2534results about "Execution paradigms" patented technology

The invention discloses a hybrid enhanced enterprise smart law platform system based on a generative language large model. Four modules including a hybrid enhanced legal knowledge engine, a multi-modal legal document analysis module, a risk quantitative evaluation module and a compliance verification workflow work cooperatively. The hybrid enhanced legal knowledge engine integrates multi-source data, realizes real-time updating and semantic reasoning, and comprises map construction, a rule base and an incremental learning mechanism; the multi-modal legal document analysis module performs structured analysis on the heterogeneous document to generate a feature vector; the risk quantitative evaluation module is combined with Monte Carlo simulation and an analytic hierarchy process, quantifies the risk according to a compliance reference and analysis characteristics, and outputs a thermodynamic diagram and a report; a compliance verification workflow is driven by a finite-state machine, a verification module and a conflict detection module are integrated, a generative language large model is called to generate an improved scheme, and audit records are solidified and fed back for optimization. And the system runs according to the processes of analysis, supply rule, bias calculation and verification correction, so that the intelligence and accuracy of legal affair processing are improved.

The invention discloses an interface parameter extraction method and device based on slot filling and a medium, and relates to large model application. The method comprises the steps that semantic analysis is conducted on natural language query through a pre-trained large language model, an intermediate parameter set containing intention types and slot filling parameters is generated, and accurate conversion from natural language parameters to structured interface specifications is achieved through multi-stage standardization processing; based on a dynamic matching mechanism of parameters and interface metadata, an interface calling chain including parallel calling, transaction compensation and data conversion nodes is constructed, and a multi-source aggregation data set is generated through cross-interface data verification and space-time alignment processing; and finally, in combination with dynamic template selection and semantic generation rules, outputting a multi-modal interaction report fusing the structured visual component and natural language interpretation.

The invention discloses a wind resource assessment report generation method based on a large model technology. The method comprises the following steps: processing multi-source data, and constructing a domain knowledge base and a fine tuning database; field adaptive RAG enhancement is carried out, professional literatures, historical cases and industry specifications in a field knowledge base are retrieved by adopting a retrieval enhancement generation technology, and wind resource assessment contents meeting technical standards are generated through a multi-modal fusion word vector technology; performing large model fine tuning, and optimizing the generative large model through a gradient-free optimization algorithm Wind-DFO on the basis of a fine tuning database by adopting a field self-adaptive fine tuning strategy; and automatically generating a report, arranging and matching multiple templates through an AI workflow, embedding a data visualization chart, executing verification of a grammar layer, a logic layer and a compliance layer, and outputting a standardized wind resource assessment report. The accuracy, specialty and efficiency of report generation are remarkably improved, and the problems that a traditional method depends on artificial experience, the data utilization efficiency is low, and standardization is insufficient are solved.

The invention discloses an intention understanding tool calling method based on a large model function call technology, and the method comprises the following steps: S1, obtaining a natural language instruction of a user, and carrying out the preprocessing of the natural language instruction; s2, based on the structured instruction fragment and the context dialogue information, constructing a semantic vector for intention recognition, and inputting the semantic vector into a large language model; s3, screening function description information from a function definition library according to an intention recognition result; s4, extracting parameter content from the semantic vector under the constraint of a parameter limiting condition; s5, constructing a call request format conforming to a function call interface specification, and executing a corresponding tool call task; and S6, receiving a function calling result, generating a natural language response text, and outputting the natural language response text to a user interface to complete an instruction response closed loop. According to the method, full-automatic semantic mapping from a natural language instruction to tool function calling is realized, and the analysis accuracy and the calling success rate are improved.

The invention discloses an MCP-based multi-agent collaboration method and device and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: obtaining a model registry form through a standardized model registration interface, analyzing model metadata in the registry form, and extracting a core field of a current registration model; creating a model node in the model map, and initializing the model node based on the core field; receiving a task request, triggering a scheduling engine, determining a plurality of candidate model nodes in the model atlas according to task content, respectively calculating task execution scores of the candidate model nodes, and generating a model execution chain based on the task execution scores; and based on the model execution chain, scheduling a candidate model corresponding to the candidate model node, and executing the corresponding task content. Dynamic access of multiple types of models is supported through a standardized model registration interface, system expansibility and integration efficiency are improved, and matching precision of tasks and models and multi-model cooperation continuity are enhanced.

The invention discloses a tool calling method based on a large language model, and the method comprises the steps: obtaining a question text which comprises task information; obtaining a matched first candidate tool set in the question text and a preset tool library; screening the first candidate tool set on the basis of locally available current hardware resources and / or available current online resources of a cloud application program interface (API) to obtain a second candidate tool set; determining a target tool matched with the task information from a second candidate tool set based on a large language model, and generating a tool calling instruction based on the target tool; selecting a called target tool based on the tool calling instruction, and obtaining an output result of the target tool; based on the question text and the output result, reply text corresponding to the large language model is obtained. According to the method, the tool with a higher matching degree can be obtained in combination with the resource occupation conditions of the tools of multiple platforms, and efficient utilization of resources and dynamic scheduling of task tools are realized.

