the tth moment, α is the learning rate, and L is the loss function;

[0115] The reinforcement learning unit dynamically optimizes the risk warning strategy based on the Q-learning algorithm, and its update formula is:

[0116]

[0117] Among them, Q(s,a) is the reward for selecting action a in state s, α is the learning rate, and γ is the discount factor.

[0118] Specifically, the risk prediction and real-time dynamic adjustment module includes a static risk prediction unit, a real-time data stream processing unit, an online learning unit, and a reinforcement learning unit. Each unit works closely together to achieve accurate prediction and real-time response to social risks. The static risk prediction unit makes a preliminary prediction of social risks based on fusion feature representation, and uses models such as multi-layer perceptron (MLP), long short-term memory network (LSTM) or Transformer to process the features after multimodal fusion to obtain preliminary prediction results of social risks. Through the static prediction model, the platform can identify potential risks under the condition of unchanged data, providing a stable reference basis for subsequent real-time dynamic adjustments. However, in the face of a dynamic social environment, relying solely on static models cannot quickly respond to risk changes. Therefore, this module introduces a real-time data stream processing unit to receive real-time data streams and update the prediction model. Based on real-time data streams, such as the latest news events and monitoring data, this unit can continuously update data and keep the model sensitive to the latest risk situation, thereby improving the real-time and accuracy of the prediction. The processing of real-time data streams ensures that the model can instantly obtain new social dynamic information when the data changes, enabling the platform to maintain efficient prediction performance in a rapidly changing risk environment.

[0119] In addition, in order to adapt to the needs of dynamic changes in risks, the online learning unit dynamically updates the model parameters through incremental learning technology. The online learning unit makes small updates to the model parameters when new data streams arrive, without the need to retrain the entire model from scratch. Through incremental learning, the platform can digest new information in a short period of time, continuously optimize the model weights and prediction results, and ensure the adaptability of predictions in a rapidly changing social environment.

[0120] The reinforcement learning unit is used to optimize the risk prediction strategy based on real-time feedback, ensuring that the model gradually improves the accuracy and robustness of its own predictions in different environmental feedback. Based on the Q-learning algorithm, this unit dynamically optimizes the risk warning strategy based on real-time feedback in each decision. The reinforcement learning unit adjusts the reward value between different states and action choices to help the model find the optimal decision-making strategy in repeated real-time feedback, thereby enhancing the flexibility and stability of risk prediction. Through this mechanism, the platform can continuously adjust its own strategy in actual operations when facing complex and uncertain social dynamics, ensuring the accuracy of risk prediction results and the timeliness of response. The entire risk prediction and real-time dynamic adjustment module, through the multiple collaborations of static prediction, real-time data stream processing, online learning and reinforcement learning, realizes efficient prediction and response to social risks, effectively responds to the complexity and suddenness of social risks, and greatly improves the practicality and intelligence of the platform.

[0121] Please see attached Figure 6 -Attached Figure 7 , the decision support and visualization modules include:

[0122] A risk scoring and grading unit, used to grade the predicted risk scores;

[0123] A decision support unit, which is used to provide intervention and response suggestions to users based on risk levels;

[0124] Visual display unit, used to display risk assessment results in real time, including risk distribution heat map, trend analysis chart and explanatory analysis results;

[0125] The risk scoring and grading unit uses the SHAP and LIME methods to show the impact of features on risk scores. The specific steps include:

[0126] S1. Feature sensitivity calculation: Based on the SHAP method, the Shapley value of each feature to the risk score is calculated to represent the marginal contribution of the feature to the prediction result.

[0127] S2. LIME local explanation generation: Using the LIME method, a local linear model is constructed to analyze the impact of each feature in specific input data on the risk score;

[0128] S3. Explanatory chart generation: The analysis results of SHAP and LIME are visualized through charts to generate feature contribution charts, waterfall charts, and feature importance ranking charts;

[0129] S4. Visual interaction: allows decision makers to select different time periods or regions on the interactive dashboard and dynamically view the sensitivity analysis results of each feature on the risk score;

[0130] The risk scoring and grading unit grades the risk scores according to the following formula:

[0131]

[0132] Among them, R is the risk score and τ1 is the risk level threshold.

