1985 results about "JSON" patented technology

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

The invention discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention provides a multi-knowledge-granularity text retrieval method and device for RAG, and the method comprises the steps: sorting an original text into a JSON file, carrying out the recursive character and semantic dicing processing, generating a target JSON file, and storing the target JSON file in a BM25 database and a vector database; when a user queries, preprocessing to generate a keyword list and a query embedded vector; searching for a target text in the BM25 database, and calculating a score of correlation with the keyword to determine a first to-be-selected text; determining a second to-be-selected text according to the query embedding vector; and performing duplicate removal processing on all the first to-be-selected texts and the second to-be-selected texts, and determining and outputting a final recommended text by utilizing a sorting model. By considering the problem of different search granularities, the keyword granularity is solved by adopting a BM25 algorithm. According to the semantic retrieval strategy combining sentence granularity and segment granularity, challenges of different text information lengths and different retrieval purposes are effectively handled, and therefore the advantages of traditional retrieval and semantic retrieval are fully utilized.

The invention provides an integrated metadata management method and system based on data source expandability, and relates to the technical field of data sources, and the method comprises the steps: executing real-time detection on a plurality of heterogeneous data sources through a PF4J plug-in framework, collecting connection information, and establishing a dynamic connection pool; obtaining a table structure, a field attribute and an association relationship list, and assembling the table structure, the field attribute and the association relationship list into JSON format data; performing analysis and standardization processing on the data, and constructing an association degree calculation matrix; generating a standardized data structure description file; establishing a three-layer index directory, and detecting structure change and updating; and calculating a data table reference dependency relationship, and generating a DAG data flow path. According to the method, efficient integration and management of heterogeneous data sources are realized, and the metadata processing efficiency is improved.

Some embodiments of a method may include: obtaining Avatar JSON Interchange File (AJIF) data; decoding the AJIF data to generate avatar data; obtaining animation parameters from the avatar data; obtaining user movement data corresponding to a movement of a user; generating updated avatar data based on the user movement data; and sending the updated avatar data to a client device. For some embodiments, a method may include: generating Avatar JSON Interchange File (AJIF) data corresponding to an avatar; sending, to an application server, the AJIF data; obtaining user movement data corresponding to a movement of a user; sending, to the application server, the user movement data corresponding to the movement of the user; receiving, from the application server, scene update data, wherein the scene update data includes updated avatar data corresponding to the movement of the user; and rendering a scene update based on the scene update data.

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 provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention discloses a medical image report generation method and system based on a large language model, and relates to the field of image report generation, and the method comprises the steps: firstly obtaining original image data and a clinical background text of a patient, and respectively extracting an image embedding vector and a background embedding vector; then, case retrieval based on priori knowledge is carried out by utilizing the embedded vectors, and K highly related historical case reports are screened out from massive historical data; and inputting the image embedding vector and the historical case report into an observation large language model, and outputting the image embedding vector and the historical case report in a structured JSON (JavaScript Object Notation) format. And finally, a large language model is written to integrate the visual evidence JSON, the clinical background text and the historical case report, and a final medical image report is generated. According to the mode, through a staged and multi-modal fusion mode, the problems of incoherent report logic, inaccurate information and the like are effectively solved, and the report quality and the generation efficiency are remarkably improved.

The invention discloses a text classification method, device and system based on a large language model, a storage medium and a product, and relates to the technical field of text classification, the method comprises the following steps: receiving a to-be-classified text and a classification label input or selected by a user through a user interaction interface of the text classification system; and according to the to-be-classified text and the classification label, constructing prompt words, and calling a pre-trained large language model to perform different-level classification on the to-be-classified text to generate a classification result. According to the technical scheme, the low-cost and low-use-threshold text classification method is provided based on the large language model, classification of different levels can be achieved, in addition, due to the fact that the text classification result is in a light-weight and convenient-to-analyze JSON format, the system can visually display the classification result to a user and can also provide an interface for a third-party application to call, and the user experience is improved. And the application scene of text classification is expanded.

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 an abnormal behavior detection method and system based on a multi-source log and electronic equipment, and the method comprises the steps: collecting log data, carrying out alignment processing and data preprocessing, and generating normalized log data; carrying out attribute screening on the standardized log data set, and packaging the screened logs into a JSON format; extracting features of the log data, converting the features into data points, clustering the data points, and retaining a log cluster according to a clustering result; mining a hidden association mode between log attribute items in the log cluster by using an improved Apriori algorithm, generating an association rule, and constructing a knowledge graph based on the association rule; and constructing a time sequence of the log data by using a self-adaptive time window, and inputting the time sequence and the tag sequence of the log data into the LSTM neural network by combining context information in the knowledge graph and a tag sequence generated by an association rule to obtain an abnormal behavior detection result. According to the method, the log data format is unified, correlation analysis is enhanced, and the accuracy of an anomaly detection result is obviously improved.

