3354 results about "Intent recognition" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention provides an AI intelligent customer service response method and system based on a remote digital service, and the method comprises the steps: firstly receiving a session request data flow inputted by a user through a remote session terminal, and extracting a dynamic semantic feature sequence containing a grammatical structure, an emotional tendency and a context dependence feature; inputting the data into an intention recognition model trained based on a historical session data multi-dimensional semantic relationship, and outputting an intention category label and confidence; matching a target dialogue stream node from a preset dialogue process library according to the label and the confidence coefficient; dynamic response content is generated based on the node response logic rule, and a response data stream is returned; and finally, collecting user feedback behavior data, updating the association weight of the intention recognition model according to an interaction validity index, optimizing the semantic mapping precision of the intention recognition model to similar session requests, and realizing more accurate, intelligent and adaptive customer service response.

The invention discloses an LLM-based customer intention recognition and response system, method, device and medium, and relates to the technical field of intelligent dialogues, the system comprises a multi-modal input processing unit used for collecting and fusing multi-source interaction data of an intelligent voice outbound system to form multi-dimensional input representation; the dynamic context enhancement LLM unit is used for performing context understanding based on the multi-dimensional input representation and a preset knowledge base and generating a user feature portrait; the reinforcement learning dialogue strategy generation unit is used for dynamically generating a dialogue strategy according to the user feature portrait and generating response content based on the dialogue strategy; and the multi-modal output unit is used for converting the response content into multi-modal output information and performing adaptive adjustment on the multi-modal output information according to the real-time scene parameters. According to the invention, accurate identification and intelligent response to the intention of the user are realized, and the interaction experience and service quality of the intelligent dialogue system are remarkably improved.

The invention relates to a control method and device for an intelligent agent with a body and a readable storage medium. The method comprises the steps of obtaining an instruction input by a user; performing intention recognition on the instruction to generate a current task; calling a large language model, and generating a planned path conforming to the current task based on the large language model and the knowledge graph; decomposing the current task to generate an operation instruction comprising a series of executable action sequences; calling a rule base to ensure that the generated action sequence conforms to various preset rules and operation standards included in the rule base; in the task execution process, environment data fed back by different sensors in the multi-mode perception sensor are collected in real time; and inputting the environment data into the large language model, understanding and processing the environment data by adopting the large language model, and adjusting an operation instruction or planning a path in real time according to a current task and an analysis result of the environment data. The control method provided by the invention is more intelligent, safer and highly adaptive, and can adapt to a more complex task environment.

The invention discloses an intention recognition intelligent dialogue method and system based on a large model, relates to the technical field of dialogue processing, and is used for solving the problem that multiple conflict intentions in multiple rounds of dialogues cannot be efficiently recognized and processed in the prior art. The method comprises the following steps: performing semantic analysis on input data in multiple rounds of conversations by utilizing a pre-trained large-scale language model, and extracting a plurality of initial intentions of a user; establishing an intention conflict detection mechanism based on a predefined intention conflict rule base, analyzing the extracted preliminary intention, and screening to obtain a logic conflict intention combination; the opposition degree of the logic conflict intention combination in logic mutual exclusion and fuzzy overlapping is evaluated through a semantic graph and a graph neural network, the dynamic relation entropy of the logic conflict intention combination is analyzed, the fluctuation change degree of semantic dependency in multiple rounds of dialogues is evaluated, and finally the processing sequence of the logic conflict intention combination is dynamically adjusted. And the intention conflict processing accuracy and the priority distribution reasonability are improved.

The invention discloses a question answering method for graph-driven fusion retrieval in the field of computer networks, and relates to technologies such as intention recognition, multi-channel fusion retrieval, hierarchical semantic matching and answer traceable generation. Aiming at the problems of professional term matching, incomplete knowledge, single retrieval and low answer credibility of an existing question-answering system, a dual-channel fusion retrieval and hierarchical semantic matching mechanism based on a knowledge graph is provided, a retrieval strategy is dynamically controlled through intention recognition, accurate matching of structured knowledge is realized by adopting phrase preliminary screening and triple fine matching, and the accuracy of the structured knowledge is improved. In combination with graph subgraph dynamic construction and Prompt generation, a large language model is guided to output high-quality answers, traceable knowledge sources are attached, and the accuracy and credibility of the answers are improved. The method is widely applicable to computer network professional question and answer scenes such as network protocols, configuration management and troubleshooting.

