400 results about "Information support" patented technology

The invention discloses an enterprise global data analysis method based on a knowledge graph and a large language model, and the method comprises the following steps: collecting internal and external multi-source data of an enterprise, cleaning, standardizing and mapping, and constructing a unified data graph; after a user inputs a natural language query, a reasoning target and task description are generated in combination with semantic understanding, two reasoning paths are formed through structural reasoning and semantic reasoning respectively, and the two reasoning paths are fused for interactive verification and optimization. Logic conflicts and data missing are detected in real time in the reasoning process, and supplementary knowledge is automatically called for local reasoning correction. Through the corrected reasoning result, the system carries out semantic alignment and correlation analysis on multi-source data, a unified enterprise data analysis view is dynamically generated, a reasoning path and evolution information are recorded in the reasoning and analysis process, and subsequent traceable query is supported. According to the method, the data integration, intelligent reasoning and decision support capabilities of an enterprise in a complex data environment are improved.

The invention relates to the technical field of knowledge maps and large models, and discloses a fully-mechanized coal mining equipment large model decision support method and system. The method comprises the following steps: defining entities in data information of fully-mechanized coal mining equipment and a relationship between the entities to form triple data; converting the triple data into question and answer pairs based on the question and answer template, and optimizing the question and answer pairs; establishing a large language model, training the large language model based on question and answer pairs, performing fine tuning on the large language model based on a LoRA technology, and performing quantification processing on the equipment state in combination with fuzzy comprehensive evaluation to obtain a quantification result; the system uses the fine-tuned large language model to carry out entity and intention recognition on a natural language input by a user, a Cypher query statement is constructed to carry out query in a graph database, sub-graph information is obtained, the sub-graph information and the quantization result serve as cue words and are transmitted to the large language model, and retrieval information supporting large model decision making is obtained. The operation and maintenance efficiency of the fully-mechanized coal mining equipment is improved.

The invention discloses a network security situation awareness and analysis platform based on AI, and relates to the technical field of network security situation awareness and analysis, and the platform comprises a multi-source data collection module which integrates flow, logs, assets and threat intelligence data, and carries out encryption transmission and standardization; the data preprocessing module purifies and optimizes data, and guarantees data quality and sensitive information security; the AI situation awareness analysis module extracts features through a deep learning model, dynamically evaluates the situation and identifies threats; the threat early warning and decision-making module triggers graded early warning and generates a targeted emergency response scheme; the visual display and interaction module displays information in multiple dimensions and supports query and report generation; and the data storage and tracing module adopts a mixed storage architecture, so that the data security and traceability are ensured. The platform integrates multi-source data and realizes situation accurate perception and intelligent decision by means of an AI technology; the early warning is accurate, the visual interaction is convenient, and the intelligent and efficient level of network security protection is comprehensively improved.

The invention relates to a data packet transmission method and device, computer equipment, a computer readable storage medium and a computer program product. The method is applied to a receiver and comprises the steps that under the condition that configuration information of the receiver supports a custom extension header, a first data packet in a sending window is acquired through a transmission channel, and the first data packet carries a serial number and the custom extension header; the user-defined extension head comprises a step length; and under the condition that an out-of-order data packet is received, writing the out-of-order data packet into the corresponding first storage position according to the serial number of the out-of-order data packet, the serial number of the first data packet, the step length and the length of the custom extension head, and returning a confirmation signal of the out-of-order data packet through the transmission channel. By adopting the method, the transmission efficiency of the out-of-order data packet can be improved.

The invention discloses a tunnel surrounding rock multi-source data dynamic fine rapid sensing method, which comprises the following steps: based on a mobile terminal multi-view image sequence, arranging shooting positions according to a preset space interval and an orthogonal angle, generating a sparse point cloud through a motion recovery structure algorithm, and generating a dense point cloud model in combination with multi-view stereo matching optimization; and for the dense point cloud model, delimiting a local neighborhood based on k-nearest neighbor search, resolving a neighborhood point covariance matrix through principal component analysis, extracting a feature vector corresponding to a minimum feature value as a normal vector, and constructing a rock mass surface microscopic geometric feature field. The invention provides a dynamic fine rapid sensing method based on multi-source data, and aims to improve the efficiency, precision and timeliness of tunnel surrounding rock information acquisition and provide accurate surrounding rock information support for tunnel construction and support design.

