1859 results about "Categorization" patented technology

An AI-based Cybersecurity System and Method enable real-time detection, analysis, and mitigation of cyber threats within computing networks using adaptive artificial intelligence. The system continuously monitors network traffic, extracts behavioral and contextual attributes, and applies deep learning-based inference to identify anomalous activities indicating security breaches. The method integrates several computational units, including a network monitoring unit, feature extraction unit, artificial intelligence processor, contextual reasoning processor, and decision synthesis unit, to compute a composite risk index quantifying threat likelihood and severity. A classification processor categorizes detected threats into types such as ransomware, phishing, or unauthorized access, while a mitigation control processor initiates automated response actions to isolate compromised nodes and restore network integrity. An adaptive learning processor updates AI models using feedback from confirmed incidents. This provides a scalable, self-evolving cybersecurity framework that minimizes human intervention and enhances resilience against dynamic and zero-day threats.

In a described embodiment, a system for information processing is provided including a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system further includes a cognitive reasoning module configured to evaluate the reasoning process of the language model, emulate cognitive functions including metacognitive processes, and generate an assessment based on an analysis of the received feedback, wherein the assessment includes classifying the one or more outputs into components, assigning quality scores for each component, and identifying an improvement corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive reasoning module for adjusting the reasoning process of the language model based on the assessment is provided. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated assessment until a performance threshold is met.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The invention discloses an intelligent approval rule modeling method oriented to process automation, and relates to the technical field of business process management, and the method comprises the following steps: S100, in a process of constructing a rule candidate set, extracting scene features, field semantic hierarchy and participation role information of each piece of historical approval data, generating a context semantic tag set, and establishing a rule candidate set; the method is used for subsequent rule difference modeling. According to the method, context semantic tags are introduced to be aligned with ternary features, so that the semantic boundary recognition capability of the rule is enhanced; constructing a rule feature matrix and a differentiation candidate set, and realizing accurate classification and processing of ambiguity rules; in combination with expression sensitivity enhancement and simulation verification, approval offset and risk are identified in advance; finally, the dynamic optimization of the rule model is realized through backtracking correction, the stability and accuracy of the rule model in multiple scenes are improved, and a closed-loop credible intelligent approval rule system is constructed.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

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 discloses an explosion-proof equipment full life cycle digital management system based on artificial intelligence, and relates to the technical field of equipment management. The working process of the system comprises the following steps: integrating equipment attributes, operation and maintenance records and environment variable data, calculating a performance attenuation value through a weighting formula, and standardizing the data; correcting an abnormal timestamp by adopting a dynamic time window, realizing cross-system equipment identity mapping in combination with Hash similarity and parameter matching degree, and reconstructing a three-dimensional feature tensor; equipment is divided into three types, and differential weighted pooling processing is executed to generate a classification feature matrix; a reference parameter curve is generated through exponential decay weighting, a normalized deviation score of the fusion environment factors is calculated, and a grading early warning mechanism is triggered; generating an early warning report; and implementing a closed-loop strategy according to the early warning level. The system solves the problems of equipment identity confusion, environment-parameter coupling quantification and the like, realizes full-chain intelligent management from data acquisition to risk disposal, and improves the safety and operation and maintenance efficiency of explosion-proof equipment.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

The invention provides a natural language text data intelligent classification method and system based on deep learning, and relates to the technical field of natural language processing, and the method comprises the steps: 1, employing a context awareness mechanism to analyze the real semantics of a target vocabulary according to an antagonistic variant existing in a text, and obtaining a target vocabulary; in combination with a word meaning library and a pre-training process of a dynamic learning rate adjustment strategy, generating a candidate replacement vocabulary set with consistent semantics; and step 2, based on the candidate replacement vocabulary set, performing multi-dimensional semantic similarity calculation and emotional tendency discrimination, determining applicable vocabularies conforming to an original culture background through a context adaptation strategy, and generating a standardized text sequence. According to the method, through multi-dimensional semantic analysis, cultural context fusion, cross-granularity feature construction and dynamic parameter correction, the accuracy and adaptability of natural language text classification are realized.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical model cascade to classify queries into agent domains. Queries are processed iteratively by a series of hierarchical levels containing one or more AI models, where each layer is more complex and imposes fewer resource constraints. Each level generates a classification and a confidence score pertaining to the classification. A dynamic bypass mechanism analyzes the classifications and confidence scores at each level to dynamically determine if one or more levels of the hierarchy can be bypassed while resulting in an accurate classification. The final classifications are matched to one or more agents that process the query. Responses from the candidate agents are aggregated into an output that is responsive to the input.

