49568 results about "Theoretical computer science" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

A computing system is disclosed for transforming document data into schema-conformant structured outputs. The system obtains document data comprising multi-format structured documents and classifies each document by type and class using vector-based modeling and structural feature analysis. An extraction configuration is selected for each document, the configuration comprising machine-executable instructions for parsing based on semantic and layout characteristics. The system extracts semantic data using structured inference, transforms the semantic data into schema-conformant outputs, and validates the outputs using temporal and domain-specific constraints. Validated structured data may be used for downstream processing, visualizations, or optimization based on performance metrics.

The invention relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to a power distribution network, and carrying out structured disassembly and preprocessing to construct a space-time double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the space-time double graph structure, performing multi-scale topological feature extraction, and generating a space-time separated feature vector set; performing deep coupling fusion on the feature vector set subjected to time-space separation to generate corresponding unified topological feature representation containing abnormal topology; and constructing a dynamic topology state prediction model based on the unified topology feature representation to optimize a space-time joint loss function and output a corresponding real-time topology connection relationship and an equipment state change trend, and meanwhile, performing dynamic topology reconstruction to generate a current-moment reliable topological graph corresponding to potential branch disconnection and temporary tripping. The topology analysis accuracy of the power distribution network can be improved.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention relates to the technical field of information data analysis. The invention provides a public policy case analysis knowledge graph fusion reasoning method and system. The method comprises the following steps: generating standardized preprocessing data; performing extraction processing on the standardized pre-processed data to generate a structured triple set; processing the structured triple set to generate a multi-dimensional knowledge graph; constructing a hybrid inference engine, and processing the multi-dimensional knowledge graph to generate inference result data; performing incremental updating processing of nodes and relationships on the multi-dimensional knowledge graph, and performing parameter optimization processing on the hybrid inference engine to generate an updated knowledge graph and an optimized inference engine; and reasoning result data are processed, and a visual analysis result is output, so that the problems of limitation of a rule engine on causal reasoning, semantic fuzziness and logic illusion of a large language model in the policy field and insufficiency of a single-field knowledge graph on cross-field interaction influence revelation are solved.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph construction method and system based on a large language model technology. The method comprises the following steps: receiving a multi-source heterogeneous data stream, and completing semantic space mapping and cross-modal feature fusion to generate a unified semantic representation vector set; constructing an initial knowledge graph skeleton; performing incremental optimization on the skeleton, and performing entity relationship disambiguation and conflict detection; and iteratively updating the knowledge representation, and outputting a target knowledge graph meeting semantic consistency. The system comprises a data receiving module, a semantic fusion module, a skeleton construction module, an optimization module and a knowledge updating module. According to the method, multi-source heterogeneous data is effectively processed, the accuracy, the dynamic updating capability and the semantic consistency of the knowledge graph are improved, and the method has wide application prospects in the fields of intelligent question answering, information retrieval and the like.

The invention discloses a Text2SQL (Structured Query Language) implementation method and system, and relates to the field of data processing, and the method comprises the following steps: firstly, receiving a natural language query, and analyzing a query intention, field classification and a key entity through a planner; the searcher obtains domain knowledge, entity information, a database table structure and a historical query mode in a multi-path parallel mode based on the planning result; the generator constructs an SQL framework according to the retrieval result and generates an initial statement; the verifier carries out grammar, table field, authority and logic multi-dimensional verification on the SQL, and if the verification fails, iteration adjustment is carried out to generate logic; when the SQL is executed, the result is formatted and a natural language explanation containing query logic, a data source and a calculation method is generated if the SQL is executed successfully, and a diagnosis and error correction mechanism is started for correction and then rechecking is performed if the SQL is executed unsuccessfully. According to the method, through deep fusion of domain knowledge, whole-process verification error correction and interpretability enhancement, the accuracy, robustness and user interaction experience of SQL conversion in a professional scene are improved.

