535 results about "Consistency analysis" patented technology

The invention discloses an underground construction decision-making method based on three-dimensional geological modeling and risk hot area identification, and relates to the field of fusion of artificial intelligence and geological engineering. The method comprises the following steps: firstly, acquiring drilling data, geological radar images and seismic reflecting layer information, constructing a three-dimensional geological voxel model with spatial topology constraints, and accurately describing a geological unit structure by adopting an irregular grid mode; and then, extracting a time sequence characteristic index under construction disturbance, forming a continuous time sequence characteristic vector, inputting the continuous time sequence characteristic vector into a convolutional recurrent neural network model with a space attention aggregation mechanism and a deep memory unit, and predicting a risk heat value of each space position. And on the basis, through heat gradient clustering and neighborhood consistency analysis, a dynamic high-risk hot area is identified, and a risk hot area map is constructed. And finally, in combination with the construction stage, the equipment plan and the sensor feedback information, constructing a multi-target auxiliary decision function, and generating a construction decision result including operation path reconstruction, rhythm adjustment and power limit and control suggestions.

The invention relates to the technical field of adaptive control, in particular to an unmanned aerial vehicle adaptive flight control system and method based on multi-modal fusion, and the system comprises an airspace analysis module, a signal screening control module, a state recognition module, a parameter matching module and a feedback adjustment module. According to the method, through a multi-source task data analysis and trend signal screening mechanism, the accuracy and real-time performance of path planning and adjustment are improved, through combination of channel fluctuation trend identification and abnormal state locking, the stability of sensing data input is enhanced, and the identification accuracy of an unbalance state is analyzed and optimized by using attitude change direction consistency. The attitude error trend and control parameter segment similarity comparison mode is adopted, parameter matching has dynamic responsiveness, control weight configuration is adjusted through collaborative analysis of the path direction component and the attitude adjustment trend, rhythm self-adaptive adjustment of aircraft control output is achieved, and the control precision of the aircraft is improved. And the dynamic adjustment capability and the safety redundancy response capability of flight control are enhanced.

The invention relates to the technical field of grinding wheel monitoring, in particular to a centerless grinding machine full-life-cycle grinding wheel state monitoring system and method.According to the centerless grinding machine full-life-cycle grinding wheel state monitoring system and method, through combined difference extraction of a main shaft current mean value fluctuation ratio and a power gradient continuous variation value and assisted by transverse comparison of periodic response intervals, effective recognition of time period boundaries of working condition states is achieved; according to the method, operation stage division does not depend on static threshold judgment any more, a track trend is established through linkage judgment of an amplitude kick point and a slope extreme value, a dynamic evolution path of signal response is clearer, the identification resolution of abnormal changes and local deviation trends is improved, response synchronous identification of parameter state changes is achieved, and the identification accuracy is improved. In multi-signal collaborative analysis, directivity comparison between a temperature rise mutation fragment and a current residual error response is carried out, so that the consistency analysis capability of response logic between different monitoring paths is enhanced, and the real-time performance, stability and anti-interference capability of state identification are improved.

The invention discloses a public institution kitchen energy data management system based on cloud data, particularly relates to the field of energy management, and comprises a multi-source heterogeneous data acquisition module, an edge intelligent preprocessing module, a cloud data lake storage module, a multi-dimensional analysis engine module, an intelligent decision center module and a visual interaction module. According to the method, space-time alignment of multi-source data is realized through protocol adaptive conversion and millisecond time synchronization technologies, a high-consistency analysis basis is constructed, real-time index calculation and adaptive model optimization are implemented by edge calculation and cloud dynamic modeling collaborative architecture, hidden energy loss is accurately identified, and the real-time performance of the system is improved. The intelligent decision center is combined with digital twinborn verification to generate a control strategy, a minute-level closed-loop response mechanism is formed, automatic adjustment and optimization of equipment parameters and intelligent distribution of maintenance work orders are achieved, manual intervention delay is eliminated, timeliness and execution accuracy of an energy strategy are guaranteed, and multi-system collaborative management efficiency is remarkably improved.