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

The invention provides a dam leakage intelligent identification method based on multi-modal fusion and knowledge enhancement, and the method comprises the steps: collecting real-time data of a multi-source sensor disposed at a key part of a dam in a preset monitoring time period, and generating seepage characteristic data; identifying a seepage form entity based on the seepage characteristic data and extracting an instantaneous characteristic entity, and associating the entity into a structured knowledge unit according to a space-time proximity principle; knowledge units are classified according to spatial positions and influence ranges, association rules of the knowledge units are complemented, and knowledge graph construction is achieved; and then, a map inference engine is triggered in a real-time feature matching mode, and graded early warning is implemented. According to the method, physical enhanced seepage characteristics are constructed, seepage forms, dynamic characteristics and inducements are deeply associated by utilizing a knowledge graph technology, accurate diagnosis and reasoning from data abnormity to seepage types, causes and risk levels are realized, and finally, the seepage characteristics are analyzed and analyzed through a dynamic conflict resolution and self-evolution mechanism. And a reliable dam leakage intelligent identification and decision-making system is formed.

Disclosed herein are devices, systems, and methods for detecting, understanding, and classifying malicious actions and / or behaviors in software (e.g., malware), including hidden malicious actions. Specifically, disclosed embodiments use natural language approaches to understand malicious software and provide explanations for classification results. At least one embodiment constructs a knowledge graph that includes textual explanations from source materials (e.g., articles), collecting one or more sets of dynamic program traces from one or more instances of malware, and constructing and training a model (also referred to herein as Trace-BERT) using the one or more sets of dynamic program traces. Forced execution of sample segments of computer code can also be used to identify hidden or novel malicious actions.

The embodiment of the invention provides an intelligent agent memory indexing method and system based on intention recognition. The method is applied to the technical field of artificial intelligence and comprises the steps of obtaining real-time question-answer data, and performing preliminary intention classification on the real-time question-answer data by utilizing a domain knowledge rule library; extracting a structured description from the real-time question and answer data after the preliminary intention classification, performing deep intention analysis in stages, and outputting a standardized intention description text; according to the standardized intention description text, acquiring an Agent operation context, performing multi-dimensional retrieval to obtain an adaptive strategy, executing the adaptive strategy, and returning a strategy evaluation result; according to a strategy evaluation result, carrying out microscopic feedback and macroscopic feedback to update a strategy library; the Agent operation context is obtained through the following steps that semantic features of a standardized intention description text are captured, and the Agent operation context corresponding to the deep semantic features is recorded based on a fine-grained metadata labeling system. According to the invention, a complete closed loop from intention identification to strategy multiplexing to strategy optimization is realized.

The invention discloses an AI-based large-model-driven contract review and regulation interpretation method and system, and the method comprises the steps: S1, collecting a regulation and institute document, and carrying out the text extraction and semantic disassembly, and obtaining term information; s2, based on a LawCheckLM + BERT-CRF named entity recognition model, performing entity recognition and classification labeling on clause information, and storing recognized key information fields into a knowledge base; s3, encoding each piece of clause information into a semantic vector through an embedded model, and storing the semantic vector into a knowledge base; and S4, performing text extraction and semantic disassembly on the uploaded contract document to obtain clause information, repeating the steps S2-S3, retrieving similar semantic vectors of laws and regulations or institutional documents similar to the contract from the knowledge base as references, if the similar semantic vectors are not retrieved, taking Top-N laws and regulations or institutional documents with similar semantics as outputs, and marking the Top-N laws and regulations or institutional documents as required to be examined. The problems that an existing management system is difficult to adapt to regulation changes, complex contract review and cross-scene deployment are achieved, and the overall iteration period is long are solved.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention relates to a bidding document total-factor blind-state desensitization method fusing multi-modal artificial intelligence, and the method comprises the following steps: S1, obtaining an original bidding document package, carrying out the data preprocessing, and obtaining a standardized document set; s2, according to the standardized document set, performing multi-modal content identification and alignment to obtain a multi-modal content library; s3, constructing a sensitive information classification system based on domain expert knowledge and a historical case library; s4, constructing a dynamic rule base according to the sensitive information classification system; s5, according to the multi-modal content library and the dynamic rule library, in combination with deep learning, multi-modal sensitive information intelligent detection is carried out, and a sensitive area set is confirmed; and S6, based on the dynamic rule base, performing intelligent desensitization on the sensitive area set to obtain a desensitized document. According to the method, the risk of sensitive information leakage is effectively reduced while the bidding file expression effect is improved.

