1288 results about "JSON" patented technology

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

The invention discloses a data analysis problem generation method based on image input and large model combination, and the method comprises the following steps: S1, obtaining original image data, and carrying out the preprocessing of the original image data; s2, constructing a visual language model based on a Qwen-VL architecture, and performing bidirectional alignment to generate a visual feature vector; s3, constructing a cue word template, and fusing the cue word template through a cross attention mechanism to generate structured text description; s4, constructing a large language model based on a Transform architecture, and performing supervision and fine tuning by adopting a LoRA method to generate a data analysis problem candidate sequence; s5, semantic consistency detection and structural rule matching are carried out, and sequences which do not meet semantic specifications or structural constraints are removed; and S6, constructing an image-text alignment triple, and writing the image-text alignment triple into the data structure in the JSON format for coding and storage. According to the method, the image can be converted into a data analysis problem, and the text generation quality and efficiency are remarkably improved.

The invention discloses a heterogeneous data conversion method and system based on a multi-modal large model, and aims to solve the problems of single-modal limitation, high migration cost and the like in traditional heterogeneous data processing. The method comprises the following steps: firstly, carrying out format adaptation and classification preprocessing on multi-source heterogeneous data such as images, texts, audios and videos; then, through a ViT, BERT and Transform cross attention mechanism, deep analysis and cross-modal semantic association of visual, text and audio features are realized, and visual-text-audio semantic mapping is established; and based on a dynamic prompt project, guiding the multi-modal large model to generate structured data according to a preset Schema zero sample. And then error correction and format normalization are carried out through a Drools rule engine to ensure that the data meet the business standard. And finally, supporting multi-format output of JSON / XML / database table and the like, and meanwhile, associating original data storage to realize data traceability. According to the method, the limitation of single-modal processing is broken through, and cross-modal data deep association is realized.

The invention discloses a circuit netlist generation method supporting deep learning model multi-stage reasoning, and belongs to the technical field of electronic circuit design and automation, and the method comprises the steps: obtaining a circuit diagram image through optical scanning, carrying out the preprocessing of the image, carrying out the recognition of a component through a deep learning model YOLOv5, and determining the boundary frame coordinates and class information. And further performing port fine identification on the component, and determining the position and the direction of the port. And eliminating character interference by using an optical character recognition technology, performing wire recognition, including Hough straight line detection and jumper processing, and determining starting point and ending point coordinates of the wire. And matching the identification results of the components, the ports and the wires, constructing a topological structure of a circuit diagram, finally generating a circuit netlist, and performing error elimination and JSON format output. According to the method, automatic netlist generation of the analog circuit diagram can be realized, the analog circuit diagram file is converted into the netlist file with device function annotations, and the efficiency and quality of circuit design and simulation are improved.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention discloses a food production traceability management system based on a block chain, which belongs to the field of food management systems, and is characterized in that a unique identifier ID of an inheritor is generated based on an SHA-256 Hash algorithm, a Merkle tree root Hash value of food production data is dynamically associated, a non-tampering digital identity file is constructed, a data island of traditional decentralized management is broken through, and the traceability of food production is improved. According to the technical scheme, full-chain credible association between the non-abandoned technology and the production process is ensured, validity of an electronic signature is automatically verified by means of an intelligent contract, data tampering or identity failure risks are recognized in real time by comparing a public key decryption result with a signature hash value, JSON format alarms are pushed by means of Apache Kafka message middleware, cross-department collaborative response is achieved, and the security and reliability of the electronic signature are improved. The risk prevention and control efficiency is remarkably improved, encryption signature is performed in combination with an ECDSA key pair of a hardware security module, strong binding of an inheritor identifier, a production batch and equipment is ensured, identity fraudulent use or qualification counterfeiting is prevented, and traceability of the whole life cycle of non-abandoned skill inheritance is achieved.

The invention belongs to the technical field of mine ventilation monitoring, and aims to solve the problems that a traditional knowledge system based on a rule base has bottlenecks in the aspects of dealing with sudden working conditions, knowledge updating and semantic reasoning, and is high in construction cost and complex to maintain. The invention provides a mine ventilation knowledge graph construction method based on a large model, and the method comprises the following steps: S100, obtaining and cleaning a multi-source heterogeneous text related to mine ventilation, and obtaining a JSON format knowledge fragment of a unified structure; s200, constructing an ontology model and defining a semantic structure; s300, constructing four types of knowledge extraction tasks, and extracting entities, attributes and relationships; s400, constructing an entity alignment module; and S500, constructing a graph database structure. According to the method, automatic structured expression, semantic consistent fusion and intelligent visual query of knowledge in the mine ventilation field can be realized, and an interpretable, extensible and reasonable intelligent support platform is provided for a mine ventilation system.

