Multi-modal retrieval enhanced generation government affair intelligent system

Abstract

The invention relates to the technical field of government affair service intellectualization, in particular to a multi-modal retrieval enhanced generation government affair intelligent system which comprises multi-level government affair knowledge base construction, retrieval enhanced agent collaborative fine adjustment, a credible generation mechanism of government affair agents and multi-modal fused personalized interaction application. Multi-source heterogeneous data semantic unification is realized through a dynamic cross-modal alignment framework and a knowledge graph; a dual-path parameter efficient fine tuning architecture is adopted to improve the retrieval and generation capability; the tool chain is verified by integrating logic, law and ethics to ensure the compliance of the generated content; and guiding and optimizing complex scene demand understanding by using distributed user portraits and progressive dialogues. According to the application, the intelligent level and the user experience of government affair services can be remarkably improved, and the government affair services are promoted to transition from passive response to active service.

Description

Technical Field

[0001] The present invention relates to the field of intelligent government service technology, and specifically to a multimodal retrieval enhanced generation government service intelligent system. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, intelligent agents—entities capable of autonomously sensing environmental changes and responding to achieve specific goals—are increasingly demonstrating broad application potential in government services. However, current efforts to apply intelligent agents to government services still face numerous technical bottlenecks. Existing systems typically rely on structured data input and fixed process execution, lacking the ability to dynamically parse complex user intent and plan cross-scenario tasks. This leads to three core issues: First, in multi-terminal interaction scenarios, first-time users can easily become lost in a maze of processes due to a lack of operational guidance. This is particularly true for cross-departmental joint operations, where users must independently navigate multiple heterogeneous systems, consuming significant time. Second, barriers exist in understanding policy semantics, leading to errors and omissions in the preparation of documents due to insufficient understanding or misunderstanding of policies. Finally, intelligent agents are insufficiently optimized for special groups, such as those with dialect recognition and the visually and hearing impaired, hindering the delivery of diversified, personalized, and high-quality services.

[0003] In response to the above problems, there is an urgent need to build a set of government intelligence solutions that can overcome the difficulties of multimodal intent understanding and generated content credibility. Specifically, existing technologies have significant deficiencies in multimodal data semantic alignment and intent modeling, making it difficult to cope with the diverse expressions of complex user intentions in government service scenarios. At the same time, government scenarios have extremely high requirements for content accuracy. Traditional methods rely solely on rule engines for compliance verification, lack an automated multi-dimensional verification mechanism, and cannot effectively guarantee the logical consistency, legal text matching, and ethical compliance of the generated text. In addition, the existing system is weak in interactive adaptability, making it difficult to handle non-standard inputs such as dialects and ambiguous expressions, and unable to meet the barrier-free interaction needs of special groups. Therefore, the development of a government intelligence technology system that integrates multimodal retrieval enhancement generation, dynamic knowledge graph verification, and a barrier-free interaction framework has become the key to solving the pain points of the intelligent transformation of government services.

[0004] This project aims to address these issues through technological innovation. It proposes a comprehensive solution encompassing the construction of a multi-level government knowledge base, lightweight search enhancement generation, multi-dimensional verification toolchain design, and multimodal interactive applications. This approach will enable a paradigm shift in government services from "passive response" to "active service." This technological system will not only improve the efficiency and fairness of government services but also provide an innovative paradigm for modernizing social governance. Summary of the Invention

[0005] This paper addresses the problems of existing government intelligence agents' insufficient understanding of intent and low credibility of generated content in complex scenarios, and proposes a multimodal search-enhanced government intelligence system. To this end, the present invention adopts the following technical solutions:

[0006] The present invention provides a multimodal retrieval-enhanced government affairs intelligence system, including the construction of a multi-level government affairs knowledge base, retrieval-enhanced intelligent agent collaborative fine-tuning, a trusted generation mechanism for government affairs intelligent agents, and a multimodal fusion government affairs intelligent agent personalized interactive application.

[0007] The multi-level government knowledge base construction achieves semantic unification and dynamic knowledge fusion of multi-source heterogeneous government data through a dynamic cross-modal alignment framework and knowledge graph construction technology. Furthermore, the retrieval-enhanced intelligent agent collaborative fine-tuning improves the ability to align government data representations and accurately align policy keywords in generated content through multimodal retrieval enhancement and a lightweight collaborative fine-tuning architecture.