[0133] Specifically, the decision support and visualization module includes a risk scoring and grading unit, a decision support unit, and a visualization display unit, which together provide users with intuitive and comprehensive risk analysis results and corresponding response suggestions. First, the risk scoring and grading unit is used to divide the predicted risk scores into levels. Through a risk threshold-based grading mechanism, the platform can divide the predicted risk scores into different levels to help users quickly understand the severity and urgency of the risks. Through pre-set risk level thresholds, this grading mechanism can effectively simplify risk information into grading labels that are easy for users to understand, helping decision makers to quickly allocate resources and respond to risks of different levels.

[0134] The decision support unit provides users with specific intervention and response suggestions based on the above risk levels. The unit automatically generates corresponding response measures and resource allocation suggestions based on the characteristics and possible impacts of each risk level. For example, in the case of higher-level risks, the platform can recommend increasing resource investment, deploying manpower, or taking preventive behavioral interventions; in the case of low-level risks, the platform may only recommend moderate monitoring. This automated decision support function not only improves the efficiency of risk management, but also reduces subjective bias in user decision-making, ensures the timeliness and scientific nature of risk response, and enhances the actual application effect of the platform in emergencies and daily monitoring.

[0135] The visualization display unit is used to display the risk assessment results in real time, making the risk information clear at a glance through various forms such as risk distribution heat maps, trend analysis charts and explanatory analysis results. The risk distribution heat map can display the risk distribution in different regions, making it easier for users to identify high-risk areas from a geographical dimension; the trend analysis chart can dynamically display the trend of risk changes and help users predict possible future development directions. Combined with the explanatory analysis results, the module can also generate intuitive explanatory charts to help users deeply understand the relative contributions of risk causes and characteristics. Through these visualization methods, the platform transforms complex risk information into easy-to-understand visual presentations, allowing decision makers to quickly obtain and evaluate important information, improving the visual interaction effect of risk assessment.

[0136] In the process of risk scoring and grading, the platform introduces the SHAP and LIME methods to show the impact of features on risk scores, so that users can clearly understand the specific contribution of each feature to risk prediction. First, through feature sensitivity calculation, the Shapley value of each feature to the risk score is calculated based on the SHAP method, which represents the marginal contribution of the feature to the prediction result. The SHAP value can provide a global feature contribution, allowing users to identify the features that have the greatest impact on the risk score, which helps decision makers accurately grasp the source of risk. Next, the platform generates a local explanation through the LIME method, builds a local linear model under specific input data conditions, and analyzes the local impact of each feature on the risk score. The LIME method can effectively reveal the contribution of features under specific input conditions, thereby providing users with a more detailed feature impact analysis, further improving the transparency and credibility of the prediction results.

[0137] Explanatory chart generation is the process of visually displaying the analysis results of SHAP and LIME through charts, including feature contribution charts, waterfall charts, and feature importance ranking charts. The feature contribution chart shows the positive and negative impact of each feature on the risk score, allowing users to intuitively see which factors are increasing or reducing the risk; the waterfall chart gradually shows the superposition process of each feature on the risk score, which is convenient for users to understand the composition and cumulative effect of the risk score; the feature importance ranking chart ranks the features by contribution, allowing users to quickly identify key risk drivers. The generation of these explanatory charts makes complex risk analysis results transparent and easy to understand, which is convenient for users to refer to in the decision-making process.

[0138] In addition, the visualization interaction function allows decision makers to select different time periods or geographical areas on the interactive dashboard to dynamically view the sensitivity analysis results of each feature to the risk score. Users can deeply analyze the changes in risk characteristics according to specific time and regional dimensions, thereby improving the flexibility and applicability of risk assessment. Overall, the decision support and visualization module enables users to understand and respond to social risks from different dimensions through risk classification, intelligent decision support, interactive visualization and explanatory analysis functions, providing comprehensive and scientific support for social stability management.

[0139] 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 stability risk assessment and prediction software platform, characterized in that: include: Data collection and preprocessing module, used to collect multimodal data from text, images, videos, audio and structured data, and normalize, clean and extract features from the data; Feature extraction and multimodal fusion module, used to extract features from each modality data and form a unified risk feature representation through multimodal fusion technology; The risk prediction and real-time dynamic adjustment module is used to predict social risks based on the fused features and adjust the model parameters in real time; Decision support and visualization module, which is used to display risk assessment results and forecast data, and provide users with decision support suggestions and real-time visualization information; The data acquisition and preprocessing module further includes: A text data processing unit, used to collect text data from social media and news, and perform word segmentation, denoising and sentiment analysis on the text data; An image data processing unit, used for collecting image data and extracting image features through a convolutional neural network; A video data processing unit, used to collect video data and extract the temporal features of the video through a 3D convolutional network and a long short-term memory network; An audio data processing unit, used for collecting audio data and extracting audio emotional features through Mel frequency cepstral coefficients and convolutional networks; A structured data processing unit, used to collect structured data and extract time features using a time series model; The text data processing unit performs quantitative analysis on the text sentiment using the following formula: Among them, S sent represents the text sentiment score, w i is the weight of sentiment words, s i The emotional intensity of each sentiment word.

