Public sentiment brain data intelligent analysis and risk early warning system based on multi-modal RAG engine

Abstract

This invention discloses a public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine, belonging to the field of e-government and intelligent social governance technology. Specifically, it is a public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine, including modules for multi-source modal data access, structure-preserving data processing, multimodal retrieval enhancement engine, public opinion situation analysis, risk classification and early warning, closed-loop handling, data security management and control, and visualization interaction. Through distributed network collaboration, a full-process governance link from data collection to risk mitigation is formed. The module supports real-time collection of heterogeneous data from more than 24 government departments, preserving data correlation; the dual retrieval link integrates results, and more than 65 risk scenarios are divided into four levels of early warning; the closed-loop handling covers three-level units, and a transparent governance and three-level computing system are set up to achieve efficient public opinion analysis and precise risk prevention and control.

Description

Technical Field

[0001] This invention relates to the field of intelligent technology in electronic work and social governance, specifically to a public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine. Background Technology

[0002] Traditional methods of collecting and processing public opinion data primarily rely on single text or tables. Data sources are scattered across different business lines such as public security, civil affairs, human resources and social security, health, and education, lacking a unified access and integration mechanism. This leads to difficulties in cross-departmental data sharing and severe information silos. Furthermore, existing systems have limited capabilities in processing unstructured data (such as on-site photos, public voice requests, video surveillance footage, and geolocation information), often relying on manual sorting and experience-based judgment. This is time-consuming and prone to missing crucial information, failing to meet the needs for rapid perception and accurate assessment of public concerns and potential risks.

[0003] At the data analysis level, most work information systems remain at the stage of statistical summarization and simple querying, lacking the ability to deeply explore the potential correlations between multimodal data. For example, a citizen's complaint voice recording may contain information such as geographical location, event type, and emotional intensity. If it cannot be linked with related images and table statistics from the same period for analysis, it is impossible to fully reconstruct the whole picture of the event and assess its scope of impact. In addition, traditional risk warnings mostly rely on fixed thresholds or post-event retrospective analysis. The warning models are singular and have narrow coverage, making it difficult to capture complex risks across fields and regions in a timely manner, resulting in delayed responses and affecting the effectiveness of handling.

[0004] In recent years, artificial intelligence (AI) technology has made significant progress in fields such as natural language processing, computer vision, and knowledge graphs, enabling unified representation and joint analysis of multimodal information. Among these advancements, Retrieval Augmentation (RAG) technology, by combining large-scale retrieval with generative models, can generate context-aware answers or analytical conclusions while maintaining factual accuracy, and has already been applied in question-answering systems and intelligent customer service. However, introducing this technology into the field of public opinion governance still faces several challenges: First, the standardized access and real-time synchronization of multi-source heterogeneous data is a problem, as different departments have significantly different data formats and transmission protocols, requiring the design of flexible multi-protocol adaptation solutions; second, the preservation of the structure and semantic alignment of multimodal data is a challenge, avoiding the loss of key contextual elements such as spatial location and temporal sequence due to format conversion; and third, the construction and dynamic updating of risk scenario models, which need to cover multiple fields such as education, healthcare, employment, and elderly care, and be continuously optimized in response to policy and social environment changes.

[0005] While some existing smart work platforms have attempted to incorporate big data analytics and visualization technologies, they still have shortcomings in terms of closed-loop governance throughout the entire process. For example, some systems only collect and display data, lacking a complete chain from analysis to early warning, dispatching, handling, and feedback; risk classification standards are inconsistent, and early warning response mechanisms are rigid, making it difficult to adapt to the differentiated needs of city, town, and village-level governance units; data security and privacy protection mechanisms are inadequate, and the access and use of sensitive public information lacks traceability, easily triggering a crisis of public trust. In addition, system interfaces are often designed for a single user group, failing to fully consider the different needs of decision-makers, implementers, and grassroots grid workers in terms of information granularity and ease of operation, thus affecting actual usage effectiveness.

[0006] In conclusion, the current field of public opinion governance needs an intelligent analysis and risk warning system that can integrate multimodal data, utilize advanced retrieval and enhancement technologies, possess full-process closed-loop processing capabilities, and is safe and controllable. This system would address issues such as data fragmentation, superficial analysis, and delayed response in the traditional model, thereby enhancing the predictability, accuracy, and credibility of relevant departments in social governance. Summary of the Invention

[0007] The purpose of this invention is to provide a public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine. Through enhanced retrieval and situational analysis, it can accurately identify public opinion risks, achieve rapid response based on hierarchical early warning and closed-loop handling, and improve the efficiency of grassroots governance and the scientific nature of decision-making.