The invention discloses a chemical material field knowledge graph construction method and device based on an inference large model prompt instruction. The method comprises the following steps: acquiring basic data information and experimental chart data; establishing a domain rule base and a knowledge graph basic framework document; establishing structured JSON data; constructing a Neo4j database according to the structured JSON data and a field rule base to perform structured data quality verification and dynamic enhancement so as to obtain the updated field rule base and the verified structured data; acquiring preprocessed atlas data according to the verified structured data; acquiring a multi-modal fusion preliminary map according to the preprocessed map data; and establishing a knowledge graph containing a reasoning result according to the multi-modal fusion preliminary graph. According to the method, the logical relationship in the article content can be better extracted, so that the content and the relationship of the established knowledge graph are more comprehensive.

The invention discloses a complex task structured decomposition method, system and device based on a large language model and a medium, and the method comprises the steps: converting natural language query into a standardized atomic task flow through the combination of a dynamic prompt template and a strong type verification mechanism; the core innovation comprises the steps that a structured prompt template is adopted to guide a large language model to generate a subtask description object meeting the JSON Schema specification, it is ensured that an output result meets the expected structure requirement in combination with a format constraint mechanism, and the logic completeness of a task flow is ensured through a dependency relationship consistency verification mechanism; the system realizes an end-to-end processing process from natural language input to structured task output through task processing link series prompt template construction, model reasoning and result analysis links; according to the method, the problems of adaptability limitation, difficulty in unstructured output docking and weak dependency relationship verification of a traditional rule engine are effectively solved, and efficient and reliable task decomposition support is provided for a complex business scene.

The embodiment of the invention provides a dynamic page rendering method and system based on a front-end framework, electronic equipment and a storage medium, and belongs to the technical field of page rendering. According to the scheme, rule expansion processing is conducted on JSON structure specifications, a metadata structure model is obtained, and the metadata structure model is used for defining page rendering rules; obtaining JSON configuration data, and performing data preprocessing on the JSON configuration data based on the metadata structure model to obtain structure data; dynamic structure analysis is conducted on the structure data through an analysis engine, a virtual document object model tree structure is obtained, and the analysis engine adopts a layered architecture; and performing difference rendering according to the virtual document object model tree structure to obtain a target page. According to the method, seamless connection between a low-code platform and professional development can be realized, the development efficiency is improved, and the page rendering dynamic capability is enhanced.

The invention belongs to the field of intelligent music generation, and relates to an intelligent music production system and method based on a model context protocol, and the system comprises a large language model, the model context protocol, and a digital audio workstation execution layer. The large language model is used for deconstructing a natural language instruction into structured operation logic and generating a semantic instruction based on the music knowledge base; the semantic instruction at least comprises a device parameter and a chord sequence; the model context protocol is used for compiling the semantic instruction into a JSON command and synchronizing working state data of the digital audio workstation in the digital audio workstation execution layer to a large language model so as to update a model context; the model context comprises track configuration, effect chain parameters and time sequence positioning information of the digital audio workstation; the digital audio workstation execution layer is used for converting the JSON command into music operation to obtain a music operation file; a man-machine cooperative music production mode is reconstructed, and the quality of music production is improved.

The invention discloses a multi-modal large model-based seal detection and identification method and system, and the method comprises the steps: carrying out the size standardization processing of obtained document image data through an image preprocessing module, and generating a standardized image which accords with the input specification of a multi-modal large model; the standardized image and the structured recognition instruction are combined and then input into a multi-modal large model subjected to fine tuning training for end-to-end reasoning, and a recognition result character string conforming to the JSON format specification is output; and analyzing the recognition result character string to extract the bounding box coordinate, the type label, the text content and the text recognition confidence coefficient of each seal object, performing hierarchical decision processing based on the text recognition confidence coefficient, and finally outputting a structured recognition result. According to the invention, the accuracy and the automation level of seal identification are obviously improved, the normalization and the reliability of an output result are ensured, and the problems of low seal identification precision and non-standardized output in a complex scene are effectively solved.

