1255 results about "Big data processing" patented technology

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to the technical field of transformer monitoring, and discloses a comprehensive online transformer monitoring system which comprises a sensor layer, an edge computing layer, a cloud platform layer and a communication module. The cloud platform layer comprises a data warehouse, a big data processing engine, a comprehensive diagnosis engine, a model training engine and a system management module, the edge calculation layer is in communication connection with the cloud platform layer through a communication module, the comprehensive diagnosis engine integrates a deep neural network and a knowledge graph inference engine, and cross validation of data driving and knowledge guiding is achieved; a model training engine utilizes historical data and online incremental data to continuously optimize a model, and a dynamic knowledge graph updates a fault rule through a real-time diagnosis result and expert feedback, so that the system can adapt to novel faults and complex working conditions, and the diagnosis accuracy and adaptability are greatly improved.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a marketing content generation method and system based on big data, and relates to the technical field of big data processing and artificial intelligence, and the system comprises a multi-source data collection and fusion module which is used for receiving original multi-source data from different behavior sources, content sources and business scene sources, and performing structured processing, time relationship coordination and feature fusion on the original multi-source data to generate a fused portrait data set for describing the relationship between the user and the product. In the invention, through the multi-source data acquisition and fusion module, deep integration of heterogeneous multi-source data and unified representation of multi-modal features are realized, a high-quality data basis is provided for subsequent knowledge graph construction and content generation, and the problems of isolated island and insufficient feature utilization of traditional marketing data are solved; through a ternary knowledge graph construction module, information such as users, products, scenes and the like is constructed into a structured knowledge graph, and the semantic understanding ability and relevance of content generation are greatly enhanced.

The invention discloses a multi-source power distribution network energy-saving optimization scheduling method, system, equipment and storage medium based on big data processing, and relates to the technical field of power systems and big data processing. After power distribution network multi-source data is collected, through preprocessing and feature extraction, loads are predicted and classified through a long and short-term memory network; the load dynamic optimization module combines cost, network loss and photovoltaic consumption, generates a scheduling instruction through a mixed integer programming model, compares power loss before and after scheduling after CPLEX solution, and generates a difference thermodynamic diagram. According to the scheme, accurate load prediction and dynamic optimization scheduling of the multi-source power distribution network are realized through big data processing, the cost and energy efficiency are effectively improved, the consumption capability of photovoltaic energy is enhanced, the energy-saving effect is visually displayed through the power loss difference thermodynamic diagram before and after scheduling, and powerful support is provided for intelligent management of the power distribution network.

The invention relates to the field of artificial intelligence and big data processing, and discloses a professional field agent construction method and system based on a multi-modal hybrid expert model and a knowledge graph. The method comprises the following steps: collecting multi-source heterogeneous data such as user behaviors, texts, images, structured data and voices, and vectorizing and coding the multi-source heterogeneous data; performing feature extraction on different modal data and fusing to form unified representation; constructing a hybrid expert network based on a gating mechanism to perform task specialization processing on the fusion representation; introducing a domain knowledge graph and performing semantic enhancement through a graph neural network; on the basis of fusion representation, tasks such as recommendation, intention recognition and question and answer are executed at the same time through a multi-task learning framework; and an output result is fed back through the multi-modal interaction interface, and user feedback is collected to update the model. According to the method, the semantic understanding, task prediction and personalized recommendation capabilities of the model in a complex scene can be improved.

The invention discloses a policy matching and pushing method and system based on a large model technology, and relates to the technical field of big data processing, and the implementation of the method comprises the following steps: constructing a multi-dimensional enterprise portrait library comprising enterprise basic information, honor information, qualification information, judicial risks, administrative punishment and project information; for the first policy, vectorizing a policy text and an enterprise portrait through an Embedding model, calculating semantic similarity and generating a recommendation list; for a non-first policy, policy conditions are deduced based on a historical reward list, condition credibility is adjusted through a large model and a machine learning model, and recommendation priorities are calculated in a weighted manner; and a data real-time updating and model feedback optimization mechanism is established, and the matching precision is dynamically improved. According to the method, cross-modal semantic alignment can be realized, recommendation accuracy is improved, matching rules are dynamically updated, and the method can adapt to real-time changes of policies and enterprise data.