[0008] To achieve the above objectives, this invention provides the following technical solution: a public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine, comprising a multi-source modal data access module, a structure-preserving data processing module, a multimodal retrieval enhancement engine module, a public opinion situation analysis module, a risk classification and early warning module, a closed-loop handling module, a data security management module, and a visualization interaction module; wherein, the multi-source modal data access module is used to aggregate various public opinion-related data, the structure-preserving data processing module is used to categorize the raw data and maintain its inherent connections, and the multimodal retrieval enhancement engine module is used to retrieve and integrate cross-category data according to the query target. According to the resources, the public sentiment analysis module is used to identify trends and concerns in public sentiment; the risk classification and early warning module is used to determine the degree of risk and trigger corresponding prompts; the closed-loop handling module is used to organize task flow and tracking; the data security management module is used to ensure the compliance and controllability of data during flow and storage; and the visualization and interaction module is used to provide intuitive information presentation and operation entry points for users with different roles. Each module relies on a distributed network architecture to establish data channels and calling mechanisms, realizing bidirectional transmission and status synchronization of task instructions and data content, forming a continuous governance process from data acquisition, processing, analysis and identification, early warning release to task handling and result verification.

[0009] Furthermore, the multi-source modal data access module adopts a multi-protocol adaptation architecture, with a built-in extensible protocol library and data access scheduler. It can connect in parallel to text-based public opinion records, image-based on-site photos and video screenshots, tabular statistical reports, voice-based recordings of public calls, and geospatial coordinate positioning data. The module has interface template management capabilities, and can configure data access standards according to the business specifications of working departments. It is compatible with heterogeneous data channels of 24 or more departments, including public security, civil affairs, human resources and social security, health, education, and housing and construction. It supports data capture by planned polling and event-driven methods, realizing real-time writing of new data and periodic incremental synchronization of historical data, ensuring that the data sources are wide, updates are timely, and the content is complete.

[0010] Furthermore, the structure-preserving data processing module employs a modality-specific processing mechanism, independently setting conversion strategies and rule sets for data from different sources and formats. For text data, it performs semantic segmentation and identifies entity relationships and behavioral orientations. For image data, it identifies elements such as people, buildings, facilities, and landmarks in the image and generates corresponding text descriptions. For tabular data, it parses the row and column structure and units of measurement, and reconstructs them into a multi-dimensional indicator logical relationship table. All converted data is uniformly encoded into an internal standard format, fully preserving its original temporal and spatial arrangement order and hierarchical position, ensuring that the data can still reflect the original collection context and situational association in subsequent calls.

[0011] Furthermore, the multimodal retrieval enhancement engine module constructs a dual retrieval link architecture. The first link performs field matching and content positioning based on keywords, time range, spatial region, and data category attributes in the input conditions, returning highly relevant data units. The second link expands the retrieval boundary based on topic classification tags and matter similarity models, incorporating indirectly relevant but valuable auxiliary materials. The module has a built-in fusion scheduling mechanism that assigns scores and merges the results obtained from different links according to source credibility, timeliness weight, and content coverage, forming a data set around a certain public opinion matter. This set is sorted according to semantic coherence and logical reasoning, allowing the analysis module to directly access and reference it.

[0012] Furthermore, the public opinion situation analysis module incorporates a multi-dimensional analysis model, covering methods such as high-frequency word distribution statistics, issue clustering, regional concentration calculation, and time series evolution fitting. It can automatically generate a visualized public opinion heat map covering demand density, problem type proportion, and regional attention differences, and regularly generate quantitative indicators reflecting the overall state of people's livelihood, namely the public opinion index. It can also output special analysis reports based on specified fields. The module has horizontal integration capabilities, and can reveal cross-domain problem clues such as the tension of educational resources and the concentration of school enrollment demands, and the overlap between insufficient medical services and the distribution of the elderly population by cross-comparing key fields in different modal data. This helps to identify practical directions and areas of conflict that require priority attention.

[0013] Furthermore, the risk classification and early warning module is pre-configured with more than 65 typical risk scenario models covering fields such as public safety, urban management, environmental protection, and social security. Each model defines triggering conditions, critical thresholds, and evolution paths. By comparing real-time collected data with historical patterns, the module identifies potential problems that may evolve unfavorably and classifies risks into four levels: red, orange, yellow, and blue, based on the scope of impact, urgency, and controllability. Each level of early warning is bound to a corresponding response strategy and push scope, and has self-identification, self-judgment, and self-prompting functions. It can complete the process from anomaly detection to early warning information release within seconds and locate the specific location and related responsible units.

[0014] Furthermore, the closed-loop handling module constructs a complete work chain of "collection-analysis-early warning-dispatch-handling-feedback-supervision," clearly defining the implementing entity and handover node for each step; relying on the task work order mechanism, early warnings or assigned matters are issued to governance personnel at the city, town, and village levels, reducing intermediate approval steps and enabling tasks to reach the front-line handlers directly; for general and simple matters, an initial response and preliminary handling opinions are required within 24 hours; for situations involving multiple overlapping functions or complex causes, the system automatically switches to a joint handling process and coordinates relevant departments to discuss countermeasures; for major hidden dangers with regional or systemic impacts, the system directly reports to core decision-making positions and simultaneously initiates emergency response arrangements, improving overall response efficiency and handling quality.

