271 results about "Logical consistency" patented technology

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches alongside quantum-inspired token management. The invention uses hierarchical and fuzzy deontic logic implementations and quantum-inspired state representations that combine complex amplitudes and phase information to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve complex goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration and information-theoretic metrics. The platform is capable of operating through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining system coherence and logical consistency using quantum-inspired token operations and phase alignment transformations for optimizing information transfer between states.

The invention relates to the technical field of artificial intelligence, in particular to an AI-driven multilingual intelligent document abstract and mind map generation system, which realizes unified representation of data such as multilingual texts, images and tables through a multi-modal semantic feature generation module, and constructs a dynamically optimized causal semantic map; generating a semantic flow path with clear logic based on a dynamic semantic flow modeling method, wherein the semantic flow path is used for multi-language abstract generation; the structure of the mind map is optimized by using the generative adversarial network, and the content of the map is further perfected through user interaction adjustment and logic consistency verification. The system supports a user to efficiently extract document core information, a logic structure is displayed in a visual mode, the accuracy, logicality and efficiency of document generation are remarkably improved, and the system is suitable for generation and decision support of complex multi-modal documents.

The invention discloses an automatic label labeling and classifying method and system oriented to unstructured system documents, and relates to the technical field of artificial intelligence. The method comprises the steps that semantic structure pre-analysis is conducted on an original system text, and a system semantic structure tree is constructed; establishing a system semantic enhancement vector space based on the semantic units and the logic relationship thereof; performing semantic deconstruction on the preset tag and extracting a feature vector; realizing cross-space semantic matching of the document and the tag through a system semantic attention mechanism; a confidence evaluation module is introduced to screen high-confidence labels from the three dimensions of structural integrity, coverage and logic consistency; and outputting a final label and a score through semantic conflict detection and resolution. According to the method, the problems that in the prior art, unstructured system text labeling accuracy is low and large-scale labeling samples are dependent on polysemy ambiguity, high context dependency, complex semantic structure and the like are solved, and labeling accuracy and robustness are remarkably improved.

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

The invention discloses a voice interactive console based on artificial intelligence, and relates to the technical field of intelligent consoles, a large language model is combined with context tracking and logic analysis technologies, a composite instruction is disassembled into independent tasks, a task chain is generated, a semantic weight is added to each task through a weight regression model, and the intelligent consoles are obtained. The task execution priority and the logic dependency relationship are identified, the limitation of a traditional system on the complex semantic analysis capability is overcome, a composite instruction can be accurately understood, a logic tree analysis method based on rules is adopted, a task chain is converted into a directed graph structure, the priority is dynamically adjusted, and a task execution sequence table is generated. In the scheduling process, multi-task parallel processing is carried out on tasks without logic dependence, and the resource utilization rate is increased; tasks with a dependency relationship are gradually scheduled by adopting topological sorting, so that the logic consistency is ensured.

The invention discloses a multi-source heterogeneous agricultural data element fusion system and method, and relates to the technical field of data fusion processing, and the method comprises the steps: carrying out the data screening of cleaned data; the screened data is preprocessed by the constructed agricultural field knowledge base, the logic consistency of the data is checked through a rule engine, and a corresponding coping strategy is adopted according to a check result; identifying abnormal points in the multi-source data by adopting a Gaussian mixture model and a constructed logic rule combination, and correcting or complementing the detected abnormal points in combination with a specific scene; key features are selected through information gain, multi-source fusion is carried out on the key features and original multi-source data, an abnormal mode library is established after abnormal data modes are classified, and source categories of the abnormal data are matched from a fault matching rule library according to abnormal features of the abnormal data; and the fault matching rule base is established, so that the source category of the abnormal data can be accurately identified, and a targeted solution is provided for processing the abnormal data.

The invention discloses an intelligent storage system supporting multi-source heterogeneous data fusion management, which relates to the technical field of multi-source heterogeneous data fusion, and comprises a data source adaptation module, an intelligent data source adapter is configured in a DataWorks data integration module, multi-source heterogeneous data is accessed through the intelligent data source adapter, and the data source adaptation module is connected with the DataWorks data integration module; extracting a semantic feature vector and a technical feature set; the neural symbol hybrid inference module inputs the semantic feature vector and the technical feature set into a neural symbol hybrid inference engine, calculates a similarity matrix between multi-source heterogeneous data fields through a BERT-based neural network, and imports the technical feature set into a field knowledge graph constructed by Neo4j for symbol logic verification to generate a unified metadata model; according to the method, a semantic similarity matrix calculated by a BERT-based neural network is combined with symbol logic verification of a Neo4j knowledge graph through a neural symbol hybrid inference engine, so that automatic semantic alignment and logic consistency verification of multi-source heterogeneous data are realized.