The invention discloses an intelligent fire-fighting AI collaborative early warning system and method fusing GIS and multi-source data, and relates to the technical field of fire-fighting safety, and the system comprises a GIS space modeling module which is used for constructing a digital twinborn model through integrating a building BIM model, fire-fighting facility distribution, a population density thermodynamic diagram, sensing equipment layout and real-time environment data; the multi-source data fusion module is used for integrating sensing equipment data, real-time environment data, meteorological information, manually reported hidden dangers and a historical fire case library, and outputting the data after standardization processing; the AI collaborative early warning engine module is used for accessing standardized data, realizing building fire dynamic risk assessment and outputting graded warning information; the 3D simulation drilling platform is used for calling the digital twinborn model, receiving graded alarm information and supporting fire spreading simulation, emergency drilling and plan optimization; and the block chain evidence storage module is used for storing alarm information and drill records. According to the invention, risk assessment and accurate prediction of the fire hazard can be realized.

The invention discloses a behavior recognition method and device based on dynamic context, equipment and a medium, and the method comprises the steps: obtaining a dynamic context graph through cross-modal modeling among multi-modal data, fusing long / short-term events in a monitoring video through dynamic upper and lower graphs, carrying out the cross-modal semantic connection, carrying out the multi-modal feature fusion, and carrying out the multi-modal feature fusion. Determining a gating strategy vector and a gating weight matrix according to a splicing result; and performing multi-modal fusion according to the gating strategy vector, the gating weight matrix and the multi-modal sequence feature to obtain a multi-modal fusion feature, and performing abnormal behavior identification on the monitoring video based on the multi-modal fusion feature. Richer information support is provided for decision making through feature fusion, so that the decision making accuracy in a complex scene is improved. The method can be applied to security and protection monitoring scenes in the financial field or the medical field, so that the abnormal behavior recognition accuracy in the security and protection monitoring scenes is improved.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

The invention provides a tunnel three-dimensional geologic body modeling method and system based on in-hole camera shooting and while-drilling lithology perception, and relates to the technical field of tunnel and underground engineering and intelligent geological information processing.The method comprises the steps that firstly, a drill hole wall optical image sequence and while-drilling engineering parameters are synchronously collected and preprocessed respectively; then establishing a depth-time mapping relation to realize accurate alignment of the multi-source data in a depth domain; then fracture intelligent identification and parameter extraction are realized through an improved U-Net network, and lithology classification is completed by using an XGBoost model; feature level and decision level fusion is carried out on the fracture features and the lithologic features, and rock mass quality grades are generated; and finally, constructing an integrated geologic model fusing lithologic distribution and a three-dimensional fracture network by adopting an implicit modeling and discrete fracture network technology. According to the method, the problem of splitting of in-hole visual information and while-drilling physical information is solved, automation and precision of geological recognition are improved, and reliable geological information support is provided for tunnel construction safety and support design.

The invention relates to the technical field of blank visual inspection, in particular to a blank defect visual inspection method and system for a plastic extruder, and the method comprises the steps: carrying out the region segmentation of a real-time image, extracting a first-stage defect region, completing the preprocessing, and selecting a specific analysis path according to the defect type. Extracting multi-level defect features and forming a structured feature vector; the reliability degree of each feature is judged through feature integrity judgment and feature quality evaluation, real defects are recognized by adopting multi-stage judgment logic, defect type classification is carried out, and quality influence level multi-stage judgment logic and a branch processing strategy are given, so that accurate detection, classification and analysis of the defects of the plastic extrusion blank are realized. The detection accuracy and adaptability are remarkably improved, the optimal processing path can be automatically selected according to different production conditions and defect types, meanwhile, potential systematic problems are identified through defect correlation analysis, and comprehensive and in-depth information support is provided for quality control.