The invention relates to the technical field of positioning and navigation, and discloses a multi-modal Transform-based UWB multi-sensor fusion positioning method and system, and the method comprises the steps: collecting the signal features of a target at different positions through deploying a plurality of UWB sensors and auxiliary sensors, and constructing a multi-modal data set; performing feature extraction and fusion on the multi-modal data set by using a multi-modal Transform model to generate global feature representation of the target; and according to the global feature representation, in combination with a dynamic weight distribution mechanism, predicting position information of a target, and carrying out adaptive correction on an abnormal signal. By introducing a multi-modal Transform model and combining a dynamic weight distribution mechanism and a self-adaptive correction strategy, high-precision positioning of a target position in a complex environment is realized. The positioning robustness is optimized through weight adjustment and track consistency analysis; modeling is carried out by utilizing deep fusion of multi-modal data and a time-space relationship, and the positioning efficiency and adaptability in a multi-source heterogeneous data scene are remarkably improved.

The invention belongs to the technical field of digital content security, and discloses a multi-mode AI content security risk traceability and identification detection system. Through obtaining multi-source heterogeneous data of content creation, editing, distribution and presentation of a full link, a content full link feature fingerprint is constructed, and cross-modal consistency analysis and link continuity verification are realized. According to the method, traceability map construction is introduced, potential tampering points are used as map nodes, content risks are quantified through tampering probability weights and risk transfer intensity, and tampering sources and propagation paths are accurately identified. The method has comprehensiveness and accuracy, deep analysis and risk assessment can be carried out on the multi-modal content, and the tampered content is effectively recognized and synthesized.

The invention discloses a three-dimensional map reconstruction method, a three-dimensional map reconstruction device and three-dimensional map reconstruction equipment without dynamic object interference, which are used for realizing high-precision three-dimensional map reconstruction based on dynamic obstacle shielding background restoration. According to the invention, a deep learning technology, multi-view information fusion and prior frame data are combined, a dynamic obstacle detection, segmentation and elimination scheme is carried out, and a high-precision background restoration strategy is designed at the same time. Wherein the dynamic object is identified and segmented through the deep learning model, and the influence of the dynamic object is accurately eliminated in combination with motion consistency analysis, so that mismatching and pose drift in a dynamic scene are avoided. Besides, by means of fusion of multi-view information and historical frame data, the static background shielded by the dynamic object is repaired, high-precision complementation of the background and accurate restoration of the three-dimensional map are achieved, the three-dimensional map without interference of the dynamic object can be effectively constructed, the precision and robustness of the map are enhanced, and especially in a complex dynamic environment, the method has the advantages of being high in practicability and easy to popularize. And relatively high stability can be maintained.

The invention relates to the technical field of artificial intelligence and information processing, and particularly discloses a multi-modal file intelligent classification and label generation method and system. The method comprises the steps of obtaining text paragraphs, image segments and video key frame data in a file, performing format recognition and region separation, and generating multi-modal content structure data; semantic features are extracted based on the data, and fused semantic representation is constructed; generating a main label, a sub-label and a keyword set by using the fused semantic vector, and constructing a multi-level label structure; and further performing label redundancy elimination and structure optimization to generate a label atlas, and performing consistency analysis and feedback optimization through the label atlas and the semantic representation structure. Compared with the prior art, the method has the advantages that various modal information can be effectively fused, the accuracy, hierarchical structure and semantic consistency of archive label generation are improved, and the method has high intelligence, self-adaption and sustainable optimization capabilities and is suitable for various scenes such as archive management, content auditing and semantic archiving.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention relates to the technical field of building structure health monitoring, and particularly discloses a building group safety inspection method based on external structure size displacement detection, and the method comprises the steps: obtaining three-dimensional laser scanning point cloud data of a target building group at a plurality of time points, adding timestamp information, and constructing a time sequence point cloud data set; initial registration is carried out by adopting a feature matching and motion recovery structure technology, and the registration precision and stability in a dynamic environment are improved in combination with a global optimization model fused with space-time consistency constraints; identifying and eliminating pseudo displacement point clusters through track consistency analysis, and extracting a static background structure; finally, key part deformation identification is carried out on the differential point cloud data, and a structure health state evaluation report is generated. According to the invention, full-process automatic processing from data acquisition, optimization registration to intelligent evaluation is realized, the dynamic interference influence is effectively inhibited, the precision and reliability of structural deformation monitoring are improved, and technical support is provided for long-term safety inspection of building groups.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention relates to the technical field of artificial intelligence, in particular to a large model training system and method based on multi-modal medical data fusion, and the method comprises the steps: collecting multi-modal medical data, and carrying out the feature extraction and pre-calculation of the multi-modal medical data; performing fusion analysis on the information distinguishing value evaluation of the sample features and the label association strength to obtain a context adaptability regulation factor of the sample data; performing modal consistency analysis and modal significance analysis on the multi-modal medical data sample to obtain an information value deepening regulatory factor of the sample data; performing optimization adjustment on the original attention score through a context adaptability adjustment factor and an information value deepening adjustment factor to obtain a final attention score; and multi-modal data feature fusion and model training are performed through the final attention score, so that the accuracy of learning medical knowledge by the model is improved, and the final performance of the model trained by the training system on a target task is improved.