The invention discloses a fault determination method and device for a micro-service system, a product and electronic equipment. Relates to the technical field of artificial intelligence. The method comprises the following steps: determining an abnormal event flow according to abnormal monitoring information; an abnormal event causal graph is determined based on an abnormal event flow, and then a fault point, a fault chain and a fault classification are determined by utilizing an intelligent agent driven by a pre-training model and combining the event causal graph and a fault mode standard. According to the method, anomaly detection, fault classification and root cause positioning of the micro-service system are realized, and closed-loop diagnosis from anomaly discovery to root cause positioning is realized; secondly, performing depth feature engineering and semantic abstraction on the refined data from the perspective of events and causal relationships, fusing system behaviors and dependency relationships dispersed in different modal data into a unified event causal graph through a graph modeling technology, and providing comprehensive and high-dimensional input for fault reasoning of an intelligent agent; and the accuracy of micro-service fault determination is improved.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention provides a classification model training method, device and equipment based on a large language model, and relates to the technical field of natural language processing and reinforcement learning. The method comprises the steps of performing classification prediction on a first training set through a base large language model to generate an initial prediction category, and performing fine adjustment on the base large language model according to the initial prediction category and a corresponding real category to obtain a fine-adjusted large language model; through the fine-tuned large language model, screening difficult sample texts which are wrongly classified from the first training set, and constructing a second training set based on the difficult sample texts; and based on the second training set, a reinforcement learning strategy is adopted to update strategy parameters in the fine-tuned large language model, and a target classification model is obtained.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention discloses a multi-scene self-adaptive man-machine interaction system and method based on emotion recognition, relates to the technical field of man-machine interaction, and solves the technical problems of realizing fusion perception of multi-modal emotion features and improving the accuracy of emotion judgment in complex scenes. According to the method, facial, voice and text emotion features are extracted by adopting a multi-modal fusion technology, the limitation of single-modal recognition is solved, an emotion-scene association rule base and a user portrait are constructed, real-time scene classification is combined, accurate mapping of emotions, scenes and demands is realized, one-step interaction of strategies is avoided, and the user experience is improved. Language interaction adaptation is designed from the form, content and style three-dimensional degree, it is ensured that languages are natural and fit scenes, functional response adaptation improves efficiency through priority ranking and execution mode optimization, environment linkage adaptation is combined with user emotion dynamic adjustment directions, collaborative linkage of languages, functions and environments is achieved, and strategy splitting is avoided.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal information analysis and scheme reminding method, device, equipment and medium, which comprises the steps of receiving input data and converting the input data into multi-modal data, executing optical character recognition and image classification recognition to generate a recognition result, and sending the recognition result to a server; a natural language processing model is used for analyzing fuzzy description to generate an analysis result, a knowledge base is inquired, a knowledge graph is combined to generate an association result, the analysis result, the association result and user feature data are fused to generate an execution scheme, the execution scheme is compared with an abnormal list, supervision confirmation is triggered, and a compliance instruction is generated. And personalized reminding contents are generated. The information analysis integrity is improved through multi-modal recognition, natural language processing and the knowledge graph, supervision confirmation and personalized reminding are introduced, and intelligent, compliant and reliable reminding management is achieved.

