Method and system for enhancing understanding of professional domain knowledge by large model

Abstract

The invention relates to the technical field of natural language processing, knowledge engineering and artificial intelligence, and particularly discloses a method and system for enhancing understanding of professional domain knowledge by a large model. The method comprises the steps that a professional domain entity classification system composed of a core entity, an auxiliary entity and a relation entity is constructed, attributes are expressed in a layered labeling and multi-granularity modeling mode, and semantic vectors are generated through ontology modeling and an embedding algorithm; based on a mixed extraction framework fusing expert rules and a neural network model, high-quality extraction of professional domain knowledge is realized; the method comprises the following steps: integrating multi-source heterogeneous data, and constructing a dynamically updated domain knowledge graph through semantic mapping, entity normalization and metadata weighting strategies; a knowledge graph is embedded into a Transform architecture, a knowledge perception attention mechanism and a multi-hop inference engine driven by reinforcement learning are introduced, and the knowledge fusion and inference ability of a large model is improved; and meanwhile, a triple check mechanism is designed to ensure entity consistency, relation logicality and numerical reasonability of the generated content. According to the method, the knowledge understanding and reasoning capability of a large model in professional scenes such as water conservancy is effectively improved, and the method has good universality and engineering application prospects.

Description

Technical Field

[0001] This invention relates to the fields of natural language processing, knowledge engineering, and artificial intelligence, and in particular to a method and system for enhancing the understanding of professional domain knowledge by large models. It is applicable to intelligent question answering, decision assistance, and language generation applications in fields such as water conservancy, medicine, finance, and law that require professional knowledge understanding and semantic reasoning support. Background Technology

[0002] With the development of artificial intelligence technology, large language models (LLMs) have been widely used in general-domain tasks such as question answering, translation, and summarization due to their powerful language generation and understanding capabilities. However, current large language models still face significant challenges when dealing with highly specialized and resource-constrained tasks in niche domains such as water conservancy, healthcare, and finance.

[0003] First, specialized domain knowledge is highly sparse and specialized. The pre-training corpora of existing large-scale language models are mostly general texts, which are difficult to cover the deep knowledge system of these sub-domains. This leads to misunderstandings and irrelevant answers when the model encounters specific domain terms, concepts or reasoning chains, and even produces "illusion" outputs.

[0004] Secondly, to compensate for the shortcomings of large models in terms of professional knowledge, the mainstream approach is to introduce structured knowledge such as knowledge graphs to enhance model capabilities. However, traditional knowledge graph construction methods have poor applicability in professional fields. On the one hand, professional documents involve diverse terminology and complex contextual relationships, making it difficult for rule-based or shallow machine learning extraction methods to efficiently and accurately identify entities and their relationships. On the other hand, knowledge graph construction usually relies on a large amount of manual annotation and expert proofreading, resulting in high construction costs and long cycles, which seriously affects the timeliness and scalability of knowledge graphs.

[0005] Furthermore, existing methods are still immature in integrating knowledge graphs and language models. Many systems only introduce knowledge results at the retrieval level, lacking deep semantic fusion mechanisms, resulting in limited knowledge utilization and difficulty in supporting complex logical reasoning or context-consistent generation, thus restricting the in-depth application of large models in professional fields.

[0006] Therefore, there is an urgent need for a new technical solution for professional fields that can improve the quality of professional knowledge extraction, construct an efficient, accurate, and easily scalable domain knowledge graph, and achieve deep integration with large-scale language models, thereby effectively enhancing its ability to understand, reason about, and generate professional domain knowledge. Summary of the Invention

[0007] In specialized fields, large language models often struggle to accurately understand complex terms and their relationships due to a lack of structured knowledge support, leading to issues such as unclear logic or factual bias in the generated content. To address this technical bottleneck, this invention proposes a method and system to enhance the understanding of specialized domain knowledge in large models. By constructing a professional knowledge graph and deeply integrating it with the language model, collaborative optimization is achieved across multiple stages, including semantic modeling, knowledge extraction, graph construction, and intelligent reasoning. The implementation of this invention will be further described in detail below with specific technical solutions.