The invention belongs to the technical field of data processing, and particularly relates to a medical big data processing method, device and equipment based on lake and warehouse integration and a medium, the method comprises the following steps: collecting original medical data, and uniformly accessing the original medical data to a data access layer of a lake and warehouse integrated architecture in an original format and full-amount information to form a source pasting library; preprocessing the original medical data in the source library, performing unified coding and format conversion according to the medical data standard set, generating a standardized data set, and storing the standardized data set in a normalization library; on the basis of data in the normalization library and the source pasting library, multi-modal medical data are associated and integrated through cross-source federated query and computational flow technologies according to business scene requirements of clinical diagnosis and treatment, scientific research analysis or management decision, a special topic library oriented to a specific business topic is constructed, and a special topic library view is generated; and providing a query interface and an analysis service for the data application platform through the thematic database. The data processing speed and efficiency are improved, delay is reduced, and the data throughput efficiency is ensured.

The invention discloses a multi-mode enterprise intelligent brain system based on artificial intelligence, and belongs to the technical field of big data processing. The method and the device are used for solving the technical problems of poor multi-modal data processing precision and poor system robustness in the existing scheme. Semantic alignment of multi-modal features can be realized through a closed-loop process of pre-extraction, comparative learning, dimension alignment, dynamic optimization and verification and fine adjustment, cross-modal semantic association is established through comparative learning, space mismatching is solved through dimension alignment, dynamic matrix optimization fits task requirements, and actual effects are ensured through verification and fine adjustment. Through a progressive process of confidence evaluation, task coding, weight calculation, feature fusion and end-to-end optimization, intelligent weighting of multi-modal features is realized, and through confidence evaluation, reliable data is screened, a decision target is determined through task coding, a gating network dynamically allocates weights, and end-to-end optimization ensures that a strategy meets service requirements.

The invention relates to the technical field of big data processing, and particularly discloses a computer data management system based on big data. The system comprises a distributed data acquisition module which captures multi-source data through an API gateway and a crawler and embeds a quality label; the intelligent storage scheduling engine is used for realizing automatic migration of cold and hot data by adopting a column-type and distributed hybrid architecture; fusing a computing framework, and dynamically coordinating flow processing and batch processing resource execution feature extraction; the self-adaptive strategy center is used for generating data partitions and encryption strategies based on reinforcement learning; and a multi-level security protection system is adopted, and attribute base decryption and dynamic desensitization are deployed. The problems of resource scheduling rigidity and security protection lag in the prior art are solved, the storage cost is reduced by more than 40%, the query delay is compressed to be within 200ms, and the method is suitable for real-time decision-making scenes in the fields of e-commerce and finance.

The invention provides a data retrieval method, system and device, and relates to the technical field of big data processing, the method comprises the following steps: obtaining a search text of a user; performing data permission assembly according to the search text to obtain a user permission set of the user; according to the user permission set, generating a retrieval filtering condition of the search text; according to the retrieval filtering condition, performing multi-source data search in combination with the search text to obtain an initial retrieval result corresponding to the search text; and performing unauthorized rejection operation on the initial retrieval result to obtain a final retrieval result of the search text. According to the method, the range is defined through permission assembly, the condition focusing target is filtered, the efficiency is improved through pre-matching, and the precision is guaranteed through post-removing, so that the retrieval data volume is compressed to improve the efficiency, and the matching pertinence is enhanced to improve the precision.

Embodiments of the present application provide an operating parameter recommendation method and apparatus, used for in a big data processing scenario, recommending operating parameters for a computing device on the basis of the resource consumption under different operating parameters, without depending on historical data. The method comprises: first, on the basis of information obtained during the processing of historical tasks in a big data system, establishing a first relationship among the corresponding data information, parameters of a computing device processing the tasks, and time required to process the tasks, and a second relationship among the data information, the parameters, and resources of the computing device consumed for processing the tasks; inputting data information of a task to be processed, and the first relationship and the second relationship to a genetic algorithm, and outputting multiple sets of time values and resource values, i.e., modeling the process of the computing device executing data processing tasks, so as to fit the relationship between the time and resource consumption of the computing device and the operating parameters of the computing device; and determining operating parameters on the basis of actual available resources of the computing device or resource consumption that a user desires.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

A context-aware intelligent retrieval method suitable for multi-domain heterogeneous data relates to the technical field of intelligent retrieval and big data processing, combines context-aware domain semantic enhancement with a cross-domain semantic hash retrieval model, and realizes effective alignment between a source domain and a target domain in a unified feature space. A domain sharing encoder and a domain special encoder are constructed, and a residual fusion strategy is adopted to integrate domain sharing features and domain special features, so that the relation between image detail information and contexts is reserved, and cross-domain feature migration is realized. In the training process, effective modeling of domain specific information and domain shared information is ensured by utilizing a domain classification loss and distribution alignment strategy.