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

The invention relates to the technical field of intelligent recommendation, and discloses a hybrid retrieval RAG-based resource recommendation method and system, and the method comprises the steps: collecting resource text data, and constructing a vector library and a tag library; expanding the user question based on the language model to obtain a plurality of semantic extension questions; performing intention recognition, judging whether the user question is a resource recommendation question, and if yes, determining a target classification type; screening the data according to the field definition in the tag library to obtain a candidate knowledge fragment set; obtaining candidate vectors, mapping the user question and the semantic extension question into query vectors, calculating the similarity between the query vectors and each candidate vector, and selecting knowledge supplement content; and performing resource splicing on all the knowledge supplement contents to generate resource recommendation answers. According to the method, a structured label screening mechanism and a semantic vector fine arrangement mechanism are fused, and the problems of recall redundancy, matching deviation and the like caused by the fact that an existing RAG system only depends on semantic similarity retrieval are solved.

The invention discloses a dynamic intention recognition and GraphRAG fusion method and system for open domain questions and answers, and relates to the technical field of intelligent and natural language processing. The method comprises the following steps: constructing and dynamically updating a heterogeneous knowledge graph; performing dual-stage intention recognition on user query; subtasks are disassembled based on an intention result, and cooperative execution is carried out through multiple agents; service standardization integration is realized by means of a model context protocol; and closed-loop optimization is carried out in combination with user feedback. The system comprises an image knowledge base, an intention recognition module, an agent collaboration module, an MCP service integration module and a feedback optimization module. The system is a novel open domain question-answering system integrating structured knowledge modeling, dynamic intention understanding, intelligent collaborative reasoning and standardized service interaction, and through fusion of structured knowledge modeling and intelligent collaborative reasoning, question-answering accuracy and complex intention understanding ability are improved, system adaptability and efficiency are enhanced, multi-system collaboration is promoted, and the system has a wide application prospect. The method is suitable for scenes such as knowledge services and intelligent assistants.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice intention recognition method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-processed voice signal, carrying out the voice activity detection processing of the voice signal, dividing the voice signal into a plurality of voice segments, analyzing semantic contents of the plurality of voice segments, determining semantic correlation information of each voice segment, analyzing sound source attributes of the plurality of voice segments, determining sound field type information of each voice segment, screening out a target voice segment from the plurality of voice segments according to the semantic correlation information and the sound field type information, and executing intention recognition processing based on the target voice segment to generate an intention recognition result. According to the invention, through a dual analysis mechanism of semantic correlation information and sound field type information, effective screening of voice segments is realized before voice recognition, non-target voice or interference segments are effectively prevented from being sent to an intention recognition model, and the accuracy of a recognition result is improved.

The invention relates to the technical field of process automation, in particular to an RPA service automation method capable of enabling a large language model MCP. The method comprises the following steps: acquiring user input data, and performing intention understanding through a multi-round intention recognition model according to the user input data to obtain semantic instruction data; performing standard semantic protocol conversion according to the semantic instruction data to obtain standard semantic protocol data; performing flow chart node matching according to the standard semantic protocol data to obtain flow chart node data; and generating a business process according to the flow chart node data to obtain business process data. According to the method, the multi-round intention recognition capability of the large language model and the standard semantic protocol conversion are fused, so that the accurate mapping from the natural language to the business process is realized, and the intelligent level and the user friendliness of the RPA system are improved. Through process node matching and process logic verification, the execution effectiveness of the automatic generation process is ensured, and the labor cost and configuration errors are reduced.

The invention discloses a method for correcting dialogue intention information of old people based on a deep neural network, and particularly relates to the technical field of dialogue optimization, and the method comprises the steps: collecting and cleaning multi-topic multi-round dialogue data, marking multi-round dialogue corpora, and unifying the format; performing feature extraction on the labeled corpus by using the multi-intention recognition sub-model to generate a multi-intention representation vector capable of representing each intention; screening context content related to the target intention in combination with a hierarchical attention mechanism of user context dynamic weight adjustment; processing context content related to the target intention through gating operation based on multiple rounds of interaction history, and reserving semantic fragments with reference value; performing parallel analysis on the reference semantic fragments based on a multi-task learning structure of a deep neural network, performing classification processing on different intentions, and calculating scores; and executing multi-intention fusion correction operation according to classification and scoring results, and outputting intention information capable of distinguishing each intention.

