1707 results about "Relvar" patented technology

The invention provides a case quality intelligent evaluation method and system based on a large language model, and the method comprises the steps: building an evaluation standard library covering various cases based on a law normative file and judgment practice; designing a complete evaluation reasoning chain based on the evaluation standard library; constructing a multi-agent system based on the evaluation reasoning chain; based on the multi-agent system, realizing distributed task allocation for unplanned online tasks by applying an imprecise alternating direction multiplier method algorithm; based on the distributed task allocation result, combining planning and reinforcement learning technologies to solve a relational multi-agent case association problem; based on the multi-agent case association processing result, applying a forced zero method sparse graph technology to realize effective learning based on a case graph; and constructing a case quality assessment knowledge graph based on the effective learning result of the case graph. By adopting the technical scheme, the accuracy, comprehensiveness and efficiency of case quality evaluation are 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 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 relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

The invention relates to a digital finance and taxation auditing method based on artificial intelligence, and the method comprises the following steps: S1, obtaining electric finance and taxation multi-source data, and carrying out the preprocessing of the data, and obtaining the preprocessed electric finance and taxation multi-source data; s2, constructing a finance and taxation knowledge graph according to the preprocessed power finance and taxation multi-source data; s3, constructing a multi-modal risk detection model, and obtaining a risk event list and a whole-course data traceability path according to the finance and taxation knowledge graph and the real-time electric finance and taxation multi-source data; s4, according to the risk event and traceability, based on a rule model, generating a structured decision instruction set including all key information and traceability links; and S5, converting the structured decision instruction set and the finance and tax knowledge graph into a standard relation graph for input, and dynamically displaying fund flow, risk events, abnormal aggregation and traceability paths among enterprises through D3. Js rendering. According to the invention, the efficiency and reliability of electric power digital finance and taxation auditing are significantly improved.

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention relates to a government affair file information extraction and question answering method and device and a medium, and the method comprises the steps: carrying out the entity extraction of a government affair file through employing a BERT-CRF joint model, and obtaining a structured entity set; performing relation extraction on the structured entity set to generate a semantic relation set between the entities; constructing a knowledge graph according to the structured entity set and the semantic relationship set, storing entity nodes into a graph database, and storing an embedded vector of an entity text into a vector database; when a query request of a user is received, relation query of the graph database and semantic retrieval of the vector database are carried out, sub-graph structures and semantic matching vectors related to query are extracted, and a mixed retrieval result is obtained; and inputting the mixed retrieval result into a large language model, and generating a question and answer response text conforming to a preset format by applying a dynamic prompt template. According to the method, the document processing efficiency and accuracy are effectively improved, and a solid technical support is provided for intelligent management of government affair documents.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses a multi-table combined natural language query SQL (structured query language) generation method, which comprises the following steps of: 1, constructing a database meta-knowledge graph, and establishing a triple storage comprising a table structure, a primary and foreign key relationship and business description for each data table; 2, receiving a natural language query request, and calculating the topic relevancy between query semantics and each database table through a pre-trained topic matching model; 3, dynamically constructing a view, and logically associating the database tables of which the theme relevancy exceeds a threshold value to form a temporary view; 4, generating a context enhancement prompt, and combining the temporary view structure, the field semantic description of the view and the view content sample to form a structured prompt; 5, inputting the natural language query and the structured prompt into the large language model to generate candidate SQL statements; and step 6, executing verification and iterative optimization on the candidate SQL statements, verifying logic correctness through SQL execution plan analysis and result sampling, and triggering and prompting a reconstruction mechanism when detection is abnormal.

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

The invention discloses a method for reducing a large model illusion problem based on an RAG technology, and the method comprises the steps: extracting semantic entities, relation phrases and context features in a natural language query, and constructing a multi-source heterogeneous hypergraph; fusing the graph structure and sequence context information by using a graph attention network and a sequence perception network to form unified semantic representation; evaluating the illusion risk based on the confidence score, the evidence coverage rate and the semantic deviation index, and triggering reverse retrieval and fusion reinforcement; and the content is generated through causal consistency discrimination feedback control. According to the method, the illusion phenomenon of the generation result is remarkably reduced, and the method is widely applied to the field of intelligent question answering and information retrieval.