[0008] In particular, the trusted generation mechanism of the government intelligence entity ensures the comprehensive protection of the generated content in terms of logical consistency, legal text matching and ethical compliance through a multi-dimensional verification tool chain and a "generation-feedback-correction" dynamic optimization mechanism.

[0009] Furthermore, the multimodal fusion government affairs intelligent agent personalized interactive application achieves robust demand understanding and transparent interaction in complex scenarios through a distributed user portrait model, a progressive dialogue guidance mechanism and an adaptive interactive presentation technology.

[0010] The beneficial effects of the present invention are: through the above technical solution, the intelligence level and user experience of the government service system are significantly improved, the core problems of existing government intelligent bodies in complex scenarios are solved, and the paradigm shift of government services from "passive response" to "active service" is promoted.

[0011] This invention first addresses the semantic fragmentation problem of multi-source heterogeneous government data, and achieves efficient integration and semantic unification of multimodal information through a dynamic cross-modal alignment framework. Specifically, S1 uses a dynamic graph attention network to construct an intra-modal feature association model, capturing the internal semantic associations of single-modal data through hybrid similarity calculation; S2 uses a cross-modal Transformer to achieve inter-modal alignment, and establishes an inter-modal semantic mapping through a multi-head cross-attention mechanism to alleviate the cross-modal matching problem; S3 designs a task-adaptive dynamic optimization strategy, dynamically adjusting the alignment weights based on real-time feedback from downstream tasks.

[0012] Furthermore, the present invention constructs a multi-dimensional knowledge graph with three levels of dynamic evolution: national, local, and departmental. This graph covers multi-dimensional knowledge, including policies and regulations, approval rules, historical cases, and more. Specifically, S1 adopts a hybrid construction strategy of "top-down specification import + bottom-up case extraction" to integrate the national policy framework with local practices. S2 integrates the rule engine and graph neural network reasoning module to achieve dynamic knowledge verification and conflict resolution. S3 constructs a user behavior-driven knowledge evolution mechanism to transform high-frequency consultation questions into incremental update signals for the knowledge graph.

[0013] To improve the intent understanding capability of government agents in complex scenarios, this paper proposes a retrieval-enhanced agent collaborative fine-tuning technology. Specifically, S1 implements government data representation alignment based on a cross-modal pre-training model, unifying the semantic spatial representation of modalities such as text and images through a contrastive learning loss function. S2 constructs a multi-head attention re-ranking module, dynamically assigns modal weights based on business type labels, and uses a rolling window mechanism to implement incremental index updates. S3 proposes a dual-path parameter efficient fine-tuning architecture. In the retrieval enhancement path, the BERT retriever is fine-tuned through the LoRA adapter, only a small number of parameters are updated, reducing computational overhead. In the trusted generation path, a reinforcement learning reward function is constructed based on the knowledge base verification results to balance generation accuracy and policy relevance.

[0014] In particular, the present invention dynamically adjusts retrieval weights through distributed collaborative training and a closed-loop feedback optimization mechanism to ensure precise alignment of policy keywords with generated results. Specifically, S1 enables cross-departmental joint training through a federated knowledge subgraph, employing quantization techniques to reduce communication overhead. S2 decouples roles, freezing the semantic encoding layer of the retrieval agent and the reasoning logic layer of the generation agent, avoiding conflicting objectives through asynchronous parameter updates. S3 constructs a "retrieval-generation-verification" enhancement loop, triggering dynamic adjustment of the retrieval threshold when the generated content repeatedly disagrees with the knowledge base verification results.

[0015] To ensure the high credibility of generated content, the present invention proposes a multi-dimensional verification tool chain and a "generate-feedback-correction" dynamic optimization mechanism. Specifically, S1, through logical consistency verification technology, uses a formal verification engine and policy semantic network to detect logical contradictions in generated content, such as converting policy clauses into first-order logic expressions and constructing a loop detection algorithm to identify circular dependencies; S2, through legal text matching verification technology, constructs a structured knowledge base and a multi-level semantic matching engine, which operates in three levels: literal matching, semantic matching, and analogy matching, to improve the matching degree of key clauses and the accuracy of understanding; S3, through ethical alignment verification technology, optimizes ethical scoring based on an ethical indicator system and a reinforcement learning framework, combines regional knowledge bases to achieve sensitive word replacement and cultural adaptation, and reduces the cultural conflict rate and ethical violation rate in generated content.