The invention discloses a distributed photovoltaic optimization coordination control system and method, and relates to the technical field of distributed photovoltaic power generation, and the control system comprises a multi-dimensional data collection module, a self-adaptive cluster division module, a robust dominant node election module, a three-dimensional voltage state evaluation module, and a hierarchical coordination control execution module. According to the distributed photovoltaic optimization coordination control system and method, real-time operation data, meteorological prediction data and power grid load prediction data of each node are continuously monitored, comprehensive information support is provided for a control strategy, and when it is detected that voltage abnormity is caused by sudden change of illumination intensity in a certain area, the real-time operation data of each node, meteorological prediction data and power grid load prediction data are monitored. And immediately finishing cluster structure adjustment in an extremely short time by adopting a fuzzy clustering algorithm with constraint conditions based on the electrical distance and the voltage fluctuation trend characteristic value, and ensuring that the node voltage change characteristics in the same cluster are highly consistent.

The invention provides a cultural field-oriented knowledge graph space-time map display method, which comprises the following steps of: extracting culture entities and relationships thereof from multi-source heterogeneous data through a knowledge graph construction module, and constructing a structured knowledge graph; analyzing and normalizing the time and space information of the culture entity through a space-time processing module; through a map display module, displaying the processed culture entity and the time-space relationship thereof on a user terminal in a map form; through the interaction module, interaction operation between the user and the knowledge graph displayed in the map is achieved, and the user can more conveniently explore culture information; the method supports the display of multi-level cultural relationships, can effectively extract and display target information in a complex cultural network, has good applicability, can be widely applied to the fields of cultural heritage, historical research, archaeological research and the like, and enables cultural entities and relationships thereof to be more visually and dynamically presented in front of users.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention relates to the technical field of cigarette supervision, in particular to a cigarette retail store supervision route planning method, which solves the problems of data missing and heterogeneity through data cleaning and standardization processing, and provides high-quality basic data for subsequent analysis. Due to the application of the analytic hierarchy process, scientific quantification of multi-dimensional indexes is realized, and the reliability of an evaluation result is improved; the improved ant colony algorithm is combined with pheromone concentration parameter adjustment, so that the routing inspection path planning efficiency and pertinence are optimized; key problems can be quickly positioned by introducing an anomaly detection algorithm, and the timeliness and accuracy of supervision are enhanced; a clustering analysis algorithm reveals a potential rule of a regional problem by grouping abnormal stores and calculating spatial distribution density; the thermodynamic diagram visualization technology presents the distribution of supervision key areas in a visual mode, and provides clear information support for decision makers; dynamic supervision and trend analysis are achieved through interactive function design, and scientificity and flexibility of supervision decision making are further improved.

The invention relates to the field of remote sensing image processing, in particular to a remote sensing image cloud processing and semantic segmentation method based on adaptive information fusion. An encoder is used for carrying out feature extraction on an original image shielded by cloud and mist, accurate modeling is carried out on a task relation through three elaborately designed expert neural networks, and each expert neural network focuses on learning features which have a key effect on a specific task. And the shared expert neural network is used as a key hub, and performs deep interaction with the other two tasks to realize joint optimization. Through the gating unit, the weight distribution between the shared information and the specific information in the specific task is automatically and intelligently adjusted according to the characteristics of the input image, and it is ensured that each task can obtain the most suitable information support. And the intermediate result is converted into a clear image after cloud and mist removal and an accurate semantic segmentation result through a decoder. According to the method, the cloud and mist removal quality of the remote sensing image and the semantic segmentation accuracy are effectively improved, and redundant calculation is remarkably reduced.

The invention relates to a recording method and system of an electronic medical record, and aims to comprehensively and systematically collect health information of a patient through diversified input terminals, improve information input efficiency, ensure information integrity and promote team cooperation. The system comprises a medical professional terminal, a patient direct related terminal and other patient related terminals, supports information input in various forms of pictures, characters, videos, audios and the like, and covers various input devices such as portable devices, professional examination instruments and the like. And the information storage module ensures accurate homing of the data and supports cross-mechanism data integration and sharing. The system also has the functions of electronic medical record automatic generation, treatment tracking, symptom and sign extraction, intelligent analysis and early warning and the like, provides efficient and accurate information support for medical services, optimizes medical resource configuration, and enhances medical experience of patients. The method is suitable for modern medical management, and particularly has remarkable advantages in the aspect of improving the intelligence and integration level of an electronic medical record system.