The invention discloses an intelligent bid invitation file generation method and system based on text automation, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: carrying out the semantic analysis of a bid invitation demand text through a project type recognition model, and extracting a feature vector and a keyword set of a project; based on the feature vector, a cosine similarity algorithm is adopted to retrieve a matching template in a preset bid invitation file template library, a candidate template list is generated, and label classification and parameterization setting are carried out on templates of the candidate template list; based on the keyword set, carrying out content adaptation on templates of the candidate template list, and generating an initial bid invitation file; and performing segmented auditing on the initial bid invitation file, adjusting the keyword set in combination with auditing opinions, and synchronously updating the keyword set to the parameterized database through the mapping relationship to generate the bid invitation file. According to the invention, closed loop from demand to achievement is realized, labor cost of bid invitation file compiling is reduced, and bid invitation file quality and generation efficiency are improved.

The invention discloses a cross-domain heterogeneous data query system and method based on a large model and a knowledge graph, belongs to the technical field of information retrieval, and aims to solve the technical problem of complex relation reasoning in cross-domain heterogeneous data query. Comprising a data input and preprocessing module used for collecting multi-modal data to obtain feature vectors; the knowledge graph construction and management module is used for constructing a knowledge graph and providing query service based on the knowledge graph; the bidirectional enhancement module is used for writing the reasoning result of the large language model into a knowledge graph and carrying out version management; the domain adaptation layer is used for carrying out model training on the large language model based on a lightweight adapter and a domain adaptation mechanism; the real-time query and reasoning module verifies and supplements the candidate answers based on a knowledge graph to generate an initial answer, and explains a reasoning path based on a causal reasoning network to generate a final answer; and the interpretability and transparency module is used for displaying knowledge in the knowledge graph through a visual interface and providing auditing service based on the operation day.

The invention provides a fault inference analysis method and system based on a large language model, and the method comprises the steps: firstly obtaining a multi-source operation and maintenance data set of a to-be-diagnosed system, including a structured performance index, a semi-structured service log and unstructured text description data, carrying out the semantic enhancement of the multi-source operation and maintenance data set, and carrying out the semantic enhancement of the multi-source operation and maintenance data set; obtaining a semantic data unit containing entity, relation and attribute semantic tags, constructing a semantic association network based on the semantic data unit, calling a pre-trained large language model to carry out root cause reasoning analysis on the network, generating a candidate root cause set sorted according to confidence, and carrying out root cause reasoning analysis on the candidate root cause set; and finally, according to the candidate root cause set, generating an operation and maintenance decision instruction containing an entity operation sequence and a priority sequence, and sending the instruction to a system management terminal to trigger an automatic repair process. Therefore, the operation and maintenance efficiency and reliability of the system can be improved.

The invention discloses an RAG intelligent retrieval question-answering system and method based on enhanced metadata, and relates to the technical field of information processing and intelligent retrieval, multi-source heterogeneous knowledge data is preprocessed to obtain unified knowledge data, and structured metadata is extracted from the unified knowledge data based on different text forms; vectorizing a document text in the structured metadata by combining with embedding of the knowledge graph to obtain document representation, and outputting the document representation, the structured metadata and the enhanced keyword set as an enhanced metadata object; labeling a display relationship between different enhanced metadata objects, and constructing to obtain a knowledge database; according to the intelligent knowledge service system and method, restrictive conditions and question intentions are extracted from user questions, mixed retrieval is performed from a knowledge database based on the restrictive conditions and the question intentions, a candidate literature semantic set is output, then structured statistical visualization reports and structured answers are output, and accurate, explainable and multifunctional intelligent knowledge services are achieved.