2. A social stability risk assessment and prediction software platform according to claim 1, characterized in that: The feature extraction and multimodal fusion module includes: Feature extraction unit, used for deep learning feature extraction of text, images, videos, audio and structured data; The multimodal fusion unit is used to fuse the multimodal features obtained by the feature extraction unit and map the features of each modality into a unified embedding space to form a comprehensive feature representation; The multimodal fusion unit calculates the interaction between multimodal features based on the self-attention mechanism. The calculation formula of the self-attention mechanism is: Where Q, K, and V are query, key, and value matrices, respectively. k is the dimension of the matrix.

3. A social stability risk assessment and prediction software platform according to claim 2, characterized in that: The multimodal fusion unit dynamically weights the contribution of each modal feature through a gating mechanism to form a fusion feature, and its calculation formula is: F fused =g text F text +g img F img +g vid F vid +g aud F aud +g struc F struc where F fused is the fusion feature representation, g text F text , g img F img , g vid F vid , g aud F aud , g struc F struc is the dynamic weight of each mode.

4. A social stability risk assessment and prediction software platform according to claim 2, characterized in that: The multimodal fusion unit performs feature normalization on data of different modalities in the embedding space. The specific steps are as follows: S1. Feature normalization: Normalize the feature vectors of each mode to ensure that the value range of each mode feature is consistent; S2, feature scale adjustment: adjust the normalized feature vectors to the same scale range to reduce the difference in feature values ​​between modes; S3. Feature Embedding: The standardized features of each modality are mapped into a shared embedding space through a pre-trained embedding model to generate a unified feature embedding. S4. Feature distance alignment: Through contrastive learning or distance alignment methods, the feature distances embedded in the same type of data are closer, and the feature distances embedded in different types of data are farther.

5. A social stability risk assessment and prediction software platform according to claim 1, characterized in that: The risk prediction and real-time dynamic adjustment module includes: Static risk prediction unit, used to make preliminary predictions of social risks based on fused feature representation; A real-time data stream processing unit, used to receive the real-time data stream and update the prediction model; An online learning unit for dynamically updating model parameters through incremental learning; Reinforcement learning unit for optimizing risk prediction strategies based on real-time feedback.

6. A social stability risk assessment and prediction software platform according to claim 5, characterized in that: The online learning unit updates the model parameters in real time using the following formula: Among them, θ t represents the weight of the model at the tth moment, α is the learning rate, and L is the loss function.

7. A social stability risk assessment and prediction software platform according to claim 5, characterized in that: The reinforcement learning unit dynamically optimizes the risk warning strategy based on the Q-learning algorithm, and its update formula is: Among them, Q(s,a) is the reward for selecting action a in state s, α is the learning rate, and γ is the discount factor.

8. A social stability risk assessment and prediction software platform according to claim 1, characterized in that: The decision support and visualization module includes: A risk scoring and grading unit, used to grade the predicted risk scores; A decision support unit, which is used to provide intervention and response suggestions to users based on risk levels; Visual display unit, used to display risk assessment results in real time, including risk distribution heat map, trend analysis chart and explanatory analysis results.

9. A social stability risk assessment and prediction software platform according to claim 8, characterized in that: The risk scoring and grading unit uses the SHAP and LIME methods to display the impact of features on risk scores. The specific steps include: S1. Feature sensitivity calculation: Based on the SHAP method, the Shapley value of each feature to the risk score is calculated to represent the marginal contribution of the feature to the prediction result. S2. LIME local explanation generation: Using the LIME method, a local linear model is constructed to analyze the impact of each feature in specific input data on the risk score; S3. Explanatory chart generation: The analysis results of SHAP and LIME are visualized through charts to generate feature contribution charts, waterfall charts, and feature importance ranking charts; S4. Visual interaction: Allows decision makers to select different time periods or regions on the interactive dashboard and dynamically view the sensitivity analysis results of each feature on the risk score.

10. A social stability risk assessment and prediction software platform according to claim 8, characterized in that: The risk scoring and grading unit grades the risk scores according to the following formula: Among them, R is the risk score and τ1 is the risk level threshold.

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