The invention discloses an electronic lead seal data management method based on a block chain, relates to the technical field of internet data services, and aims to solve the problems of node data asynchronization, state updating failure and potential safety hazards of an intelligent contract in the prior art. The data consistency is ensured through a distributed clock synchronization algorithm and a version number verification mechanism; the reliability and fault tolerance of state updating are improved by adopting an improved Byzantine fault-tolerant algorithm and an asynchronous two-stage transaction submission mechanism; data integrity and traceability are guaranteed through a state machine detection mechanism and transaction log records; the security and maintainability of the smart contract are improved based on an automatic code auditing algorithm and a proxy contract mode of a graph neural network; real-time data monitoring and exception repair are realized by using an under-chain auditing mechanism and a cross-chain data consistency verification protocol; according to the invention, the data consistency, the system safety and the reliability of electronic lead seal data management are obviously improved.

The invention discloses a task autonomous processing method and device, a storage medium and electronic device.The method comprises the steps that a target transaction task instruction input by a user is obtained, task autonomous reasoning is conducted on the target transaction task instruction in an equipment simulation environment, and candidate task execution action tracks are obtained; performing task execution motion line optimization on candidate task execution motions in the candidate task execution motion track by adopting a Monte Carlo tree search mechanism through a collaborative search agent to obtain a target task execution motion track; and determining a task autonomous processing action sequence for the target transaction task instruction based on the target task execution action trajectory.

The invention provides a large model active tool calling method and system based on multi-step reasoning, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting tool information, generating a structured tool description set, automatically generating an executable code sample and corresponding natural language explanation based on the tool description set, and constructing a reverse training data set; the method comprises the following steps of: marking tool use necessity tags in a real problem, prompting a large model to generate a multi-step reasoning path combined with a natural language and codes, verifying and filtering the correctness of the reasoning path, and constructing a forward training data set; and performing three-stage training on the large model based on the reverse training data set and the forward training data set. According to the method, the problem that the active tool calling capability of a large model is insufficient is solved, and the code execution rate and problem solving efficiency of a complex reasoning task are improved.

The invention relates to the technical field of agent systems, and provides an MCP agent-oriented dynamic tool discovery and adaptive system construction method, which comprises the following steps of: designing a uniform interface to register tools, and storing tool data through a dictionary structure; environment parameters are collected, external input and context information are received, three types of data are fused, task types are judged according to priorities, and task requirements are analyzed; according to the capability description during tool registration, the suitability of task execution is judged, a correlation score is calculated, and tools with the score lower than a preset score are screened; comprehensive calculation is carried out by combining the correlation score of the tool, the cost and the delay value, and the tool for executing the task is selected according to the score; according to the selected tool and the current operation environment, resources needed by tasks and an execution sequence are judged, and an execution chain is automatically constructed; and recording and collecting feedback data, evaluating an execution result, and iterating tool parameters.

The invention discloses an automatic compiling and adapting method for an RISC-V extension instruction, and belongs to the technical field of compilers. According to the method, a dynamic DSL (Digital Subscriber Line) for registering a custom instruction, adding register use constraints and defining a specific code mode is designed. The LLVM plug-in is used for automatically integrating the registered custom instruction in the compiling process. The invention relates to a self-defined instruction rapid adaptation mechanism which is specially provided for a continuously provided RISC-V self-defined instruction expansion scene, and by automatically generating correct assembly codes of registered self-defined instructions, correct registers are automatically distributed and reserved according to register use constraints; the method comprises the following steps of: searching a defined specific code mode and semantics, and automatically generating and inserting a registered custom instruction in a program, so as to realize the rapid adaptation of a compiler to an RISC-V custom extension instruction and the automatic use of a program code to the custom instruction, and the whole process does not need a developer to manually modify the compiler or a program source code.

The invention provides a large model tool calling method and device based on multiple constraint sets and electronic equipment. The method comprises the steps that a current constraint set comprises a permission constraint set, a state constraint set and a context constraint set; based on the current constraint set and a tool set cue word builder, obtaining a tool set cue word corresponding to the current constraint set, and generating a calling intention based on the task demand instruction and the tool set cue word; and generating a tool call description based on a cue word formed by the large language model and the call intention, and executing tool function call under the condition that grammar and semantic check of the tool call description is passed. On one hand, a current constraint set uniformly manages authority, state and context constraint conditions, and the constraints are flexibly subjected to set operation to dynamically judge the availability of a tool; and on the other hand, the tool set cue words are dynamically screened, redundancy of the tool set cue words is avoided, the calculation amount of a large language model is reduced, and the possibility of calling wrong tools is reduced.

The invention discloses a code vulnerability detection method, a code vulnerability rule configuration method, a static single assignment SSA code acquisition method, devices corresponding to the methods, and electronic equipment. The code vulnerability detection method comprises the following steps: acquiring a plurality of source code files and code vulnerability rules of an application program; generating an abstract syntax tree of the source code file, and obtaining a function call relationship; and traversing the abstract syntax tree, and obtaining a cross-function risk propagation path according to the function call relationship and the code vulnerability rule. By the adoption of the processing mode, the function calling relation and the abstract syntax tree are fused to conduct cross-function code vulnerability detection, cross-function vulnerability detection of context sensitivity, flow sensitivity and domain sensitivity is achieved, and vulnerability false report and missing report are avoided; therefore, the accuracy and recall rate of vulnerability detection can be effectively improved.

