583 results about "Schema for Object-Oriented XML" patented technology

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses an enterprise knowledge base retrieval and intelligent answering method and system based on a large language model. The method comprises the following steps: performing clause-level segmentation on an enterprise knowledge base document, associating document metadata to form structured knowledge entries, and establishing a keyword reverse index and a semantic vector index for the structured knowledge entries; analyzing the natural language query of the user, and performing multi-strategy expansion to generate an enhanced query expression and a query semantic vector; performing dual-channel mixed retrieval, performing duplicate removal, version filtering and weighted fusion sorting on a result, and generating a final candidate knowledge item list; and based on the candidate list and a predefined instruction, calling a large language model to generate a structured answer with complete traceability information. The method is compatible with an existing retrieval framework, precise understanding, knowledge point-level positioning, cross-document content integration and version consistency control of natural language problems are achieved, and the retrieval accuracy, answer availability and service intelligence level of an enterprise knowledge base are remarkably improved.

A system for extracting a number of data elements from one or more data sources. A text representing tables using a markdown language is extracted from spreadsheets and or other grid-based documents. The text is provided to a language model with a prompt. The prompt may be a chain-of-thoughts prompt. The prompt includes several requests and / or steps that cause the language model to extract one or more tables from the spreadsheet and output the tables using the markdown language or a different markdown language. The tables extracted from the spreadsheet are converted into table chunks and indexed for retrieval by a retrieval augmented architecture. When a prompt to extract particular information from the spreadsheet is provided, one or more relevant table chunks are identified and provided to a language model for extraction. Using the language model to separate tables improves information extraction accuracy while maintaining downstream instructions.

The invention discloses an engineering drawing compliance intelligent review method and system constructed based on a knowledge base and a large model. The method mainly comprises the following steps: intelligently analyzing a design specification in a natural language form by utilizing a large language model, and dynamically constructing a machine-readable rule base and a knowledge base; uniformly converting the multi-format engineering drawing into a structured intermediate format; functional areas and primitives in the drawing are recognized through a computer vision model, and natural language information describing attributes of the functional areas and the primitives is generated; and finally, in combination with the knowledge base and natural language description, performing compliance judgment by utilizing the reasoning ability of the large language model, and generating an interpretable review report. According to the method, computer vision and a large language model technology are fused, so that the defects that traditional manual review is low in efficiency and prone to making mistakes and a traditional automatic tool lacks semantic understanding ability are overcome, and efficient and accurate engineering drawing automatic compliance review with deep semantic understanding ability is achieved. Figure 1 of the abstract is a system architecture block diagram.

The invention provides an intelligent number asking method suitable for a sales data query scene, which belongs to the technical field of data processing and comprises the following steps of: (1) inputting a natural language; (2) dialogue type number asking; (3) accessing a plug-in model; (4) carrying out multi-modal analysis; and (5) carrying out platform service. According to the intelligent data asking system architecture, an industry knowledge graph is used as a cognitive footstone, a large language model is used as an interaction engine, and dynamic data association is used as execution blood vessel three-in-one. The core pain point of'water and soil disability 'in vertical industry application of a general AI model is solved, the method is a key for really changing the AI from'chatting' to'drying ', and unprecedented agility and accuracy are provided for data-driven decision making.

The invention relates to the technical field of network security, and discloses a multi-agent cooperative RESTful API vulnerability test method. Constructing a parameter structured representation by analyzing an API interface specification, generating a test value conforming to service semantics by using a large language model, and establishing a semantic request baseline representing normal service logic; constructing a multi-agent collaborative architecture including vulnerability reasoning, case generation, test execution, test analysis and test management; all the agents are coordinated to carry out iterative vulnerability testing through a signal mechanism and a state transition table, and API service analysis, test strategy customization, use case directional variation and execution, result study and judgment and strategy optimization are achieved; and finally generating a structured vulnerability report. According to the method, the problems of semantic blind areas, shallow analysis layers, insufficient adaptive capacity and the like are solved, security risks such as business logic vulnerabilities and sensitive data leakage in the RESTful API can be deeply detected, and the accuracy and coverage rate of vulnerability detection are remarkably improved.