The invention relates to the technical field of financial analysis, and discloses a multi-agent financial analysis system and method based on a large language model, and each agent of a multi-role agent module realizes specialized analysis through a field customization LLM; the hybrid communication protocol module integrates a structured report and a natural language dialogue, and the structured report adopts a JSON or XML format; the dynamic coordinator module is configured to dynamically allocate an agent weight according to the difference between the market stage and the confidence coefficient; the interpretability enhancing module is used for outputting an inference logic chain through a natural language generation technology and visually presenting the inference logic chain; and the data fusion and strategy generation module is used for integrating basic surface, emotion, technology and macroeconomic data and generating a dynamic investment strategy. According to the method, by simulating the organization structure and the cooperation process of a real financial analysis team, the multi-source data are integrated, and the interpretable comprehensive investment strategy is generated, so that the efficiency, the depth and the credibility of financial analysis are remarkably improved.

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 relates to the technical field of large models, in particular to a TNM (Tumor Necrosis Model) staging prediction method and system in a clinical text of a large language model. Comprising the steps of data acquisition and preprocessing, instruction data acquisition, large language model instruction fine tuning, clinical text preprocessing, medical event extraction, preliminary event processing and screening, cue word engineering optimization, RAG technical framework combination, TNM staging result generation and JSON formatting output. By combining the natural language processing capacity of the large language model and the external knowledge retrieval function of the RAG technical framework, the efficient, accurate and wide-adaptability TNM staging prediction method and system are provided, the automation degree of clinical tumor staging can be effectively improved, a reliable staging basis can be provided for doctors, and the clinical tumor staging prediction efficiency is improved. With the continuous development of medical big data and artificial intelligence technology, the method has a wide application prospect and potential market value.

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 belongs to the technical field of artificial intelligence, and relates to a multi-modal large model-based document identification method, system and device and a medium, and the method comprises the following steps: 1) image preprocessing: preprocessing a document image input by a user; 2) multi-modal large model reasoning: based on the pre-processed document image, the configured JSON template and the cue word template, performing reasoning by a multi-modal large model to obtain a JSON result; 3) OCR identification: identifying the document image input by the user by using an OCR identification technology to obtain an OCR identification result; and 4) verification: performing similarity comparison on the OCR recognition result and the JSON result, and determining a document recognition result based on a similarity comparison result. The method is high in generalization, can adapt to various types of receipts, and can provide an efficient and accurate recognition result.

The invention provides a conflict resolution method and system for realizing multi-person collaborative editing based on WebSocket and JSON-PATCH, and relates to the technical field of I T and software development, the method comprises the following steps: obtaining an editing operation of a user at a client of a low-code platform, and converting the editing operation into operation information in a JSON format; the method comprises the following steps: receiving operation information in a JSON format sent by a plurality of clients through a WebSocket server; storing the received operation information in the JSON format in a database according to a user account; based on the operation type defined by the JSON-PATCH, conflict detection is carried out on the concurrent operation of the multiple users; according to a preset priority rule, carrying out merging processing on the detected conflict operations; a result obtained after combination processing is broadcasted to all the clients through a WebSocket server; and the client receives the broadcast combination result and updates the low-code platform interface. According to the method, the user operation is formatted by using the JSON-PATCH, so that operation conversion is realized to solve conflicts.

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.

A method, system, computer program product of providing a FiniteStateMachine (FSM) with embedded business rules, processing data & events, where each state is individually saved to blockchain ledger, a data record including a unique identifier / name, a set of input field(s) or input data slots, state rules in a rule language, and set of output field names or output data slots, method including: executing of a Smart Contract by identifying state by id or name, loading corresponding Component State from blockchain, adding / executing code for processing the State's behavior, where the code includes rules for the state in the rule language (e.g., but not limited to. Open Policy Agent, Rego, Prolog, DataLog, RuleML, Drools Rule Language, NRules, and JavaScriptObjectNotation(JSON), etc.). System includes processor(s), application program interface (API(s)) for communicating, blockchain, rule processor, and memory. This enhances transparency, immutability, and secure execution of business rules within FSM architecture.

The invention relates to the technical field of configuration editing, in particular to an intelligent page editing method and system based on component dynamic generation, and the method comprises the steps: extracting data binding, event response and rendering rules by analyzing teaching component codes, generating JSON Schema, an event mapping table and a view template, and storing the JSON Schema, the event mapping table and the view template in a database after logic optimization and intelligent completion. And setting priority rules of equipment performance, user intention and domain knowledge, generating a preliminary strategy instruction by utilizing a rule engine in combination with stored data, predicting component combination probability through machine learning, and fusing to generate a final strategy. And generating a DOM tree based on the strategy instruction dynamic loading component, collecting page interaction data and feeding back the page interaction data to the rule engine to form a strategy optimization closed loop. According to the method, dynamic generation and intelligent adaptation of the teaching component are realized, and the flexibility and cross-end adaptation capability of page editing are improved.

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 belongs to the technical field of information extraction, and discloses a text structuring method and system based on a large language model. The method comprises the following steps: performing training data processing by analyzing a JSON format of a large language model; performing training and fine tuning on the large language model, performing training by using vLLM, and deploying the fine-tuned large language model; and performing text structuring on the deployed large language model by adopting a CoT prompt mode, and further improving the structured output capability of the large language model by adopting a reflection strategy of a JSON analysis result for wrong JSON format data. According to the method, the problem that JSON character strings generated by a large model in a knowledge extraction task do not meet requirements is solved through combination of fine adjustment and instructions; and the large model generates a JSON (JavaScript Object Notation) of a knowledge graph specification according to a schema. Org requirement, so that end-to-end text structuring is realized, further knowledge processing is not needed, and the workload of subsequent knowledge processing is saved.

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.