[0015] Furthermore, the data security management module establishes a hierarchical authorization system and a data lineage tracking record library. It sets access permissions for viewing, editing, and exporting based on data security level and usage purpose. Data containing sensitive fields such as personal identity, home address, and health status is encrypted, stored, and displayed anonymously. The module continuously records the source account, time, and operation content of each data access, modification, and copy, forming an operation trajectory chain that can be reverse-tracked and located. A public interface is also provided, offering functions such as submitting requests via QR code, evaluating service quality online, and supplementing reports of missed issues, allowing public participation in the governance process and promoting a transparent, verifiable, and interactive governance mechanism.

[0016] Furthermore, the visualization interaction module adopts a layered layout design, differentiating the displayed content and operational depth according to the user. The decision-making layer view gathers the dynamics of public sentiment, risk distribution, and resource allocation across the entire region, presenting the macro situation and changing trends in the form of comprehensive charts. The execution layer view highlights task assignment details, current progress status, and timeout reminders, facilitating supervision and implementation. The grassroots grid worker view integrates mobile information collection tools, on-site problem upload channels, and quick feedback options, adapting to mobile phones, tablets, and other terminal devices. The module supports user-defined homepage components and filtering conditions, enabling personalized arrangement of interface content and operation processes, improving adaptability and work efficiency for different positions.

[0017] Furthermore, the system adopts a three-tiered computing architecture of "central-regional-community". The central layer deploys core computing resources and model libraries, uniformly maintains data structure definitions, topic tag systems, and risk assessment standards, and provides strategic-level situation summaries and contingency plan suggestions. The regional layer is configured with local node servers to store, clean, and routinely analyze data within its jurisdiction, respond to management instructions at its level, and support data exchange between regions. The community layer is equipped with front-end data collection devices and lightweight computing terminals to complete the initial sorting, classification, packaging, and uplink transmission of public opinion information. The various levels are interconnected via dedicated optical fibers and virtual private networks to ensure response speed and system stability when multiple nodes access concurrently and when large amounts of data are processed.

[0018] This invention provides a public opinion data intelligent analysis and risk early warning system based on a multimodal RAG engine, which has the following beneficial effects: 1. This system, through a multi-protocol-adaptive multi-source modal data access module, can simultaneously collect cross-modal data such as text, images, tables, voice, and geographic information, supporting real-time access and incremental updates of heterogeneous data from more than 24 departments. This design breaks down the barriers of traditionally scattered public opinion data across departments such as public security, civil affairs, and petitions, solving the "information silo" problem caused by inconsistent data standards and closed interfaces. For example, recordings of public demands (voice), on-site photos (images), and community statistical reports (tables) can be simultaneously imported into the system, forming a complete data chain covering "demand expression - scene reconstruction - data statistics," providing a comprehensive and vivid foundation of public opinion for subsequent analysis, and enabling administrators to possess, for the first time, a "panoramic" digital capability to perceive public sentiment.

[0019] The structure-preserving data processing module employs modality-specific processing mechanisms such as semantic segmentation, scene element extraction, and dimensional analysis to address the characteristics of different data types. Even after conversion, it retains the original contextual relationships and spatial locations in a standardized format. Traditional data processing often loses key information due to format uniformity requirements (such as the spatial binding between image capture location and complaint content, and the time-series logic of tabular data), while this module avoids such losses. For example, in an image of a community elderly care complaint, the system can simultaneously retain the text annotation "poor canteen hygiene" and the latitude and longitude of the capture point. Combined with data on the distribution of surrounding elderly care facilities, it can more accurately pinpoint the root cause of the problem, upgrading subsequent situational analysis from "single-dimensional description" to "multi-modal relational insight," significantly improving the accuracy of identifying the focus of the conflict.

[0020] The multimodal retrieval enhancement engine module constructs a dual-link system of "precise matching + topic expansion." It integrates and weights different search results through a modal fusion algorithm, generating a context-aware dataset. Traditional retrieval methods either rely on keyword matching (which easily misses implicit connections) or perform only simple topic clustering (lacking detailed support). This module, however, balances "precise search" and "comprehensive search": the first link quickly identifies text and recordings directly related to the current request; the second link extends to historical cases and policy documents on similar topics, and then the algorithm assigns weights based on relevance.

[0021] The risk grading and early warning module has over 65 preset scenario models, enabling second-level warnings at four levels: red, orange, yellow, and blue. Combined with the closed-loop handling module's "collection-analysis-early warning-dispatch-handling-feedback-supervision" chain, it forms a complete governance closed loop of "perception-response-resolution." Traditional risk handling often leads to minor issues escalating due to delayed warnings and departmental buck-passing. This system, however, identifies early risk signs through trend analysis (e.g., if a region experiences three consecutive days of complaints about long waiting times for medical care, the system can predict an orange warning). It then dispatches tasks in a flat, three-tiered system at the city, town, and village levels: general matters receive a 24-hour response, complex matters require joint coordination, and major hazards are directly reported to the decision-making level. For example, if a village experiences a sudden public outcry over drinking water pollution, the system triggers a red warning within seconds, directly pushing the alert to the county-level emergency command center, simultaneously coordinating with environmental protection and health departments for joint handling. This reduces response time by over 70% compared to traditional processes, significantly lowering the probability of risk escalation.