The invention discloses an SQL (Structured Query Language) statement generation method and equipment based on a natural language and a medium, and the method comprises the following steps: constructing an RAG knowledge base and a data table knowledge graph of a project, then obtaining a to-be-converted natural language of the project, and performing keyword retrieval on the natural language through the RAG knowledge base to obtain an association table set; performing graph traversal on the natural language through the data table knowledge graph to obtain an associated node set; packaging the screening condition of the natural language to be converted, the association table set and the association node set to generate a context knowledge base of the natural language; and finally, according to the context knowledge base and the semantic conversion model, obtaining an SQL statement of a natural language. The problem of low efficiency of multi-table association path discovery is solved by constructing a dynamic extensible database relationship graph; a unified retrieval enhancement mechanism for the RAG knowledge base, the extensible knowledge graph and the historical query features is established, and the problem that target context retrieval is low in efficiency is solved.

The invention discloses a root cause analysis method and device based on a large language model, equipment and a medium. The method comprises the steps that a target entity and a topological relation are extracted in response to a root cause analysis request; searching matched historical root cause cases in a vector database, inputting the topological relation, the historical root cause cases and cue words into a large language model to generate a doubtful point list, and extracting downstream entities in the doubtful point list; calling a detection tool to collect entity diagnosis data and identify abnormal downstream entities; returning to execute the operation of retrieving the historical root cause case until a preset iteration ending condition is met; and inputting the final doubtful point list, the abnormal diagnosis data, the topological relation and the historical root cause case into the large language model again to obtain a root cause reasoning result and generate a root cause report. According to the embodiment of the invention, through the full-chain design of natural language understanding, topological constraint, historical cases, dynamic detection and iterative reasoning, the fault positioning efficiency and the root cause accuracy are improved, and the operation and maintenance labor cost and the service fault time consumption are remarkably reduced.

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

The invention discloses a structured query language generation method and device, equipment and a storage medium, and relates to the technical field of database query, and the method comprises the following steps: obtaining a natural language input by a target user, and analyzing the natural language by using a preset large model to obtain structured data; determining a corresponding mapping relationship based on the structured data and a preset database knowledge graph, determining an initial structured query language based on a preset large model and the mapping relationship, and performing query by using the initial structured query language to obtain a query result; determining a first reward value based on the structured data, determining a second reward value based on the query result, and adjusting a preset large model by using the first reward value, the second reward value and a strategy gradient algorithm to obtain an adjusted large model; and evaluating the adjusted large model by utilizing a preset verification set, and if the evaluation is passed, generating a target structured query language by utilizing the adjusted large model. Therefore, the SQL generation accuracy can be improved.

The invention discloses a natural language-to-SQL (Structured Query Language) method and system based on RAG (Random Access Gateway) enhancement, and the method comprises the steps: constructing a query intention graph through dependency syntax analysis, recognizing and complementing semantic missing components, and forming complete semantic representation; vector representation is carried out on the business term segments by adopting vectorization coding, accurate definitions of business terms are obtained from a factory structured knowledge base, and an enhanced context set is formed through expansion retrieval in a low-confidence region; an SQL template mapping network is established based on historical query records, a mapping relation matrix is generated through field candidate matching, mapping conflict positions are identified, and multiple SQL candidate sequences are generated; grammar verification is carried out on the candidate sequence to identify grammar errors, semantic consistency verification is carried out to calculate the intention alignment degree, and an optimal SQL statement is selected through a deviation correction factor; and performing formatting processing and statistical abstract on an execution result, and providing data query capability for scenes such as factory quality management and equipment maintenance.

The invention provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention belongs to the technical field of mine ventilation monitoring, and aims to solve the problems that a traditional knowledge system based on a rule base has bottlenecks in the aspects of dealing with sudden working conditions, knowledge updating and semantic reasoning, and is high in construction cost and complex to maintain. The invention provides a mine ventilation knowledge graph construction method based on a large model, and the method comprises the following steps: S100, obtaining and cleaning a multi-source heterogeneous text related to mine ventilation, and obtaining a JSON format knowledge fragment of a unified structure; s200, constructing an ontology model and defining a semantic structure; s300, constructing four types of knowledge extraction tasks, and extracting entities, attributes and relationships; s400, constructing an entity alignment module; and S500, constructing a graph database structure. According to the method, automatic structured expression, semantic consistent fusion and intelligent visual query of knowledge in the mine ventilation field can be realized, and an interpretable, extensible and reasonable intelligent support platform is provided for a mine ventilation system.