[0008] In one embodiment of the present invention, a method for enhancing the understanding of domain-specific knowledge in large models is provided, the method comprising the following steps:

[0009] In one embodiment of the invention, domain-adaptive data modeling is first performed. This stage achieves comprehensive coverage of professional domain knowledge elements by constructing an entity classification system that includes core entities, auxiliary entities, and relational entities. For example, in the field of water conservancy engineering, "reservoir" is classified as a core entity, "rain gauge" as an auxiliary entity, and "scheduling plan" as a relational entity. Preferably, a hierarchical annotation strategy is used to perform structured annotation of the text to ensure clear logic between hierarchical concepts, and terminology alignment is performed in conjunction with an industry terminology database (such as "Technical Terminology of Water Conservancy and Hydropower Engineering").

[0010] Furthermore, through a multi-granularity modeling approach, the structural attributes (e.g., dam height, dam type) and functional attributes (e.g., flood control capacity, design flood level) of core entities, the spatial attributes (e.g., latitude, longitude, elevation) and observational characteristics (e.g., sampling frequency, measurement accuracy) of auxiliary entities, and the dynamic attributes (e.g., water diversion ratio, execution period) of relational entities are systematically modeled. Optionally, to alleviate ambiguity caused by differences in the expression of technical terms, an ontology modeling approach is adopted to construct hierarchical relationships between terms and a synonym merging network. Knowledge graph embedding technology is then used to transform the logical structure into a learnable vector representation, providing high-quality semantic input for subsequent model learning.

[0011] In another embodiment of the present invention, based on the above modeling results, a hybrid domain knowledge extraction is performed. This step employs a hybrid extraction framework combining expert rules and neural network models. Preferably, the expert rule template is constructed according to industry standards (such as the "Reservoir Dispatch Regulations"), enabling structured extraction of key condition-action rules. For example, the statement "When the water level exceeds the limit water level, flood discharge will be initiated" is extracted as a rule triple for condition triggering and action execution.

[0012] Furthermore, a BiLSTM-CRF model pre-trained on domain corpora is used for named entity recognition, and relation pairs are jointly extracted. The model supports the recognition of composite relations such as "reservoir-scheduling scheme" and "rain gauge-observation data". To improve generalization ability, the system introduces a dedicated labeling system (such as OB-scheduling rules and IB-observation characteristics) and optimizes the loss function structure, prioritizing the learning of the weights of key samples, which effectively improves the model's performance in specialized tasks.

[0013] In another embodiment of the present invention, a domain knowledge graph is constructed and dynamically optimized. Specifically, this includes: integrating multi-source heterogeneous data (such as PDF procedure documents, structured databases, XML hydrological reports, etc.), using semantic mapping technology to unify the concepts of synonymous entities (such as unifying "dam" and "gravity dam" into "dam body"), and determining whether entities are normalized and merged through context similarity and graph neural networks.

[0014] Preferably, to address knowledge conflicts across different data sources, the system introduces a metadata weighting mechanism, prioritizing data with more recent time tags from authoritative standards. After construction, the system employs a domain-adaptive optimized TransE graph embedding algorithm, using low-dimensional dense vectors to encode core entities and high-dimensional sparse vectors to represent peripheral entities, balancing representational power and computational efficiency. To maintain the graph's timeliness, the system supports access to real-time data streams, such as hydrological monitoring data, and provides a visual review interface for manual knowledge verification and rapid revision.

[0015] In a preferred embodiment of the present invention, the aforementioned knowledge graph is deeply integrated with a large model. First, entity link information is injected into the self-attention layer of the Transformer architecture. By constructing a dual-channel attention mechanism that integrates text representation and graph representation, the model can dynamically acquire and utilize structured knowledge during the generation process.

[0016] Furthermore, a multi-hop inference engine is trained based on a reinforcement learning framework to generate complex inference chains across levels, entities, and paths. Taking the "impact of continuous heavy rain on scheduling schemes" as an example, the system can automatically infer the path of "rainfall → inflow → flood control capacity → flood discharge strategy" and calculate the confidence score for each step. When a step has a low confidence interval, the system will trigger a manual review process to ensure the reliability of the inference path.

[0017] Optionally, to ensure that the generated response is highly consistent with the knowledge graph, the system introduces a triple verification architecture in the response generation stage, which verifies the compliance of the generated content from three aspects: entity matching, relational logic, and numerical rationality. In conjunction with the "knowledge anchoring" mechanism, key response fragments are pointed back to knowledge graph nodes, and the language model parameters and knowledge selection strategy are continuously optimized through a feedback learning mechanism.

