5053 results about "Service system" patented technology

The invention discloses an intelligent customer service system based on an AI large model, and belongs to the technical field of intelligent customer service systems. Through the multi-modal interaction technology, the flexibility of the system and the user experience are improved, the data type is intelligently recognized and correspondingly processed, the language style and emotional intonation of the response content are automatically adjusted according to the mood and intention of the user, the knowledge base integrates static data and dynamic interaction data, and the user experience is improved. Real-time updating and comprehensiveness of knowledge content are ensured, rapid retrieval is performed based on a knowledge base, accurate reply is performed in combination with a large language model, high-quality answers and solutions are generated through rule reasoning and model generation, state information of each round of interaction of a user is recorded and managed by constructing a dialogue state tracker, and the user experience is improved. And precise tracking and updating of the context are realized. For a complex task, the system splits a user request into a plurality of sub-tasks, and the sub-tasks are executed in sequence according to the priority and the dependency relationship, so that the high efficiency and continuity of task processing are ensured.

The invention relates to an intelligent computing power and storage scheduling method and system for a multi-service system, and belongs to the technical field of intelligent computing resource scheduling and storage management, and the method comprises the steps: collecting the real-time monitoring data of each node in the multi-service system, and carrying out the feature extraction; predicting a computing power demand value and a storage demand layering strategy in a future preset time window; dynamically correcting the prediction result, and generating a dynamic resource demand table and a cross-service priority weight matrix; generating a task allocation scheme through a hybrid scheduling algorithm; generating a storage data migration instruction through a storage scheduling engine; executing calculation node capacity expansion, task migration and storage data redistribution operation; and based on the executed latest state snapshot of the resource pool and the scheduling failure case library, updating a weight parameter of the time sequence prediction model and a scheduling strategy rule library to obtain an updated strategy version, and applying the updated strategy version to a next scheduling period. According to the invention, efficient scheduling of resources can be realized in a multi-service system environment.

XCB poly-radio devices as finders, locators, scanners, sensors, and Bluetooth (BT) radio topology reporters as part of a global lost-and-found services system. XCB devices function as “smart objects” and combine dual transceivers operating in the telecommunications and the low power ISM radio spectra, with default power management overrides under local control of the BT modem. “Familiar zones” are defined, and a “familiarity index” versus a “strangeness index” based on meta-analysis of BT radio contact logs are used to assign location. Location, in concert with other sensor data, is coupled to conditional logic rules programmable by the user or the system administrator. Locations, determined by monitoring familiar and unfamiliar BT radio topology, trigger executable commands to smartphones, to smart devices generally, and to remote machines, and can for example, generate alerts to a user if radiotagged assets, children or pets have strayed, are lost, or are left behind. When tied to cloud predictive services or increased edge computing capacity, incipient loss of radiotag objects can be detected before an owner knows the smart object is lost. In another embodiment, XCB devices having a novel chip architecture for improved power management of cellular and MCU circuits mediated by BT radio modem that achieves “situational awareness” by monitoring the BT signal environment around the device.

The invention discloses a low-code platform based on a component library and a form engine, and relates to the field of data processing. The platform comprises a design system, an application system and a service system. The design system is provided with a form designer and a template manager, the form designer can configure a form component to generate a description file, and the template manager supports separate storage of a page template; the application system comprises a form analysis engine, a UI rendering engine and a reusable resource management component, the analysis engine analyzes configuration generation logic, the rendering engine loads a resource rendering page, and the reusable resource management component stores resources and generates configuration; the service system comprises a metadata analysis engine and an SQL assembler, the metadata analysis engine analyzes business rules to generate SQL and filtering strategies, and the SQL assembler generates operation statements adaptive to multiple data sources. Through the modular architecture design and the dynamic analysis mechanism, the problem that a low-code platform is insufficient in flexibility and usability is solved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention provides a multi-round dialogue interaction method and system based on context reconstruction and multi-library retrieval, and the method comprises the steps: firstly obtaining a multi-round dialogue data set of a target user, which comprises a plurality of dialogue round sequences composed of user input statements and system response statements; performing context reconstruction processing on the multi-round dialogue data set to generate context reconstruction characteristics of each dialogue round sequence, covering current round semantic focus offset and historical round semantic dependence intensity, and retrieving a matched multi-database retrieval result from a preset heterogeneous database based on a dynamic context window, a candidate response statement set and confidence are included. Performing multi-strategy matching on context reconstruction features and retrieval results, generating a response generation strategy to adjust candidate response semantic priorities and confidence thresholds, and finally feeding back the response generation strategy to a dialogue service system to trigger response optimization operation, and updating heterogeneous database retrieval weights and context window dynamic adjustment parameters.