[0022] The visualization and interaction module provides differentiated interfaces for decision-makers, implementers, and grassroots grid workers (e.g., decision-makers can view an overall situation overview, while grid workers can collect feedback using mobile terminals). Combined with a three-tiered computing system (central-regional-community, central modeling, regional processing, and community preprocessing), it ensures low-latency interaction under large-scale data, manages sensitive data through hierarchical authorization and lineage tracing mechanisms, and provides open interfaces for public supervision (QR code reporting and service evaluation). Traditional systems often suffer from inefficient operation due to a "one-size-fits-all" interface or security risks due to centralized data storage. This design allows decision-makers to "see everything," implementers to "grasp accurately," and grassroots workers to "collect data quickly," while also enabling public participation in supervision. This creates a dual advantage of "technology empowerment + transparent governance," shifting governance from "passive response" to "proactive co-governance." Attached Figure Description

[0023] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the overall governance process of the system of this invention. Figure 2 This is a flowchart of the multi-source modal data access process of the present invention; Figure 3 This is a flowchart of the multimodal retrieval enhancement engine of the present invention; Figure 4 This is a flowchart of the closed-loop processing of the present invention; Figure 5 This is a flowchart of the three-level operation system of the present invention. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] How to use: I. Multi-source modal data access and aggregation First, the multi-source modal data access module is activated. Leveraging its multi-protocol adaptation architecture, it simultaneously accesses text-based public opinion records (such as complaint work orders), image-based scene materials (such as on-site photos), tabular statistical data (such as livelihood indicator reports), voice recordings of requests, and geospatial location information. During use, standardized adaptation of cross-departmental data interfaces must be completed to ensure real-time collection and incremental updates of heterogeneous data from more than 24 departments, laying a data foundation for subsequent analysis.

[0028] II. Structure-Preserving Data Processing and Storage After data access, modality-specific processing is performed through a structure-preserving data processing module: semantic segmentation and relational annotation are performed on text data to clarify the logical connections between key demands; scene elements (such as crowd gatherings and facility damage) are extracted from image data and associated text descriptions are generated; dimensional analysis and logical reconstruction are carried out on tabular data to clarify the hierarchical relationships of statistical indicators. All processed data is stored in a standardized format, strictly preserving the contextual connections (such as the causal chain between demands and time) and spatial location relationships (such as the geographical coordinates of the event) of the original data to avoid information distortion.

[0029] III. Enhanced Multimodal Retrieval and Data Support Generation The multimodal retrieval enhancement engine module is invoked to initiate a dual retrieval chain: the first chain performs precise matching retrieval based on content features (such as keywords and image features) to quickly locate directly relevant data; the second chain performs extended retrieval based on public sentiment theme associations (such as associating "elderly care" with "medical resources" and "community services") to uncover potentially related data. A modal fusion algorithm is then used to weight and integrate the two types of retrieval results, generating a context-aware public sentiment data set, providing comprehensive and accurate data support for subsequent analysis.

[0030] IV. Analysis of Public Sentiment and Demand Positioning Utilizing the built-in multi-dimensional analysis model of the public sentiment analysis module, the system automatically generates public sentiment heatmaps (intuitively presenting areas with high-frequency problems), public sentiment indices (quantifying public satisfaction and the intensity of conflicts), and special analysis reports (focusing on key areas). Combined with data association algorithms, it uncovers potential connections between different modalities of data (such as the correlation between an increase in medical complaints in a certain area and surrounding hospital bed data), accurately pinpointing public needs and points of conflict in key areas such as education, healthcare, employment, and elderly care.

[0031] V. Risk Classification, Early Warning, and Response Configuration The risk grading and early warning module is activated. The system uses data feature comparison (such as a sudden increase in the number of complaints or the proportion of negative sentiment exceeding a threshold) and trend analysis to classify identified public sentiment risks into four levels: red, orange, yellow, and blue. Red level (major hidden dangers) triggers a second-level early warning and is directly reported to the core decision-making level; orange level (high risk) initiates an emergency response; and yellow and blue levels are configured with regular response mechanisms, achieving early detection, tiered early warning, and precise location of risks.

[0032] VI. Closed-loop handling and coordinated response Based on the closed-loop response module, the process proceeds according to the "collection-analysis-early warning-dispatch-response-feedback-supervision" chain: general matters are automatically dispatched to the corresponding level (city / town / village) and responded within 24 hours; complex matters trigger a joint coordination mechanism to coordinate resources from multiple departments; major hidden dangers are directly reported to the core decision-making level and an emergency response is initiated. Progress is tracked in real time during the response process, and feedback is collected and rectification is supervised after completion, forming a complete governance closed loop.

[0033] VII. Data Security Management and Transparent Governance The data security management module implements a tiered authorization and lineage traceability mechanism: sensitive data is stored in encrypted form, and access requires authorization verification; all operations are traceable and accountability is ensured. Simultaneously, a public oversight interface is provided, supporting QR code submission of issues, service evaluations, or feedback, promoting transparency in governance.