The invention provides an intelligent baking temperature control system and method based on the Internet of Things. The system comprises a data sensing module, a dynamic modeling module, an intelligent decision-making module and an interactive monitoring module. The method comprises the following steps: firstly, collecting multi-source heterogeneous data, performing data cleaning and feature alignment, and outputting a standardized baking data set; thirdly, constructing a temperature field space-time model based on the standardized baking data set, and generating a digital twinborn body; then, on the basis of the digital twin, a control instruction set is generated through conjoint analysis of a temperature control strategy and energy consumption optimization, equipment is driven to execute temperature control operation, and execution state data is fed back in real time to form closed-loop control; and finally, the interactive monitoring module provides a visual interface, displays temperature field evolution, an equipment state and material response information, and supports a user to adjust a control instruction. Through multi-source data fusion, temperature modeling, intelligent optimization analysis and user interaction, the temperature control precision, the energy efficiency and the user operation experience are improved.

The invention discloses an RTO real-time data processing and analysis system fused with edge computing, and relates to the technical field of RTO.Industrial production real-time data including process parameters and key index time sequence data are acquired through a data acquisition module, an analysis module extracts trend term and periodic term time sequence data by using an MSTL decomposition algorithm, initial standard indexes are determined through comprehensive analysis, and the initial standard indexes are analyzed and analyzed; a final standard index is obtained based on local data fluctuation and numerical feature correction, a construction module classifies data according to process parameters and screens reference data to construct a case library, an edge calculation module preprocesses data on site and improves efficiency, and a dynamic optimization decision module combines the case library and real-time data modeling prediction, provides optimization suggestions and updates the case library. The visual module visually displays information and supports interaction, and the system enhances data processing and analysis capability, optimizes industrial production decisions, and improves efficiency and quality.

The invention relates to the technical field of natural language processing, in particular to a network public opinion hot topic recognition method, device and equipment, and the network public opinion hot topic recognition method comprises the steps: embedding social media text data through a Chinese pre-training language model to obtain corresponding high-dimensional vector representation; performing dimension reduction on the high-dimensional vector representation by adopting a dimension reduction algorithm based on manifold learning to obtain dimension-reduced text data; and performing density clustering on the text data subjected to dimension reduction, and extracting keywords from a clustering result as recognized topics. According to the method, public opinion topics with high attention and representativeness can be accurately recognized and summarized from massive social media data, the automation level of public opinion hotspot recognition is improved, and more accurate and timely information support is provided for public opinion analysis and decision making.

The invention discloses a power distribution network area low voltage prediction method, and relates to the technical field of voltage prediction, and the method comprises the steps: S1, data preprocessing; s2, establishing a GA-BP neural network load prediction model; s3, GA-BP load prediction model simulation is carried out; s4, establishing an LSTM neural network voltage prediction model; s5, performing model simulation and result analysis; and S6, voltage prediction and treatment suggestion. According to the power distribution area low voltage prediction method, firstly, a GA-BP neural network is utilized to predict area loads, influence factors such as temperature, humidity, date types and different moments in one day are fully considered, and predicted load data are utilized as input to construct an LSTM model to predict area voltages; the transformer area low voltage is effectively predicted through the GA-BP-LSTM combined model, the model is high in fitting degree and low in prediction error, the low voltage degree of a user is reflected in real time, and more information support is provided for follow-up transformer area low voltage treatment.

The invention discloses a bank data analysis processing method and system, particularly relates to the technical field of bank data processing, and is used for solving the problem that multi-source data is difficult to trace uniformly. Multi-service system records are accessed through a unified time reference and a subject identifier, scattered records are recombined into event frames with event types, time and subject sets based on event anchor point rules, conflict resolution is performed on multi-source same-name fields according to authoritative source priorities and delay propagation thresholds, main field values and alternative field values are reserved, and multi-source same-name fields are obtained. On this basis, an event frame table, a state sequence table and a participating entity table are constructed, indexes are established according to subject identifiers, time intervals, event types and state codes, retrieval and statistics around customer events are achieved, data consanguinity among source fields, event frame fields and analysis fields is synchronously established when the analysis fields are generated, and the data consanguinity among the source fields, the event frame fields and the analysis fields is established; and in combination with the version information, tracing of index sources in a rule change and late data scene is supported.