The invention discloses a building engineering lighting resource scheduling method and system based on big data, and relates to the technical field of intelligent control and big data processing. According to the method, the multi-source time sequence data is collected, the resource scheduling association diagram is constructed, the illumination terminals are grouped by using the multi-target adaptive scheduling clustering algorithm, the differentiated illumination control strategies of the resource scheduling partitions are generated, and the operation states of the illumination terminals in the corresponding resource scheduling partitions are controlled; the building engineering lighting resource utilization efficiency is remarkably improved, on the premise that operation safety and lighting quality are guaranteed, precise lighting according to needs, energy conservation and consumption reduction are achieved, the capacity of self-learning and strategy optimization is achieved through feedback data, lighting scheduling is converted from static and extensive states to dynamic and refined states, and the lighting scheduling efficiency is improved. And the intelligent level and the self-adaptive capability of illumination management are effectively improved.

The invention discloses a road traffic inspection method and system based on big data processing, and particularly relates to the technical field of road inspection. Multi-source data such as a floating car Beidou, a car detector, an accident platform, a structure sensor and weather are accessed in real time and cleaned uniformly, and multi-dimensional indexes of congestion, accidents, road surfaces, structures and environments are constructed; dynamically dividing each kilometer of road section into 1-4 levels of risks; according to a grading result, introducing three factors including a risk change rate, a last inspection interval and an emergency, and calculating a dynamic inspection priority of each road section in real time; and finally, by using an improved VRPTW-PW algorithm, generating a routing inspection path with the shortest driving and the highest priority under the constraints of vehicle endurance and real-time road conditions. The system comprises a multi-source data fusion module, a dynamic grading module, a frequency tuning module, a path planning module and a closed-loop feedback module, the hidden danger discovery rate can be remarkably increased, the empty driving rate is reduced, and the precision and intelligence of road inspection are achieved.

The invention relates to the technical field of big data processing and analysis, in particular to a commodity traceability management method and system based on big data. The method comprises the steps of determining a data range and association logic of each type of events by constructing a semantic event system of a commodity life cycle, performing data acquisition, preprocessing and credibility evaluation at edge nodes and generating an evidence header, and further converting data into multilayer verifiable commodity fingerprints of an L1 edge abstract, L2 event level fusion and L3 full link fusion, the method comprises the following steps: realizing feature fusion and dynamic incremental updating, then performing distributed notarization and multi-party verification on fingerprints through a federated anchor point mechanism to ensure that a full link is traceable and cannot be tampered, and finally supporting full link traceability query and anomaly tracking based on a unique commodity identifier. And closed-loop traceable management is realized through visual display and multi-role authority management. According to the invention, the reliability, interpretability and cross-organization trust of commodity traceability are effectively improved.

The invention relates to the technical field of big data processing, in particular to a hydrological information forecast data collection automatic processing method, which comprises the steps of multi-source data automatic acquisition and access, data quality intelligent diagnosis processing and abnormity early warning, multi-index comprehensive drought evaluation model construction and drought trend intelligent prediction. GIS spatial analysis and visual display are carried out, and drought evolution is dynamically reflected; and user-defined configuration and adaptive learning are carried out. The equipment state can be displayed, and related personnel can be prompted to maintain the observation station in time; existing hydrological data of the hydrological bureau are fully applied, rainfall, river flow, soil moisture content and other single items are normatively determined to determine the drought level, and meanwhile, according to multiple indexes, comprehensive drought judgment is performed according to the proportion, and based on the GIS technology, an interpolation method and a proximity association method are utilized to determine a Xinjiang whole administrative division plane drought map; and according to the analysis data, automatically sorting out soil moisture content brief report materials.

The invention discloses an abnormal data processing method and system for a virtual game, and relates to the technical field of big data processing. Receiving an interaction data frame sequence uploaded by the terminal equipment, and analyzing to obtain an input equipment sampling point set and a displacement coordinate vector; performing feature extraction on the sampling point set of the input equipment to obtain a high-dimensional feature vector; mapping the high-dimensional feature vector to a preset behavior feature space, calculating a multi-dimensional statistical distance of the high-dimensional feature vector in the behavior feature space, and generating a first abnormal confidence coefficient; calling space mapping data corresponding to the virtual scene; performing discretization ray stepping detection in the space mapping data based on the displacement coordinate vector to generate a second abnormal confidence coefficient; performing fusion calculation on the first abnormal confidence coefficient and the second abnormal confidence coefficient based on a dynamic weighting algorithm to obtain a comprehensive abnormal score; and judging abnormal data through the comprehensive abnormal score, and executing a state rollback operation for the virtual object.