[0034] 8. Visual Interaction and Hierarchical Adaptation When using the visual interaction module, decision-makers log in to the overall public sentiment overview interface to grasp the overall dynamics; the execution level manages tasks through the precise task assignment and progress tracking interface; and grassroots grid workers collect data and report the handling results through mobile terminals. The system supports multi-terminal (PC, mobile phone, etc.) adaptation and personalized interface configuration to improve ease of use.

[0035] IX. Three-level computing system ensures operation The system defaults to a three-tiered computing architecture: the central layer is responsible for global data modeling and decision support; the regional layer handles localized data storage and analysis; and the community layer completes terminal data collection and preprocessing. High-speed network interconnection must be ensured to guarantee low-latency data interaction and support stable operation in large-scale data scenarios.

[0036] Example: Example 1: Response to Public Demands and Risk Warning in the Renovation of Old Urban Residential Communities The modules involved include: multi-source modal data access module, structure-preserving data processing module, multi-modal retrieval enhancement engine module, public opinion trend analysis module, risk classification and early warning module, closed-loop handling module, data security management and control module, visualization interaction module, and three-level computing system.

[0037] A certain urban area plans to renovate old residential communities. During the preliminary research phase, a multi-source modal data access module was activated to simultaneously access text-based renovation suggestions submitted by residents (including requests for elevator installation, green space optimization, etc.), on-site image-based scene materials (damaged corridors, insufficient parking spaces), tabular statistical data provided by the street office (resident age structure, vehicle ownership), resident voice recordings (expressing concerns about construction noise), and geospatial location information (specific building distribution within the community). Through a multi-protocol adaptation architecture, real-time collection and incremental updates of data from more than 24 departments, including housing and construction, civil affairs, and urban management, were achieved.

[0038] Subsequently, the structure-preserving data processing module performs modality-specific processing: semantic segmentation and relational annotation are performed on text suggestions (e.g., a strong correlation between "elderly people" and "elderly people installing elevators"); scene elements are extracted from image materials (e.g., "lack of corridor lighting" and "blocked fire exits") and associated text is generated; dimensional analysis (layered by building age and population density) and logical reconstruction are performed on tabular data. The processed data is stored in a standardized format, preserving the temporal order and spatial location of the original requests (e.g., parking conflicts in front of a certain unit entrance).

[0039] The multimodal retrieval enhancement engine module initiates a dual retrieval link: the first link accurately matches text and images related to "old residential areas + elevator installation"; the second link expands the retrieval to related data on topics such as "inconvenience for elderly people to travel" and "construction disturbance". The results are integrated through a modal fusion algorithm to generate a context-aware data set containing residents' real pain points and similar historical cases.

[0040] The public sentiment analysis module automatically generates a public sentiment heat map (showing that Buildings 3 and 5 are the areas with concentrated demands), a public sentiment index (the support rate for renovation is 78%, but the noise concern index is 62%), and a special analysis report based on this set. It uncovers the potential connection between "high proportion of elderly residents" and "strong demand for elevator installation" and identifies "age-friendly renovation" as a key area of ​​demand.

[0041] The risk classification and early warning module compares data characteristics (more than 40% of the residents in a certain building are elderly and 35% of the recorded complaints against construction noise) and deduces the risk of "the construction period may trigger group complaints". It is judged as an orange warning and triggers the emergency response mechanism (publicizing the construction period in advance and setting up temporary rest points).

[0042] The closed-loop handling module proceeds according to the "collection-analysis-early warning-dispatch-handling-feedback-supervision" link: the task of "optimizing construction time" is assigned to the street and construction unit, and the negotiation is completed within 24 hours; after handling, residents provide feedback on their satisfaction through scanning the code (92% approval), and supervision is carried out to rectify the unmet requirements.

[0043] The data security management module encrypts and stores sensitive data such as information on elderly residents. Access requires authorization from the civil affairs department, and operations are traceable. At the same time, a public supervision interface is opened, allowing residents to scan a code to report new problems or evaluate services.

[0044] The visual interactive module provides district leaders with an overview of the overall transformation situation (including the distribution of demands and risk levels in each community), street-level implementation staff can view the progress of precise task assignment, and grid workers can collect new demands and report the handling results via mobile terminals. The system relies on a three-level computing system of "central-regional-community". The central level models the overall transformation strategy, the regional level processes local data, and the community level completes door-to-door data collection, ensuring low-latency interaction.

[0045] Example 2: Response to Public Sentiment Arising from Shortages of Medical Resources in Rural Areas The modules involved include: multi-source modal data access module, structure-preserving data processing module, multi-modal retrieval enhancement engine module, public opinion trend analysis module, risk classification and early warning module, closed-loop handling module, data security management and control module, visualization interaction module, and three-level computing system.

[0046] A county launched a special campaign to improve rural healthcare. This involved first integrating multi-source modal data access modules with text-based patient records from village clinics (primarily for colds and chronic illnesses), image-based scene data uploaded by villagers (showing outdated clinic equipment and empty medicine cabinets), tabular data from township statistical offices (number of doctors per thousand people, drug inventory cycles), villagers' voice-based requests ("long queues for IV drips," "out of commonly used medications"), and geospatial location information (distribution of remote villages). Real-time data collection was achieved from over 24 departments, including health, medical insurance, and market supervision.