The invention discloses a comprehensive service system and method for intelligent safety protection, and relates to the technical field of safety service systems.After state data of a park are intelligently analyzed, potential risk event types are identified, the risk propagation trend is judged through a space-time association algorithm, the risk level of each area in the park is evaluated, and the risk level of each area in the park is evaluated. And generating a risk level map, performing path planning and scheduling optimization on the inspection equipment according to the current available inspection resources and the risk level map, and cooperatively controlling all the inspection equipment to operate based on path planning and scheduling optimization results. The service system generates a high-credibility regional risk level map, takes a current available resource state and the risk level map as input conditions, dynamically generates a routing inspection path and task allocation, effectively improves reasonable utilization and allocation of routing inspection resources, automatically adjusts a scheduling task under the condition that individual routing inspection equipment fails, has sudden obstacles or is limited in path, and improves the routing inspection efficiency. And stable proceeding of the inspection task is ensured.

The invention provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

Systems and methods for directing and enforcing zero trust control on requests to destination services. In various embodiments, steps include receiving a request from a user to access a destination service; directing the request to a control layer; enforcing one or more controls, via the control layer, on the request based on a configuration provided by an owner of the destination service; and providing access to the destination service to the user based on the one or more controls.

The invention discloses a dynamic interaction method based on cooperation of a multi-modal dynamic fusion large model and an intelligent agent. The method comprises the following steps: performing feature extraction on user voice information to obtain a voice coding vector, a text semantic vector and an emotion feature vector; performing dynamic weight feature fusion on the voice coding vector, the text semantic vector and the emotion feature vector through a multi-modal dynamic fusion large model to obtain a fusion feature vector; inputting the fusion feature vector into an intention-scene coupling network, and identifying to obtain a user intention label; and identifying according to the user behavior log to obtain a user portrait tag, inputting the user intention tag and the user portrait tag into an autonomous decision-making agent, generating a target decision-making action through a lightweight policy network, and then interacting with the user according to the target decision-making action. The intelligent interaction efficiency and accuracy of the customer service system are improved, the interaction experience of the user is also improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention relates to the technical field of natural language processing, in particular to an intelligent customer service system based on natural language processing, which comprises a dynamic term ambiguity resolution module, a context intention chain construction module, a domain emotion vectorization module, a compliance response decision module and an aging knowledge injection module. Wherein the dynamic term ambiguity resolution module is used for receiving an original statement of a user and generating a standard term set; the context intention chain construction module is used for generating a structured intention chain; the domain emotion vectorization module is used for generating a three-dimensional emotion vector; the compliance response decision module is used for generating a to-be-verified response scheme; and the timeliness knowledge injection module is used for verifying the timeliness of legal provisions in the response scheme to be verified. According to the method, through fusion of dynamic term disambiguation, context intention chain modeling, emotion vector quantization and law and regulation aging verification, accurate semantic understanding and compliance response generation of the intelligent customer service system in multiple rounds of dialogues are realized.

The invention discloses an AI generation content detection and review method, device and equipment and a storage medium, and the method comprises the steps: receiving multi-modal input data which comprises text data, image data and video data; calling a large language model to carry out compliance analysis on the text data to obtain an analysis result; when the analysis result is compliance, calling a multi-modal model to identify and detect the image data and the video data, and respectively obtaining an image detection result and a video detection result; and aggregating the analysis result, the image detection result and the video detection result to obtain a content detection result. According to the method, the processing paths are automatically allocated according to the content types, redundant calculation is avoided, hardware consumption is remarkably reduced, the multi-modal detection results are aggregated into structured output, the large-batch content processing efficiency is remarkably improved, calculation resource occupation is greatly reduced, and seamless integration of the detection results and a downstream service system is achieved.