The invention relates to the technical field of personnel examination scoring, in particular to an end-to-end-based multi-agent personnel examination scoring method, which comprises the steps of generating fusion features based on image data of an examinee answer sheet, inputting the fusion features into a heterogeneous OCR engine for processing, and outputting an examinee answer text; establishing a knowledge base, analyzing a question requirement text, and screening out a reference answer text from the knowledge base; constructing a plurality of dimension scoring agents, determining an original state based on the question requirement text and the reference answer text, training each dimension scoring agent through the original state, inputting the examinee answer text into the dimension scoring agents, outputting dimension scoring scores, and forming dimension scoring vectors; carrying out self-consistency analysis by integrating the dimension score vector and the original state, and outputting a final score; the whole-process closed-loop processing from paper answer sheet scanning to high-precision, automatic and interpretable scoring is realized, and the marking efficiency, fairness and credibility are improved.

The invention belongs to the technical field of pathological diagnosis, and discloses an artificial intelligence-based lung cancer pathological section analysis and system, which introduces identification basis visual information generated by an artificial intelligence model, and combines the correction operation of a plurality of pathologists on a model identification result and the acquisition of correction reasons, so that the pathological section analysis of the lung cancer is realized. Further performing consistency analysis on the correction results to generate uniform data, and finally performing incremental training on an artificial intelligence model by using the data, thereby constructing a closed-loop feedback optimization mechanism combining artificial intelligence and expert knowledge. The model performance problem caused by insufficient training data coverage, expert labeling efficiency and result inconsistency of an artificial intelligence model in pathological diagnosis is solved, and model learning and performance improvement are achieved.

The embodiment of the invention provides a reliability analysis method and system of a body model, a medium, equipment and a product. The method comprises the following steps: receiving a target video of a target agent executing an operation task generated by the body model; performing one or more of motion consistency analysis, visual consistency analysis and logic rationality analysis on the target video to obtain one or more of a motion consistency index value, a visual consistency index value and a logic rationality index value; and determining a reliability analysis result of the body model based on one or more of the motion consistency index value, the visual consistency index value and the logic rationality index value. According to the embodiment of the invention, the key index is obtained by analyzing the target video of the operation task executed by the target agent generated by the body model, so that the reliability of the body model in an actual task scene can be effectively evaluated.

The invention relates to the technical field of ocean remote sensing and satellite monitoring, in particular to a satellite-borne laser radar ocean water color product quality evaluation and calibration method, system and program, and particularly aims at improving the inversion precision of laser radar ocean water body parameters. The method comprises the steps of multi-source data acquisition and standardization, dynamic space-time matching window optimization, abnormal data filtering, seasonal conversion factor dynamic calculation, multi-source cross validation, consistency analysis, regional interpolation modeling, full-link uncertainty quantification, day and night difference correction and dynamic feedback optimization. By constructing a self-adaptive calibration mechanism and an error control process, the inversion precision and stability of a laser radar water color product are remarkably improved, and high-credibility data support is provided for marine ecological monitoring and carbon flux evaluation.