The invention discloses a large-model complaint intention recognition method based on sentiment analysis, and the method comprises the steps: obtaining call voice data of a customer, and converting the call voice data into text data; preprocessing the text data, and removing noise and marking components; performing emotion feature analysis on the text data, extracting an emotion feature value, and generating an emotion vector; the emotion vectors and the text data are input into a large model together, and the large model combines the emotion feature values and context semantics to generate intention feature vectors; constructing an emotion-intention state vector, inputting the emotion-intention state vector into an asynchronous dominant actor commentator algorithm model, and generating a corresponding complaint intention probability value; judging a complaint intention probability, and generating risk early warning; and preferentially distributing high-risk customers and responding to customer demands. Through combination of voice data preprocessing, text semantic feature extraction, emotion intensity quantitative analysis and a multi-modal fusion algorithm and efficient risk assessment based on an asynchronous dominant actor reviewer model, the early warning and response capabilities of customer complaint risks are significantly improved.

The invention discloses a large-model real-time voice text intention recognition method and system based on text prediction, and the method comprises the steps: obtaining the real-time voice data of a user, carrying out the real-time voice recognition processing through a streaming voice recognition interface, and obtaining a part of transcriptional text; inputting the partial transcription text into a mask language model for text prediction, and generating a plurality of high-credibility complete sentence candidates; based on the complete sentence candidates, the complete sentence candidates are input into a large language model in parallel for intention recognition, a corresponding intention result is obtained, and a mapping relation between the candidate sentences and the intention recognition result is established; and obtaining a sentence completely expressed by the user, calculating the similarity between the complete actual sentence and a plurality of high-credibility complete sentence candidates through a multi-level text similarity algorithm, selecting the candidate sentence with the highest similarity score, and directly obtaining a corresponding final intention recognition result based on the mapping relationship. The objective of the invention is to solve the technical problem of high response delay of an existing voice intention recognition system.

Embodiments of the present disclosure provide a report generation method and system, an electronic device, and a storage medium. The report generation method comprises: acquiring requirement text information of a report to be generated in a target application scenario; performing requirement intent recognition on the requirement text information, to acquire a report parameter matching the report to be generated; on the basis of the target application scenario, selecting and arranging a preset intelligent agent, to obtain an intelligent agent call chain; on the basis of the report parameter, performing chain-type calling on the preset intelligent agent in the intelligent agent call chain, to generate a report matching the requirement text information. The present method can automatically and quickly generate a report matching a user requirement, effectively increasing report generation efficiency. Using the present method to generate a report does not require manual data acquisition and analysis; efficiency is higher, and costs are lower.

The invention relates to the technical field of artificial intelligence and Internet of Things, and discloses an AI interaction intelligent module based on a hybrid architecture, comprising a user interaction unit which supports voice, text and image multi-modal input and integrates intention recognition and context understanding algorithms; the data processing unit is used for carrying out structured processing on the electric appliance specification and the historical fault data and constructing a dynamically updated knowledge graph; the hybrid architecture core unit comprises a deep learning subunit for realizing natural language understanding and generation based on a Transform model, and a knowledge reasoning subunit; a fault diagnosis unit; and a feedback optimization unit. According to the method, seamless cooperation of deep learning and symbol logic is realized through a dynamic routing strategy, a high-confidence-coefficient scene generates a response through a Transform model, a medium-confidence-coefficient scene calls a knowledge graph rule for verification, and a low-confidence-coefficient scene supplements information through multiple rounds of interaction, so that the effect of improving balance efficiency and safety is achieved.

The invention provides a cache-based data query analysis result optimization method and system, and relates to the technical field of data query analysis result optimizing.The cache-based data query analysis result optimization method comprises the steps that user query statements are understood through a semantic understanding model and an intention recognition model, query features are constructed and matched with a preset template, and query rules are determined; based on data distribution prediction and index optimization technologies, an optimal execution path is determined, a database executor is scheduled to obtain a query result, the result is cached, anomaly detection and resource scheduling optimization are performed according to performance data, and a preloading strategy is generated to improve query efficiency.