The invention relates to the technical field of intelligent customer service, and discloses an intelligent customer service dialogue generation method and system based on user intention recognition, and the method comprises the steps: obtaining an input first user text from an intelligent customer service system, and carrying out the feature decomposition and attention fusion of the first user text, and obtaining a fusion intention feature matrix; performing intention migration analysis and dynamic Bayesian analysis on the dialogue sequence data in the historical dialogue corpus to obtain an intention state transition probability matrix; an improved Transform network model is input for joint training, and a dialogue generation model is obtained; a newly input second user text in the intelligent customer service system is decoded and generated based on the dialogue generation model, meanwhile, the intention state transition probability matrix serves as a constraint condition, a reply text conforming to the dialogue intention is obtained, the accuracy and robustness of intention recognition are improved, the reply smoothness is guaranteed, and meanwhile the user experience is improved. And the generated content is ensured to conform to an expected intention transfer rule.

The invention discloses a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

The invention discloses an information consultation service system and method based on a cloud computing platform, and the method comprises the steps: accessing a user request through a plurality of channels, analyzing multi-modal data through a natural language processing technology, and carrying out the analysis in combination with the historical data of a user; data formats are unified through data cleaning and standardization tools, the data formats are stored in a cloud computing platform, and user requirements are understood through knowledge maps and semantic analysis; providing related consultation contents based on the user behavior data by using deep learning and machine learning recommendation algorithms; a / B testing and performance analysis are carried out regularly, and the system and the model are optimized; safety is guaranteed by adopting TLS and AES encryption and multi-factor authentication, the performance is monitored in real time, the response type design is adapted to different devices, and user feedback is collected to improve the experience; according to the information consultation service system and method based on the cloud computing platform, the accuracy and adaptability of the AI model are improved through reinforcement learning and deep learning technologies, and more accurate consultation services are provided for users.

The invention provides an intelligent memory leak prediction and tracking method and system for a micro-service architecture, and relates to the technical field of memory leak prediction, and the method comprises the steps: collecting the runtime data of a service node, constructing a service calling topological graph, extracting the time sequence and topological features, and carrying out the feature fusion. And constructing a deep learning prediction model by combining a bidirectional long-short term memory network with a multi-head attention mechanism and a graph attention network, and predicting a memory use trend and a risk score of a service node. Potential memory leak nodes are identified according to the risk score and the abnormal degree predicted value, a key propagation path is identified through a service calling topological graph and a minimum cut algorithm, and finally a root service of memory leak is positioned by using a belief propagation algorithm, so that accurate prediction and rapid positioning of the memory leak in the micro-service system are realized. And the system stability and reliability are improved.

The invention belongs to the technical field of artificial intelligence, and discloses a government information consultation system based on a large language model, which comprises a user interaction module, a large language model core engine, a knowledge base integration module, a multi-level authority management module, a feedback optimization mechanism and a risk control module. The comprehensive intelligent government affair service system is constructed through the six core modules, remarkable advantages are shown in government affair service digital transformation, the system innovatively adopts multi-mode interactive design, multiple input and output modes of voice, text and images are supported, an intelligent authority management mechanism is matched, and the intelligent authority management mechanism is matched with the intelligent authority management mechanism. According to the technical scheme, the accessibility and convenience of government affair services are greatly improved, precise services for different user groups are achieved, it is guaranteed that sensitive data are safe and controllable while wide spreading of government affair information is guaranteed, and a large language model of a system core is subjected to professional government affair scene optimization training and is combined with a dynamically-updated knowledge graph technology.