The invention relates to a knowledge graph construction method and system based on a large language model, and the method and system achieve the automatic construction and dynamic maintenance of a knowledge graph through multi-modal data fusion, reinforcement learning and comparative learning joint optimization, teacher-student model knowledge migration, time sequence dynamic analysis and an incremental updating mechanism. Constructing a reinforcement learning framework, and taking accuracy and integrity as reward indexes to train a large model to extract an entity relationship; a large-scale knowledge graph is used as a teacher model, and conflict resolution and semantic alignment of newly added knowledge and an existing graph are realized through a graph attention network; a verification rule is dynamically generated based on historical data and domain knowledge, and relation periodicity and mutation points are detected in combination with Fourier transform and a CUSUM algorithm; and finally, generating a traceable knowledge graph through incremental updating and version control. And the multi-modal data processing precision, the entity relationship extraction dynamic adaptability and the knowledge graph maintenance efficiency are improved.

The invention relates to the technical field of judicial data security, and discloses a judicial system confidential data security circulation method based on a block chain technology. Collecting multi-source judicial data, identifying sensitive information through a large language model, and performing differential privacy desensitization processing; constructing an SM4 encryption and TLS 1.3 end-to-end secure channel, generating a data hash fingerprint, and writing the data hash fingerprint into an alliance chain evidence based on a PBFT consensus mechanism; designing a multi-modal classification engine to perform feature extraction and intelligent classification; establishing a hybrid authority model to integrate an XACML strategy and a Kafka queue, and implementing dynamic authority control in combination with an RBAC / ABAC mechanism; a hierarchical encryption storage architecture is constructed, and homomorphic encryption retrieval and erasure code distributed storage are adopted; constructing a judicial knowledge graph based on a BERT model; and deploying a block chain auditing system, and combining LSTM anomaly detection and a DREAD risk assessment model to form a closed-loop risk control system. According to the method, the problems of security risk and privacy disclosure in judicial data cross-department circulation are solved.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

The invention relates to a cross-domain intelligent reasoning method based on knowledge graph enhancement, and the method achieves the precise reasoning in a complex scene through the construction of a hierarchical knowledge expression framework and a dynamic optimization mechanism. A multi-source heterogeneous data fusion technology is adopted, subject fine-grained knowledge units are generated through multi-modal feature extraction, and a three-dimensional knowledge graph structure comprising a core common concept layer, a subject feature ontology layer and a dynamic semantic mapping layer is established; based on a path exploration algorithm driven by reinforcement learning, cross-domain implicit association is mined while subject independence is reserved, and controllability and interpretability of the reasoning process are achieved in combination with an attention fusion mechanism of a large language model. According to the method, the limitation of traditional unified ontology modeling is broken through, the problems of concept drift and path deviation existing in reasoning in the cross fields of medicine-finance, engineering-law and the like are effectively solved, and the accuracy and knowledge traceability of complex decision tasks are remarkably improved.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

The invention discloses a hybrid enhanced enterprise smart law platform system based on a generative language large model. Four modules including a hybrid enhanced legal knowledge engine, a multi-modal legal document analysis module, a risk quantitative evaluation module and a compliance verification workflow work cooperatively. The hybrid enhanced legal knowledge engine integrates multi-source data, realizes real-time updating and semantic reasoning, and comprises map construction, a rule base and an incremental learning mechanism; the multi-modal legal document analysis module performs structured analysis on the heterogeneous document to generate a feature vector; the risk quantitative evaluation module is combined with Monte Carlo simulation and an analytic hierarchy process, quantifies the risk according to a compliance reference and analysis characteristics, and outputs a thermodynamic diagram and a report; a compliance verification workflow is driven by a finite-state machine, a verification module and a conflict detection module are integrated, a generative language large model is called to generate an improved scheme, and audit records are solidified and fed back for optimization. And the system runs according to the processes of analysis, supply rule, bias calculation and verification correction, so that the intelligence and accuracy of legal affair processing are improved.