The invention belongs to the field of intelligent question-answering systems, and relates to a financial intelligent question-answering method and system based on mixed retrieval and dynamic query, and the method comprises the steps: constructing an FAQ library and a financial office database; receiving a user question, calling a historical chat record of the user, and calling the large language model to perform intention recognition; calling a large language model to perform semantic rewriting in combination with the real intention of the user to generate a standardized query statement; obtaining a candidate list from the FAQ library by adopting a mixed retrieval mode; if no candidate questions and answers exist in the candidate list, replying the standardized answers; otherwise, screening candidate questions and answers of Top-N, judging whether the candidate questions and answers of Top-1 are personalized questions or not, if yes, calling a financial office database to generate a query result, and replying according to the query result and the candidate questions and answers of Top-N; according to the method, semantic retrieval and keyword retrieval are mixed through the user intention classification result and the conflict detection rule, the candidate list is obtained according to the mixed retrieval mode, and the retrieval recall rate is increased.

The invention discloses a reverse question guide question answering implementation method and system, and belongs to the technical field of artificial intelligence and natural language processing. Context-aware intention dynamic correction is realized through a three-level intention classification system, and cross-modal knowledge matching is realized by adopting a distributed semantic index technology; based on the reinforcement learning strategy, optimizing a cooperative work mechanism of the dialogue strategy and the knowledge base; comprising the steps of intention recognition: analyzing a session of a user by using an intention recognition model, and constructing a three-level intention classification system based on deep semantic understanding, including main class recognition, fine-grained analysis and context perception; question rewriting: constructing a dynamic rewriting engine to rewrite the user question; recalling and cleaning multi-source item knowledge; generating a reverse question; locking items and acquiring item data; generating questions and answers. According to the method, the robustness, the real-time performance and the scene adaptation capability of a professional question answering system can be improved, and the government affair service question answering accuracy, the intention recognition precision and the cross-region recommendation adoption rate are improved.

The invention discloses an airport intelligent service question answering method and system based on AI, relates to the technical field of artificial intelligence, and comprises the steps of performing combined modeling by using a time sequence and a multi-head attention mechanism, extracting key behavior characteristics, fusing intention recognition and behavior prediction, and improving the answer reasoning accuracy. The method comprises the following steps: selecting a semantic knowledge base, constructing deep semantic representation and accurately matching the deep semantic representation with the semantic knowledge base, extracting high-weight phrases in combination with a sliding window mechanism, generating unique semantic fingerprints by utilizing a Karp-Rabin hash function, improving the speed and accuracy of answer matching, and performing fuzzy control and redundant information elimination on candidate answers by adopting a rough set tolerance model. Semantic similarity judgment is carried out according to the intersection and union set proportion between the upper and lower approximate sets, the problems of semantic drift and repeated candidates in traditional matching are effectively relieved, and through reinforcement learning and A / B testing, the question and answer strategy is continuously optimized, and the response effect of the system and the user satisfaction degree are enhanced.

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 provides a multi-round dialogue intention recognition method and system based on self-adaptive semantic understanding, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a natural language dialogue text of a current round of a user, and taking the natural language dialogue text as original input data; based on original input data, multi-level semantic features are extracted through a dynamic semantic coding algorithm, and semantic vector representation of a current round of dialogue is generated; setting three fixed anchor points in a semantic vector space based on a current round semantic vector and a historical dialogue state vector to form a triangular analysis structure; performing gridding segmentation on the triangular analysis structure, and generating a feature adjustment value according to distribution characteristics of segmented grids; and dynamically correcting the extraction process of the context-related features by using the feature adjustment value to obtain the corrected context-related features. According to the method, end-to-end optimization is realized in multiple rounds of interaction scenes such as customer service and intelligent assistants through full-process design.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention discloses an intelligent customer service dialogue model construction method and application, and relates to the technical field of artificial intelligence. Comprising an intention recognition module, a knowledge graph management module, a multi-modal interaction module and a reply generation optimization module. Wherein the intention recognition module judges the intention category consulted by a client; performing semantic analysis on a text input by a client by adopting a natural language processing technology based on deep learning in combination with word vector representation and a bidirectional long-short term memory network; and training is performed through the tagged corpus, and feature expressions of different business intentions are autonomously learned, so that questions of clients are accurately classified into a predefined intention category system. The designed intelligent customer service template covers multiple personality dimensions, the score can be dynamically adjusted according to the feedback of the customer, and then the template most conforming to the personality requirement of the customer is recommended for the customer; therefore, the interactive experience between the customer and the customer service can be enhanced, and the recognition of the customer to the service is improved.