[0018] In another embodiment of the present invention, a system for enhancing large model understanding of domain knowledge is provided, comprising the following modules:

[0019] Data modeling module: used to realize entity system construction, terminology ontology modeling and vector generation;

[0020] Knowledge extraction module: used to execute a hybrid rule template and deep model extraction strategy;

[0021] The graph construction module supports multi-source heterogeneous data fusion, knowledge normalization, and conflict resolution, and is continuously updated iteratively.

[0022] Model integration module: used to implement knowledge perception attention mechanism, multi-hop reasoning path generation and response generation verification.

[0023] Optionally, each module of the system can be deployed in a cloud or local server environment using programmable logic devices (such as FPGAs) or software programs to adapt to different practical application scenarios.

[0024] Based on the above technical solutions, the method and system for enhancing large-scale model understanding of professional domain knowledge in this invention combines key technical aspects such as domain adaptive modeling, hybrid knowledge extraction, dynamic knowledge graph construction, and deep fusion of large-scale models, forming a knowledge enhancement mechanism with high accuracy, high adaptability, and high interpretability, effectively solving the following problems commonly existing in the prior art:

[0025] First, by constructing a professional entity classification system and adopting a multi-granularity modeling method, this invention systematically distinguishes and defines the attributes and relational features of core entities, auxiliary entities, and relational entities. Combined with ontology modeling and knowledge graph embedding technology, it not only improves the modeling and expression capabilities of complex professional concepts, but also significantly enhances the model's recognition accuracy of domain terminology, structural semantics, and relational logic, thereby fundamentally alleviating the problem of insufficient understanding capabilities of general large models in professional domains.

[0026] Secondly, by introducing a hybrid extraction framework that integrates expert rules and neural networks, and combining joint modeling algorithms and domain adaptation mechanisms, this invention can achieve high-quality extraction of key entities and domain relationships under the premise of limited professional corpus resources. This effectively reduces the problems of error propagation and rule dependence in traditional pipeline extraction methods, while enhancing the system's portability and scalability.

[0027] Furthermore, this invention achieves effective fusion and conflict correction of knowledge from complex data sources by constructing semantic mapping, entity normalization, and conflict decision-making mechanisms for multi-source heterogeneous data. Combined with graph neural networks and the domain-optimized TransE algorithm, it significantly improves the representation ability and structural completeness of the knowledge graph, and maintains the timeliness and accuracy of the graph content through a dynamic update mechanism.

[0028] More importantly, this invention deeply integrates the constructed domain knowledge graph with a large-scale language model, injects semantic information from the knowledge graph into the Transformer structure, enhances the model's ability to focus on knowledge through a dual-channel attention mechanism and a knowledge gating structure, and, in conjunction with a reinforcement learning-driven reasoning path generation algorithm, achieves a multi-hop, traceable reasoning process. Through a triple verification mechanism and a knowledge anchoring generation mechanism, it ensures that the output of the large model meets the high standards of the professional field in terms of logical structure, entity consistency, and numerical accuracy, thereby significantly reducing the probability of the "illusion" phenomenon.

[0029] In summary, the technical solution of this invention not only has significant improvements in accuracy and robustness, but also good interpretability and transferability. It is applicable to many scenarios with extremely high requirements for the accuracy of professional knowledge understanding, such as water conservancy engineering, medical diagnosis, financial supervision, and legal analysis. It provides complete, efficient, and reliable technical support for promoting the application and value realization of large-scale language models in vertical fields. Attached Figure Description

[0030] To more clearly illustrate the technical solution of the present invention, the following description, in conjunction with the accompanying drawings, describes a method and system for enhancing large-scale model understanding of specialized domain knowledge, wherein:

[0031] Figure 1 This is a system architecture diagram of the domain knowledge enhancement method based on data modeling and knowledge graphs proposed in this invention. The diagram shows that the system consists of a data modeling module, a knowledge extraction module, a knowledge graph construction module, and a model integration module. The modules work collaboratively through data flow, knowledge flow, and control flow, forming a complete closed loop from the input of the original domain corpus to the output of the enhanced model.