[0047] The structure-preserving data processing module annotates text records with semantic blocks such as "chronic disease follow-up visits" and "children's vaccinations"; extracts elements such as "aging blood pressure monitor" and "no antihypertensive drugs on the medicine shelf" from images and generates text; and parses and reconstructs table data according to the dimensions of "administrative village-population size-medical resources," preserving the correspondence between the original data and geographical location (e.g., a village is 15 kilometers away from the nearest health center).

[0048] The multimodal retrieval enhancement engine module accurately matches text and images related to "rural areas + drug shortage" in the first link; the second link expands the search to topics such as "inconvenient management of chronic diseases" and "long distance to medical treatment", integrating related data sets such as "untimely drug supply in remote villages" and "insufficient professional capacity of village doctors".

[0049] The public sentiment analysis module generates a public sentiment heat map (three villages in the north are high-risk areas for COVID-19), a public sentiment index (medical resource satisfaction rate is 51%), and special reports. It uncovers a strong correlation between "aging of the permanent population" and "high demand for chronic disease medications" and identifies "grassroots drug supply" as the key contradiction.

[0050] The risk classification and early warning module comparison revealed that a village in the north had been without commonly used hypertension medication for more than 7 days and that 45% of the patients' voice-based medical requests were for treatment. This was deduced as a risk of "potentially causing serious delays in treatment", and a red alert was issued. The county party committee was notified directly and an emergency response was initiated (coordinating with the county-level hospital to urgently supply medication).

[0051] The closed-loop treatment module dispatches orders to the county health commission and township health centers, and completes drug delivery within 24 hours; for complex matters (village doctor training), joint assistance is initiated (health and human resources departments organize online courses); after treatment, feedback is provided by villagers through scanning codes (drug accessibility is improved to 90%), and village doctors who have not completed supplementary training are supervised.

[0052] The data security management module encrypts villagers' health information, authorizing only village doctors and health departments to access it, and the operations are traceable; an open supervision interface allows villagers to report new shortages of medicines or evaluate services.

[0053] The visual interactive module allows county leaders to view the overall healthcare situation, township-level administrators to track dispatch progress, and village doctors to report inventory and receive treatment instructions via mobile terminals. In the three-tiered computing system, the central layer models county-wide healthcare balancing strategies, the regional layer processes county-level data, and the community layer completes data collection for village doctors, ensuring stable operation.

[0054] Example 3: Public Opinion Insight and Risk Intervention in Employment Assistance for College Graduates The modules involved include: multi-source modal data access module, structure-preserving data processing module, multi-modal retrieval enhancement engine module, public opinion trend analysis module, risk classification and early warning module, closed-loop handling module, data security management and control module, visualization interaction module, and three-level computing system.

[0055] A municipal human resources and social security bureau conducted employment assistance for college graduates, utilizing a multi-source modal data access module to integrate text-based employment intention surveys submitted by universities ("preferring government positions" and "demand for skilled jobs"), image materials from job fairs (showing deserted company booths and graduates gathering to inquire), tabular statistical data from the human resources and social security department (professional matching rate and contract signing rate), voice requests from graduates ("low job matching rate" and "inconvenient transportation to interviews"), and campus geolocation information (distribution of various colleges). Real-time data collection was completed from over 24 departments, including education, human resources and social security, and transportation.

[0056] The structure-preserving data processing module annotates text surveys with semantic blocks such as "STEM majors prioritize technical positions" and "Liberal arts majors seek stability"; extracts elements from images such as "no visitors to the booth" and "crowded information desk" and generates text; and analyzes tabular data according to the dimensions of "major-employment direction-regional enterprise demand," preserving the correlation between survey time and college location.

[0057] The multimodal retrieval enhancement engine module accurately matches relevant text and images related to "college graduates + job matching" in the first link; the second link expands the search to topics such as "skills gap" and "commuting costs", integrating related data sets such as "insufficient supply of some professional positions" and "low attractiveness of remote enterprises".

[0058] The public opinion situation analysis module generates a public opinion heat map (engineering buildings and liberal arts buildings are the areas where demands are concentrated), a public opinion index (employment confidence index 63%), and special reports, uncovering the potential connection between "insufficient publicity of emerging industry positions" and "graduates' cognitive biases", and identifying "the accuracy of school-enterprise docking" as a key area.

[0059] The risk classification and early warning module found that the supply of job positions in the mechanical engineering major of a certain college was only 60% of the demand, and the related voice requests accounted for 38%. It was deduced that this was a risk of "potentially leading to an expansion of the slow employment group", so a yellow warning was set and a routine response was configured (organizing special recruitment events).

[0060] The closed-loop processing module dispatches orders to university employment centers and company HR departments, and pushes matching positions within 24 hours; for complex matters (cross-city company commuting subsidies), joint assistance is initiated (human resources and social security departments and finance departments formulate plans); after processing, graduates scan the code to provide feedback (the job click rate increases by 40%), and companies that did not participate in the meeting are urged to make up for the missing recruitment.

[0061] The data security management module encrypts graduates' employment intentions, authorizing only universities and human resources departments to access them, and leaves a record of all operations; an open supervision interface allows students to report false job postings or evaluation services.