The invention provides a multivariate document analysis method and system, relates to the field of computer information processing, and solves the technical problems of low information extraction efficiency and low accuracy caused by incapability of uniformly analyzing and integrating formats of various types of documents. The method comprises the following steps: identifying the type of a to-be-processed document; the document types comprise a table type, a text type and a demonstration type; the text class comprises a Word format and a PDF format; calling a corresponding analysis function according to the type of the to-be-processed document, and analyzing the to-be-processed document to obtain an analysis result, the analysis result comprising the extracted structure information and content data of the to-be-processed document; and converting an analysis result into a standard JSON format and outputting the standard JSON format. The method and the device are used in a document analysis process.

The invention relates to the technical field of multimodal transport, in particular to a multimodal transport one-box system intelligent credible collaboration method based on an artificial intelligence technology, comprising the following steps: S1, obtaining a standardized JSON data packet from transport data according to an agreed rule, performing triple encryption to obtain an encrypted data packet, and broadcasting the encrypted data packet to a block chain; s2, the verification node carries out consensus verification on the Internet of Things equipment data and the electronic waybill key field, and if the verification is passed, the verification is written into the block chain; s3, the digital twin system fuses the multi-source data and extracts calculation data, and artificial intelligence calculates an optimal loading and unloading sequence and a transportation path according to the calculation data; and S4, uploading the longitude and latitude data to the block chain node through the Beidou positioning terminal and the 5G / LoRa gateway along the way, and automatically identifying the abnormity in the transportation stage based on the geofence technology. According to the method, data rules are unified, efficient connection of cross-transportation modes is achieved, manual input errors or violation operation is prevented, the risk of goods damage is reduced through dynamic route planning, the transfer efficiency is improved, and logistics time and cost consumption are reduced.

The invention belongs to the technical field of artificial intelligence, and discloses a knowledge extraction method and system based on semantic consistency evaluation and hybrid verifiable reward, and the method comprises the steps: constructing a training data set with evidence labeling; based on the training data set, reinforcement learning training is carried out on a pre-trained large language model, a group strategy optimization GRPO algorithm is adopted, and model output is evaluated by using a mixed reward function; and on the basis of an evaluation result of the mixed reward function, updating parameters of a large language model so as to generate structured knowledge which is correct in format, accurate in content and provided with verifiable evidence. According to the method, in reinforcement learning training, effective decoupling format, content and credibility evaluation is realized, and refined feedback is provided for the model; when the model outputs knowledge, traceable original text evidence is provided for the model, so that the credibility and the interpretability of the model are enhanced, the accuracy of outputting the JSON format by the model is improved, and the accuracy and the integrity of the model in the aspect of content extraction are enhanced.

The invention relates to the technical field of data processing, in particular to an HTML (Hypertext Markup Language)-based webpage word cutting annotation generation method, system and equipment, and aims to solve the problems of low storage efficiency, poor dynamic webpage adaptability, lack of fault tolerance and insufficient cross-browser compatibility of an existing word cutting annotation method. The method comprises the steps of capturing a selected area; obtaining DOM node path data and text offset, and generating positioning data; recursively storing the path data to generate a unique fingerprint identifier; storing the fingerprint data as historical fingerprint data in a JSON format; performing matching and positioning based on the historical fingerprint data; and mapping information in the historical fingerprint data to an HTML page to realize bidirectional rendering. According to the method, the label name, the class name and the hierarchical relationship of the DOM node are recorded through the node path to construct the fingerprint identifier, and cross-platform data exchange is realized through the JSON structure, so that webpage labeling which is efficient, accurate, high in fault tolerance and adaptive to a dynamic page is realized.

The invention discloses a knowledge document-oriented method for structurally and stably presenting large language model output. The method comprises the following steps of: preprocessing a knowledge document and converting the knowledge document into a text by a system; the back-end service module adopts a prompt word engineering technology based on a preset JSON mode, constructs a structured prompt containing clear instructions and output format constraints, calls a large language model to carry out analysis and information extraction on text contents, and forces the model to generate structured JSON data following the preset mode; the back-end service provides access to the structured JSON data for the front-end application; and the front end executes templated mapping rendering according to a preset user interface component template isomorphic to the JSON mode, and fills the content of each field of the JSON into a corresponding visual component. According to the method, through the cooperation of the back-end constraint prompt word engineering and the front-end modularized template rendering, the defects that the output content format of a large language model is unstable and is difficult to be directly applied to standardized interface presentation are relieved.

