328 results about "Natural language understanding" patented technology

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

Techniques are described for identifying functionalities (i.e., user experiences) that are requested by users but are not supported by natural understanding (NU) processing. Some embodiments may involve identifying functionalities by transforming user inputs to functionality-based representations. The functionality-based representations may be grouped into individual functionalities. The user inputs associated with an individual functionality may be evaluated using an NU component to determine whether the functionality is supported. These techniques may enable discovery at a functionality level, rather than at a user input level, an intent level, or an entity level. These techniques may also be used to group user inputs to determine trending functionalities.

The invention discloses a knowledge graph generation method and system based on a large model, and the method comprises the steps: carrying out the preprocessing of data, obtaining a data set, and determining a target mode of a knowledge graph; extracting a triple conforming to the type of the target mode from the data set to obtain a candidate knowledge triple; carrying out multi-dimensional verification on the candidate knowledge triad to generate a confidence score; performing consistency verification and conflict resolution on the candidate triad set with the confidence score higher than a preset threshold value to obtain a verification candidate knowledge triad; performing entity linking and relationship standardization processing on the verification candidate knowledge triples, and mapping the verification candidate knowledge triples to a knowledge graph of a corresponding target mode to obtain standard candidate knowledge triples; and fusing the standard candidate knowledge triples into the knowledge graph database, and updating the knowledge graph database. According to the method, the powerful natural language understanding and knowledge reasoning potential of the LLM can be utilized to the maximum extent, and the inherent defects of the LLM are actively and systematically overcome by introducing an innovative mechanism.

The invention relates to the field of artificial intelligence medical application, and discloses a lightweight intelligent traditional Chinese medicine inquiry system and a construction method thereof, and the system comprises a multi-dialect adaptive speech recognition module, a traditional Chinese medicine intelligent dialogue large language model module, a natural speech synthesis module, and a continuous learning mechanism module. The multi-dialect adaptive speech recognition module is used for converting dialect speech input of a patient into a standard text; the traditional Chinese medicine intelligent dialogue big language model module is the core of the system and is used for carrying out natural language understanding, dialectical reasoning and inquiry dialogue generation, and the natural speech synthesis module is used for converting a text response generated by the system into speech output; and the continuous learning mechanism module realizes continuous optimization of the large language model through incremental learning architecture and clinical feedback integration. According to the method, while the professional traditional Chinese medicine diagnosis capability is maintained, the calculation complexity is remarkably reduced, and the universality and sustainable development capability of system application are improved.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention provides a battlefield environment information generation method and system based on natural language understanding, electronic equipment and a storage medium, and relates to the technical field of data processing. Enemy force deployment information and communication characteristic parameters are extracted from historical battlefield data and reconnaissance data, an association model is constructed to generate a behavior pattern library, and the behavior pattern library is used for generating battlefield environment information. Extracting communication protocol characteristic parameters from battlefield electromagnetic data, matching the communication protocol characteristic parameters with a behavior pattern library to form a communication characteristic mapping relationship, generating associated data in combination with a military strength deployment rule and the communication characteristic mapping relationship, and inputting the associated data into a strategy deduction framework to construct a strategy space of multilevel decision nodes of an enemy; a node interaction behavior is calculated in a strategy space topological structure, a decision tree is generated, decision tree semantics are reconstructed through battlefield environment feedback data, a natural language understanding model is converted into a tactical logic chain, an intelligence report is generated, and intelligent conversion from multi-source battlefield data to understandable tactical intelligence can be achieved.

The invention discloses an unauthorized vulnerability detection method, device and equipment and a readable storage medium, and is applied to the field of security detection, and the method comprises the steps: carrying out the semantic recognition of real business flow data through a large language model, and determining a to-be-detected interface; performing semantic analysis on the parameters of the to-be-detected interface by using a large language model to determine target parameters; extracting a parameter value with an unauthorized vulnerability risk in the target parameter from the historical real service flow data; generating a test effective load of the to-be-detected interface based on the target parameter and the parameter value by utilizing a large language model and a preset rule base; and performing unauthorized vulnerability detection on the to-be-detected interface by using the test payload, and determining a detection result. According to the method, the natural language understanding capability of a large language model is utilized, the limitation of traditional regularization preprocessing and effective load generation is broken through, and the method is adaptive to diversified scenes of a complex system.