[0032] Figure 2 This is a flowchart illustrating the domain data modeling process proposed in this invention. The flowchart details the relationships between sub-steps such as domain analysis, entity classification, attribute modeling, terminology standardization, and ontology construction, as well as the input and output logic of each step, providing a structural foundation for subsequent knowledge extraction and graph construction.

[0033] Figure 3This is a flowchart illustrating the knowledge extraction and graph construction process proposed in this invention. The diagram shows how expert rule templates and neural network models in the hybrid extraction framework work together to extract high-quality knowledge triples, and how the structured construction and optimization of the graph are completed through steps such as semantic mapping, entity normalization, and conflict resolution.

[0034] Figure 4 This diagram illustrates the knowledge graph and large model integration architecture proposed in this invention. It shows how the knowledge graph and Transformer model are integrated at the input and attention layers, including entity link injection, knowledge gating mechanisms, and a dual-channel attention structure, achieving deep coupling between knowledge and text.

[0035] Figure 5 This diagram illustrates the reasoning process of knowledge enhancement proposed in this invention. It shows how, in dialogue or question-answering scenarios, the system drives a multi-hop reasoning engine trained by reinforcement learning to generate reasoning paths through dynamically constructed vectorized queries, and ensures the reliability and interpretability of the reasoning results through confidence evaluation and a triple-verification mechanism. Detailed Implementation

[0036] To better understand the technical solution and application effects of the present invention, the typical embodiments of the present invention will be described in detail below with reference to the accompanying drawings and specific application scenarios. The present invention is applicable to various professional fields; however, for clarity, the following uses water conservancy engineering scheduling as an example to demonstrate its specific technical implementation process and operational effects in terms of professional knowledge extraction, graph construction, and deep integration with large models.

[0037] Example 1:

[0038] like Figure 1 As shown, the overall system architecture proposed in this invention includes a data modeling module, a knowledge extraction module, a graph construction module, and a model integration module. The system takes domain-specific corpora as input, and after data preprocessing, knowledge modeling, extraction, graph fusion, and semantic enhancement, ultimately achieves knowledge-driven large-scale model response generation.

[0039] During implementation, the first step is to perform data modeling for the target professional field. For example... Figure 2 As shown, taking water conservancy project scheduling as an example, the system collects a large amount of text data from industry standards, business procedures, and academic literature. Through cleaning, format standardization, word segmentation, and preliminary entity recognition, a structured corpus is obtained. Subsequently, with the participation of domain experts, the system constructs a complete conceptual system, classifying core entities (such as reservoirs and sluice gates), auxiliary entities (such as rain gauges), and relational entities (such as scheduling schemes). To describe the semantic hierarchy and logical relationships between entities, the system establishes an ontology model based on standard terminology databases such as "Technical Terminology of Water Conservancy and Hydropower Engineering" and uses embedding technology to convert it into vector representation.

[0040] After modeling is completed, the system enters the knowledge extraction phase. For example... Figure 3 As shown, the system employs a hybrid extraction framework that integrates expert rules and neural network models to automatically identify and structure key information in the modeling corpus. The expert rule component primarily constructs templates based on business logic to extract condition-action structures such as "when the reservoir water level exceeds a certain threshold, flood discharge is initiated." The neural network component uses a BiLSTM-CRF model fine-tuned on a specialized corpus to automatically label and extract domain-specific terms and relational phrases. Through this joint modeling approach, the system simultaneously performs entity recognition and relation classification, improving the overall consistency and accuracy of the extraction results.

[0041] After extraction, the system imports the extracted knowledge triples into the graph construction module. In this stage, the system first performs semantic mapping and entity normalization on the multi-source heterogeneous data. It then calculates entity context similarity using BERT vector representation and graph neural networks to merge synonymous entities and unify concept labels. Subsequently, the system uses an ontology inference engine to handle concept hierarchy relationships, resolving terminological ambiguity and structural conflicts. When knowledge conflicts exist between different sources, the system performs a weighted judgment based on the data's credibility level, timestamp, and contextual consistency to determine the optimal knowledge path. To improve the graph's representation quality, the system uses low-dimensional dense vector encoding for core entities and high-dimensional sparse representation for peripheral entities. It also introduces causal chain modeling such as "rainfall → runoff → reservoir capacity → scheduling instructions" to enhance the graph's ability to express dynamic logic. Furthermore, to ensure the timeliness of knowledge, the system supports periodic incremental updates to the graph and provides a manual review interface for knowledge maintenance.