The invention discloses an automatic integrated system from an asynchronous pipeline to an asynchronous circuit, which adopts a modular architecture, takes a high-level structure description file in a JSON / XML format as input, and gradually maps to generate a gate-level netlist and a standard delay format file so as to be in butt joint with a commercial back-end design process. According to the method, asynchronous design based on an asynchronous structure can be automatically mapped into an asynchronous circuit, the automatic mapping process can seamlessly process multi-level design representation, the mapping result is stable and cooperatively operated, asynchronous circuit time sequence detection and delay matching are automatically completed, the design efficiency is greatly improved, and the design cost is reduced. An automatic delay matching mechanism enables the circuit to have a good data ready detection and handshake mechanism, and the characteristics of low power consumption, high throughput and no global clock drift sensitivity are realized; in addition, automation of asynchronous design key links such as structure abstraction, time delay modeling and testability insertion is achieved while design readability, hierarchical consistency and time sequence controllability are guaranteed.

The invention discloses a cross-department collaborative approval digital processing method based on a block chain and a smart contract, and relates to the technical field of block chains, and the method comprises the steps: submitting an approval material through a front-end application, converting the approval material into standardized JSON format data, generating a data fingerprint, writing the data fingerprint into a block chain, and generating an NFT in a to-be-pre-audited state; the intelligent contract retrieves a list of departments participating in examination and approval according to examination and approval type fields in the standardized JSON format data, verifies the validity of the examination and approval right NFT of each department, and generates an encrypted pre-examination task package; and encrypting the encrypted pre-auditing task package by using a fully homomorphic encryption algorithm. The risk analysis request packet is generated after the threshold signature is collected through the smart contract, real-time risk analysis is performed through the neural network model deployed in the smart contract, the risk disposal report or the plaintext approval document is output, the result is synchronized to the related service chain through the cross-chain protocol, and the verifiable certificate is generated.

The invention provides a multi-operator card AIOT management method and system based on the Internet of Things, and the method comprises the following steps: carrying out API butt joint, calling a mobile CMNet API, a Unicom WorAPI and a Telecom CTWing API, and obtaining the state, flow and signal intensity data of an Internet of Things card; a heterogeneous protocol is converted into a unified JSON format through a protocol conversion gateway; network delay and base station loads are monitored in real time, and network scores of all operators are calculated based on the base station loads, the network delay, the signal strength, the tariff cost and the historical stability; and judging whether a switching trigger condition is met or not, and when the switching trigger condition is met, determining to switch the network based on the network scores of the operators, and executing a network switching process. According to the method, the network condition is monitored in real time, and the score of each operator network is calculated based on multiple factors, so that the available network can be quickly switched to when a single operator network has a fault or a poor signal, and the stability of the Internet of Things system is improved.

The invention discloses a method and a system for realizing hosted API automatic testing by an API gateway, belongs to the technical field of computer software, and aims at solving the technical problems of how to realize non-intrusive and automatic API testing, ensuring that monitoring and verification can be directly carried out on an API flow level, improving the testing efficiency and the coverage rate and improving the testing efficiency. According to the technical scheme, the method comprises the steps of test case generation, wherein an interface structure is automatically analyzed based on an API document, and a basic test case set is generated; flow capturing: monitoring the actual calling condition of the API in real time, and recording the specific content of each request and the corresponding response data; carrying out flow playback: carrying out playback test based on the captured historical flow, and verifying the compatibility of the API; assertion verification: assertion judgment is performed on indexes of the return value and the response time through an assertion mechanism, and abnormity is found; and test report generation: aggregating test result data, generating a test report containing success rate, response time and error log detailed information, and supporting HTML / PDF / JSON export.

The invention relates to the technical field of OA examination and approval, in particular to a multi-modal file intelligent examination and approval method and system based on a large language model, and the method comprises the steps: configuring exclusive examination and approval templates for different examination and approval roles, and generating a role exclusive intelligent examination model; the method comprises the following steps: receiving a to-be-approved multi-modal file, analyzing file content, and outputting structured JSON (JavaScript Object Notation) data; inputting the structured JSON data and a configured approval template into a corresponding intelligent review model, performing step-by-step reasoning through a thinking chain prompt strategy, and outputting a structured review intermediate result; encoding the review intermediate result into a semantic vector, and retrieving Top-K similar historical approval cases from a knowledge base module based on a vector database to generate a reference suggestion set; fusing the review intermediate result with the reference suggestion set to generate a review result; based on the review result and the feedback of the approver, the standardized approval opinions are generated, and the accuracy and consistency of review are improved.