The invention relates to the technical field of intelligent customer service control, and discloses a federated learning driven customer service robot cooperative control method and system. The method comprises the following steps: deploying a local intention recognition model at a plurality of nodes, collecting a user dialogue stream, extracting a semantic behavior track fragment, and generating a behavior feature vector set containing a time sequence and context association; the federal cooperative controller performs periodic aggregation, constructs a cross-node feature alignment mapping table based on trajectory similarity, and generates a global behavior feature distribution map; calculating node feature offset, screening high-contribution-degree nodes in combination with a sparse activation threshold, and allocating aggregation tasks; a knowledge distillation compression model is used at the high-contribution-degree nodes, weight updating parameters are extracted, compensation coefficients are added, and an encrypted updating package is generated; and the federal cooperative controller carries out heterogeneous fusion on the encrypted packet, reconstructs a global intention decision tree and carries out segmentation and distribution, so that efficient cooperation and optimization are realized, and privacy protection and service adaptability are considered.

The invention discloses a method and system for context semantic extraction and intention recognition in voice dialogue, and the method comprises the following steps: S1, obtaining a voice input signal, converting the voice input signal into text data, and carrying out the preprocessing; s2, constructing a dialogue context cache, and updating the cache content; s3, constructing a sparse hybrid expert Transform network, and carrying out semantic coding on the sparse hybrid expert Transform network; s4, calculating a semantic dependency relationship between dialogue statements by adopting an ordered graph neural network; s5, calculating intention probability distribution, and adjusting the weight of the sparse hybrid expert Transform network and the weight of the ordered graph neural network; and S6, updating a dialogue state, and generating a system response. According to the method, the sparse hybrid expert Transform network and the ordered graph neural network are combined, voice dialogue context semantic extraction and intention recognition are achieved, and the method has the advantages of being accurate in semantic understanding, high in calculation efficiency and high in context sensing capacity.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a government affair industry intelligent information retrieval and pushing system and method based on a large model, and the system comprises an intelligent semantic understanding and query analysis module, a semantic-driven efficient retrieval module, a generation-enhanced intelligent content generation module, and a personalized pushing and feedback optimization module. The method has the beneficial effects that a natural language query request input by a user is received through the intelligent semantic understanding and query analysis module, semantic analysis and intention recognition are performed by utilizing a pre-trained large language model, key information is extracted, and query logic is optimized through a context sensing mechanism. Then, a semantic-driven efficient retrieval module quickly retrieves document fragments most relevant to user query from mass data of government affair cloud, precise matching is achieved through semantic vectorization and an efficient vector retrieval technology, and a retrieval strategy is dynamically optimized in combination with user feedback.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention is applicable to the technical field of data processing, and discloses a knowledge base sensitive question test method, which comprises the following steps of: firstly, constructing a semantic vector model of a knowledge base for efficiently retrieving knowledge points with high relevancy with a to-be-tested question to form a candidate answer set; performing intention judgment on user questions by using an intention recognition model, and distinguishing factual verification or emotion expression; selecting knowledge points with the highest confidence degree from the candidate answer set as answers according to the fact verification type questions; responding to emotion expression questions based on an emotion response library; on the basis, the answers are further optimized in combination with the context of the questions; judging whether the optimized answer contains sensitive content or not, and performing necessary revision on the answer according to a sensitivity judgment result; and outputting the revised answer as a test result. The method effectively improves the question answering accuracy and sensitivity processing capacity of the knowledge base, and is suitable for various scenes needing intelligent question answering and knowledge base application.

The invention discloses a service report generation method, device and system. The method comprises the steps that intention recognition is conducted on an input problem through a large language model, an index list is obtained, the input problem is used for describing the generation requirement of a service report, and the index list is used for recording a plurality of data analysis indexes associated with the generation requirement; constructing a business index system based on index categories of a plurality of data analysis indexes in the index list; utilizing a large language model and a business index system to query and obtain index data from a business database; and generating a service report based on the index data. According to the method and the device, the technical problems of low accuracy and low efficiency of a service report generation method in related technologies are solved.