[0016] In particular, the present invention constructs a closed-loop feedback system architecture, integrating multi-source feedback channels from public opinion, expert review, and system self-checking. Specifically, S1 develops small sample rapid adaptation technology, shortening the policy iteration cycle while ensuring policy continuity through prompt vocabulary fine-tuning and a flexible knowledge retention mechanism. S2, combined with knowledge anchoring technology, generates a policy knowledge fingerprint library based on the SimHash algorithm, detects parameter deviations during model updates, and triggers manual review or automatic release.

[0017] To achieve personalized interactive applications for multimodal government intelligence, this paper proposes a distributed user profiling model and a progressive dialogue guidance mechanism. Specifically, S1 uses a federated learning framework to establish a distributed user profiling model, balancing the accuracy of understanding user needs with privacy and security. S2, in terms of multimodal data fusion, extracts input features such as text, speech, images, and forms, and designs a cross-modal attention mechanism to align heterogeneous features and eliminate semantic gaps. S3, uses a federated clustering algorithm to mine cross-terminal user group characteristics, models the temporal characteristics of user interaction sequences based on an LSTM network, and outputs a user dynamic preference feature vector.

[0018] Furthermore, the present invention constructs a progressive, dialogue-guided demand mining mechanism to analyze explicit user needs and mine implicit ones. Specifically, S1 builds a multi-round dialogue reinforcement learning model, integrating dialogue history, user profiles, and scene context to define the state space and action set. S2 designs a composite reward function, employing a proximal policy optimization algorithm to maximize the expected cumulative reward. S3 develops a robustness enhancement module, generates enhanced training samples using Mixup technology, and fine-tunes the dialect adapter module to improve its understanding of dialect speech and fuzzy semantics.

[0019] In particular, the present invention develops an intelligent context-awareness module and constructs a dynamic adjustment mechanism for interactive feedback. Specifically, S1 collects multi-dimensional behavioral data to quantify users' cognitive level, motor skills, and personality traits; S2 dynamically optimizes the interface layout based on the user's real-time cognitive state, triggering a layout re-optimization mechanism; and S3 constructs an interpretable graph that breaks down government regulations into condition-action rules, records the triggered rule chains, and dynamically renders a visual decision path to meet the transparent supervision requirements of government services.

[0020] Through the above technical solutions, the present invention has achieved a number of technological breakthroughs. Specifically, the cross-modal retrieval accuracy rate reaches ≥95%, the generated content compliance rate reaches ≥98%, the knowledge graph update response time is ≤30 minutes, the user demand understanding accuracy rate reaches ≥90%, and the interaction satisfaction rate reaches ≥92%. These technical indicators have significantly improved the efficiency of government services and public trust, shortened the processing time of government affairs by more than 50%, reduced manual review costs by 70%, and promoted the development of domestic AI chips and lightweight large-scale model deployment tool chain industries, increasing the localization rate from 20% to 50%.

[0021] The core innovations of this invention include: proposing a dynamic cross-modal alignment framework to solve the problems of dynamic fusion and conflict resolution of multi-source heterogeneous government data; developing a dual-path parameter efficient fine-tuning architecture to realize retrieval enhancement generation driven by government knowledge base; integrating a three-layer verification tool chain of logic, law, and ethics to build a full-process trusted generation system; developing a progressive dialogue guidance mechanism and a distributed user portrait model to improve the robustness of demand understanding in complex scenarios.

[0022] The dynamic cross-modal alignment method and its application in government scenarios cover the specific implementation methods of multimodal data representation alignment and dynamic weight adjustment.

[0023] Furthermore, the dual-path parameter efficient fine-tuning architecture and its application in collaborative fine-tuning of intelligent agents cover the design details of the LoRA adapter and reinforcement learning reward function.

[0024] In particular, the multi-dimensional verification tool chain and its application in government content generation cover the specific implementation steps of logical consistency verification, legal provision matching verification and ethical alignment verification.

[0025] Furthermore, the progressive dialogue guidance mechanism and its application in personalized demand mining, the scope of protection covers the specific implementation methods of the multi-round dialogue reinforcement learning model and the robustness enhancement module.