The invention discloses an AI-based logistics document intelligent identification and filling system, and the system comprises an image collection module which is used for obtaining the image information of a logistics document, supporting a plurality of image input modes, such as scanner, mobile phone photographing, camera capturing and the like, carrying out the preprocessing of a collected image, including the operation of image enhancement, denoising, graying, binaryzation and the like, and obtaining the image information of the logistics document; the image quality is improved, and preparation is made for follow-up recognition; the document type identification module is used for carrying out document type judgment on the preprocessed document image based on a convolutional neural network (CNN) algorithm in deep learning, and can automatically identify common logistics document types; the invention relates to the technical field of logistics information, and the AI-based logistics document intelligent identification and filling system can efficiently and accurately identify various logistics documents, realize intelligent filling and improve the automation level and accuracy of logistics document processing.

The invention discloses a geographic information element analysis method and system based on a multi-modal large model. According to the method, the flexibility and pertinence of the analysis process are remarkably improved through a dynamic self-adaptive multi-modal fusion mechanism. Through a task sensing module and real-time contribution degree calculation, key data types in different scenes can be automatically identified, the dynamic adjustment enables an analysis process to better meet actual requirements, interference of irrelevant data is avoided, the capture precision of a target geographic phenomenon is directly enhanced, and the accuracy of target geographic phenomenon capture is improved. And the comprehensiveness and the reliability of an analysis result are enhanced through deep collaborative fusion of multi-modal data. Various types of data are complemented and cooperated through unified feature space mapping and dual-drive weight adjustment, not only are multi-dimensional features of geographic information covered, but also limitation of a single data source is reduced through real-time verification and iterative optimization, and a finally output analysis result is closer to a real geographic environment. And a more solid information support is provided for subsequent decision making.

The invention discloses a vehicle data transmission method and device, electronic equipment and a storage medium. The method is applied to a data sending end, and comprises the following steps: in response to a received data transmission instruction, determining to-be-transmitted data corresponding to the data transmission instruction and a data receiving end; determining a target compression algorithm based on target compression configuration information and the multi-dimensional decision information, the target compression configuration information being used for representing compression configuration information jointly supported by the data sending end and the data receiving end, the multi-dimensional decision information at least comprises two of the following: a network resource parameter, a computing resource parameter and a data attribute parameter of the to-be-transmitted data; compressing the to-be-transmitted data by using the target compression algorithm to obtain processed data; and transmitting the processed data to a data receiving end. According to the invention, the technical problem of low transmission efficiency and adaptability of vehicle data transmission in the prior art is solved.

The invention relates to the technical field of power systems, and discloses a power system node attribution and reasoning method, system and device and a medium, and the method comprises the steps: obtaining first device data of a target power system; performing first preprocessing on the first equipment data, and establishing a time-varying causal graph model according to a first preprocessing result; and solving the time-varying causal graph model to obtain a decision strategy. According to the method, the accuracy and efficiency of power system safety monitoring are improved, and faults can be quickly positioned and repaired. Through feature extraction of preprocessing, key information is extracted to support data requirements of a time-varying causal graph model. The introduced time-varying causal graph model considers the time factor and the causal relationship, so that the operation state of the system is reflected more accurately. A decision strategy obtained by solving the model ensures safe operation of the power system, and power failure and economic loss caused by faults are reduced.