The invention relates to a method and apparatus for generating a project-specific network architecture. The invention relates to a method for generating a project-specific network architecture, said method comprising the steps of:-providing (S1) a base model, in particular a large language model, with LoRa network adaptation; providing (S2) a model library having training data pairs, each of which consists of input data having a model application and / or model and / or hardware and / or software specification, and output data having at least one network architecture belonging to the respective input data; -selecting (S3) item-specific training pairs on the basis of said library of models; and-training (S4) the LoRa network of the base model on the basis of the item-specific training pair for generating the item-specific network architecture.

The invention relates to an intelligent map making system and method based on a large language model, and the method comprises the steps: enabling a client to input data analysis information and a map making demand to a task planning Agent of a server, and carrying out the reasoning and disassembly of the input information into preliminary planning information through a double-LLM mechanism, the method comprises the following steps that Agent is used as a tool library, a tool library document, a knowledge library and a memory library are further inquired, a tool is selected through RAG and sent to a client side, if an existing tool cannot meet requirements, the Agent will generate a special code to complete a task, the client side calls the tool to carry out specific drawing according to received information, and a user can re-plan and adjust the drawing process through dialogue. The memory bank records session information and charting interaction operation information of this time and history. The problem of system fragmentation is solved through a unified architecture, stability and flexibility are balanced by combining large language model driving, tool agent and code generation, domain knowledge is introduced to enhance decision and improve specialty, and a user preference memory mechanism is constructed to realize personalized services.

The invention relates to the technical field of front-end application development, in particular to a micro-front-end architecture-oriented cross-application state management, style isolation and resource collaborative loading method, which comprises the following steps of: constructing a state management module, establishing a unified state communication protocol based on a publishing / subscribing mode, and realizing safe state synchronization through an authority control mechanism; a style isolation module is constructed, a Shadow DOM and CSS rule rewriting double isolation strategy is adopted, dynamic style elements are automatically processed, and the problem of style pollution is thoroughly solved; constructing a resource coordinator module, and realizing dependent intelligent management and multi-version coexistence based on semantic version specifications; and the configuration management center uniformly coordinates the work of each module. According to the method, safe communication, reliable isolation and efficient resource utilization between micro front-end sub-applications are realized, three core problems of difficult state management, pattern conflict and resource redundancy in the prior art are solved, and the maintainability, the stability and the development efficiency of a large front-end project are remarkably improved.

The invention discloses a complex system comprehensive multi-view consistency detection method based on a large model, and relates to the technical field of information system architecture design. The method comprises the steps that N view models to be detected are determined, and a rule base is constructed and formed; forming a triple set; performing rule retrieval on the triple set to obtain a corresponding rule subset; and constructing a complete prompt statement according to the prompt template and the rule subset, and reasoning the prompt statement by the large language model to obtain a consistency detection result. Based on the MBSE method, the ability of understanding, reasoning and applying knowledge in the field of system architecture and system engineering is remarkably improved, the automation degree, accuracy and robustness of multi-view consistency detection are improved, and the correctness, completeness and consistency of complex information system architecture design are guaranteed.

Systems and methods for an artificial intelligence model architecture that involves a first artificial intelligence model trained to map a plurality of entities to ranked documentation from a documentation source, and a second artificial intelligence model that comprises a language model trained to generate an additional query to run on the plurality of documents from the documentation source. By training the second model to generate additional queries as entities and / or links are discovered, the system may quickly and efficiently determine links and / or potential resolutions as well as received feedback thereon.

The invention relates to the technical field of knowledge questions and answers, in particular to a large language model knowledge question and answer method and system fused with a multi-modal knowledge graph. The method comprises the following steps: acquiring structured and semi-structured data and unstructured multi-modal data in an enterprise business scene; capturing data change through a real-time synchronization technology and executing targeted preprocessing to ensure the consistency of data quality and modal characteristics; constructing a dynamically updated multi-modal knowledge graph based on the preprocessed data, and integrating a multi-modal embedded model, a reordering model and a fine-tuned generative large model to obtain a knowledge storage-feature matching-semantic generation integrated architecture; according to the method, the defect of knowledge lag of a traditional system is overcome, and seamless fusion of the AI capability and the enterprise core business process is realized.