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

The invention relates to document verification, in particular to a heterogeneous document set-oriented cross-modal semantic alignment and logic consistency verification system, which is used for heterogeneous document input, supports multi-format document input and comprises multi-modal elements including texts, pictures, tables and charts. The document analysis module is used for carrying out structured extraction on document contents; extracting multi-modal elements, identifying and classifying various elements in the document, and establishing position and type labels of a foundation; the knowledge graph construction module is used for uniformly modeling heterogeneous elements into a multi-modal knowledge graph; the graph neural network semantic alignment module is used for realizing accurate cross-modal semantic alignment by using a specially designed graph neural network based on the multi-modal knowledge graph; the hybrid consistency verification engine is used for performing logic consistency verification in combination with a symbol logic verification mechanism and a semantic consistency verification mechanism; according to the method, the defect that accurate cross-modal semantic alignment and logic consistency verification are difficult to carry out on professional documents with multi-modal elements can be effectively overcome.

The invention provides an electronic medical record information extraction system and method, and belongs to the technical field of medical information extraction. The method aims to solve the problems of inaccuracy, nonstandardization and inconsistent logic when information is extracted from a free text medical record. According to the scheme, the method comprises the steps that cue words containing structured field definitions and rules are generated; inputting the cue word and the medical text into a large language model to obtain a preliminary result; calling a medical knowledge base to correct terms in the result; and performing logic verification on a corrected result, including time sequence and cross-field consistency verification, so as to generate final structured medical record information. According to the method, a closed loop is formed by guiding type extraction and multi-stage verification and by introducing a feedback correction mechanism, so that the accuracy, the normalization and the logic consistency of the medical record information are remarkably improved, and a high-quality data basis is provided for clinical application.

The invention relates to the field of artificial intelligence and natural languages, and discloses an artificial intelligence-based semantic understanding and generation model, which comprises an input analysis module, a generation module, a verification module and an optimization module, the generation module generates preliminary text output based on the semantic logic diagram, the verification module evaluates the logic consistency and the fact credibility of the generated text, and the optimization module optimizes the generated text through a dynamic weight adjustment mechanism to ensure that the generated content achieves balance between the logic consistency and the fact credibility; and the optimization module dynamically adjusts a weight ratio of logic verification to fact verification according to the input complexity score and a verification result, and guides text optimization of the generation module through a credibility objective function. The method is suitable for the fields of medicine, law, education, intelligent customer service and the like needing high-credibility and logic consistency text generation, and has wide application value and expansibility.

The invention provides a large model thinking chain knowledge distillation method and system based on anti-factual reasoning, and belongs to the technical field of artificial intelligence and natural language processing. Comprising the steps that minimum semantic disturbance is applied to a key position of an original reasoning task data set, and an anti-fact problem is constructed; based on the original problem and the anti-fact problem, calling a large language model to generate a multi-view reasoning chain; a high-loyalty distillation data set is screened out through causal intermediary analysis, and thinking chain consistency optimization training is carried out on the large language model; and deploying the large language model after optimization training to a downstream task. According to the method, by analyzing the anti-fact space of the input problem and quantifying the causal contribution of the inference chain to the final decision, the causal reliability and logic consistency of the large model thinking chain can be improved, and then effective data support is provided for downstream inference application.

The invention provides a question and answer model training method and device and a question and answer method and device. The method comprises the steps of obtaining sample question and answer data; the sample question and answer data comprises sample questions and real answers corresponding to the sample questions; based on the pre-training model, the semantic information of the sample problem is applied, the sample problem is converted into a pseudo code in a preset format, and the logic of the pseudo code is consistent with the logic of the corresponding sample problem; based on the illusion evaluation model, generating a sample correct answer and a sample wrong answer of the sample question by applying a pseudo code; and training a pre-training model by taking the sample question and answer data, the sample correct answers and the sample wrong answers as training samples to obtain a question and answer model. According to the question and answer model, the logic consistency of input and output in the natural language generation task can be guaranteed, the accuracy of generated answers can be guaranteed, and the illusion problem is avoided.