The invention provides a security information retrieval reasoning method, device and equipment and a storage medium, and the method comprises the steps: carrying out the analysis processing of an input security case document, and extracting the entity and relation information; then, constructing a character relationship graph and carrying out coding processing to obtain a coded graph; performing retrieval and expansion processing on the encoded atlas according to user query to obtain a retrieval sub-graph; and finally, evidence chain reasoning is carried out based on the retrieval sub-graph, and an analysis report containing a relation network graph and an evidence chain view is generated. Through organic combination of case document analysis, map coding, retrieval extension and evidence chain reasoning, effective extraction of entity relationships is realized in a document information processing stage, structural expression of information is enhanced in a map construction stage, complete associated information is obtained in a retrieval stage, and the retrieval efficiency is improved. Reliable evidence support is established in the reasoning stage, and finally more accurate information support is provided for case analysis.

The invention discloses a reservoir landslide evolution state sensing method fusing distributed inclinometry and GNSS, and relates to the technical field of landslide monitoring, comprising the following steps: arranging a distributed inclinometer and installing a global navigation satellite system; the method comprises the following steps: acquiring monitoring data through an inclinometer and a global navigation satellite system, and processing noise; data fusion is carried out, and landslide dynamic states are output in real time. According to the method, organic combination of macroscopic deformation information and local deformation data is realized, the overall motion trend and local deformation characteristics of the landslide can be more accurately captured, the timeliness and practicability of data acquisition are improved, noise and abnormal values in the data are effectively removed, the signal-to-noise ratio of the data is improved, and the data acquisition efficiency is improved. The method provides a more accurate basis for subsequent data analysis, improves the precision and reliability of landslide dynamic estimation, enables the monitoring result to be more accurate, provides timely information support for landslide early warning and emergency response, and ensures the continuity and stability of a monitoring system.

The invention discloses an unmanned vehicle cloud cooperative wide area sensing method and system based on sky-ground feature fusion, and the method comprises the steps: vehicle end data processing: carrying out the processing of a laser radar point cloud through PointPill volume cylindricity in combination with an FPN-ResNet backbone network, generating a BEV feature map, outputting a target detection result containing the information of a three-dimensional coordinate, a course angle and the like through a detection head, and carrying out the detection of the target detection result; then, an AB3DMOT multi-target tracking algorithm is utilized to associate detection results into a track set, and then the BEV feature map, the track set and vehicle pose information are packaged, compressed and uploaded to a cloud end; cloud data processing: after receiving the data, the cloud decompresses the data to obtain a BEV feature map, a track set and vehicle pose information, and then performs feature fusion and track fusion; and the vehicle end fuses the received information fused by the cloud end with the characteristics of the vehicle at the current moment, eliminates the delay, and finally obtains a vehicle-cloud cooperative sensing result. The real-time performance of the sensing result can be guaranteed, and timely and accurate environment information support is provided for automatic driving decision making.

The invention provides a mind map generation method and device based on a large model and electronic equipment, and relates to the field of artificial intelligence, in particular to the technical field of large models and natural language processing. Determining N types of reference elements; according to the N types of reference elements, generation data of the mind map is determined, the generation data comprises nodes and node information of the nodes, and the node information carries node filling data obtained by the large model based on the reference elements; generating data of the mind map are transmitted to a front end, a target mind map is generated at the front end, at least one piece of node information of the target mind map is filled with chart information and supports any type and number of reference elements, the mind map is generated, and at least one piece of node information of the target mind map is filled with chart information and supports any type and number of reference elements. The visualization degree of the target mind map is improved, the generated target mind map better meets user requirements, and the user experience is improved.

The invention relates to the technical field of natural language processing, in particular to a verifiable large model retrieval enhancement generation system and method based on an evidence chain, and the method comprises the steps: receiving an initial query, recognizing the fuzziness and information gap of the initial query in combination with an associated retrieval document, and generating a supplementary query set; based on the initial query, the supplementary query and the corresponding retrieval document, generating candidate answers with references and verifying the information supportability of the candidate answers; for the candidate answers passing the verification, extracting support information and constructing a hierarchical attribution mapping relation; and integrating the information to form to-be-evaluated information, and if the current verified to-be-evaluated information meets a preset sufficiency condition, integrating the generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The method effectively overcomes the defects that a traditional RAG system is fragmented in information integration, has one-sided fuzzy query and answer, is low in attribution efficiency and excessively depends on retrieval content, and has the advantages of answer comprehensiveness, verifiability and deployment lightweighting.