The invention provides an adaptive retrieval enhancement generation method and system based on question classification, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a policy question and answer question input by a user and an external knowledge base; user questions are input into the question classification model, question classification results are obtained, and the question classification results comprise any one or more of explicit facts, implicit facts, interpretable reasoning, policy matching and recommendation; according to a question classification result, a corresponding retrieval strategy module is adaptively selected, and related policy fragments are retrieved from an external knowledge base by utilizing the selected retrieval strategy module; taking the related policy fragments as context information, inputting the context information into an answer generation module, and generating a plurality of candidate answers; performing multi-dimensional quality evaluation on the plurality of candidate answers through an answer scoring module to obtain a comprehensive score of each candidate answer; and selecting the candidate answer with the highest comprehensive score as a final policy question and answer output.

The invention discloses a human resource intelligent management method and system based on man-post matching, and the method comprises the steps: receiving an unstructured post description text, extracting key information through a natural language processing technology, generating a structured multi-dimensional post portrait, and classifying the structured multi-dimensional post portrait; on the basis of historical recruitment data, predicting the number of future post demands by means of a time sequence analysis model; for the candidate resumes and the target post portraits, keyword correlation scores, depth semantic similarity scores and predictive stability scores are calculated in parallel; according to the post portrait classification application dynamic weight, performing weighted summation on the scores to generate a comprehensive matching score; and sorting the candidates according to the comprehensive matching scores, and outputting a sorted candidate list. According to the method and the system provided by the invention, the man-post matching accuracy and the recruitment efficiency are effectively improved, the core pain points of intelligent recruitment, man-post matching and demand prediction in human resource management are solved, and the method and the system are particularly suitable for the demands of human resource outsourcing and labor dispatching industries for stable service selection.

The invention discloses a multi-modal open intention recognition method and system based on pellet characterization, and belongs to the technical field of artificial intelligence and multi-modal intention understanding, and the method comprises the steps: carrying out the feature extraction and modal fusion of multi-modal input data; carrying out structural modeling on the feature representation of each mode and the fusion mode through an adaptive particle and ball clustering method, and generating a multi-granularity particle and ball set; the mass centers of the pellets serve as multi-granularity anchor points, and the pellets with the same labels in different modalities are aligned; introducing a weighting mechanism based on purity and sample scale into the fusion mode; generating a boundary-constrained pseudo-distribution outer sample in the fusion modal space; and constructing a self-adaptive decision boundary based on the fusion modal particle ball obtained by training, and carrying out known class classification and unknown class detection. According to the invention, by introducing the multi-granularity anchor point and the structure perception particle-ball representation mode, the joint recognition of the known category and the unknown category in the multi-modal scene is realized, and the accuracy and robustness of intention recognition are remarkably improved.

The invention discloses a code generation and restoration method and device based on multi-round feedback of a large language model, and the method comprises the following steps: 1, converting problem description into a structured tetrad (target, input, output and constraint) through a two-stage task demand deconstruction mechanism, and generating a pseudo-code frame according to the structured tetrad; 2, generating a plurality of candidate code schemes in parallel based on different implementation strategies; 3, selecting an optimal code implementation scheme through multi-dimensional evaluation; 4, generating an extended test case through boundary condition analysis and public test reasoning, and expanding a verification range; 5, classifying error types in combination with static analysis and dynamic execution information, and applying a special repair strategy; 6, providing detailed repair reasons and ideas by an interpretable repair mechanism; and 7, establishing a multi-round feedback repair verification iteration mechanism composed of testing, analysis, repair and verification. According to the method, the performance of an existing large language model in processing a complex programming task is effectively improved.

The invention belongs to the technical field of intelligent question-answering systems, and particularly relates to an intelligent question-answering inference system based on a knowledge graph, which is characterized in that firstly, a knowledge graph construction module fuses multi-source data to generate a structured graph, and after a user inputs a natural language question, a question-answering analysis module completes intention classification and entity disambiguation and converts the question into structured query; an inference engine module fuses symbol rules and graph neural network inference through a hybrid inference sub-module, and a dynamic weight adjustment sub-module optimizes weights according to errors and attenuation factors to generate an inference result; the knowledge updating module incrementally updates the atlas in real time and detects conflicts, the interactive interface module visually presents a result, and the evaluation optimization module iteratively optimizes parameters in combination with offline evaluation and online feedback. The whole process is from user question asking to result output, accurate reasoning and continuous performance improvement are achieved, and multi-field question and answer requirements are met.