[0026] In particular, the distributed user portrait model and its application in privacy and security modeling cover the specific implementation details of the federated learning framework and cross-modal attention mechanism.

[0027] Through the above technical solutions and innovations, the present invention significantly improves the intelligence level and user experience of the government service system, and has important social value and technological innovation significance.

[0028] In order to make the above and other objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 Schematic diagram of the overall research framework of the present invention;

[0031] Figure 2 This is a diagram related to the research contents of the present invention;

[0032] Figure 3 A schematic diagram of constructing a hierarchical knowledge base of the present invention;

[0033] Figure 4 Schematic diagram of the retrieval-enhanced intelligent agent collaborative fine-tuning of the present invention. DETAILED DESCRIPTION

[0034] The present invention provides a multimodal retrieval-enhanced government affairs intelligent system. Through the construction of a multi-level government affairs knowledge base, the collaborative fine-tuning of retrieval-enhanced intelligent agents, the trustworthy generation mechanism of government affairs intelligent agents, and the personalized interactive application of multimodal fusion government affairs intelligent agents, it achieves a comprehensive improvement in the credibility of intention understanding and generated content in complex scenarios. Figures 1 to 4 The specific structures and numbers in the figure are used to describe the specific embodiments of the present invention in detail.

[0035] During the implementation process, the semantic unification and dynamic knowledge fusion of multi-source heterogeneous government data are first achieved through the dynamic cross-modal alignment framework. Figure 3 As shown in the figure, a dynamic graph attention network is used to construct an intra-modal feature association model to extract the internal semantic associations of single-modal data such as text and images. For example, for different versions of policy documents, hybrid similarity calculation is used to capture their core differences, laying the foundation for subsequent cross-modal alignment. Furthermore, a cross-modal Transformer is used to establish an inter-modal semantic mapping, and a multi-head cross-attention mechanism is used to alleviate the matching problem between user license images and policy texts. On this basis, a task-adaptive dynamic optimization strategy is designed to adjust the alignment weight according to real-time feedback from downstream tasks. For example, when the system detects a decrease in the recognition accuracy of the "material supplement" type of intent, it automatically increases the alignment weight of the image and table data, thereby improving the robustness and accuracy of the system.

[0036] Knowledge base driven semantic alignment: For users' multimodal input, a cross-modal pre-trained model (such as BLIP) is used to achieve cross-modal semantic space mapping. Heterogeneous data representation alignment is achieved through contrastive learning loss function, realizing the representation of government data and streaming business data: t =Enc t (s pol ),v i =Eoc ima (G flow )

[0037] Where vt is the policy text embedding vector and vi is the flowchart image embedding vector.

[0038] Dynamic weight allocation mechanism: Construct a multi-head attention re-ranking module, taking image and text as examples:

[0039] α t =σ(w q ·[v t ;M kb ]),α i =1-αt

[0040] Where Mkb is the corresponding business type metadata vector in the knowledge base.

[0041] Incremental index update: Update multimodal indexes through a rolling window mechanism:

[0042] ind t+1 =ind t Field (Δv.Deplot(G new ))

[0043] The DePlot model implements the linear table conversion of the newly added policy chart.

[0044] Knowledge-guided PEFT architecture. Using dual-path parameter efficient fine-tuning, the process is shown in the figure

[0045] 4 shown:

[0046] Retrieval enhancement path: Insert the LoRA adapter into the BERT retriever, and only update a small number of parameters (usually less than 1%):

[0047] Where Ar and Br are adaptation matrices.

[0048] Credible Generation Path: Building a Reinforcement Learning Award Based on Research 1 Knowledge Verification Results VKB

[0049] Reward function:

[0050] R=Acc(vJen,vkb).log(1+Rel(Q,D))

[0051] Where Acc represents the accuracy, and Rel represents the correlation coefficient.

[0052] Distributed collaborative training. Cross-departmental joint training is achieved through the "federated knowledge subgraph" constructed in research. Gradient compression: Quantization technology is used to reduce communication overhead and GPU memory usage. Role decoupling: Retrieve the frozen semantic encoding layer of the agent and generate the frozen reasoning logic layer of the agent, avoiding goal conflicts through asynchronous parameter updates.