[0062] The visual interactive module allows city leaders to view the overall employment situation, university executives to track job posting progress, and counselors to collect and respond to students' new needs via mobile devices. A three-tiered computing system ensures that the central level models employment trends, the regional level processes local data, and the community level completes campus data collection, all with low-latency interaction.

[0063] Example 4: Public Opinion Regulation of Imbalance Between Supply and Demand in Community-Based Elderly Care Services The modules involved include: multi-source modal data access module, structure-preserving data processing module, multi-modal retrieval enhancement engine module, public opinion trend analysis module, risk classification and early warning module, closed-loop handling module, data security management and control module, visualization interaction module, and three-level computing system.

[0064] A certain subdistrict is promoting the improvement of community-based elderly care by integrating multi-source modal data access modules. This includes text-based registration of elderly needs (e.g., meal assistance, home care), image-based materials from home care service centers (activity room vacancy, queues at meal assistance points), subdistrict table-based data (percentage of the population aged 60 and above, service personnel ratio), voice-based requests from elderly family members (e.g., slow response from caregivers, unpalatable meals), and community geographic location information (building distribution and distance to service centers). Real-time data collection from over 24 departments, including civil affairs, health, and market supervision, has been achieved.

[0065] The structure-preserving data processing module labels text registrations with semantic blocks such as "bathing assistance for disabled elderly" and "accompanying elderly to medical appointments"; extracts elements such as "high vacancy rate in activity rooms" and "long queues at meal service windows" from images and generates text; and analyzes tabular data according to the dimensions of "building - elderly self-care ability - service coverage" to preserve the correlation between registration time and address.

[0066] The multimodal retrieval enhancement engine module accurately matches relevant text and images related to "community elderly care + meal assistance queuing" in the first link; the second link expands the search to topics such as "insufficient nursing staff" and "misaligned service hours", integrating related data sets such as "insufficient service capacity during peak hours" and "low rate of personalized needs being met".

[0067] The public sentiment analysis module generates a public sentiment heat map (Area A and Area B, where the elderly population is dense, are high-incidence areas), a public sentiment index (satisfaction with elderly care services is 58%), and special reports. It uncovers a strong correlation between "high proportion of elderly living alone" and "strong demand for in-home care" and identifies "dynamic allocation of service resources" as a key need.

[0068] The risk classification and early warning module found that 35% of the elderly living alone in Area A had voice requests for care that were delayed, and 42% of these requests were deemed to be "potentially causing home safety risks". An orange warning was issued, triggering an emergency response (increasing the number of mobile caregivers and extending meal assistance hours).

[0069] The closed-loop handling module dispatches orders to community service centers and elderly care institutions, and adjusts the schedule within 24 hours; for complex matters (cross-community resource sharing), joint assistance is initiated (civil affairs + street-level personnel are coordinated); after handling, family members scan the code to provide feedback (response timeliness rate increased to 85%), and the rectification of substandard service points is supervised.

[0070] The data security management module encrypts the elderly's health information, authorizing only the community and civil affairs departments to access it, and the operation is traceable; an open supervision interface allows family members to report service oversights or provide feedback.

[0071] The visual interactive module allows street leaders to view the overall elderly care situation, the execution layer to track the progress of scheduling adjustments, and grid workers to collect and report new needs of the elderly through mobile terminals. A three-tiered computing system ensures stable operation, with the central layer modeling the allocation of elderly care resources, the regional layer processing data for its own street, and the community layer completing door-to-door data collection.

[0072] Example 5: Resolving the Conflict of Limited Educational Resources in Urban-Rural Fringe Areas The modules involved include: multi-source modal data access module, structure-preserving data processing module, multi-modal retrieval enhancement engine module, public opinion trend analysis module, risk classification and early warning module, closed-loop handling module, data security management and control module, visualization interaction module, and three-level computing system.

[0073] To address the enrollment difficulties in urban-rural fringe areas, the education bureau of a certain district implemented a multi-source modal data access module. This module integrated textual records of school enrollment applications ("high proportion of children of migrant workers," "demand for cross-district enrollment"), images of school gates (parents queuing for registration, crowded classrooms), data from the education bureau's tables (school-age population in the area, teacher-student ratio), parents' voice requests ("cumbersome registration materials," "insufficient quality teachers"), and school geographic location information (school district boundaries and residential area distribution). Real-time data collection was completed from over 24 departments, including education, public security, and housing and construction.

[0074] The structure-preserving data processing module annotates text applications with semantic blocks such as "residence permit for more than one year" and "property certificate and household registration are inconsistent"; extracts elements such as "excessively long registration queue" and "insufficient per capita classroom area" from images and generates text; and analyzes table data according to the dimensions of "district-student structure-degree supply" to preserve the relationship between application time and address.

[0075] The multimodal retrieval enhancement engine module accurately matches text and images related to "urban-rural fringe areas + shortage of school places" in the first link; the second link expands the search to topics such as "admission threshold for children of migrant workers" and "teacher mobility", integrating related data sets such as "low efficiency of document review" and "loss of high-quality teachers".