The invention discloses a monitoring data trend analysis and decision suggestion generation method based on a large language model agent. The method comprises the following specific steps: step 1, constructing a domain knowledge enhancement module; 2, developing and registering a callable tool set, and compiling a series of performance functions by using Python; 3, constructing an agent arrangement platform and a core agent, and creating a master control agent in the agent arrangement platform as a scheduling center of an analysis task; step 4, executing an intelligent agent analysis process, and when an analysis request is received, enabling the intelligent agent to autonomously run according to the following process; 5, system integration and interface calling are carried out, the whole agent is packaged into RESTful API service and deployed in an enterprise server, by introducing a large language model agent architecture, an RAG enhanced reasoning mechanism and a callable tool set, the technical transition from passive data display to active insight generation is achieved, and the method has the advantages of being high in practicability and easy to popularize. And the analysis efficiency, the decision accuracy and the system intelligence level are remarkably improved.

The invention discloses a two-stage multi-dimensional fusion retrieval enhancement generation method and system, and belongs to the field of large model language processing. Comprising the following steps: dual-stage mixed retrieval: performing BM25 retrieval and vector retrieval in parallel and performing dynamic weighted fusion to realize file-level rough selection and content-level fine selection so as to give consideration to literal matching and semantic understanding; performing intelligent content integrity judgment: performing three-dimensional evaluation on a retrieval result by utilizing a lightweight language model, and triggering supplementary retrieval when information is insufficient; and multi-source information fusion generation: fusing multi-source information by adopting a model based on an attention mechanism, and driving a large language model to generate accurate and traceable answers. The method is also matched with a continuous training optimization method of a small model and an efficient system management architecture based on an asynchronous queue. According to the method, the accuracy and the integrity of the answers are remarkably improved, the retrieval efficiency and the expandability of the system are improved, and the calculation cost is effectively reduced.

The embodiment of the invention provides a modeling method and device for an airborne system function architecture model, computer equipment and a medium, and relates to the technical field of aviation architecture design, the method comprises the following steps: inputting a design requirement document into an airborne large language model, and in the large language model, under the structural constraint of an airborne system knowledge graph, establishing a design requirement document; the method comprises the following steps: extracting a logical relationship between entities required by airborne system modeling from a design demand document, screening a corresponding airborne meta-model according to the extracted logical relationship between the entities, and constructing a demand graph; constructing and outputting a text model of a concept scene, a text model of a task scene, a text model of a system function, a text model of a logic architecture and a text model of a physical architecture of the airborne system based on the demand graph; and inputting each text model into a system modeling language modeling terminal for visualization to obtain a multi-level functional architecture model of the airborne system. According to the scheme, the modeling efficiency, consistency and normalization are improved.

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

The invention relates to a cross-architecture code automatic migration method, system and device based on a large language model agent and a medium. The method comprises the following steps: acquiring a source code, a construction script and an architecture related header file of a target project; based on a pre-constructed transplantation target system architecture knowledge base, analyzing system architecture differences in combination with construction scripts and positioning architecture related positions needing to be modified in source codes to obtain a preliminary modification scheme; constructing scripts through static analysis and carrying out abstract syntax tree analysis on source codes to obtain a global dependency directed graph; based on a transplantation target system architecture knowledge base, performing migration code semantic matching on the source code according to the preliminary modification scheme and the global dependency directed graph, and generating a semantic equivalent replacement code to obtain a patch list; and sequentially executing incremental compiling, function consistency testing and performance regression verification on the migration project copy after the patch list is applied to generate verification information and a verification result. By adopting the method, cross-architecture code automatic migration can be realized.

The invention relates to the technical field of auxiliary writing systems, in particular to a structured auxiliary writing system based on multi-agent collaboration. The system specifically comprises a writing task demand analysis and modeling module, a multi-agent system architecture construction module, a writing task disassembly and subtask distribution module, a multi-source knowledge retrieval and integration module, a structured content generation module, a format specification and quality evaluation module, a logic verification and content adjustment module and a user feedback iteration and model optimization module. The writing task demand analysis and modeling module firstly performs surface analysis on a writing task of a user, determines the type, theme, target audience, core demand and quality requirement of the writing task, and converts a fuzzy writing demand into a quantifiable and executable structured task model; according to the invention, the limitation of a traditional writing mode and an existing single-function auxiliary tool is solved.