[0053] Closed-loop feedback optimization. Building a "retrieval-generation-verification" enhancement loop:

[0054] βt+1=βt+η.ReLU(1-Acc(vJen,vkb))

[0055] When the generated content is inconsistent with the knowledge base verification results for multiple consecutive times, the dynamic adjustment of the retrieval threshold β is triggered, forming a balanced optimization of the policy keyword recall rate and generation accuracy.

[0056] In order to further enhance the integration capability of government data, the present invention constructs a multi-dimensional knowledge graph with three levels of dynamic evolution: national, local, and departmental. Figure 3 As shown in the figure, a hybrid construction strategy of "top-down specification import + bottom-up case extraction" is adopted to integrate the national policy framework with local practices. For example, the "acceptance with missing information" rule in the "Government Service Regulations" is mapped to a conditional constraint edge in the knowledge graph and dynamically updated based on actual cases. At the same time, the rule engine and graph neural network reasoning module are integrated to achieve dynamic knowledge verification and conflict resolution. For example, when a city updates its "construction permit" policy, the system automatically triggers version comparison and conflict resolution in the knowledge graph to ensure that policy changes can be quickly synchronized with the system. In addition, a user behavior-driven knowledge evolution mechanism is constructed to convert high-frequency consultation questions into incremental update signals for the knowledge graph. For example, for the "medical insurance reimbursement in different places" process frequently consulted by elderly users, knowledge nodes are automatically generated and linked to local implementation details, thereby improving the real-time and practicality of the knowledge graph.

[0057] In terms of intelligent collaborative fine-tuning of retrieval enhancement, this invention significantly improves the government data representation alignment capability and the policy keyword precise alignment capability of generated content through multimodal retrieval enhancement and lightweight collaborative fine-tuning architecture. Figure 4As shown in the figure, the representation alignment of government data is achieved based on a cross-modal pre-training model, and the semantic space expression of modalities such as text and images is unified through a contrastive learning loss function. For example, the policy text embedding vector is semantically aligned with the flowchart image embedding vector, inheriting the "business type matching" results of the first study, and the entity classification labels of the knowledge base are used as prior constraints for cross-modal alignment. Furthermore, a multi-head attention re-ranking module is constructed to dynamically assign modal weights according to business type labels, and a rolling window mechanism is used to achieve incremental index updates. For example, when a new policy chart is added, a linear table conversion is implemented through the DePlot model to ensure the timeliness and accuracy of the index update.

[0058] In terms of multimodal data fusion, multimodal data such as text, speech, images, and forms are preprocessed to extract input features. For text input, the BERT pre-trained model is used to extract semantic features and encode government forms and consultation texts: htext = BERT(xtext)∈Rd. For speech input, acoustic features are extracted based on the Whisper model, and dialect recognition is optimized through phoneme alignment: Hspeech = whisper(xspeech)∈Rd. For image input, ResNet-50 is used to extract visual features, and optical character recognition (OCR) technology is used to parse the structured information in the document image: Himage = ResNet(ximage)∈Rd. For form input, the structured data entered by the user (such as age and occupation) is parsed, mapped into a sparse vector, and embedded: Hform = Embedding(xform)∈Rd. Then, a cross-modal attention mechanism is designed to align heterogeneous features, mapping features from different modalities (such as vision, text, and audio) into a unified semantic space, resulting in a multimodal alignment feature halign, thereby eliminating the semantic gap and achieving effective fusion and understanding of cross-modal information.

[0059] In terms of privacy and security modeling, a technical framework that balances privacy and security with policy semantic integrity is constructed through the deep integration of federated learning and knowledge distillation. Local datasets Dk are maintained on local clients, i.e., on government terminals (such as self-service kiosks). A lightweight user feature encoder fθ is trained, and the FedAvg algorithm is used to aggregate global model parameters θG. A privacy protection mechanism is established through gradient encryption transmission and differential privacy perturbation. Encrypted gradients are uploaded to the client, and Gaussian noise is injected during global model aggregation. This allows for localized modeling and secure aggregation of behavioral features such as user interaction habits and preferences, avoiding cross-domain transmission of raw data. Furthermore, cross-modal comparative learning is used to optimize feature alignment and design a loss function.

