792 results about "JSON" patented technology

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 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 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 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.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention discloses an intelligent routing and unified adaptation method for a large language model, and the method comprises the steps: defining all access details of the model through a declarative configuration file, and achieving the zero-code access of the large language model without writing any code for a newly-added model; a completely consistent calling interface is provided for an upstream application, the isomerism of all downstream large language models is shielded, and a unified request and response abstraction layer is constructed; through a strategy engine and a JSON path technology, complex streaming response including content thinking is precisely processed, and intelligent analysis and content extraction are carried out; dynamic configuration and intelligent strategy hot update are supported; intelligent routing of the model is realized, and an optimal large language model instance is dynamically selected; and meanwhile, enterprise-level governance capability is provided, governance functions such as fusing, degradation, current limiting and monitoring are integrated, and stability guarantee is provided for model calling. Therefore, the maintainability, the expandability and the user experience consistency of the system are comprehensively improved.

The invention discloses a graphical file editing system and method. The system is deployed in the form of an IDE plug-in, and comprises an editor extension module of a JBCefBrowser embedded browser, an editor extension module of a JBCefBrowser embedded browser, and an editor extension module of a JBCefBrowser embedded browser, the file identification module is used for scheduling a corresponding editor based on a file suffix; the graphical engine is used for performing classified rendering on the JSON / XML data based on the component metadata registry; the event processing module supports three-stage dragging and eight-direction zooming; an event driving communication module based on window.post Message, and an event driving communication module based on window.post Message; the operation module is used for synchronizing files and codes through a PSI interface; and a file template management module based on a. Ft template. The method comprises the steps of editor registration and embedding, file identification and loading, content analysis and rendering, interaction event processing, data persistence synchronization and template file creation. According to the method, graphical dragging and editing of the structured file are realized in the IDE, and the development efficiency and the normalization are remarkably improved.

The invention discloses a plant protection field literature information batch structured extraction method based on an AI large model, and belongs to the technical field of computer data processing and artificial intelligence application. According to the method, a PDF format literature is converted into a Markdown format text through a PDF literature self-adaptive preprocessing module and a Markdown conversion module; then, a double-strategy self-adaptive AI extraction module is adopted, different modes are adopted for processing according to a text length threshold value, and JSON data are extracted in combination with a JSON format data restoration strategy; then, the CPU intensive conversion task and the I / O intensive AI analysis task are processed in parallel through a two-stage parallel scheduling module, and a structured database file is generated through aggregation according to a predefined mapping rule through a multi-dimensional aggregation export module. According to the method, end-to-end automation from literature acquisition to structured data output is realized, and the problem of low extraction efficiency of manual literature reading information is solved.

The invention discloses an automatic SCADE test case generation method based on a large language model, which comprises the following steps of: analyzing various SCADE related documents to obtain an analysis result; the analysis result is converted into JSON data; performing key information extraction on the JSON data, and performing block processing on the extracted key information to obtain text blocks; vectorizing the text blocks, storing the vectorized text blocks into a vector database, converting a natural language query requirement of a user into a query vector, and performing approximate nearest neighbor search with the query vector in the vector database by utilizing RAG retrieval to obtain a preliminary retrieval result; based on metadata in the preliminary retrieval result, filtering, timeliness sorting and deduplication fusion processing are carried out, and a retrieved context information set is obtained; adopting a dynamic cue word template to construct an enhanced cue word based on the retrieved context information set and the natural language query of the user; and calling a large language model API, inputting an enhanced cue word, and generating a structured test case.

The invention discloses an Internet of Things process engine automatic arrangement method and system based on a large model. The method comprises the steps of obtaining a process demand described by a natural language of a user; the method comprises the following steps: analyzing a natural language of a user through a first-layer large model, and identifying related equipment, variables, conditions and action information; information identified by the first-layer large model serves as input of a second-layer large model, and the second-layer large model matches actual corresponding equipment and variable information under the user permission based on the user token, the equipment and the variable information; transmitting actual corresponding equipment, variables, conditions and action information under the user permission to a third-layer large model; and the third-layer large model matches the corresponding flow nodes according to the received information, the matched flow nodes are connected in series through a connecting line, and a structured flow JSON is generated. According to the method, the fuzzy natural language intention is gradually converted into an accurate and executable structured process through the three-layer large model, and the accuracy of an output result of each link is improved.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

The invention discloses an information system existing interface-based AI large language model interaction method, which comprises the following steps of: constructing an interface operation feature set according to an information system operation log; identifying a service intention category and a function granularity level corresponding to the interface, and generating an interface function granularity identification table; constructing an action rule chain according to the interface service intention category and the function granularity level; performing field consistency calibration in combination with a field time sequence identifier, a stage identifier and a trigger point identifier to form standardized intermediate JSON data; according to the standardized intermediate JSON data, generating cue words including field operation instructions, business semantic instructions and time sequence background instructions; model request sending, model output result analysis and consistency verification are carried out through an existing API interface, and system state updating information is recorded. According to the method, the system transformation cost is reduced, the accuracy of interface semantic expression is improved, and an enforceable and extensible technical path is provided for intelligent upgrading of a traditional information system.