The invention provides a code agent construction system, a construction method thereof and a code generation method, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as large models. According to the specific implementation scheme, the code agent construction system comprises an intelligent cooperation engine, a layered template library, a semantic understanding and layered retrieval system, a T-RAG generation and optimization system and a closed loop verification system; wherein the layered template library comprises a project-level template, a function-level template and an API-level template; the semantic understanding and hierarchical retrieval system is configured as follows: analyzing user requirements through a natural language understanding module to generate an intention vector, and sequentially executing project-level, function-level and API-level three-level progressive template retrieval; and the T-RAG generation and optimization system is configured to inject the hierarchical retrieval result into the cue word of the large language model to generate deployable codes containing front-end and rear-end logics.

Dialogue state tracking for voice assistants involves correctly tracking intent and entities of a task that a user is performing. A dialogue state, having a tracked intent and one or more tracked entities, can then be used to perform the task. Building a dialogue state tracking system within a voice assistant is not trivial. In some embodiments, a dialogue state tracking system involving one or more large language models can be implemented downstream of a natural language understanding system to produce the tracked intent and the one or more tracked entities. In some embodiments, a dialogue state tracking system involving one or more large language models can be implemented upstream of a natural language understanding system to produce rephrased natural language text, which is in turn processed by the natural language understanding system to produce the tracked intent and the one or more tracked entities.

The invention discloses an elevator intelligent operation and maintenance question-answering system based on edge computing and computing network integration. The system obtains running state data in real time through an edge data acquisition module, and performs preprocessing and dynamic sampling optimization on an edge side; the computing network fusion analysis module performs multi-dimensional comprehensive analysis on the operation state data by utilizing computing power cooperation of the edge node and the cloud node to generate an elevator health state result and a fault risk result; the knowledge graph reasoning module executes semantic association and logical reasoning in the elevator knowledge graph according to the analysis result to obtain a fault diagnosis result and a fault reason speculation result; the intelligent question and answer interaction module analyzes questions input by a user based on a natural language understanding technology, and generates question and answer responses containing running state instructions, potential risk prompts and maintenance suggestions. According to the system, a complete closed loop from data acquisition, intelligent analysis and knowledge reasoning to semantic interaction is realized, and the intelligence, real-time performance and interpretability of elevator operation and maintenance diagnosis can be remarkably improved.

The invention discloses a multi-context semantic recognition and understanding method based on a large language model. The method comprises the following steps: S1, generating a semantic unit sequence; s2, constructing a context nested vector sequence; s3, constructing context priori representation, and generating a semantic representation sequence; s4, outputting a state vector of the semantic path by adopting a gating loop unit, obtaining a matching degree score according to a feedforward neural network, and determining a deliberate map tag and an alternative intention tag; s5, slot field extraction and semantic filling are completed, and a structured semantic task unit is generated; and S6, completing semantic recognition and service response closed loop. According to the method, by introducing a prefix regulation and control mechanism and a multi-context semantic modeling structure, the accuracy of intention recognition in multiple rounds of dialogues and the consistency of the context generated in response are remarkably improved, and the method is suitable for natural language understanding scenes of multi-language intelligent customer service, cross-context man-machine interaction and complex task driving.

The invention provides a 3D application generation method and device and electronic equipment, and relates to the technical field of computers.The method comprises the steps that natural language information input by a user is analyzed through a large language model, and structured scene description information is generated; generating interactive three-dimensional scene preview according to the scene description information; receiving an operation instruction sent by a user for the scene description information based on the three-dimensional scene preview, and determining a final scene configuration parameter according to the operation instruction; and according to the final scene configuration parameters, constructing the 3D application in stages, and after at least one stage is completed, providing a runnable 3D application intermediate version for the user. According to the method, the natural language understanding ability of the large language model is deeply fused with a visual interaction guide mechanism, so that low-threshold and high-efficiency 3D application creation is realized, a user with zero programming basis can quickly and accurately convert the creation into a high-quality immersive application, and the flexibility of 3D application creation is improved.