[0060] In terms of user portrait modeling and demand understanding, a federated clustering algorithm (FedProx+DBSCAN) is used to mine user group characteristics across terminals, constraining the differences between local models and global models to generate group portraits; based on the LSTM network modeling of the temporal characteristics of user interaction sequences, the feature vector hpreference of user dynamic preferences is output to achieve personalized behavior modeling; finally, the multimodal alignment feature halign is spliced with the user preference feature hpreference, and user needs are predicted through the fully connected layer.

[0061] (2) Demand mining mechanism guided by progressive dialogue

[0062] By using reinforcement learning and adversarial training techniques, we build a multi-round dialogue guidance mechanism to achieve collaborative mining of explicit and implicit needs, and design a robustness enhancement module to improve the robustness of the intelligent agent in complex language scenarios. The specific implementation method is as follows:

[0063] In the multi-round dialogue reinforcement learning modeling, the dialogue history, user profile and scene context are integrated to define the state space. The state vector st is:

[0064] st=concat(Transformer(H1:t),huser,hcontext)

[0065] Among them, H1:t represents the conversation history (text sequence) of the previous t rounds, which is encoded into a fixed-length vector through Transformer; huser is the user portrait vector generated by federated learning; hcontext is the scene context encoding (such as service type and urgency).

[0066] Define the dialogue policy action set A = {Ask, Confirm, Recommend, Clarify}, and output the action probability distribution by the policy network πθ(a|st). Design a composite reward function R_t = R_explicit + λR_implicit + R_satisfaction, and use the proximal policy optimization (PPO) algorithm to maximize the expected cumulative reward:

[0067]

[0068] in, is the strategy probability ratio, is the estimated value of the advantage function.

[0069] In the robustness enhancement module, in order to achieve enhanced recognition of dialect speech, the Mixup technology is used to mix the dialect and the standard speech to generate enhanced training samples:

[0070] xmix=λxdialect+(1-λ)xstd,λBeta(α,α).

[0071] Based on the Wav2Vec2.0 framework, a dialect adapter module was constructed to fine-tune the model. The adapter is a lightweight, fully connected network that only fine-tunes dialect-related parameters. To build a fuzzy semantic error correction model, semantic similarity was optimized based on the SimCSE framework. A semantic error correction graph network was introduced, and fuzzy expressions were mapped to government knowledge graph nodes through entity linking, thereby achieving contextual disambiguation of fuzzy expressions. An adversarial training module was designed, using the TextFooler attack method to generate adversarial examples. These adversarial examples were added to the training data to optimize model parameters.

[0072] (3) Adaptive interactive presentation and enhanced interpretability

[0073] Model user capabilities to provide adaptive interactive presentation and assistance. Collect multi-dimensional behavioral data, such as click stream data (click location, response time), gesture trajectory (touch screen sliding speed / accuracy), voice commands (speech rate, pause frequency), synchronize multi-source signals with timestamps and remove noise data. Quantify user cognitive level, motor ability and personality traits from interactive behaviors, such as evaluating user memory capacity through the accuracy and response delay of N-back task embedding (such as verification code memory link), and analyzing user movement coordination by extracting the Jerk value (acceleration change rate) of the touch trajectory. At the same time, dynamically optimize the interface layout according to the user's real-time cognitive state to minimize cognitive load. Establish a real-time adjustment mechanism to trigger layout re-optimization when it is detected that the user error rate increases or the response delay exceeds the threshold. In addition,

[0074] Provide interactive assistance for special groups such as the elderly and the disabled. Based on the Bayesian principle, predict user intentions through probabilistic reasoning, realize implicit interface assisted interaction, and improve user interaction efficiency.

[0075] Constructing an explainable graph enhances user trust in the agent's decision-making. First, atomize policy knowledge and break down government regulations into condition-action (If-Then) rules:

[0076]

[0077] Then, a rule graph is constructed, where nodes include conditions, actions, policy clauses, etc., and edges define logical relationships (triggering, dependency, exclusion). During the interaction with the government agent, the triggered rule chain is recorded and a traceability graph Gtrace = (V, E) is constructed:

[0078]

[0079] Finally, the decision path of the agent is dynamically rendered and visualized to meet the transparent supervision requirements of government services, including:

[0080] Node tracing: Click on the action node to display the original text of the associated policy;

[0081] Path highlighting: highlight key decision branches in red (e.g., abnormal conditions that trigger manual review);

[0082] Compliance score: Calculated based on the coverage of triggered rules.