The invention discloses a pump set vibration monitoring method and system based on distributed sensing. The method comprises the steps that vibration collection signals and real-time working condition parameters of distributed sensing nodes are obtained, multi-node clock synchronization correction is executed, and synchronous vibration data frames are formed; time-frequency feature extraction is carried out on the multi-channel vibration data set, abnormal channels are identified through inter-channel consistency analysis, a local fault correlation frequency band is extracted, and a vibration feature vector is constructed; associating the vibration feature vector with a real-time working condition parameter to form a working condition-feature mapping table, identifying a feature mutation rotating speed point for the feature baseline, adaptively dividing a rotating speed interval to generate an adaptive working condition threshold value, and implementing out-of-limit detection to generate an abnormal trigger identifier; determining a traceability analysis window based on the abnormal trigger identifier, and executing multi-measuring-point coherence calculation and energy attenuation gradient analysis to construct an energy transfer link diagram; and finally, vibration source position positioning is performed to form fault source probability distribution, a fault mode label is matched, pump set vibration monitoring is completed, and quantitative positioning of a fault source is realized.

The invention discloses a multifunctional safety helmet management system and method based on the Internet of Things, relates to the field of safety production, and solves the problems that a traditional safety helmet is single in safety management function, insufficient in data processing precision and the like. A multi-dimensional state data sequence is continuously collected through a three-axis acceleration sensor, a temperature and humidity sensor, a positioning sensor, a vital sign sensor and the like integrated on the safety helmet terminal; terminal monitoring characteristics including an acceleration module value, a position drift distance and a physical sign fluctuation index are constructed, data reliability is evaluated through weighted fusion of dynamic fluctuation values, and an abnormal terminal is isolated. And carrying out space-time calibration on the reliable data to generate a unified coordinate system trajectory, analyzing and identifying abnormal behaviors through group trajectory consistency, and constructing a regional safety risk map in combination with a signal blind area coverage rate, thereby realizing safety risk level prediction based on historical data and real-time trajectory matching. According to the invention, comprehensive real-time monitoring of the state of the operator is realized, and the intelligent level of safety production management is improved.

The invention discloses a multi-agent cooperative control system based on adaptive learning, and belongs to the technical field of multi-agent system control, and the system specifically comprises the steps: collecting and analyzing the wind field data of the position of each agent; distinguishing two typical working conditions of a global uniform wind field and a local disturbance wind field based on a consistency analysis result; for a global uniform wind field, a model prediction control method is adopted to generate a wind field compensation strategy for keeping formation stability; for a local disturbance wind field, an accurate airflow disturbance state is obtained through environment three-dimensional reconstruction and fluid simulation, then the expected track deviation of each agent is predicted, and a collaborative avoidance instruction set is generated in combination with a relative position relationship. According to the method, adaptive cooperative control of multiple agents under different wind field working conditions is realized, the hidden conflict problem caused by local wind field disturbance is effectively solved, and the flight safety and task reliability of an agent cluster in a complex environment are remarkably improved.

The invention discloses a supervision method and device based on construction project quality detection and a medium, and relates to the technical field of project quality supervision, and the method comprises the steps: collecting construction site data, carrying out the preprocessing, and generating a site data set; performing real-time comparison and verification on the building information model and the field data set to obtain verification data, inputting the verification data into the building information model, performing construction consistency analysis and deviation identification, and generating a quality evaluation report; performing risk assessment and priority classification on the quality analysis data to obtain risk assessment data, and performing problem priority analysis and rectification scheme formulation on the risk assessment data to generate rectification execution data; and performing rectification effect analysis and overall quality evaluation according to the rectification execution data, and generating a project quality summary report. The real-time performance and the accuracy of construction quality monitoring are improved, the construction deviation is reduced to the maximum extent, the quality management is optimized, and the smooth proceeding of a project is guaranteed.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