The invention discloses an intelligent voice question-answering system and a knowledge reasoning method for operation and maintenance of power equipment. The system comprises a knowledge base construction module and a core question and answer engine module, the knowledge base construction module is used for constructing a power equipment knowledge graph and a fault case library, and the core question and answer engine module comprises a natural language understanding unit, a multistage semantic retrieval unit and a voice interaction ambiguity resolution unit. A natural language understanding unit identifies intent categories and power entities. The multi-stage semantic retrieval unit executes the following steps: matching in an ontology layer of the knowledge graph; performing extended query to obtain detailed information; and similar case matching is carried out in the fault case library. The voice interaction ambiguity resolution unit is used for executing disambiguation processing; analyzing the confidence and clarifying the dialogue; and triggering knowledge retrieval and reasoning and generating a sorting answer. The operation and maintenance efficiency is improved, the dependence on experts is reduced, the operation and maintenance quality and safety are improved, knowledge precipitation and sharing are promoted, the field work experience is improved, and scientific decision making is assisted.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention discloses a low-code automatic generation method based on AI agent collaboration, and relates to the technical field of computer processing, and the method comprises the following steps: S1, receiving a function demand description text input by a user through a natural language; s2, an input analysis agent reads and analyzes the function requirement description text, key information is extracted, and the key information comprises a key intention, an entity object and an operation constraint condition; s3, performing semantic similarity matching in a predefined model library by a model retrieval agent according to the key information, and retrieving and defining a basic generation model with closest semantics; and S4, receiving the basic generation model and the key information by an adaptive modification agent, and performing context semantic understanding enhancement on the basic generation model. According to the method, end-to-end automatic conversion from business demand description to executable codes is realized through natural language understanding and an automatic code generation technology.

The invention provides an enterprise-level intelligent assistant-oriented multi-modal dialogue management and task execution system establishment method, which is characterized by comprising the following steps of Step 1, multi-modal data acquisition and preprocessing; step 2, carrying out cross-modal information alignment and fusion; 3, multi-mode dialogue management is carried out, and after data fusion is completed, the system enters a dialogue management stage; step 4, multi-modal task execution: in the dialogue management process, when a user initiates a specific task request, the system enters a task execution stage; automatically distributing and executing a plurality of tasks in the enterprise by using the fused multi-modal information; step 5, designing and optimizing a system architecture; and an integrated multi-mode intelligent assistant architecture is designed. By integrating multi-modal data processing and natural language understanding (NLU) technologies, the accuracy and efficiency of the enterprise intelligent assistant in task execution are remarkably improved. The system can process various modal information such as texts, voices and images at the same time, and seamless connection of cross-modal dialogue management and task execution is achieved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention belongs to the technical field of artificial intelligence and data analysis, and discloses a data analysis method and system based on multi-layer intention recognition and multiple agents, which remarkably improves the accuracy and robustness of natural language understanding through an innovative multi-layer intention recognition technology and improves the accuracy and robustness of natural language understanding. A common user can perform complex data analysis without mastering professional skills; through a multi-agent cooperative working mechanism, specialized division of labor of different agents is realized, the problem of capability dispersion of a single agent is avoided, and the analysis efficiency and the result quality are remarkably improved; through a real-time streaming processing technology and intelligent visual matching, complete transparency of a code execution process and intelligent recommendation of chart types are realized, and the user experience and the professionality of an analysis result are greatly improved; through an end-to-end integration solution, full-link intelligent processing from data input to visual large screen generation is realized, and a complete data analysis platform is provided for enterprise decision support.

The invention relates to the technical field of computers, and discloses an application development method based on a large model technology, and the method comprises the steps: 1, carrying out the demand analysis, and converting a fuzzy business description into an executable technical scheme through natural language understanding and a structured modeling technology; 2, carrying out architecture design and planning, and constructing a code-free and low-code dual-mode collaborative architecture; 3, establishing a visual arrangement engine, and establishing a dual-mode development interface of a code-free development mode and a low-code development mode; step 4, aiming at different requirements of a code-free scene and a low-code scene, a dynamic adaptation strategy is adopted, and differentiated resource management of lightweight adaptation and elastic expansion is carried out; and step 5, performing field adaptation, and obtaining the industry exclusive capability according to the general model and the field knowledge through code-free rapid adaptation and low-code deep customization. By means of the scheme, the development threshold can be lowered, the efficiency can be improved, and the accuracy of cross-industry application is improved.