[0083] In the lightweight collaborative fine-tuning architecture, a dual-path parameter efficient fine-tuning solution is proposed. Figure 4 As shown in the figure, in the retrieval enhancement path, the BERT retriever is fine-tuned through the LoRA adapter, only a small number of parameters are updated, and the computational overhead is reduced; in the trusted generation path, a reinforcement learning reward function is constructed based on the knowledge base verification results to balance the generation accuracy and policy relevance. For example, through the design of the reward function, it is ensured that the generated content not only complies with policies and regulations, but also meets the actual needs of users. Furthermore, the retrieval weight is dynamically adjusted through distributed collaborative training and closed-loop feedback optimization mechanism. Figure 4 As shown, cross-departmental joint training is achieved through federated knowledge subgraphs, and quantization techniques are used to reduce communication overhead. Roles are decoupled, with the retrieval agent freezing the semantic encoding layer and the generation agent freezing the reasoning logic layer. Asynchronous parameter updates are used to avoid conflicting objectives. Furthermore, a "retrieval-generation-verification" enhancement loop is constructed. When the generated content repeatedly disagrees with the knowledge base verification results, the retrieval threshold is dynamically adjusted to ensure accurate alignment of policy keywords with the generated results.

[0084] In terms of the trustworthy generation mechanism of government intelligence, this invention uses a multi-dimensional verification tool chain and a "generation-feedback-correction" dynamic optimization mechanism to ensure that the generated content is fully guaranteed in terms of logical consistency, legal provisions matching, and ethical compliance. Figure 4 As shown, the logical consistency verification module uses a formal verification engine and a policy semantic network to detect logical contradictions in generated content. For example, policy clauses are converted into first-order logic expressions, and a loop detection algorithm is built to identify circular dependencies, reducing the rate of logical errors. Furthermore, the legal text matching verification module builds a structured knowledge base and a multi-level semantic matching engine, operating at three levels: literal matching, semantic matching, and analogical matching. For example, the BM25 algorithm is used to quickly retrieve precise clauses, and the LawBERT model is used to calculate the semantic similarity between generated content and legal texts. This supports case-based reasoning for similar situation determination, improving the accuracy of key clause matching and understanding. Furthermore, the ethical alignment verification module optimizes ethical scoring based on an ethical indicator system and a reinforcement learning framework, integrating regional knowledge bases to achieve sensitive word replacement and cultural adaptation. For example, the phrase "elderly entry prohibited" was revised to "establishment of a health code-free channel," reducing the rate of cultural conflict and ethical violations in the generated content.

[0085] In terms of personalized interactive applications of multimodal fusion government affairs agents, this invention proposes a distributed user portrait model and a progressive dialogue guidance mechanism, which significantly improves the robustness of demand understanding and the transparency of interaction in complex scenarios. Figure 4 As shown, a federated learning framework is used to establish a distributed user profile model, balancing the accuracy of understanding user needs with privacy and security. For example, in terms of multimodal data fusion, input features such as text, speech, images, and forms are extracted, and a cross-modal attention mechanism is designed to align heterogeneous features and bridge the semantic gap. Furthermore, a federated clustering algorithm is used to mine cross-terminal user group characteristics. The temporal characteristics of user interaction sequences are modeled using an LSTM network, and a feature vector of user dynamic preferences is output. In the progressive dialogue guidance mechanism, a multi-round dialogue reinforcement learning model is constructed, integrating dialogue history, user profiles, and scene context to define the state space and action set. For example, a proximal policy optimization algorithm is used to maximize the expected cumulative reward, analyze users' explicit needs, and mine implicit needs. Furthermore, a robustness enhancement module is developed, using Mixup technology to generate enhanced training samples, and the dialect adapter module is fine-tuned to improve the understanding of dialect speech and ambiguous semantics.

[0086] In terms of adaptive interactive presentation and enhanced explainability, the present invention develops an intelligent context-awareness module to construct a dynamic adjustment mechanism for interactive feedback. For example, multi-dimensional behavioral data is collected to quantify the user's cognitive level, motor skills, and personality traits, and the interface layout is dynamically optimized according to the user's real-time cognitive state, triggering a layout re-optimization mechanism. Furthermore, an interpretable graph is constructed to decompose government regulations into condition-action rules, record the triggered rule chain, and dynamically render a visual decision path. For example, by using the node tracing function to click on the action node to display the original text of the associated policy, by using the path highlighting function to mark the key decision branches in red, and by using the compliance scoring function to calculate the coverage of the triggered rules, the transparent supervision requirements of government services can be met.