The invention provides a bank financial management product recommendation method and system, and the method comprises the steps: firstly collecting a multi-modal dialogue data flow which is generated by interaction of a user terminal and an intelligent customer service system and contains voice input signals and text input contents, and then carrying out the semantic recognition of the voice input signals, and generating a structured demand description set; performing entity extraction and intention analysis on text input content to generate a multi-dimensional semantic tag set, performing time-space alignment and fusion on the text input content and the multi-dimensional semantic tag set to form a user portrait enhanced feature matrix, and performing multi-level association analysis on the user portrait enhanced feature matrix and a financial product knowledge graph based on a deep matching model to generate an initial recommendation result queue; and finally, according to the real-time environment perception data, queue context perception optimization is carried out, a dynamic adaptive recommendation list is generated, and visual rendering output is carried out through a multi-channel interaction interface, so that accurate and dynamic financial management product recommendation is realized by utilizing multi-modal data, deep analysis and combination with a real-time environment.

The invention relates to the artificial intelligence technology, can be applied to service system platforms such as medical health and financial science and technology, and discloses a data processing method, device, equipment and medium based on a hybrid expert model.The method comprises the steps that to-be-processed data is input into the hybrid expert model, routing probability distribution is obtained, the Tsallis entropy of the routing probability distribution is calculated, and the Tsallis entropy of the routing probability distribution is calculated; generating a prediction result according to the Tsallis entropy; evaluating loss based on an auxiliary entropy loss function, constructing a subspace, and adjusting pre-training parameters in the subspace to obtain an optimized hybrid expert model; and obtaining a target prediction result according to the optimized hybrid expert model. According to the dynamic routing mechanism, experts are flexibly selected, an entropy loss function is assisted to optimize a routing decision, uncertainty is reduced, and model convergence is accelerated; and interference of new tasks on old tasks is eliminated through re-parameterization and subspace design, so that the model achieves good balance between stability and plasticity, and generalization and adaptability of model data processing are improved.

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

The invention relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.

The invention belongs to the technical field of fermentation, and discloses a cloud computing-based fermentation tank fermentation process management and control service system and a cloud computing-based fermentation tank fermentation process management and control service method. Comprising the steps that an equipment end collects fermentation parameters in real time in the fermentation process and sends the fermentation parameters to a cloud computing platform; the fermentation parameters comprise fermentation characteristic parameters and fermentation control parameters; the cloud computing platform analyzes the fermentation characteristic parameters to obtain optimal fermentation control parameters; the cloud computing platform identifies deviation parameters in the fermentation control parameters collected in real time; the cloud computing platform judges and generates an early warning instruction or an adjusting instruction according to the deviation parameter; if the adjusting instruction is generated, the cloud computing platform calculates the adjusting quantity corresponding to each deviation parameter and sends the adjusting quantity to the corresponding equipment end; intelligent monitoring and control in the fermentation process are achieved through the cloud computing platform, the fermentation quality and yield can be effectively controlled, the fermentation efficiency is improved, and therefore the automation level and stability of the fermentation process are improved.

A payment service system receives contextual information regarding an interaction between the payment service and a user device associated with a user. A propensity metric for the user is determined based at least in part on inputting the contextual information into a machine learning (ML) model. Based on the propensity metric, a user interface is dynamically configured to comprise user interface elements arranged in a layout personalized for the user, where a user interface element represents content particular to a service offered by the payment service. Based on receiving an interaction with the user interface element, a booklet is launched corresponding to the service with which the user interface element is associated.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

The invention discloses a construction method of a real-time streaming voice intelligent question and answer service system. The construction method comprises the following steps: receiving input voice data; calling an independent voice recognition service to convert the voice data into an input text; the method comprises the following steps: constructing cue words of a large language model based on an input text, initiating an LLM streaming request to an independent large language model service, and obtaining streaming text answers generated by LLM in real time; segmenting the flow type text answer in real time through a dynamic sentence segmentation device to generate a plurality of clauses; calling an independent speech synthesis service in parallel for each clause, and converting the text into an audio data block; and combining the audio data blocks into streaming audio data according to the generation sequence, and returning the streaming audio data to a client for playing in real time. According to the invention, by decoupling ASR, LLM and TTS services and combining the asynchronous streaming framework and the strategy of "wanting while speaking", the real-time performance and flexibility of the voice question-answering system are significantly improved.