The invention provides a design rule checking method and device based on a multi-modal large model, a terminal, a medium and a product. The method comprises the following steps: acquiring a to-be-analyzed multi-modal DRC rule file; generating a JSON code corresponding to the DRC rule file by utilizing a visual language model according to a preset JSON code format; and converting the JSON code into a corresponding DRC code by using a pre-constructed knowledge base, and carrying out conflict detection on the JSON code through a large language model to obtain a corresponding conflict detection result. According to the method and the device, the multi-modal DRC rule file can be processed, the rule file analysis efficiency is relatively high, and the conflict detection efficiency and the accuracy are relatively high.

A method for managing data includes: receiving a file analysis request from a user, in which the request includes merging criteria, a first data path to access a first file, a second data path to access a second file, and a third data path to access a third file; obtaining the first file using the first data path, the second file using the second data path, and the third file using the third data path; analyzing the merging criteria; inferring, based on a determination, that the second file is suitable to be merged with the first file and the third file is not suitable to be merged with the first file; merging, based on the merging criteria, the first file and the second file to generate an output file; and initiating, via a graphical user interface (GUI), displaying of the output file to the user.

The invention discloses a system for generating structured data based on natural language processing to drive UI dynamic rendering. The system comprises a voice acquisition and recognition module, a natural language processing module, a UI dynamic rendering driving module, a control logic automatic binding module and a rendering execution module. User voice or text instructions are automatically analyzed into structured JSON data through natural language processing, Jetpack Compose is driven to dynamically generate UI components, service logic is bound, zero-code interface construction is achieved, in addition, the system is combined with a local AI model, a factory mode and a reflection mechanism, multi-end adaptation, bidirectional data binding and event automatic processing are supported, and the system is suitable for large-scale popularization and application. According to the method, the development threshold is remarkably reduced, the iteration efficiency is improved, the method is particularly suitable for scenes such as smart homes and vehicle-mounted central control which need to quickly customize UIs, and meanwhile offline availability and cross-device consistency are guaranteed.

The invention provides an event processing method, system and equipment based on Internet of Things equipment and a medium, and belongs to the technical field of Internet of Things. The method comprises the following steps: receiving an Internet of Things equipment message through an MQTT Broker, identifying an event type according to a theme, and distributing the event type to a corresponding thread pool; concurrently analyzing the JSON load in each thread pool, and converting the JSON load into a structured data object; caching the data in a batch processing queue in a classified manner, and triggering batch processing based on a queue state; extracting batch data, generating an optimized SQL statement, executing writing, and processing exceptions; screening data based on a routing rule and asynchronously forwarding the data to message middleware; meanwhile, processing parameters are dynamically adjusted according to the equipment state data and the system load. According to the invention, efficient processing of Internet of Things equipment events is realized, and the system throughput, the resource utilization rate and the data processing real-time performance are improved.

The invention relates to the technical field of picture sharing, and discloses a picture sharing method and device based on an AI platform, electronic equipment and a storage medium, and the method comprises the steps: obtaining a generated data stream of a picture uploaded by a first user, converting the generated data stream into a structured JSON file, and enabling a built-in cross-platform compatible tag to support multi-terminal sharing; based on the second user creation preference portraits and the key features, non-key parameters are screened through an AI semantic matching model, an adaptive priority adjustable component is generated, and a difference thermodynamic diagram and a new picture are generated after user adjustment; the enhanced copyright metadata containing original author identification, content fingerprints and the like are embedded into the JSON parameter stream; and calculating a hierarchical hash value and carrying out block chain evidence storage during each parameter iteration, calculating contribution degree distribution earnings based on a multi-dimensional index, generating a corresponding NFT copyright certificate, and supporting on-chain reverse tracing. According to the method and the device, the problem of low efficiency in secondary creation parameter adjustment caused by lack of standardized structured packaging and dependency relationship labeling in parameter generation can be solved.