[0042] Figure 4 This paper demonstrates a deep integration approach between knowledge graphs and large language models. In this stage, the system introduces a knowledge-aware mechanism into the Transformer architecture, injecting entity link information related to the input text from the knowledge graph into the attention layer, thereby constructing a dual-channel attention mechanism that integrates text representation and graph representation. To achieve deep integration of text semantics and knowledge structure, this invention introduces the following knowledge-aware attention mechanism into the Transformer architecture:

[0043]

[0044] Where Q, K, and V are the query, key, and value vectors, respectively, and d k M represents the dimension of the key vector. KG Let λ be the entity association matrix constructed from the knowledge graph, and λ be a learnable gating parameter. This mechanism enables the fusion control of knowledge-enhanced signals in multi-head attention.

[0045] To enhance the fusion effect, the system designed learnable gating units to dynamically adjust the level of knowledge participation in the language modeling process. Building upon this, the system further constructs a reinforcement learning-trained inference engine to generate multi-hop knowledge paths. The system can automatically generate inference chains that conform to causal and temporal logic based on the problem context. For example, in the question "How should the Longhekou Reservoir be managed during a severe rainstorm?", it automatically generates an inference path of "rainfall → inflow → water level prediction → flood discharge strategy," and scores each hop to identify high-risk or low-confidence links.

[0046] like Figure 5 As shown, during the response generation phase, the system ensures the accuracy and consistency of the generated content by constructing a knowledge constraint mechanism. This mechanism includes a triple verification architecture, corresponding to entity correctness verification (ensuring that the generated entities actually exist in the graph), relational logic verification (ensuring that the relationships between entities conform to ontology logic), and numerical range verification (ensuring that the data conforms to conventional domain indicators). In addition, the system introduces a knowledge anchoring mechanism to bind key information in the generated content to graph nodes, improving the traceability of the response; combined with an expert feedback learning mechanism, the system continuously optimizes the model's knowledge selection and generation strategies.

[0047] In practical deployments, this system can run on hardware platforms with high-performance computing capabilities, such as a deep learning server equipped with four NVIDIA L20 GPUs and running the domestically developed Kylin V10 operating system. In terms of the software environment, the system relies on PyTorch, Neo4j graph database, and Milvus vector database to support knowledge modeling and language generation tasks.

[0048] Through the above implementation methods, the present invention effectively solves the problems of knowledge gaps, comprehension biases, and uncontrollable reasoning that exist in large-scale language models when facing professional domain tasks. It realizes deep collaboration and intelligent integration of structured knowledge and language models, significantly improves the response accuracy and reliability of the model in professional scenarios such as water conservancy, and has good engineering implementation and cross-domain adaptability.

[0049] Example 2: Application of knowledge enhancement in large-scale models in water conservancy project scheduling scenarios

[0050] In a specific application embodiment of this invention, the system is applied to the professional field of "water conservancy project scheduling," aiming to achieve intelligent recommendation and reasoning question-answering support for scheduling schemes through an enhanced language model. The overall system architecture is as follows: Figure 1 As shown, it includes a data modeling module, a knowledge extraction module, a graph construction module, and a model integration module.

[0051] In this embodiment, the system first collects approximately 200 documents, including "Technical Terminology for Water Conservancy and Hydropower Engineering," "Reservoir Dispatch Regulations," and "River Basin Dispatch Scheme Manual," from the National Water Resources Ministry's standard database, a river basin management bureau, and local design units. These documents total over 1.5 million words. After being encoded in UTF-8 and cleaned using a syntax filter, Chinese word segmentation is performed using the NLPIR tool, and part-of-speech tagging and preliminary entity recognition are completed using the Stanford CoreNLP tool.