The invention relates to the technical field of large models, in particular to a large-model-oriented reasoning service system and an inter-stage resource adjustment method, and the system comprises a pre-filling processor and a decoding processor; a computing resource controller implementing computing resource control of the prefill processor and the decode processor based on a multi-process service, the multi-process service allowing a target number of streaming multiprocessors to be designated to a prefill process representing an entity of the prefill processor and a decode process representing an entity of the decode processor; and the unified storage manager is used for managing the storage utilization rate of the system and the storage access of the pre-filling processor and the decoding processor to the large model weight and the KV cache. Therefore, the problems of delay interference, low storage efficiency and the like between pre-filling and decoding in related technologies are solved.

The invention relates to the technical field of voice processing, can be applied to service system platforms of medical health, financial science and technology, communication and the like, and discloses an audio compression and reconstruction method, device, equipment and medium. Residual vector quantization and inverse quantization are carried out to generate basic spectrum features; and compensating a high-frequency component by adopting a spectrum expansion network, predicting a time domain error in combination with a long-short-term memory network, generating a time domain residual compensation signal, and superposing the time domain residual compensation signal with the reconstructed time domain audio signal to obtain a target audio signal. Through residual vector quantization and neural network modeling, the audio reconstruction quality under low-bit-rate compression is improved, spectrum detail reservation and time domain error compensation are optimized, meanwhile, the calculation complexity is reduced, and the method is suitable for high-sampling-rate and resource-limited scenes.

The invention provides an intelligent financial management business service efficiency improvement method and system and electronic equipment, and the method comprises the steps: S1, collecting customer data, and carrying out the cleaning and preprocessing of the data; s2, performing deep mining on the customer data; s3, according to the customer portrait, market dynamic and customer behavior changes are monitored in real time, and a recommendation strategy is dynamically adjusted; s4, constructing a risk assessment model, performing risk assessment on the recommended financial products, and setting a risk early warning mechanism; s5, introducing an intelligent customer service system to carry out real-time conversation with the customer, solving customer questions, collecting customer feedback, and optimizing a recommendation algorithm and a risk assessment model according to the customer feedback and transaction behavior data; and S6, regularly evaluating system performance and service effect, collecting user feedback and market data, identifying potential problems and improving space, and optimizing and iterating the system according to an evaluation result. According to the invention, accurate capture of deep demands of customers is realized, and more personalized financial management suggestions are provided.

The invention discloses an edge computing-oriented lightweight large-model intelligent customer service deployment method, and relates to the technical field of artificial intelligence and edge computing. The method comprises the following steps: acquiring a pre-trained large language model, and pruning based on a lottery hypothesis to obtain a winner ticket sub-network; remapping the sparse structure into a V: N: M structured sparse format, and loading the V: N: M structured sparse format together with the weight and the mask to an on-chip memory; inserting a lateral branch outlet in the converter layer and carrying out token pruning; monitoring the confusion degree and the confidence degree, and dynamically recovering the pruned weight which does not exceed 5% when the load is high; and uploading the 8-bit quantization hidden state of the low-confidence or sensitive token to the large cloud model for collaborative reasoning, and combining the result with local reasoning output. According to the method, the reasoning delay and the communication overhead of the edge equipment are reduced, and the response efficiency and the deployment economy of the intelligent customer service system in a low-computing-power environment are improved.

The invention discloses a scenic spot accompanying service method and a scenic spot accompanying service system based on linkage of a generative AI and a knowledge base, particularly relates to the technical field of artificial intelligence and smart tourism, and is used for solving the problems that core knowledge is forgotten and the answer accuracy is reduced due to dynamic knowledge updating of a generative AI model in an existing scenic spot guide system. The method comprises the following steps: extracting tourist question keywords in real time and retrieving a knowledge base to generate associated data; dynamically weighting the associated data priority based on the tourist aggregation density; generating an initial answer through real-time parameter adjustment; fusing the spatial-temporal clustering and the knowledge graph structure entropy to generate a weight factor; correcting the answer coverage defect and evaluating the result; outputting a final answer after directional addition of core knowledge iterative training; it is guaranteed that core knowledge of the scenic area is stably reserved in the dynamic learning process of the generative AI, meanwhile, the real-time scene requirement is accurately responded, and the reliability, accuracy and individuation level of the navigation service are remarkably improved.