The invention discloses a hybrid PDF (Portable Document Format) document analysis and knowledge fragment construction method and device, and belongs to the technical field of digital document intelligent processing. The method comprises the following steps: firstly, carrying out layout element identification and semantic classification on a PDF page through a deep learning model, and calculating the confidence of the PDF page; when the confidence coefficient is lower than or equal to a threshold value, a correction module based on vertical and horizontal projection analysis is automatically switched to carry out boundary detection and result error correction; the fused layout result is converted into structured JSON data, and the structured JSON data is further mapped into a Markdown format with a reserved title level; and finally, knowledge fragment segmentation is carried out based on a Markdown hierarchical relationship, so that each fragment carries a complete title context path. According to the method, the problems of semantic deficiency, insufficient stability and context splitting in the prior art are effectively solved, and the robustness and accuracy of complex format PDF and low-quality scanning copy analysis and the interpretability of downstream knowledge retrieval are remarkably improved.

A system and method for generating structured software application specifications through multi-phase conversational dialogue is disclosed. The system implements multiple specialized AI agents including a framework generation agent, an interactive coaching agent, specialized capsule agents for analyzing different concern categories, and a specification coaching agent. During Phase 1, the system conducts exploratory dialogue using a tailored question framework while detecting and storing user concerns in structured capsule entries. Concern injection into the dialogue is strategically timed based on algorithmic evaluation of cooldown periods and user sentiment analysis. During Phase 2, the system conducts comprehensive concern resolution dialogue and generates a final specification in structured JSON format with explicit traceability linking requirements to source conversations and concern resolutions. The system solves technical problems of preserving concern context across conversation phases, optimizing injection timing to avoid overwhelming users, and generating machine-readable specifications suitable for automated downstream processing.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

The invention discloses an automatic reference format conversion system based on Web, and belongs to the field of computer software. The system adopts a B / S architecture, receives a Word manuscript uploaded by a user and an NBIB file exported by PubMed, and provides a dynamically loaded CSL style library for selecting a target periodical format. Through regular segmentation of NBIB entries, author information is analyzed according to an FAU priority strategy to generate standardized BibTeX data; the Word manuscript is converted into a JSON-VDOM tree with a reserved style, placeholders are positioned and referenced, a three-dimensional mapping index is established, the placeholders are replaced through CSL rendering, a reference list is added, and a final document is reversely generated. Temporary data are isolated and automatically cleaned through the UUID, and safety is guaranteed. The problems of local dependence, inaccurate analysis and tedious operation in the prior art are solved, the method is efficient, accurate, flexible and easy to use, and the paper reference typesetting efficiency is greatly improved.

The invention provides a PDF analysis method and system based on image-text semantic alignment, and belongs to the technical field of document processing. The method comprises the following steps: reading PDF or picture file byte data, and converting pictures into PDF byte data in a unified format; creating an image and a Markdown directory according to the output directory, the PDF name and an analysis method; extracting byte data of a specified page of the PDF according to starting and ending page numbers; selecting a back end to analyze PDF byte data to obtain a reasoning result, an image list and the like; generating an intermediate JSON containing PDF detailed information according to an analysis result; analyzing the positions of the text and the picture, and associating information to realize semantic alignment of the picture and the text; and generating multiple types of machine readable files according to the intermediate JSON and storing the files in a specified directory. According to the method, the PDF multi-mode content can be accurately extracted and subjected to semantic alignment, the PDF multi-mode content is efficiently converted into a machine readable format, the analysis accuracy and usability are improved, and the method is suitable for PDF analysis of multiple image-text tables such as scientific and technical literatures.

The invention discloses a report generation agent method, system and device driven by a large language model and a medium, and the method comprises the steps: constructing an agent execution framework independent of a native interface, outputting a decision through a cue word constraint model in a JSON structure, and scheduling a local tool function; analyzing the reference data and constructing a local vector knowledge base supporting semantic retrieval; the intelligent agent autonomously plans and generates a report directory structure based on a local vector knowledge base, stores the report directory structure as a structured intermediate file as a persistent intermediate representation, generates chapter content in parallel by utilizing a concurrent flow control and exponential backoff retry mechanism and combining a mixed retrieval strategy, and serializes and updates the chapter content to the intermediate file in real time; and finally, converting the intermediate file into a target format report according to a mapping rule. The system, the equipment and the medium are used for implementing the method. According to the method, the efficiency and the accuracy of complex report generation are remarkably improved through agent whole-process autonomous driving and combination of persistent intermediate representation, retrieval enhancement and concurrency control technologies.

The invention discloses a multi-node sensor communication link cooperative scheduling method and system based on star flash, and relates to the technical field of new-generation wireless communication and sensors. The method comprises the following steps: constructing a star flash sensor data reporting system; calculating a node valid data volume based on parameters such as sensor types and channel numbers; estimating transmission time and distributing time slots in combination with a star flash frame structure; transmission parameters are dynamically adjusted through a real-time link quality perception and prediction model, and a data priority and preemption mechanism is supported; issuing a scheduling strategy by using the JSON configuration file; and the nodes communicate according to configuration and realize fault self-diagnosis and self-healing. The system comprises a star flash management node, a sensor node and an embedded cooperative scheduling engine. According to the method, multi-node cooperative scheduling, link adaptive optimization and system autonomous recovery are realized, and the communication reliability, the channel utilization rate and the system robustness are remarkably improved.