The invention provides a multi-granularity large language model parameter optimization method and system based on a vLLM architecture, and the method comprises the steps: obtaining a parameter data set of a to-be-optimized model, parameters in the parameter set being used for representing the probability of candidate text combination output in the to-be-optimized model; inputting the parameter data set into a preset evaluation tool to construct a mathematical model corresponding to the score; a search space corresponding to the mathematical model is classified on the basis of a granularity ball, global optimization is carried out on the basis of a classification result, an optimized parameter vector set is obtained, and the search space is a parameter combination formed after corresponding constraint conditions are introduced into parameters in the parameter data set; and determining a target optimization parameter set according to the optimization parameter vector group and the mathematical model, so that the to-be-optimized model performs dialogue output based on the target optimization parameter set. According to the method, instability in the model parameter tuning process can be effectively reduced.

The invention discloses a hierarchical self-adaptive full block pre-filling scheduling method and system for large language model reasoning, and the method comprises the steps: carrying out the hierarchical portrait analysis of a to-be-served model, and dividing the to-be-served model into partitions with different calculation characteristics according to the calculation intensity and memory access characteristics of each layer; then, making a layering and partitioning strategy based on a partitioning result, allocating a larger partitioning size to a calculation-intensive partition, allocating a smaller partitioning size to a memory bandwidth-intensive partition, and generating a layering and partitioning mapping table; and finally, when the online scheduling is executed, querying the mapping table according to the request processing progress to determine the block target size, and jointly forming a batch processing unit by the decoding task and the pre-filled block with the heterogeneous size under the constraint of the iteration time budget to be executed. According to the method, accurate matching of calculation and bandwidth resources is achieved, the system throughput can be effectively improved, tail delay and fluctuation thereof can be remarkably reduced, bubbles under pipeline parallelism are reduced, and the method is suitable for various attention mechanisms and distributed reasoning architectures.

The invention relates to a fusion method and device based on multi-source data, and relates to the technical field of multi-source data fusion, and the method comprises the steps: associating a multi-source enterprise code with a unique DID through a cross-chain identification mapping protocol based on a block chain master-slave chain architecture, and generating an enterprise unique ID to achieve identity normalization; calling a GCN-LLM intelligent unit, splitting a multi-language address and a business scope text into entity nodes, constructing a cross-language association graph, and extracting standardized fields of countries, cities and the like; a Transform model is adopted to extract historical time sequence characteristics to predict the data abnormal probability, and dynamic cleaning is triggered; and finally, the data hash value is subjected to uplink evidence storage, credibility is ensured through consistency verification, and a permission controllable interface is generated to be called by a downstream application. According to the method, unique credible association of cross-source enterprise identities, high-precision structured extraction of multi-language unstructured texts, advanced prediction of data anomalies and credible reuse are realized, and the enterprise data fusion efficiency and credibility are improved.

The invention discloses a system for realizing bidirectional conversion between a model and a code, and the system comprises a meta-model architecture module which is used for constructing a unified model specification; the meta-model engine module is used for generating, analyzing and processing the meta-model architecture module; and the bidirectional conversion module is used for realizing forward and reverse synchronous conversion between a zero code model and a Java model serving as a program code by adopting the meta-model engine module. By means of the scheme, a unified specification with the meta-model architecture as the core is established, a powerful meta-model engine is used for automatic processing, and the barrier between a zero-code visualization model used by business personnel and bottom-layer Java codes written by developers is seamlessly broken through. The synchronization capability of the model and codes ensures the continuous alignment of business design and technical implementation, and effectively supports the quick iteration of agile development and business.

Embodiments of the present disclosure relate to applications, platforms, architecture, etc. for automating software requirement verification. In particular, one or more generative language model (GLM) prompts may be generated based at least on program information that describes a software program and based on requirement information that corresponds to a requirement of the software program. Based on such prompts, the GLM may be able to automatically identify segments of the software program information that relate to the requirement. Further, based on the identified segments and the GLM prompts, the GLM may be able to automatically create (e.g., based on one or more additional prompts) testing architecture that may be used to verify whether the software program satisfies the requirement.