[0052] Subsequently, a two-week expert seminar was organized, with five experts participating, all possessing over 10 years of experience in water conservancy engineering. The seminar resulted in a core concept system for the professional field of "Water Conservancy Engineering Scheduling," identifying 18 entity types, including core entities (such as reservoirs and scheduling schemes), auxiliary entities (such as rain gauges and hydrological stations), and relational entities (such as water diversion routes and scheduling instructions). This resulted in an ontology structure of approximately 200 terms, with clearly defined hierarchical relationships, association logic, and terminology alignment rules. For example, "gravity dam" was defined as a subclass of "dam," and "sluice gate" and "gate station" were grouped as synonymous entities.

[0053] During the data modeling phase, the system employs a multi-granularity modeling approach to define the structure of various entity attributes:

[0054] For the core entity "reservoir", model its structural attributes (dam type, dam height, seismic resistance level) and functional attributes (flood control capacity, beneficial water level);

[0055] For the auxiliary entity "rain gauge", its spatial attributes (latitude and longitude: such as 117.45° east longitude and 31.92° north latitude) and observation characteristics (collection frequency of 15 minutes / time, measurement accuracy ±1mm) are associated.

[0056] For the relational entity "scheduling scheme", record dynamic information such as its operation object (e.g., reservoir name), scheduling cycle (hours / days), and execution priority (e.g., emergency / regular).

[0057] During the knowledge extraction phase, the system constructs extraction rules and a neural network model based on the above model:

[0058] The expert rules module defines approximately 45 structured rule templates, adapted to common control command expressions in reservoir scheduling, such as "If the inflow is ≥1000 m³ / s..." 3 If the reservoir water level is greater than the limit water level, then flood discharge scheduling will be implemented.

[0059] For the neural network part, a sequence labeling model was built based on the BiLSTM-CRF architecture. It was trained for 10 rounds on a professional corpus of about 80,000 words. The cross-entropy loss function was used, the number of hidden layer nodes was set to 256, and the learning rate was 0.001.

[0060] Simultaneously, a term similarity loss term is introduced. During training, to achieve collaborative optimization of Named Entity Recognition (NER) and Relation Extraction (RE) while maintaining semantic consistency of terms, this invention introduces the following joint loss function:

[0061]

[0062] Among them, L NER and L RE The loss functions T are for named entity recognition and relation classification, respectively. term v represents the set of terms associated with each other. ti v tj The term is represented by a vector, where α, β, and γ are adjustable weighting coefficients for the loss term.

[0063] For "same type entities", construct embedded constraint relationships to form a set T of term pairs. term It contains approximately 800 pairs, ensuring a consistent distribution of similar terms in the vector space.

[0064] Using a hybrid extraction model, the system extracted 810 knowledge triples from 200 documents. After manual sampling and redundancy detection, 675 high-quality triples were retained and imported into the graph construction module. During the construction process, the system introduced context vector similarity to determine entity normalization and used BERT-based embedding representations and graph neural networks for merge inference. For example, "Longhekou Reservoir" and "Longhe Reservoir" were considered the same entity and merged after comparison. For conflicting "flood control capacity" attribute values, the system made a weighted decision based on the reliability of the data source, prioritizing the retention of the latest value recorded by the official monitoring system (e.g., 315 million m³). 3 ).

[0065] The system uses a domain-adaptive TransE model for knowledge representation learning, employing 100-dimensional dense embedding for core subgraphs and 200-dimensional sparse embedding for edge subgraphs to maintain a balance between expressive power and inference efficiency. Simultaneously, it models a causal chain of "rainstorm → runoff → reservoir capacity → scheduling," injecting knowledge paths as inference constraints during the training phase.

[0066] In the embedding learning phase, to further enhance the diversity and controllability of graph representation, this invention introduces embedding distance constraints for different types of subgraphs based on the improved TransE model:

[0067]

[0068] Where h, r, and t are the embedding vectors of the head entity, relation, and tail entity, respectively, and G core and G edge Represents the core and edge subgraphs, δ core ,δ edge: These are the maximum allowed distance thresholds for core and edge entities, used to adjust the tightness of the graph embedding.

[0069] After the graph is constructed, the system integrates it with the large language model. A dual-channel attention mechanism is introduced into the Transformer architecture, using the entity link matrix M. KG The system achieves joint modeling of textual and graph information. It further trains a reinforcement learning inference engine, employing asynchronous Monte Carlo Tree Search (MCTS) to explore inference paths, enabling it to dynamically generate inference chains of up to four hops based on the problem context.