[0076] The public sentiment analysis module generates a public sentiment heat map (areas C and D are high-incidence red areas), a public sentiment index (sense of fairness in school enrollment is 55%), and special reports, uncovering the potential connection between "concentration of migrant worker families" and "high demand for cross-district schooling", and identifying "precise implementation of school enrollment policies" as a key area.

[0077] The risk classification and early warning module found that the number of applications from children of migrant workers in Area C exceeded the supply of school places by 20%, and the proportion of voice requests due to overdue document review was 40%. A red alert was issued, and the district committee was notified directly and an emergency response was initiated (adding temporary review points and coordinating with surrounding schools to divert students).

[0078] The closed-loop processing module dispatches orders to the Basic Education Section of the Education Bureau and schools, opening a green channel within 24 hours; for complex matters (teacher allocation), joint assistance is initiated (the Education Bureau and the Organization Department formulate incentive policies); after processing, parents provide feedback by scanning a code (the review time is shortened by 50%), and the assigned schools are supervised to follow up accordingly.

[0079] The data security management module encrypts students' household registration information, authorizes access only to education and public security departments, and leaves a trace of each operation; it also provides an open supervision interface for parents to report violations or provide feedback.

[0080] The visual interactive module allows district leaders to view the overall enrollment situation, the execution layer to track the student allocation progress, and schools to collect and provide feedback on new student information via mobile terminals. A three-tiered computing system ensures that the central layer models educational equity strategies, the regional layer processes local data, and the community layer completes data collection at registration points, all with low-latency interaction.

[0081] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A public opinion data intelligent analysis and risk early warning system based on a multimodal RAG engine, characterized by: It includes a multi-source modal data access module, a structure-preserving data processing module, a multi-modal retrieval enhancement engine module, a public opinion situation analysis module, a risk classification and early warning module, a closed-loop disposal module, a data security management and control module, and a visualization and interaction module. Each module achieves data interconnection and collaborative operation through a distributed network, forming a full-process governance chain from data collection to risk mitigation.

2. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The multi-source modal data access module adopts a multi-protocol adaptation architecture, which can simultaneously access text-based public opinion records, image-based scene materials, table-based statistical data, voice-based request recordings, and geospatial location information. It supports standardized adaptation of cross-departmental data interfaces and enables real-time collection and incremental updates of heterogeneous data from more than 24 government departments.

3. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The structure-preserving data processing module employs a modality-specific processing mechanism to perform structured transformations on different types of data: text data undergoes semantic segmentation and relation labeling, image data undergoes scene element extraction and associated text generation, and table data undergoes dimensional analysis and logical reconstruction. All data is stored in a standardized format after transformation, preserving the contextual relationships and spatial location relationships of the original data.

4. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The multimodal retrieval enhancement engine module constructs a dual retrieval link architecture. The first link performs precise matching retrieval based on content features, and the second link performs extended retrieval based on the association of public opinion topics. Through modal fusion algorithms, different types of retrieval results are weighted and integrated to generate a context-aware public opinion data set, providing data support for subsequent analysis.

5. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The public sentiment analysis module has a built-in multi-dimensional analysis model that can automatically generate public sentiment heat maps, public sentiment indices, and special analysis reports. It uses data association algorithms to mine the potential correlations between different modalities of public sentiment data, and accurately locate the public needs and focal points of conflicts in key areas such as education, medical care, employment, and elderly care.

6. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The risk classification and early warning module has more than 65 preset risk scenario models. Through data feature comparison and trend inference, it classifies public sentiment risks into four levels of early warning: red, orange, yellow and blue. Differentiated response mechanisms are configured for different levels of early warning to achieve early perception, second-level early warning and accurate location of risks.

7. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The closed-loop handling module constructs a full-process link of "collection-analysis-early warning-dispatch-handling-feedback-supervision", supports the flat response of the city, town and village three-level governance units, realizes the handling and response of general matters within 24 hours, initiates a joint cooperation mechanism for complex matters, and directly reports major hidden dangers to the core decision-making level and initiates emergency response.

8. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The data security management module adopts a hierarchical authorization and lineage tracing mechanism to perform encrypted storage and access control on sensitive public opinion data, so as to achieve traceability of operations and location of responsibilities throughout the data life cycle. At the same time, it sets up a public supervision interface to support reporting by scanning code, service evaluation and feedback, forming a transparent governance system.

9. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The visualization and interaction module adopts a layered display architecture, providing decision-makers with an overview of the overall public sentiment situation, the execution level with a precise task assignment and progress tracking interface, and grassroots grid workers with a mobile terminal data collection and feedback interface, supporting multi-terminal adaptation and personalized interface configuration.

10. The public opinion brain data intelligent analysis and risk early warning system based on a multimodal RAG engine according to claim 1, characterized in that: The system adopts a three-tier computing architecture of "central-regional-community". The central layer is responsible for global data modeling and decision support, the regional layer realizes local data storage and processing, and the community layer completes terminal data collection and preprocessing. Low-latency data interaction is achieved through high-speed network interconnection to ensure the stable operation of the system in large-scale data scenarios.

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