In some embodiments, a language model forward traversal with a few-shot learning forward prompt yields a primary answer from a primary question. Then at least one backward traversal yields at least one candidate question using backward prompt(s) with answer-question pairs derived from the forward prompt's question-answer pairs. Each backward prompt also includes the primary answer but not the primary question. Each backward traversal is through one or more language models, not necessarily including the forward traversal's language model. Sometimes backward traversals vary model temperature, top-p, or top-k. A vector distance calculated between at least some candidate question vectors and a primary question vector indicates whether the primary answer includes hallucination content, and in some cases how much. Some embodiments withhold hallucinated answers from user interfaces and device control interfaces. Some embodiments also loop to obtain an answer with less hallucination content.

The invention discloses a multi-modal knowledge graph rule reasoning method and device based on a large model, and the method comprises the steps: carrying out the feature extraction and cross-modal alignment of input text data and image data, and generating a multi-modal feature vector of a unified semantic space; performing knowledge graph storage on the emotion entities and the relationships by adopting an attribute graph model to complete construction of an emotion knowledge graph; generating an interpretable inference rule from the emotion knowledge graph by using a large language model, and eliminating a conflict rule in combination with logic verification; calculating the confidence coefficient of a reasoning path based on an attention mechanism, and carrying out quantitative evaluation on a rule reasoning result; the knowledge graph and the rule base are updated online according to user feedback, and the real-time performance and accuracy of the inference system are optimized through weight adjustment and a forgetting mechanism. According to the method, through innovative technologies such as multi-modal data integration, dynamic knowledge evolution and interpretability reasoning, the limitation of a traditional sentiment analysis method in the aspects of evidence dimension, adaptive capacity, interpretability and the like is broken through.

The invention provides a multi-agent collaborative question and answer enhancement method and system based on heterogeneous data knowledge, and belongs to the technical field of information management. The planning optimization intelligent agent carries out structured processing on the input complex natural language problem; a multi-modal retrieval mechanism on the heterogeneous knowledge source is constructed based on the problem disassembly and entity recognition result, and a multi-path recall agent passes through a semantic matching model of a double-tower structure; the abstract extraction agent performs semantic fusion and information extraction on the text segments and the knowledge graph sub-graphs output by the multi-path recall module; logic verification and quality evaluation are carried out on the answers generated by the abstract extraction module by the reflection iteration agent; and by setting a threshold mechanism and combining importance weights of the sub-questions, performing scoring and reflection optimization on the generated answers by utilizing a large language model LLM (Language Language Model). The method can effectively cope with cross-domain and multi-level complex question and answer tasks, and has good expandability and intelligent level.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

The invention discloses an artificial intelligence data privacy protection system based on a block chain and federated learning, and relates to the technical field of block chains and federated learning, and the system comprises a block chain module which employs a main chain-side chain double-chain architecture, a main chain stores a global model hash value and node reputation evaluation data, and a side chain module is used for storing node reputation evaluation data; the side chain stores the encrypted local model parameters through a fragmentation technology; the federated learning module comprises a dynamic difference privacy algorithm and a gradient ternary processing unit, and is used for adding noise to the gradient in a local training stage and converting the gradient into a ternary numerical format; the privacy protection module is used for integrating homomorphic encryption and zero-knowledge proof technologies and realizing ciphertext aggregation and verification of model parameters; and the malicious node detection module is used for identifying abnormal gradient update based on cosine similarity and Multi-Krum algorithm, and is linked with node reputation data in the block chain module. Through a system architecture and a privacy protection mechanism, the efficiency and performance of federal learning are improved while data privacy is ensured, and the method has a wide application prospect.

The invention provides an agent-based large language model retrieval enhancement generation system and method, and the system comprises a planning layer which is used for receiving user query, carrying out the multi-round iterative decomposition of a complex task through a task planning agent, and generating an atomic query or a direct response; the execution layer is used for executing the atomic query generated by the planning layer in parallel, calling a search module to obtain external knowledge base data, and caching an intermediate result through a memory module; the answer detection module is used for performing multi-dimensional detection on the generated result, including preference, accuracy, integrity and logicality; the dynamic decision-making module is used for adaptively adjusting a subsequent retrieval strategy and a task planning process according to a detection result and user feedback; and a cross-layer interaction mechanism enables the planning layer and the execution layer to realize collaborative optimization through context sharing and iterative feedback.