The invention discloses a health analysis report generation method based on a large model. The report generation method comprises the following steps: inputting a physical examination report; preprocessing the physical examination report to obtain a preprocessing result; performing visual language model analysis according to the preprocessing result; carrying out structured processing; compressing the cue word; generating an HTML (Hypertext Markup Language): generating webpage contents by utilizing And rendering and exporting. By introducing a large model with natural language understanding, data reasoning and code generation capabilities, an intelligent report generation mechanism is constructed, while the system development workload is reduced and the development period is shortened, specialized, precise and personalized output of health reports is realized, and the scientificity, readability and practical value of report contents are improved.

The invention discloses an artificial intelligence-based intelligent question and answer method in the human society field, and relates to the technical field of artificial intelligence and government affair services. Comprising the following steps: collecting and cleaning laws and regulations, files, common questions and case library contents related to human society through a knowledge graph construction module; constructing a structured human social knowledge graph, and performing intention recognition and slot extraction on the content input by the user by using a pre-training language model through a natural language understanding module; context memory and state tracking are carried out through a dialogue management module, and multi-round dialogue logic is controlled for an interaction mode of inquiry, clarification and guidance; providing a voice and character dual-channel input and output interface; an answer generation module generates a natural language answer based on a human society knowledge graph query result, the natural language answer is used for visualized display of structured data, and an API interface can be called to obtain real-time data; user interaction behaviors and identity information are recorded through a user portrait and personalized recommendation module, personalized policy interpretation and service recommendation are provided in combination with a user portrait, and historical question and answer record retrieval and reuse are provided; and obtaining question and answer effect feedback through a feedback learning and adaptive optimization module, carrying out question and answer strategy optimization by utilizing a reinforcement learning algorithm, automatically discovering uncovered questions and triggering a knowledge base updating process.

A system for latency-aware orchestration and performance optimization in AI-driven telephone communication, consisting of: a speech capture unit configured to capture an analog audio signal from a telephone interface and convert the analog audio signal into a digital audio signal stream; a feature extraction unit that is operationally coupled with the speech acquisition unit and is configured to generate a feature representation of the digital audio signal stream through spectral decomposition, noise reduction, and temporal segmentation; an AI inference processor communicatively connected to the feature extraction unit, configured to run one or more AI models for automatic speech recognition, natural language understanding, and emotion recognition on the feature representation to generate intermediate results for inference; a latency orchestration controller coupled to the AI ​​inference processor, wherein the latency orchestration controller is configured to monitor latency across multiple processing stages, predict cumulative delay propagation using a hybrid latency estimation model, and orchestrate the execution scheduling of the AI ​​inference processor based on the predicted latency deviation; a performance optimization unit coupled with the latency orchestration controller and configured to dynamically adjust computational accuracy, inference batch size, and feature processing resolution based on latency thresholds and quality constraints set by the latency orchestration controller; and a transmission synchronization array configured to time-align the processed output generated by the AI ​​inference processor and transmit it to a remote communication node, with the transmission synchronization array maintaining deterministic time coordination between successive packets and the orchestrated inference results.

Methods and systems for generating automation rules based on natural language inputs. In an example, the technology relates to a computer-implemented method for generating automation rules from natural language input. The method includes receiving a natural language input into a communications application for performing an action on communications received by the communications application; providing the natural language input into a trained machine learning model; receiving, as output from the trained machine learning model, a tagged primitive and an identified action from the natural language input; generating an automation rule for performing the action on a subset of communications received by the communications application, the subset of communications corresponding to the tagged primitives; and executing the generated automation rule to perform action on the subset of communications.

The invention relates to the technical field of railway traffic control and software quality detection, in particular to a railway signal safety system function performance testing method which comprises the following steps: S1, knowledge extraction: extracting multivariate entities and semantic relationships thereof from text data related to railway signal safety to form a triple set; s2, constructing and optimizing a graph, constructing a heterogeneous knowledge graph, and optimizing the heterogeneous knowledge graph through a graph neural network and an embedded learning method; s3, constructing an intelligent dialogue mechanism; s4, strategy generation and grade evaluation; s5, task scheduling and execution; s6, test data analysis; and S7, result generation and write-back. According to the method, knowledge graph construction, natural language understanding, man-machine collaborative interaction, automatic test strategy generation and graph self-evolution updating technologies are comprehensively applied, and the verification efficiency and result credibility of the special software for the railway signal safety system in the aspects of function integrity and performance stability are effectively improved.