The invention discloses a cross-system full-link metadata processing method and device.According to the method, a separated metadata storage architecture is adopted, entity attributes are stored in a JSON document form, relationships between entities are stored in an independent relationship table in a quintuple structure, and efficient storage and rapid query are achieved; through a topology driving processing framework, depth-first traversal is carried out based on a hierarchical structure of a directed acyclic graph, and multi-thread concurrent processing is started at a mode level, so that the processing efficiency of large-scale heterogeneous metadata is improved; the entity life cycle event scheduler is used for monitoring entity change events, triggering index updating, relation maintenance and other operations, and real-time response of metadata change is ensured. Meanwhile, in combination with a search index synchronization mechanism, a hierarchical entity association model, a connector abstraction factory and a fingerprint cache optimization mechanism, efficient management, real-time synchronization and deep association analysis of cross-system metadata are realized. According to the method, the storage efficiency, the real-time performance, the concurrent processing capability and the expansibility are remarkably improved.

The invention discloses an intelligent medical image diagnosis system and method based on hierarchical cross-modal conversion and dynamic feature tracking. The system adopts three-step cross-modal conversion: a first-layer small model for converting user questions to realize medical ontology matching; the second-layer multi-modal model extracts image features, and outputs text states such as JSON data with focus coordinates, density and other features; and the third-layer large model fuses the medical history and the image features to generate diagnosis suggestions, and credibility verification is carried out. A dynamic focus tracking engine is introduced, a focus evolution rule of multiple scanning is analyzed through a convolutional network, and an optical flow field is adopted to compensate artifacts. The system also integrates a multi-expert voting mechanism to simulate a clinical consultation process, and outputs consensus diagnosis and objection viewpoints. A hierarchical routing algorithm is designed for emergency treatment scenes, so that the recognition response time of emergencies such as pneumothorax is shortened. Further, the system automatically generates a full chain of evidence report that conforms to medical regulations, including a model version, a guide reference, and a data hash value.

The invention discloses a multi-person voice separation and recognition system and method for real-time voice interaction, and belongs to the technical field of voice signal processing. The technical principle comprises the following steps: segmenting streaming voice through an audio acquisition module and storing the streaming voice into a circular queue; the preprocessing module converts the wav file into a standard wav file; the voice separation module adopts a SepFormer model to separate a speaker track; the voice recognition module executes end-to-end transcription on the voice through a Paraformer model; and the result management module binds the speaker number and the text and then performs structured output. The multi-thread asynchronous architecture enables acquisition, separation and identification to be processed in parallel, and low delay is realized by combining a circular queue; the SepFormer and the Paraformer are cooperated to improve the separation precision and the recognition accuracy of the overlapped voices; jSON / database multi-format output is supported, and the interaction requirements of real-time conferences and intelligent devices are met.

The invention provides a JSON (JavaScript Object Notation) data dynamic addition and management method based on a target keyword, which relates to the technical field of data processing, and comprises the following steps of: generating a keyword affinity matrix through grammar dependency analysis for clustering, establishing a mapping relationship between semantic identifiers and JSON nodes to form a semantic addressing table, constructing a dependency topological graph to determine a data block, and establishing a database; and accurate data addition and management based on keywords are realized. According to the method, the retrieval efficiency of the JSON data is improved, the data relevance is enhanced, the storage structure is optimized, and intelligent organization and maintenance of the data are realized.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

The invention discloses a machine control system and method based on large language model structured data output, and relates to the technical field of artificial intelligence and robot control. Voice information and image information are acquired through a multi-mode acquisition module, and based on a Gemma 3 large model, a text instruction and a coding image are combined, so that a multi-mode model is obtained; a decision output module generates a structured instruction conforming to predefined JSON Schema, the structured instruction is analyzed by an execution control module to drive a mechanical arm, a memory feedback module is utilized to store interaction data and execute feedback, dynamic switching between a dialogue mode and an action mode is supported, and the mechanical arm is controlled to operate. On the basis of large language model structured output and a multi-module cooperation mechanism, the ambiguity and unreliability of natural language control are effectively solved, and the man-machine interaction precision, the execution reliability and the complex scene adaptation intelligence level are improved.