2876 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 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 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 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 discloses a software multi-agent collaboration method and system based on a large language model, and the method comprises the steps: receiving natural language task description submitted by a user at the same time, carrying out the semantic understanding and intention recognition through a pre-trained large language model center, and generating a structured task element set; based on the structured task element set, the large language model center generates a task dependency graph through multiple rounds of reasoning, and the task dependency graph comprises a plurality of atomic subtasks, logic relations among the tasks and data flow constraints; according to a topological structure and resource demand characteristics of a task dependency graph, a double-layer graph attention network is adopted to dynamically match a professional agent with specific domain capability, and a distributed collaborative network is formed. Through the dynamic graph network scheduling and cross-domain semantic alignment mechanism, the problems that the multi-agent dynamic collaborative adaptation capability is insufficient and cross-domain semantic fusion is difficult are solved.

The invention discloses a knowledge base question answering method and system based on intention recognition and a medium, and the method comprises the steps: receiving a user question, inputting the user question into a large language model subjected to intention recognition training, and carrying out the preliminary classification; determining a corresponding search strategy according to a classification result obtained by the preliminary classification; if the search strategy is knowledge base search questions and answers, selecting a knowledge base matched with the search strategy for knowledge retrieval, inputting the retrieved reference knowledge and cue word templates into a large language model, and answering the user questions by the large language model; and if the search strategy is large-model direct question answering, selecting a cue word template, inputting the cue word template into the large-language model, and answering the user question by the large-language model. According to the method, user questions can be preliminarily classified through user intention recognition, different search strategies are adopted for different categories, and knowledge base question answering efficiency and accuracy are effectively improved.

The invention relates to a multi-mode intention recognition method and system, and the method comprises the steps: carrying out the time domain and frequency domain enhancement of the features of text, video and audio modes, carrying out the splicing to obtain non-language mode fusion features, combining the features of an original text, modeling the time synchronization relation of audio-text and video-text, and carrying out the recognition of a multi-mode intention. Standardized audio features, video features and text features are obtained through context alignment processing; fusing the standardized features of the three modalities to obtain a fused feature vector, and mapping the fused feature vector back to the text modal space to be connected with the weighted residual error of the original text feature to obtain a fused semantic vector; extracting global semantic anchor points and mask positions from the fused semantic vector, and splicing the global semantic anchor points and the mask positions with the original text features and the fused semantic vector to obtain input features; and obtaining probability distribution of multiple intention categories by using the input features. Three types of heterogeneous modal input can be supported, and the accuracy and robustness of intention recognition are improved through fine-grained semantic supervision and enhancement strategies.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention discloses a sound interaction intention recognition and intelligent decision-making method based on an AI large model, and relates to the technical field of intelligent voice interaction, and the method comprises the steps: collecting a voice signal through an acoustic sensor, carrying out the noise reduction processing and acoustic feature extraction, capturing a text instruction, and carrying out the semantic segmentation and text feature extraction, splicing the acoustic feature vector and the text feature vector to form a multi-modal data packet; retrieving a historical memory library based on the enhanced fusion feature vector to generate a memory context vector, identifying the category of a deliberate map through a two-stage intention reasoning model, analyzing operation parameters, and outputting a structured intention instruction; and performing parameter legality verification, equipment state verification and security risk assessment on the structured intention instruction, and packaging the structured intention instruction into an executable instruction set after correcting abnormal parameters. According to the method, through double screening of the frequency band energy ratio and the lexical item importance score, the acoustic-text feature scale difference is reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a task scheduling execution method, device, equipment and medium based on intention recognition, and the method comprises the steps: fusing an interaction request and a dialogue history library to generate a session context, and carrying out the analysis to obtain an intention recognition result; task planning is carried out based on the intention recognition result and a knowledge base, and a task subitem set and an execution sequence are generated; matching the task subitem set with a resource directory to generate an execution resource list; scheduling the task subitem set by using the execution sequence to generate an ordered task sequence; generating an execution plan in combination with the ordered task sequence and the execution resource list; and triggering execution based on the execution plan, and aggregating output to generate an execution result and a process tracking record. According to the method, automatic connection of session analysis, task planning, resource matching, sequential scheduling and execution triggering is achieved, manual operation splitting among multiple systems is eliminated, and task processing efficiency and execution accuracy are improved.

The invention relates to the field of artificial intelligence, discloses a large language model driven complex manufacturing and production scheduling method and system, and aims to solve the problems that a traditional scheduling system depends on a static rule, is difficult to cope with multi-constraint dynamic disturbance, is weak in semantic understanding ability and is poor in execution interpretability. The method comprises the steps that a special large language model base is constructed and manufactured, and work orders, equipment, materials, processes and abnormal event data are packaged in a unified mode through a semantic collection module; a task intention recognition sub-module, a resource matching sub-module and a time sequence conflict detection sub-module are used for jointly analyzing the semantic unit; and the scheduling engine based on reinforcement learning generates an optimal action sequence under the multi-target weighting constraint. According to the method, minute-level high-dimensional scheduling, anti-disturbance re-planning and man-machine cooperative execution are realized through semantic driving and dynamic evolution architecture, the equipment efficiency is remarkably improved by more than 15%, the delivery delay is reduced by 30%, the line change loss is reduced by 20%, and intelligent manufacturing is promoted to evolve from rule driving to semantic self-adaption.