The invention relates to the technical field of enterprise project management, and discloses an enterprise project management method and system based on multi-source heterogeneous data integration, and the method comprises the steps: receiving heterogeneous data from a plurality of data sources; classifying the heterogeneous data into different-speed data streams of a high-speed stream, a medium-speed stream and a low-speed stream according to the updating frequency; allocating a logic timestamp for the different-speed data stream; the different-speed data flow is processed based on the logic timestamp, and the logic consistency of data processing is guaranteed; converting, aggregating and analyzing the processed data according to a preset data stream processing logic; the analysis result is applied to enterprise project management decision support; real-time performance, consistency and integrity of data in enterprise project management are achieved, efficiency and decision quality of enterprise project management are improved, and the problems of data islands, data inconsistency and data processing lag in traditional enterprise project management are solved.

The invention relates to an intelligent bidding document compiling method, system and equipment based on a large language model. The method comprises the following steps: constructing a bid invitation knowledge graph; obtaining project demand information to determine demand fields, calling a knowledge graph association interface to match bid invitation file modules and terms for each demand field, and generating a demand field-association module mapping table; according to the mapping table and bid invitation industry specifications, a generation prompt template embedded with a dynamic nesting slot position and a knowledge graph rule is constructed, and a pre-trained bid invitation big language model is input to output a bid invitation clause draft; traversing the knowledge graph according to a demand change field to calculate a change influence radius, constructing an update prompt template in combination with a slot association rule and a constraint condition, inputting a large model to generate revised content only aiming at the demand field and an association module, replacing a corresponding position of a draft, and performing secondary verification on logic consistency through the knowledge graph, and obtaining the updated complete bid invitation file. By adopting the method, intelligence, precision and high efficiency of bid invitation file compiling can be realized.

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention discloses a material data quality evaluation method based on a large model Agent, and relates to the technical field of material science and intelligent data management. According to the method, firstly, based on a rule set and through a real-time feedback and cyclic detection mechanism, original material data is corrected, and it is ensured that data entering follow-up evaluation has high quality; and secondly, based on the knowledge base, deep analysis is performed on the speciality, logic consistency and rationality of the to-be-evaluated material data by utilizing an RAG technology and an LLMs model, and a data quality evaluation result based on the knowledge base is generated. And meanwhile, various machine learning algorithms are used for carrying out multi-aspect detailed detection on the to-be-evaluated material data to obtain a plurality of data quality evaluation results based on the machine learning algorithms, and visual and quantitative evaluation results are provided through a visual method. And then, multi-dimensional evaluation information is integrated through a large model Agent, and a high-interpretation and structured comprehensive quality evaluation result is generated.

The invention discloses an intelligent legal contract generation method and device based on a contract template and contract term semanteme, and belongs to the technical field of intelligent contracts, and the method comprises the steps: analyzing a legal contract text in combination with a plurality of natural language processing technologies, and constructing a contract meta-model; dynamically generating an intelligent legal contract template based on the contract meta-model; an executable intelligent legal contract code is generated through the contract template; and finally carrying out consistency check on the generated code and the original contract. Therefore, the problems of unclear structure, difficulty in semantic mapping, poor universality, law compliance and the like in the traditional process of converting the contract into the intelligent contract are solved, structured expression of the law text, automatic generation of the contract code and logic consistency verification are realized, and the generation efficiency and reliability of the intelligent law contract are improved.

The invention discloses a cue word logic configuration method based on context semantic enhancement, and belongs to the technical field of artificial intelligence and natural language processing. The method comprises the steps of data collection, data preprocessing, dynamic semantic anchor point generation, semantic field strong quantitative modeling and cue word logic configuration. Dynamically generating a semantic anchor point set capable of representing core semantics based on a semantic network structure contribution degree calculation and aging attenuation mechanism; further, a three-dimensional semantic field model is constructed, the timeliness intensity, the emotional intensity and the credibility intensity of each semantic anchor point are quantified, and comprehensive semantic field intensity data are obtained in combination with a multi-dimensional attenuation weighting and credibility correction method; according to the method, the context adaptability, the logic consistency and the dynamic adjustability of prompt word configuration can be remarkably improved, and the method has good application value and popularization prospects.

The invention discloses an operation data processing method and system based on a compliance verification library and a risk control plan, and belongs to the technical field of data compliance and risk control, and the method comprises the steps: obtaining an execution plan of a target operation task and risk control strategy information of the execution plan, analyzing operation data items, distributing corresponding compliance verification time windows and levels, and forming an operation data verification scheduling table; according to the verification level and the business domain, a rule set is extracted from a compliance verification library, and rules comprise predefined rules, model output rules and historical violation rules; field-level matching, logic consistency verification and cross-source linkage verification are executed in the verification time window; when the verification result meets the triggering condition, generating an abnormal event, and executing response operations such as delay, recheck or interception; and finally, writing the execution state and the verification result into an abnormal log queue for risk control strategy parameter updating or re-evaluation. According to the method, the dynamic compliance verification and strategy closed-loop optimization of the operation data are realized, and the intelligent compliance control capability of the system is improved.