The invention belongs to the technical field of computer data security, and particularly relates to a data contract-oriented strategy real-time compiling and consistency hot deployment method and system.The method comprises the steps that according to a strategy input by a user, strategy definition and standardized representation are based on formalized semantics, and a data contract-oriented strategy is compiled in real time; constructing a strategy abstract syntax tree with complete semantic information; inputting the abstract syntax tree into a multi-stage compilation optimization pipeline, and generating a high-performance intermediate representation (IR) code by using a cost model driven optimization decision mechanism; a policy distribution architecture based on a publishing-subscribing model is adopted, and an improved Gossip protocol and a Paxos atomic submission algorithm are combined to realize distributed consistency synchronization and real-time hot deployment of a policy; and efficient and safe execution of the strategy is realized through a register type micro virtual machine and a self-adaptive execution mode. The problems that in the prior art, execution performance has bottleneck, strategy updating is delayed, complex strategy supporting capacity is weak, and cluster strategy consistency is difficult to guarantee are solved.

Techniques may generate realistic synthetic data by programmatically generating a configuration file object type and relationship data. This configuration file may be used to retrieve source data matching the object type(s) and / or specific records indicated by the configuration file. The techniques may detect and anonymize private / proprietary information and may determine statistical characteristic(s) of the source data. A batch of prompt(s) may be generated using the source data, the statistical characteristic(s), and the configuration file and may be transmitted to one or more instances of a transformer-based machine-learned model. Sets of synthetic data received from the model instance(s) may be de-duplicated, checked for similarity to the source data (e.g., via embedding the synthetic data and the source data), and may be used to generate synthetic object(s) using the relationship(s) and / or other data indicated by the configuration file. These synthetic object(s) may then be deployed in a software environment.

Embodiments described herein relate to systems and methods for generating and displaying content for an entity card interface of a collaboration platform. The entity card interface may be displayed in response to a natural language input or inquiry of a search interface or the entity card interface may also be displayed in response to a user selection of text or a designated object within user-generated content of an editor. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

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 discloses a database query system and method for converting a natural language into an SQL (Structured Query Language) statement based on a Transform architecture, and the system captures a long-distance dependency relationship and complex semantic information in the natural language based on the Transform architecture, so that the semantic accuracy is improved, the wrong and repeated operation of generating the SQL statement is reduced, and the data acquisition efficiency is improved. On the other hand, the problems of grammar errors, semantic inconsistency and the like are verified, recognized and corrected through the SQL error correction module, database query failures are reduced, the user operation experience is improved, meanwhile, the error correction module can conduct compensation through reverse verification and correction, the dependence of the system on the precision of a single module is reduced, and the overall stability is improved; and for similar related problems, SQL statements can be directly extracted from the mapping table, so that time-consuming processing links such as Transform semantic analysis and SQL generation are omitted, and the waiting time and the computing power consumption of a user are remarkably reduced.

The invention provides a software architecture governance system based on automatic modeling and holographic penetration analysis. The software architecture governance system comprises multiple modules: a project management module used for managing all analysis projects; the source code management module is used for pulling, updating and deleting project source codes; the analysis drive management module is used for uniformly managing and calling analysis drive programs; the multi-language analysis driving cluster is integrated with analysis driving programs corresponding to multiple programming languages and is responsible for analyzing source codes, generating a standardized software chart model and storing the standardized software chart model in a database; the rule engine module is used for supporting a complex code analysis service; the cross-project integrated analysis module generates a system architecture diagram according to the plurality of software models and stores the system architecture diagram into a diagram model library; the multi-dimensional data fusion interface module provides penetrating analysis interfaces of a code layer, a design layer and a framework layer; and the architecture model visualization module is responsible for displaying the system architecture in a multi-dimensional visualization manner. The method can be used for automatic modeling, tracing analysis and interactive visual presentation of a complex system architecture.

A computer-implemented natural language processing method can include storing a criteria embedding generated from a criteria document containing natural language or otherwise unstructured text in a vector database; for each individual document in the throughput of electronic documents: generating, with a first language model, a text summary of the individual document; conducting, using retrieval augmented generation, a semantic search to identify relevant passages from the vector database based on the text summary of the individual document; generating, with a second language model, retrieval-augmented text by performing semantic textual similarity with the identified passages and the text summary of the individual document; and generating, with a third language model, a set of assessment outputs based on the text summary of the individual document and the retrieval-augmented text.