The invention discloses a dynamic and steady state characteristic evaluation method for a main fuel oil system of an aero-engine, which is used for carrying out systematic evaluation on dynamic and steady state performance of the main fuel oil system in different design stages. Respectively establishing differential evaluation mechanisms of three stages of design scheme demonstration, principle prototype development and product shaping and batch production; multi-dimensional evaluation of system response characteristics, control performance and robustness is realized by constructing a nonlinear system model and a linearized frequency domain model and combining typical working condition simulation and a principle prototype test; in the batch production stage, a standardized input working condition and a simplified index system are adopted, and batch production prototype performance consistency analysis and rapid quality judgment are achieved. According to the method, systematic evaluation of the aero-engine main fuel system in the full design stage can be achieved, a comprehensive evaluation system combining a model and a test is constructed, the design efficiency and reliability are improved, and the evaluation precision and credibility are improved.

The invention discloses a document understanding and question-answering method and system based on multiple modes and multiple agents, and the method comprises the steps: firstly carrying out the preprocessing of a document, generating a text paragraph and a page image, and then employing a retrieval enhancement generation frame to retrieve and query related contexts from two channels of a text and the page image, obtaining a text candidate set and an image candidate set; performing preliminary fusion by using five types of functional agents and forming a candidate conclusion, forming cross-modal key information to limit a subsequent analysis range, performing fine-grained examination and analysis in text and image modalities, and outputting intra-modal conclusions with position indications such as page numbers, paragraphs and regions; and finally, performing consistency analysis and weighted fusion on the multi-source result to generate a final answer. According to the method, the text and the visual clues can be integrated at the same time, the complex multi-modal reasoning capability is improved, and the accuracy in tasks such as document question and answer is improved.

The invention discloses a data-enhanced fine-grained multi-mode false information detection method and system, and aims to improve the accuracy of image-text false news detection. The method comprises the following steps: acquiring a news text and associated pictures thereof, extracting a text core entity semantic sequence and a visual entity semantic sequence by respectively utilizing a pre-training language model and a visual entity recognition model, and performing knowledge enhancement on original word-level text representation and low-level visual features through an attention mechanism; calculating a correlation matrix between the enhanced text and the visual features, dividing consistent and inconsistent regions, and respectively extracting consistent and inconsistent features; and finally, fusing the two types of features and global text representation to generate classification features so as to judge the authenticity of the news. Through double-channel knowledge enhancement and fine-grained cross-modal consistency analysis, the detection performance is remarkably improved, and the method is suitable for scenes such as social media and news platforms.

The invention provides a medicine bottle array detection system based on vision and sensing fusion, and relates to the technical field of data processing, and the system comprises the steps: obtaining the spatial distribution data of a vision unit and the posture state data of a sensing unit, fusing the position parameters and state parameters of each bottle body, building a multi-dimensional data node under a unified time index, calculating the spatial aggregation degree of the medicine bottle array to obtain an array quantity, combining the aggregation change of each time point into an evolution chain table, carrying out joint operation on the difference result and the state parameter of adjacent nodes in the evolution chain table, calculating the dynamic coupling degree of the medicine bottle array to obtain a sequence potential energy value, and dividing the sequence potential energy values of the different numerical value intervals into a plurality of level nodes, and performing consistency analysis and anomaly detection on features of the different level nodes to obtain detection result data. By detecting the state of the medicine bottles and the arraying direction, bottle toppling early warning is achieved.

The invention relates to a multi-competency intelligent scoring method and system, and belongs to the technical field of intelligent evaluation and talent evaluation. The method comprises the following steps: acquiring multi-source response data of a target evaluation object, executing evidence sufficiency and consistency analysis for each competency dimension, generating a judgment state identifier, and constructing a structural description; performing responsibility distribution on the evaluation evidence, mapping the evaluation evidence into a corresponding evidence model and generating a priority constraint relationship; performing time sequence consistency and stability analysis on the evidence model, and constructing a time sequence constraint rule to prohibit unconstrained score backtracking; and carrying out resolution processing on the continuous undetermined dimension, generating a scoring result through a preset risk scoring rule and a conservative determination strategy, and synchronously generating a scoring basis path and confidence information. According to the method, standard management and control of multi-source evidences and accurate constraint of the whole scoring process are realized, the scoring accuracy and traceability are effectively improved, and reliable support is provided for multi-scene talent evaluation decision.