The invention discloses a large language model multi-source inference knowledge graph question answering method and system, and the method comprises the steps: extracting entities in a question, linking the entities with knowledge graph entities, and determining a subject entity set; searching paths in the knowledge graph by adopting a graph constraint reasoning method, and obtaining candidate answers and reasoning paths; detecting incomplete path coverage or answer dimension deficiency by using a large language model, triggering question decomposition to generate logically complementary sub-questions, and obtaining answers; establishing an inference source coordination layer, a coordination graph constraint inference source, a planning-retrieval inference source and a sub-problem inference source for multi-source evidence; and standardizing the output of each inference source into a structured evidence triple, sorting and selecting the first K evidences according to relevance, and generating a final answer by utilizing a large language model to carry out inductive inference. According to the method, reasoning can be dynamically adjusted, multi-source evidences can be effectively aggregated, logic consistency and semantic integrity are guaranteed, and the integrity and accuracy of knowledge graph multi-hop questions and answers are improved.

The invention relates to the technical field of document automatic processing, and discloses a bidding and tendering document generation and automatic review method and system based on a large language model.The method comprises the steps that firstly, a source heterogeneous constraint document is obtained and analyzed, and a structured semantic unit is obtained; secondly, based on the structured semantic unit, constructing a dynamic abstract semantic graph; then, based on the topological structure of the dynamic abstract semantic graph, controlling the large language model to generate contents node by node by adopting a multivariate constraint guide mode, and performing instant verification; when a user modifies the content, executing a bidirectional consistency propagation algorithm based on a graph, reversely mapping text change into node attribute update, and automatically detecting global logic conflicts; and finally, combining the final text of each node in the graph, and outputting a complete bidding and tendering file. According to the method, through structured modeling and bidirectional consistency maintenance, the accuracy, controllability and logic consistency of the generated content are improved, and automatic consistency maintenance of the whole life cycle of the document is realized.

The invention discloses a DCS operation and maintenance method based on fusion dynamic weight distribution and fault chain deduction, and relates to the technical field of operation and maintenance of a distributed control system, and the method comprises the steps: obtaining a plurality of groups of source data of a monitoring region; corresponding monitoring periods are determined according to the multiple groups of source data, and differential monitoring period information is obtained after combination; according to the technical key points, a multi-source data fusion module is utilized to associate data of a DCS system, an associated production system and a cross-plant cooperative system, and fragmented data such as equipment operation data, production load fluctuation and cross-plant interaction information are woven into a complete data network through adjustment of space-time alignment and multi-dimensional verification; the global sensing capability not only solves the one-sidedness of traditional single data judgment, for example, environmental interference is easily misjudged by only depending on equipment temperature data, but also can capture cross-system implicit associated faults; and secondly, when the multi-source data are mutually verified, an evidence chain is formed through logic consistency verification, so that the misjudgment rate of suspected faults is greatly reduced.

The invention relates to the technical field of information extraction, in particular to an intelligent table filling method and system based on deep semantic analysis. The intelligent table filling method based on deep semantic analysis comprises the following steps: performing content extraction and format standardization on an input document to form a standard document text; a large language model is adopted, deep semantic analysis is executed on a standard document text, an entity of the document is recognized, and corresponding semantics are analyzed; analyzing semantics, position coordinates and logic partitions of fields in the target Excel template; establishing a corresponding semantic mapping relationship based on the semantic similarity between the entity of the document and the field of the target Excel template in the vector space; and based on the semantic mapping relationship, executing a hierarchical filling operation according to a semantic similarity threshold. According to the intelligent table filling and reporting method and system based on deep semantic analysis, the accuracy of automatic filling and reporting is improved through format verification, logic consistency check and integrity verification.

The invention relates to an AI-driven code automatic completion method, device and equipment and a storage medium thereof, and the method comprises the steps: constructing a grammar structure and variable definition of a code through semantic analysis, analyzing inter-line logic association and a variable transfer chain based on context-dependent modeling, the problem that in the prior art, a completion result deviates from an intention due to semantic fault is solved. A variable function and a logic target are identified in combination with deep semantic reasoning, a multi-path candidate scheme is generated, semantic complete paths are screened, and the defect of logic consistency deficiency in a multi-path decision scene is overcome; finally, path selection is dynamically optimized by fusing historical behavior data of a developer, a basic code is generated through template instantiation, a personalized completion result is output through logic verification and style adaptation, the intention understanding accuracy and the style adaptation capacity under a complex code scene are improved, and intelligent completion which is logically coherent and conforms to developer habits is achieved.