The invention provides a workflow generation method and device based on a large model, electronic equipment, a storage medium and a computer program product, relates to the technical field of artificial intelligence, in particular to the technical fields of large models, natural language understanding, workflows and the like, and can be applied to a workflow generation scene. According to the specific implementation scheme, a workflow generation request is analyzed, and a workflow generation intention is determined; through an artificial intelligence large model, a target tool used for generating a workflow meeting the workflow generation intention is determined from a tool library, and the tool library comprises a plurality of workflow construction tools; and generating the workflow by adopting the target tool. According to the method and the device, the workflow generation request is mapped into the target tool for constructing the workflow based on the artificial intelligence large model, so that the workflow meeting the workflow generation intention is generated, the automatic process development threshold is greatly reduced, and the automatic experience that the workflow generation request is what is obtained (workflow) is realized.

The invention belongs to the technical field of automobile testing, and particularly discloses a digital twinning-based automobile instrument testing method, which comprises the following steps of: automatically generating a structured test case through a large language model, constructing a double-digital twinning architecture by relying on a high-fidelity rendering engine and a real-time simulation engine, and realizing synchronization of a virtual model and a physical instrument; multi-modal data are collected through a multi-sensor array, and defect intelligent analysis and grade judgment are completed in combination with a large language model; and test strategy self-optimization is realized through reinforcement learning based on historical test data. According to the invention, a double-engine architecture in which a high-fidelity rendering engine and a real-time simulation engine cooperatively work is adopted, and the requirements of large-scale scene and refined rendering are met at the same time. And seamless switching and data fusion between the double engines are realized through the scene transformation matrix, so that the consistency of a virtual environment and a physical entity is ensured. Meanwhile, a large language model is introduced into a test case generation and result analysis link, and natural language understanding level test script generation and anomaly analysis are achieved.

In one embodiment, a method for managing embeddings and text for enhanced natural language understanding includes dividing, by a process, a corpus of one or more documents into a first plurality of fragments based on a first threshold size for a first large language model and computing, by the process, a first set of embeddings using the first large language model to analyze the first plurality of fragments. The method further includes dividing, by the process, the corpus of one or more documents into a second plurality of fragments based on a second threshold size for a second large language model and computing, by the process, a second set of embeddings using the second large language model to analyze the second plurality of fragments.

The invention provides an intelligent customer service query system and method based on natural language understanding, and relates to the technical field of natural language processing. A query intention and a core entity of a natural language query request input by a user side are extracted; performing hierarchical deconstruction on the query intention, and constructing a query semantic tree according to a hierarchical deconstruction result and a semantic association relationship between core entities; generating a decision tensor representing a query path priority according to a node semantic weight of the query semantic tree and an access delay feature of the multi-source database; further adjusting an access sequence and a connection strategy of the multi-source data through a decision tensor and a dynamically updated query cost model to obtain a query execution link adaptive to the current query request; and calling a corresponding data source interface according to the query execution link to execute data retrieval and integration operation to obtain a target result set responding to the query demand of the user. By the adoption of the scheme, multi-source data optimal cost retrieval based on the natural language can be achieved.

The invention relates to an intelligent agricultural system based on modular collaboration and AI Agent driving. Environmental sensing, video monitoring and equipment operation data are comprehensively acquired through a data acquisition module; the data processing module carries out real-time abnormity diagnosis and mode analysis; the core framework module cooperatively schedules the data storage module, the user management module and the external interface module to realize cross-layer resource and data integration; the business logic module is connected in series with each layer of events to dynamically generate backlogs and drive multi-domain function execution; and the AI service module carries a multi-modal agricultural agent Agent, has the capabilities of natural language understanding, multi-round dialogue memory, agricultural knowledge reasoning, equipment control decision making, abnormity pre-judgment and personalized suggestion generation core AI, coordinates operation of each module, and realizes intelligent jump from passive response to active suggestion. According to the invention, the problems of response lag, operation splitting and data analysis fragmentation of an existing agricultural system are solved, and the real-time performance, the collaboration and the decision accuracy of agricultural management are remarkably improved.