The invention discloses a hierarchical semantic-driven retrieval enhancement generation method and system, a natural hierarchical relationship and a semantic boundary of a document are effectively reserved by constructing a tree hierarchical structure based on a document chapter title, and a recursive semantic boundary splitting strategy is adopted to refine overlong text nodes, so that the semantic integrity is ensured, and the retrieval enhancement generation efficiency is improved. And the model input length limitation is met, and the semantic information is prevented from being lost. Meanwhile, node knowledge point extraction and abstract generation are achieved through a large language model, top-down multi-level title path transmission and bottom-up content aggregation are combined, the structural perception and semantic expression ability of nodes is enhanced, and in the retrieval stage, based on similarity distribution of query and node semantic expression, an adaptive retrieval threshold value is dynamically calculated, and the retrieval efficiency is improved. A fixed top-k retrieval strategy is replaced, intelligent screening of different query and hierarchical nodes is achieved, information coverage and redundancy suppression are balanced, and retrieval efficiency and accuracy are remarkably improved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.

A system and method for efficient natural language processing combines large and small language models with a thought caching architecture. The system includes a router that directs prompts either to a large language model for thought generation or to a thought cache containing previously generated thoughts. When using the large model, generated thoughts are combined with the original prompt and routed through a smaller language model to produce responses. The thought cache stores reasoning patterns that can be retrieved and reused, eliminating the need to regenerate similar thoughts for related prompts. The system supports both local and cloud-based caching, enabling personal and enterprise-wide thought storage and retrieval. This architecture reduces computational overhead while maintaining reasoning capabilities, effectively extends context windows beyond traditional limits, and enables efficient scaling across different deployment scenarios. The system can operate with reduced resources by leveraging cached thoughts without requiring constant access to the large model.

The invention relates to the technical field of data processing, in particular to a method and system for processing data based on a large language model.The method comprises the following steps that based on inter-sentence punctuation positioning and part-of-speech tagging, sentence blocks are divided to generate functional partitions, themes and relational words are extracted to judge semantic chain starting points, a trigger index sequence is constructed, and logic jump points and breakpoint positions are recognized; and mapping the label structure to the language model output analysis deviation, and generating a label mapping combination list. According to the method, semantic turning nodes can be captured by analyzing the semantic direction change trend, the relation between the semantic turning nodes and verb and noun combinations is judged, the position of a trigger point of an actual information transfer effect is extracted, and the break point area of a semantic path is recognized through the positioning of key word starting and stopping blocks and the logical judgment of noun group cross combination; the path integrity has a clear fracture identifier, a traceable semantic mapping structure path is established in a language model, and the accuracy, coherence and hierarchy clearness of semantic reconstruction are enhanced.

The invention discloses a method and system for dynamically calling a Java statistical analysis interface based on LLM and MCP protocols, belongs to the technical field of data processing and analysis technology, artificial intelligence technology and application program interface (API) integration, and aims to solve the technical problem of how to improve the response speed and flexibility of statistical analysis requirements. In order to reduce the development and maintenance cost of a statistical analysis function, the adopted technical scheme is as follows: a Java annotation mechanism is utilized to mark and describe an existing Java statistical analysis system, a Java method of which the annotation is defined is obtained, and the Java method of which the annotation is defined can be automatically identified by an MCP server and registered as an MCP tool; the MCP server follows an MCP protocol and communicates with the LLM serving as an MCP client, and the LLM dynamically discovers available MCP tools through the MCP protocol; after natural language query of a user is understood through LLM, one or more appropriate MCP tools are intelligently selected, calling parameters are generated, and an MCP server is requested to execute through an MCP protocol.