[0087] Through the above technical solutions, the present invention has achieved a number of technological breakthroughs. For example, the cross-modal retrieval accuracy rate reaches ≥95%, the generated content compliance rate reaches ≥98%, the knowledge graph update response time is ≤30 minutes, the user demand understanding accuracy rate reaches ≥90%, and the interaction satisfaction rate reaches ≥92%. These technical indicators have significantly improved the efficiency of government services and public trust, shortened the processing time of government affairs by more than 50%, reduced manual review costs by 70%, and promoted the development of domestic AI chips and lightweight large-scale model deployment tool chain industries, increasing the localization rate from 20% to 50%.

[0088] The above are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. Multimodal retrieval and enhanced generation of government affairs intelligent system, characterized by: The system includes a multi-level government knowledge base construction module, a search-enhanced intelligent agent collaborative fine-tuning module, a trusted generation mechanism for government agents, and a multimodal fusion government agent personalized interactive application module. The multi-level government knowledge base construction module achieves semantic unification and dynamic knowledge fusion of multi-source heterogeneous government data through a dynamic cross-modal alignment framework and knowledge graph construction technology. The retrieval-enhanced intelligent agent collaborative fine-tuning module improves the government data representation alignment capability and the policy keyword precise alignment capability of the generated content through multimodal retrieval enhancement and lightweight collaborative fine-tuning architecture; the trusted generation mechanism of the government agent ensures the comprehensive guarantee of the generated content in terms of logical consistency, legal text matching and ethical compliance through a multi-dimensional verification tool chain and dynamic optimization mechanism; the multimodal fusion government agent personalized interactive application module achieves robust demand understanding and interactive transparency in complex scenarios through a distributed user portrait model, a progressive dialogue guidance module and an adaptive interactive presentation technology.

2. The multimodal search and enhanced generation of government affairs intelligent system according to claim 1 is characterized by: The multi-level government knowledge base construction module adopts a dynamic graph attention network to construct an intra-modal feature association model, and uses a cross-modal Transformer to realize semantic mapping between modalities.

3. The multimodal search and enhanced generation of government affairs intelligent system according to claim 2 is characterized by: A task-adaptive dynamic optimization strategy is designed to adjust the alignment weights of the dynamic graph attention network and the cross-modal Transformer based on real-time feedback from downstream tasks.

4. The multimodal search and enhanced generation of government affairs intelligent system according to claim 1 is characterized by: The retrieval-enhanced agent collaborative fine-tuning module includes a LoRA adapter and a reinforcement learning reward function. The BERT retriever is fine-tuned through the LoRA adapter to update a small number of parameters, and a reinforcement learning reward function is constructed based on the knowledge base verification results.

5. The multimodal search and enhanced generation of government affairs intelligent system according to claim 4 is characterized by: Cross-department joint training is achieved through federated knowledge subgraphs, and quantization technology is used to reduce communication overhead.

6. The multimodal search and enhanced generation of government affairs intelligent system according to claim 1 is characterized by: The trustworthy generation mechanism of the government affairs intelligent body includes a logical consistency verification module, a legal clause matching verification module and an ethical alignment verification module, which are respectively used to detect logical contradictions, key clause matching and ethical compliance of the generated content.

7. The multimodal search and enhanced generation of government affairs intelligent system according to claim 6 is characterized by: Through the layered operation of the structured knowledge base and the multi-level semantic matching engine, the literal matching, semantic matching and analogy matching capabilities of the legal text matching verification module are improved.

8. The multimodal search and enhanced generation of government affairs intelligent system according to claim 1 is characterized by: The multimodal fusion government affairs intelligent agent personalized interactive application module includes a distributed user portrait model and a progressive dialogue guidance module. It establishes a distributed user portrait model through a federated learning framework and mines user group characteristics across terminals.

9. The multimodal search and enhanced generation of government affairs intelligent system according to claim 8, characterized in that: The state space and action set are defined through a multi-round dialogue reinforcement learning model, and the proximal policy optimization algorithm is used to maximize the cumulative reward expectation.

Images