The invention relates to the technical field of electric data processing, in particular to a hydroelectric generating set operation state online monitoring method and device based on big data processing. Determining a characteristic difference value of each modal component in each type of operation data in each working condition interval; and based on each modal component and the characteristic difference value in each type of operation data in each working condition interval, optimizing a de-trending fluctuation analysis algorithm, taking each type of operation data in a preset time period as the input of the optimized de-trending fluctuation analysis algorithm, and outputting each type of de-trending operation data so as to monitor the operation data of the hydroelectric generating set. According to the method, trend fluctuation interference caused by working condition changes is inhibited, and the accuracy of monitoring the operation data of the hydroelectric generating set is improved.

The invention relates to the technical field of Internet of Vehicles and big data processing, and discloses an Internet of Vehicles user perception method and system based on multi-modal data fusion and dynamic portray.The method comprises the following steps that a vehicle terminal collects operation, interaction and environment data and sends the data to a cloud; the cloud access module cleans and vectorizes data; the real-time calculation module generates a fused context feature by using sliding window aggregation and spatial association; the offline module outputs long-term preference and attribute correction characteristics; the dynamic portrait module combines the above characteristics, and constructs a user portrait comprising basic attributes, real-time states and intention labels by using a rule discrimination and deep learning double-track architecture and an adaptive weighting algorithm; and generating and issuing a control instruction according to the matching rule. According to the invention, through spatio-temporal data alignment, static attribute dynamic correction and intention real-time prediction, the problems of portrait lag and prediction delay are solved, and the accuracy and active perception ability of the Internet of Vehicles service are improved.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The application provides a tunnel blasting scheme intelligent design method, and relates to the technical field of tunnel engineering. The application obtains blasting design parameters based on a tunnel engineering geological exploration report and a forepoling geological prediction; obtains a drill-and-blast method tunnel blasting scheme 01 based on the blasting design parameters (4) by using a tunnel engineering blasting theory and a computer deep learning technology; establishes an existing drill-and-blast method tunnel blasting scheme sample library of built or under-construction tunnels based on the blasting design parameters (10) by using the computer deep learning technology, and obtains a to-be-built drill-and-blast method tunnel blasting scheme 02 by inputting the blasting design parameters and tunnel engineering requirements; and obtains a final drill-and-blast method tunnel blasting scheme according to the economic, efficient, safe and simple principle by using an artificial selection method based on the obtained drill-and-blast method tunnel blasting scheme group. The method avoids the influence of subjective factors, combines a traditional experience design method, big data processing and artificial intelligence technology, and provides an efficient scheme for design and construction personnel.

The invention relates to the technical field of water resource scheduling, and discloses an intelligent water resource configuration and scheduling system and method, and the system comprises a data collection module which is used for collecting the related data of water resources in real time, including but not limited to the water level of a water source, the water quality, the meteorological data, the water demand of a user, and the like; the data processing and analysis module is used for cleaning and integrating the collected data by using a big data processing technology, and carrying out demand prediction and water resource supply and demand matching through an artificial intelligence algorithm; a scheduling decision module, an execution module and a feedback mechanism module. Multi-dimensional data such as water sources and weather are collected in real time, data processing and demand prediction are carried out through the artificial intelligence technology, and it is ensured that water resource scheduling can cope with rapidly-changed demand and supply conditions; through an optimization algorithm and a scheduling decision model, the water resource utilization efficiency is maximized, and the problems of excessive water pumping, resource waste and the like are avoided.

The invention discloses an electroplating production full-process monitoring system and method, and the method comprises the steps: monitoring a current density signal and plating solution temperature distribution data in an electroplating bath in real time, and generating a process coupling response value through density gradient analysis and hot spot migration track extraction; screening an abnormal process component by utilizing a process coupling response value, positioning a plating solution circulation transmission path and constructing a sensor network arrangement matrix; identifying a quality risk area through a big data processing technology, and extracting a coating thickness gradient field to determine a key monitoring node; adjusting equipment monitoring frequency according to the key monitoring nodes, forming a self-adaptive acquisition sequence and extracting a core monitoring set; performing energy consumption analysis by using the power change component, determining a monitoring period through load matching, and forming a monitoring convergence field; and a process control window is determined by using the monitoring convergence field and is converted into an intelligent monitoring instruction, so that comprehensive intelligent analysis and optimal management and control of production operation and maintenance of the electroplating bath are realized, and the quality stability and operation and maintenance efficiency of electroplating production are improved.