[0070] In actual testing, the system automatically generated the following reasoning path in response to the user's input of the question "Dispatch and response of Longhekou Reservoir under continuous heavy rain":

[0071] "If the cumulative rainfall exceeds 120 millimeters in the past 3 hours, the inflow is expected to reach 2,300 cubic meters per second. Under this flow condition, the remaining capacity of the flood control reservoir will be less than 30% of the total capacity. To cope with this severe situation, the Level I flood discharge plan will be activated, which includes implementing flood discharge measures in stages and issuing early warning information to downstream areas simultaneously."

[0072] The final generated response is verified by a triple check mechanism:

[0073] The entity "Longhekou Reservoir" has been bound to the map node, and the verification has been passed;

[0074] The relationship "triggering scheduling scheme" conforms to the scheduling logic rules;

[0075] The value "remaining capacity 30%" matches the record in the map (error <1%).

[0076] The above embodiments fully verify that the system described in this invention has the comprehensive capabilities of accurate identification, effective reasoning and reliable generation when facing professional tasks, and significantly improves the knowledge understanding and intelligent response level of large models in vertical scenarios.

[0077] This invention systematically solves the following technical problems by constructing a domain-adaptive data modeling system, designing a hybrid extraction mechanism that integrates expert rules and neural networks, introducing a multi-source heterogeneous knowledge graph construction method, and embedding a knowledge perception mechanism and a multi-hop inference engine into a large model architecture:

[0078] First, traditional language models suffer from weak terminology recognition and incomplete relation extraction in professional contexts;

[0079] Secondly, existing knowledge enhancement methods lack dynamic updating capabilities, leading to a disconnect between knowledge graphs and real-time semantics. Thirdly, the fusion of large models and knowledge graphs is superficial, making it difficult to achieve high-quality knowledge-driven reasoning and fact consistency control.

[0080] The technical solution proposed in this invention can effectively improve the depth of understanding of professional knowledge of large models, the reliability of reasoning paths, and the interpretability and accuracy of output content.

[0081] In summary, the method and system proposed in this invention for enhancing large-scale models' understanding of domain-specific knowledge significantly improves the semantic understanding ability, knowledge accuracy, and reasoning reliability of large-scale models in professional fields such as water conservancy by introducing domain-adaptive data modeling, a hybrid knowledge extraction mechanism, a knowledge graph construction strategy for multi-source heterogeneous data, and a deep fusion mechanism with language models. Compared with existing technologies, this invention has the following beneficial effects:

[0082] On the one hand, the model demonstrates higher accuracy in recognizing specialized terms, entity attributes, and relational structures, exhibiting strong adaptability to small-sample domains. On the other hand, by constructing multi-hop reasoning chains and uncertainty assessment mechanisms, the interpretability and stability of the model's reasoning process are enhanced. Simultaneously, the triple-verification architecture effectively reduces the risk of "illusion" in the language generation process, ensuring consistency between the output content and the known facts. Furthermore, the system architecture proposed in this invention is highly modular, applicable to other specialized fields with knowledge sparsity characteristics, and possesses significant promotional value and industrial prospects.

[0083] It should be noted that the above embodiments are merely preferred examples of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, any equivalent substitutions, modifications, combinations, or improvements made without departing from the core concept and essence of the present invention should be covered within the scope of protection claimed by the present invention. The scope of protection of the present invention is determined by the claims.