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

The invention discloses a financial information query method and system based on atlas analysis. The method comprises the following steps: performing task division on a query task input by a target object, and constructing a task execution graph according to a dependency relationship among a plurality of query sub-tasks obtained by division; for each query sub-task, analyzing the query sub-task and target query results of all executed query sub-tasks having a dependency relationship with the query sub-task by using a plurality of knowledge retrieval models to obtain at least one candidate query result output by each knowledge retrieval model, determining a query result of the query subtask based on each candidate query result; and according to the task execution graph, combining the query results of the query subtasks to obtain a final query result, and feeding back the final query result to the target object. According to the method and the device, the technical problem that the comprehensiveness and the accuracy of the obtained final query result are relatively poor due to the fact that a financial information query technology based on keyword matching does not deeply understand the semantics of the query task is solved.

The invention discloses a knowledge graph generation method and system based on a large model, and the method comprises the steps: carrying out the preprocessing of data, obtaining a data set, and determining a target mode of a knowledge graph; extracting a triple conforming to the type of the target mode from the data set to obtain a candidate knowledge triple; carrying out multi-dimensional verification on the candidate knowledge triad to generate a confidence score; performing consistency verification and conflict resolution on the candidate triad set with the confidence score higher than a preset threshold value to obtain a verification candidate knowledge triad; performing entity linking and relationship standardization processing on the verification candidate knowledge triples, and mapping the verification candidate knowledge triples to a knowledge graph of a corresponding target mode to obtain standard candidate knowledge triples; and fusing the standard candidate knowledge triples into the knowledge graph database, and updating the knowledge graph database. According to the method, the powerful natural language understanding and knowledge reasoning potential of the LLM can be utilized to the maximum extent, and the inherent defects of the LLM are actively and systematically overcome by introducing an innovative mechanism.

The invention relates to the technical field of databases, and discloses a multi-type database performance optimization and operation and maintenance management method and system.The multi-type database performance optimization and operation and maintenance management method comprises the steps that real-time performance indexes of a heterogeneous database cluster are collected, and a noise reduction data set is obtained; generating a cross-library performance coupling degree matrix through the resource competition coupling degree and the data dependence coupling degree; constructing a coupling relation graph; identifying a bottleneck node set based on the coupling relation graph and the abnormal level; generating an optimization strategy; and performing strategy conflict identification according to the optimization strategy, generating a processing strategy, and executing the processing strategy optimization strategy. Through the improved weighting centrality algorithm, the core bottleneck node which has the greatest influence on the whole cluster is accurately identified, a differential optimization strategy is adopted according to the database type, a perfect strategy conflict detection and avoidance mechanism is established, mutual interference in the optimization process is effectively prevented, the optimization success rate is improved, and the optimization efficiency is improved. And the optimization time is shortened.

The invention provides a mass data association fusion method and device and a medium, and the method comprises the steps: firstly receiving mass data from a plurality of heterogeneous data sources, and carrying out the preprocessing of the data; then, the data stream is integrated into an annotated data stream; wherein the labeled data stream comprises a two-dimensional semantic vector and a dynamic quality label; and finally, inputting the data into an AI association fusion model to obtain a fusion result. Wherein the association fusion model is provided with an association fusion and semantic mapping layer, and is used for carrying out association fusion according to the layered dynamic knowledge graph and a preset fusion rule, and carrying out semantic mapping according to the twin-tower sub-model and a consistency judgment rule. According to the method, real-time updating of the incidence relation is achieved through the layered dynamic knowledge graph, and the real-time scene requirement is met; the association complexity of mass data is reduced by using a double-tower model, and the processing speed is increased; and in combination with the judgment rule, the conflict resolution precision is improved. Finally, efficient, accurate and real-time fusion of mass data is realized, and a reliable basis is provided for analysis and decision of various data.

The invention provides an intelligent form processing method based on multi-modal verification and large model feedback, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: obtaining a corresponding business rule from a relational database through a rule extraction and matching request based on user input data; error verification, service logic verification and cross-field and cross-method verification are carried out on the user input data and the corresponding service rules, and verification results are obtained; based on the verification result, natural language feedback is generated through the large model to provide error prompts and improvement suggestions.