The invention discloses a big data governance method and system based on a rule engine, belongs to the technical field of big data processing, and aims to solve the technical problems of low efficiency, slow response and rule stiffness in the current data governance process. Comprising a data access layer used for collecting data from each data source; the metadata management module is used for constructing a knowledge graph based on the metadata; the rule engine module is used for constructing a rule base; the data quality evaluation module is used for analyzing and evaluating the multi-source heterogeneous data based on the rule base; the authority control and compliance auditing module is used for performing authority control and performing compliance analysis on data operation through a preset compliance scale board based on an auditing log; the governance engine module is used for carrying out governance operation on the multi-source heterogeneous data based on the data quality analysis result to form a data governance execution result; and the feedback optimization module is used for optimizing the AI large model based on the data governance execution result.

The invention discloses a video processing method and system based on a big data technology, and constructs an intelligent video processing system with semantic driving, man-machine collaboration and continuous evolution by fusing advanced technologies such as big data processing, bimodal AI analysis, knowledge graph, natural language understanding and feedback learning. Compared with a traditional video monitoring system, the scheme has the advantages that the retrieval efficiency, the semantic understanding depth, the event association analysis capability, the user interaction experience, the system self-optimization capability and the like are remarkably improved, and the problems of incomplete seeing, difficulty in finding, inaccuracy in judgment and poor use are effectively solved.

The invention relates to a meter box multi-parameter prediction method and system integrated with big data processing, and the method comprises the steps: collecting the multi-dimensional original data of the operation of a meter box in real time through a built-in RTU collection module of the meter box, encrypting the original data, uploading the encrypted original data to a cloud server, and carrying out the cleaning, standardization and feature extraction, thereby obtaining structured data; storing the structured data to a distributed storage system, and indexing and compressing the data; calling the compressed structured data to construct a multi-task prediction model, and performing supervised training on the multi-task prediction model by using historical statistical data to obtain a trained prediction model; and inputting the original data acquired in real time into the trained prediction model, outputting a single-parameter prediction result, and performing multi-parameter collaborative analysis based on the single-parameter prediction result and the original data to generate a collaborative prediction result.

The invention discloses a business flow site selection optimization method based on multi-source data fusion, and relates to the technical field of big data processing and multi-source data fusion, and the method comprises the following steps: obtaining multi-source data of a to-be-detected region, constructing multi-source features, carrying out the cross-modal fusion of the multi-source features through an adaptive weight distribution mechanism, and generating a comprehensive feature representation; based on comprehensive feature representation, passenger flow trends and causal contributions of the candidate points in different scenes are output through a causal inference method; taking the passenger flow trend and the causal contribution as input, establishing a multi-objective optimization model, performing solving, and outputting a comprehensive score and a sorting result of the candidate points; constructing an explanatory index matrix according to the comprehensive score and the sorting result in combination with a causal contribution result, and outputting a site selection scheme through a visual platform; according to the method, the problems of single data source and incomplete candidate point description in the prior art are solved by fusing the spatial geographic data, the space-time traffic data and the consumption behavior data.

The invention discloses a spoken dialogue data processing method based on an agent, and belongs to the technical field of artificial intelligence big data processing, and the method comprises the steps: receiving a voice input file sent by a user side, converting a voice signal into text data, extracting an emotion feature, generating an emotion tag and a voice rhythm feature, text and emotion information are combined to form dialogue anchor point information, a path diagram is constructed, and edge weights are calculated; constructing a propagation matrix according to the similarity between a current dialogue anchor point and a historical path node, performing intelligent path reasoning, calculating a path score, ensuring the influence of emotional fluctuation on task path selection, selecting an optimal path by an intelligent agent according to the path score to perform subsequent task processing, and performing real-time correction after user feedback; anchor point information of the current dialogue is adjusted in real time through the sentiment analysis technology, the reasoning process is adjusted according to feedback, and a closed-loop self-adaptive adjustment mechanism is formed, so that the response quality of the system is optimized, and the trust of a user to the system is enhanced.