Claims

1. A method for enhancing the understanding of domain-specific knowledge in large models, characterized in that, Includes the following steps: Adaptive data modeling is performed in the execution domain. A specialized entity classification system is constructed and a hierarchical annotation strategy is adopted. Combined with an industry standard terminology library, the model's ability to recognize professional concepts is optimized through adversarial training. Multi-granularity modeling method is used to model the attributes and relationships of entities in the professional domain. Ontology modeling is used to standardize the expression of professional terms, and knowledge graph embedding technology is used to transform the logical structure into a vector representation that can be processed by machine learning. Based on the data modeling results, domain knowledge extraction is performed using a hybrid framework that integrates expert rules and neural network models. A joint extraction algorithm is adopted to simultaneously complete entity recognition and relationship classification through a unified modeling framework, and a domain adaptation mechanism is introduced to optimize the extraction effect. Integrate multi-source heterogeneous data, achieve concept unification through semantic mapping technology, normalize entities based on context similarity, and make weighted decisions on contradictory knowledge by combining metadata to construct a domain knowledge graph; The general knowledge graph embedding method is optimized for domain adaptation, and the domain knowledge graph is updated by real-time capture of dynamic data sources, comparison of semantic consistency, and visual interactive review. The domain knowledge graph is deeply integrated with the large model, and the deep fusion of knowledge and text is achieved through the attention enhancement mechanism of knowledge perception. The domain knowledge graph is retrieved based on context awareness, and a multi-hop inference engine trained by reinforcement learning is used to generate inference paths. Confidence scores are calculated for each inference step to achieve uncertain inference. Through a triple verification architecture, knowledge anchoring generation, and feedback learning mechanism, the consistency between the output of the large model and the domain knowledge is ensured.

2. The method according to claim 1, characterized in that, The construction of a specialized entity classification system and the adoption of a hierarchical annotation strategy are as follows: in a professional field, core entities are subclassified, auxiliary entities are associated with their unique attributes, relational entities are characterized by their dynamic attributes, and annotation is carried out in conjunction with an industry standard terminology library.

3. The method according to claim 1, characterized in that, The multi-granularity modeling method specifically involves: modeling the structural and functional attributes of core entities, associating the spatial attributes and observational characteristics of auxiliary entities, and characterizing the dynamic attributes of relational entities.

4. The method according to claim 1, characterized in that, The method of extracting domain knowledge using a hybrid framework that integrates expert rules and neural network models involves: first, constructing structured rule templates using industry standards, and then identifying variable entities in the text using a neural network model.

5. The method according to claim 1, characterized in that, The domain adaptation mechanism of the joint extraction algorithm includes further training the language model on professional corpora for documents, designing a dedicated labeling system, and strengthening the weight of key samples in the loss function.

6. The method according to claim 1, characterized in that, The attention enhancement mechanism for knowledge perception includes injecting entity linking information from the knowledge graph into the self-attention layer of the Transformer, constructing a dual-channel interaction mechanism for text representation and knowledge representation, and designing a learnable knowledge gating unit.

7. The method according to claim 1, characterized in that, The triple verification architecture includes entity correctness verification, relational logic verification, and numerical range verification during the text generation process.

8. A system for enhancing large-scale model understanding of domain-specific knowledge, characterized in that, include: The data modeling module is used to perform domain-adaptive data modeling, build a specialized entity classification system and adopt a hierarchical annotation strategy, combine an industry standard terminology library, and optimize the model's ability to recognize professional concepts through adversarial training; it uses multi-granularity modeling methods to model the attributes and relationships of professional domain entities; it standardizes the expression of professional terms through ontology modeling, and uses knowledge graph embedding technology to transform the logical structure into a vector representation that can be processed by machine learning. The knowledge extraction module is used to extract domain knowledge based on the data modeling results and using a hybrid framework that integrates expert rules and neural network models. It adopts a joint extraction algorithm, which simultaneously completes entity recognition and relationship classification through a unified modeling framework, and introduces a domain adaptation mechanism to optimize the extraction effect. The knowledge graph construction module is used to integrate multi-source heterogeneous data, achieve concept unification through semantic mapping technology, normalize entities based on context similarity, and make weighted decisions on contradictory knowledge in combination with metadata to construct a domain knowledge graph. The general knowledge graph embedding method is optimized for domain adaptation, and the domain knowledge graph is updated by real-time capture of dynamic data sources, comparison of semantic consistency, and visual interactive review. The model integration module is used to deeply integrate the domain knowledge graph with the large model, and to achieve deep fusion of knowledge and text through the attention enhancement mechanism of knowledge perception. The domain knowledge graph is retrieved based on context awareness, and a multi-hop inference engine trained by reinforcement learning is used to generate inference paths. Confidence scores are calculated for each inference step to achieve uncertain inference. Through a triple verification architecture, knowledge anchoring generation, and feedback learning mechanism, the consistency between the output of the large model and the domain knowledge is ensured.

9. The system according to claim 8, characterized in that, The data modeling module, knowledge extraction module, graph construction module, and model integration module are implemented using programmable logic devices or software programs.

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