The invention relates to a potential advertisement audience identification method. Comprising the following steps of: marking time weights on all behaviors of a user by adopting a behavior delay window concept, introducing behavior silence period logic, and identifying the user which does not clearly express an intention but has a behavior sequence in an abnormal set; a potential user state classifier is constructed, and users are divided into dominant intentions, potential intentions and irrelevant users; constructing a multi-time-scale behavior flow graph by using a sliding time window, and respectively calculating a short-term impulse behavior track and a long-term habitual behavior track; performing intention prediction on the behavior flow through an intention evolution modeling network, and outputting advertisement preference trend points which are not dominant but tend to be stable; adopting a multi-dimensional interest interference discrimination mechanism and real intention clarification; establishing a behavior stability evaluation model, calculating the behavior backtracking coverage degree of a certain point of interest, and excluding one-time behaviors; a cross-modal intention consistency analysis mechanism is introduced; and constructing an interest exclusion list, and filtering unstable interests or heterogeneous behaviors.

The invention discloses an image measurement method for precise size measurement. The method comprises the following steps: step 1, selecting a laser scanner and an optical image measurement instrument to form a measurement array according to geometrical characteristics of a workpiece; 2, calibrating and verifying the measurement array, obtaining a basic error curve of the equipment through a standard gauge block, and collecting real-time environmental parameters after verification is passed; 3, calculating an environment interference coefficient Kenv, and starting measurement at the moment; 4, monitoring equipment acceleration sensor data in real time, and generating an equipment stability index Si; 5, when Si is larger than S < safety >, measurement is executed, and a result is output; and step 6, carrying out consistency analysis on multi-device results, if the results are consistent, outputting a final value, otherwise, triggering dynamic re-calibration. According to the invention, multi-dimensional cooperative control can be realized, the precision bottleneck of traditional image measurement in a dynamic environment is broken through, and the method is suitable for precision manufacturing scenes with high requirements on measurement stability and reliability.

The invention relates to the technical field of equipment configuration management, in particular to a visual equipment element configuration system which comprises an operation trend analysis module, a configuration parameter screening module, a parameter conflict detection module, a parameter dynamic adjustment module and a configuration result analysis module. According to the method, through dynamic analysis of the extraction state values of the operation parameters of the equipment elements, accurate identification and induction of the operation change trend of the equipment are realized, clear capture of dynamic change information is ensured, and through screening of the operation parameters in the target range and elimination of configuration combinations which do not accord with the range, the pertinence and accuracy of screening are improved, and the configuration coordination is enhanced; parameter conflict value grading and influence range analysis are combined, the conflict degree and influence thereof are clearly defined, a conflict value range and a logic relation are optimized, configuration flexibility and matching performance are improved, and it is ensured that an optimization result effectively adapts to actual requirements through logic consistency analysis, support degree evaluation and matching performance verification; and the configuration efficiency, the accuracy and the intelligent level are obviously improved.

The invention discloses an RAG query method, system, medium and product capable of inhibiting a hallucination problem of a generative large model, and relates to the field of RAG query, the method comprises the following steps: obtaining a preliminarily generated text obtained by answering a user initial problem by a preset generative large model; extracting the preliminarily generated text to obtain a key verification problem; performing multiple rounds of questions and answers on the key verification question by using the generative large model to obtain multiple rounds of answers; performing consistency analysis on the multiple rounds of answers to obtain a consistency analysis result; a round of questions and answers with the highest comprehensive score in multiple rounds of answers are directly output at the high grade; when the key verification question is in the middle level, performing multiple rounds of question answering on the key verification question again; when the level is low, RAG retrieval and knowledge fusion are carried out on key verification questions, and questions are asked again; and carrying out illusion identification, correction and text optimization on the answers obtained by asking the questions again to obtain high-quality answers and outputting the high-quality answers. According to the invention, illusion can be accurately positioned, and high-quality answers can be obtained.