The invention relates to the field of computer data processing and artificial intelligence, and discloses a mobile phone APP intelligent code scanning and AI automatic identification system which comprises a mobile terminal, an internet network and a local server. The mobile terminal is provided with an acquisition detection module, and a mobile phone end code scanning module automatically controls light supplement when the image brightness is lower than a threshold value. A server-side AI intelligent recognition module executes Gaussian filtering, binarization and geometric correction preprocessing on a code scanning image, and a text is extracted by using a deep convolutional neural network and a bidirectional long-short-term memory network. And the system further executes semantic analysis by traversing the business template library, judges the attribution of key fields by using a spatial Euclidean distance, and generates structured JSON data. The statistical module calculates a real-time production efficiency index, and the remote viewing module associates and displays a process optimization suggestion when the data is abnormal. According to the invention, high-precision identification, business semantic understanding and intelligent decision support of code scanning data are realized.

The invention discloses a chip IP interface information automatic analysis and connection modeling method, and relates to the technical field of integrated circuit verification, and the method comprises the steps: multi-source interface information collection: docking a design server through an FTP / SFTP protocol, automatically retrieving and capturing a target file, carrying out format preprocessing, and storing the collected original information through a database; heterogeneous information fusion analysis: extracting port physical attributes and generating machine executable rules by constructing a grammar analyzer and a semantic analyzer respectively, and automatically judging multi-source information contradictions by adopting a three-level judgment mechanism; structuring modeling of the connection relation: constructing a directed graph model of the IP nodes, the signal nodes and the constraint nodes, and automatically executing directed graph model verification; and through a standardized API interface docking verification tool, outputting a model file and a report file in standardized JSON-LD and DOT formats. Through cooperative work of cross-format acquisition, double-engine analysis, structured modeling and standardized output, full-automatic processing of IP interface information is realized.

The invention discloses a modularized multi-channel dynamic layout method for a three-dimensional scene of a data center machine room, which comprises the following steps of: firstly, defining a topological relation data structure of a directed acyclic graph model based on a machine room design drawing, and accurately describing spatial layout characteristics of multiple channels, bifurcated channels and asymmetric channels through weight parameters; equipment such as a cabinet, an air conditioner and a power distribution module are modularized into Unity preforms, and a unified data structure including equipment types, position coordinates and data interface mapping is established; a data binding component is mounted on each prefabricated body, so that digital twin linkage with a rear-end monitoring system is realized, and real-time state visualization is supported; after layout is completed, structured JSON data is generated and uploaded to a server, and a user can load a historical version in a one-key mode and rapidly reproduce a scene. According to the method, the layout generation efficiency is improved by more than 50%, dynamic generation of a complex topological structure during operation is supported, a complete digital twin closed loop from geometric layout to data linkage is realized, and the planning and operation and maintenance efficiency of a data center is remarkably improved.

The invention discloses a method and system for converting a C / S architecture HMI configuration file into a B / S architecture JSON format, and the method comprises the steps: initializing a conversion service, constructing and loading a distributed configuration center file, so as to obtain a multi-dimensional configuration parameter; performing configuration file monitoring and increment identification, monitoring file change of a target path in real time, and performing legality verification on the file change; the primitive data in the configuration file are analyzed, and the analyzed primitive data are obtained; primitive attribute mapping conversion is conducted on the analyzed primitive data, a JSON file obtained through conversion is verified, and a verified JSON file is obtained; and respectively storing the verified JSON file to an intelligent operation and maintenance platform and a local disk. According to the invention, automation, standardization and high reliability of configuration conversion from C / S to B / S architecture are realized.

The invention provides a method and system for realizing progressive user interface rendering through real-time completion, and the method comprises the steps: S1, receiving data fragments provided by a large language model and forwarded by a server based on a streaming protocol in real time, adding the data fragments to the tail of a buffer region according to a time sequence, and updating the buffer region; s2, extracting all JSON character strings from the updated buffer area, and complementing the JSON character strings on the basis of complementing logic of a heuristic algorithm to generate new JSON character strings; s3, analyzing the new JSON character string to obtain a current object; s4, obtaining an object used in the last successful rendering in the list container from the UI framework, comparing the current object with the object used in the last successful rendering, and updating the list container through difference comparison; and S5, rendering of the UI framework is carried out on the updated list container.