The invention discloses a power transmission line fault positioning method and system, and relates to the technical field of power transmission lines. The method comprises the following steps: dividing a power transmission line into a plurality of logic sections, and setting a basic logic rule; performing coarse-grained threshold judgment and fine-grained logic check on the real-time monitoring parameters of each logic section, and screening out candidate fault logic sections; establishing a node logic link, detecting a breakpoint node of the node logic link, comparing the breakpoint node in a previous time period and a later time period, judging whether the breakpoint node is a persistent fault or not, and obtaining a breakpoint judgment result; performing logic consistency check on breakpoint judgment results of different monitoring points and different parameter types, and when conflicts occur, making decisions according to priorities to generate a unified judgment result; and synthesizing the candidate fault logic sections and the unified judgment result to generate a final fault positioning result of the power transmission line. According to the invention, high-precision, reliable and intelligent positioning of power transmission line faults is realized.

The invention discloses an automatic causal structure generation method based on semantic representation and logical reasoning of a large language model. The method comprises the following steps: acquiring an input text; performing semantic coding and clustering on the obtained input text by utilizing a large language model, and establishing a candidate causal variable set; causal relationship detection is carried out on the established candidate causal variable set based on anti-fact intervention and do-calculation; performing causal direction judgment, and generating a directed acyclic causal graph meeting logic consistency; and on the basis of the generated directed acyclic causal graph, natural language interpretation is generated by using a large language model, and logic consistency closed-loop verification is carried out. According to the method, automatic generation from the natural language to the causal structure is realized, the causal variable set is automatically extracted and constructed from the unstructured natural language text, the defects that variables need to be manually defined and modeling depends on field experts in the existing causal modeling process are avoided, and the labor cost and professional threshold of causal structure construction are remarkably reduced.

The embodiment of the invention provides a knowledge graph fusion method and system based on a large model, and the method comprises the steps: carrying out the data standardization, entity feature enhancement and relation semantic annotation of heterogeneous knowledge graphs from different sources; through semantic similarity calculation, context reasoning and alignment confidence evaluation, a multi-stage and multi-mode entity alignment mechanism is constructed in combination with a large language model, and cross-source entity matching is performed on entities in heterogeneous knowledge maps of different sources; based on conflict detection, dynamic weight distribution and a conflict resolution strategy, generating a data fusion result, and unifying multi-source data; and generating a high-quality unified knowledge graph through missing relationship prediction, logic consistency verification and an incremental updating mechanism. According to the knowledge graph fusion method and device, full-process automation, precision and dynamics of knowledge graph fusion are realized, the problems of high manual dependence, weak semantic processing capability, poor cross-domain adaptability and the like in the prior art are effectively solved, and the efficiency and quality of knowledge graph fusion are remarkably improved.

The invention discloses a memory optimization system and method for an intelligent accompanying robot, and belongs to the technical field of computers, and the system comprises a multi-mode memory coding unit which is used for processing original interaction data so as to generate structured memory nodes containing emotional valence states; the memory association initialization unit is used for carrying out association weight calculation on the basis of the time proximity and the semantic relevance between the memory nodes so as to generate an initial association weight; the memory node importance evaluation unit is used for carrying out value evaluation on the memory nodes to generate node importance scores; and the association weight adaptive adjustment unit is used for carrying out iterative updating on the initial association weight in combination with semantic and emotion association between the node importance score and the memory nodes so as to generate an updated association weight. Therefore, higher logic consistency and emotional continuity are shown in long-term interaction with the user.

The invention belongs to the field of contract generation, and provides a contract generation method and system based on multi-round dialogue interaction and intelligent element extraction, and the method comprises the steps: carrying out the multi-round dialogue interaction, carrying out the intention recognition of the interactive multi-round dialogue content through a pre-trained large model, determining a keyword, and carrying out the intelligent element extraction. According to a pre-configured keyword-contract operation matching relationship, determining an operation needing to be executed for contract generation and an intention to which the operation belongs; performing field integrity verification, term logic consistency verification and law compliance verification in sequence; identifying missing contract elements in the contract by using a pre-trained model, calculating the question priority of each missing contract element, generating a question according to the question priority and the contract business scene, and complementing the missing contract elements; and assembling and generating a contract document based on the complete contract elements. According to the invention, the contract generation efficiency and accuracy are improved, and the user experience is enhanced.