The invention provides a man-machine interaction method, device, system and equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of large language models, intelligent assistants and the like. According to the implementation scheme, in response to a received user instruction, an operation instruction generation request is obtained based on the user instruction and function description information of multiple functions; an operation instruction generation request is sent to the server, and the operation instruction generation request is used for triggering the server to apply the cloud large model so as to select at least one function from the multiple functions based on the user instruction and the function description information, and an operation instruction used for completing the user instruction is generated based on the at least one function; and in response to a received operation instruction sent by the server, executing a calling operation for each function in the at least one function based on the operation instruction so as to obtain an execution result of the user instruction.

The invention discloses a transaction management method and server based on dynamic SQL (Structured Query Language) processing, and the method comprises the steps: determining a to-be-executed business process in response to a received API (Application Program Interface) request of a user terminal; decomposing the business process to be executed into a plurality of process nodes; identifying a transaction node group in each process node, and declaring a joint transaction for all the transaction node groups; for each transaction node, obtaining a corresponding target SQL statement, and dynamically modifying the target SQL statement; executing the dynamically modified target SQL statement, and packaging the dynamically modified target SQL statement into a transaction operation unit under the condition that the dynamically modified target SQL statement belongs to the joint transaction; executing all the encapsulated transaction operation units, and submitting or rolling back the joint transaction to obtain an execution result; and returning an execution result to the user terminal. According to the method and the device, the problems of difficulty in dynamic SQL construction, SQL injection risk and poor maintainability can be effectively solved.

The invention relates to the technical field of voice semantics, and discloses an information interaction method and device based on multi-agent collaboration, equipment and a medium, and the method comprises the steps: generating a target workflow through a large language model and a current user demand; determining a target AP I according to the target workflow, and generating initial interaction information; and generating current reply evaluation information based on the initial interaction information and the criticizing agent, and determining the initial interaction information as target interaction information under the condition that the current matching degree is greater than or equal to a matching degree threshold value. Through the above mode, the user demand is split through the routing agent and the built-in large language model, the target workflow is generated, and the method adapts to complex and changeable task scenes. According to the method, the target AP I is determined through the induction agent, and the initial interaction information is generated, so that the efficiency and accuracy of calling the AP I are improved, and the performance of an automatic business process system for processing complex interaction information in the fields of financial science and technology, medical treatment, health, old-age care and the like is improved.

The embodiment of the invention provides a business scene arrangement platform construction method based on a standard component, and the method comprises the steps: combining a user demand in a component demand document with a standard component, and outputting a decoupled function point and corresponding input and output information; taking the decoupled function points as input, performing retrieval in a standard component library, and if the function points are not retrieved, generating component codes based on the decoupled function points; comprehensively testing the standard component based on the component code; if the test is passed, outputting a package component; inputting and outputting parameters into the packaging component, carrying out vectorization processing, outputting a packaged standardized component, and storing the packaged standardized component into a standard component library; on the basis of the business scene data, performing scene construction on the packaged standardized component by writing a specific function module, and outputting the standard component after scene construction; and testing, optimizing and packaging the standard component after the scene is established to obtain an application program corresponding to the business scene.

The invention provides an implementation method, system and device for a cross-sub-application calling component based on a qankun framework and a medium, and belongs to the technical field of software development. Multiple sub-applications are loaded based on the qankun framework; loading resources of the target sub-application B, and configuring sandbox environment isolation styles and resources; displaying a shared component through the page in combination with the query parameter, an identifier of the shared component corresponding to a value in the query parameter; detecting the currently mounted sub-application B, executing an unloading operation, releasing resources and resetting the state of a mounting container; when the sub-application A is uninstalled by the main application, the target sub-application B associated with the sub-application A is uninstalled, and the consistency of the states of the components called across the applications is ensured. According to the method, the sub-applications and the shared components are found in the modes of universal route matching, parameter setting and the like, and the pages and the shared components are loaded through universal routes and parameters, so that the function of opening the shared components or pages in the sub-applications in a cross-application manner is realized.

A distributed task scheduling system based on RabbitMQ, Redis and MongoDB. The task scheduling system comprises a task system service, Redis, MongoDB, RabbitMQ and an SDK, wherein the task system service comprises a data and object storage management module, a message queue management module, a task management module, a timing task module and a workflow module. By means of the design of a task system service, Redis, MongoDB, RabbitMQ and an SDK, the functions of an asynchronous task, a timing task and a workflow in a distributed and micro-service environment are provided. By means of the technical solution of a message queue, trigger-based scheduling of a task is realized, and a reliable and unique task scheduling capability exists; moreover, a timing task trigger capability exists, and thus the present invention is suitable for a scenario in which the execution of a certain task is triggered in a timing or delayed manner; a workflow capability is provided, and thus serial and parallel execution of a plurality of tasks can be performed; and capabilities such as task rollback and interruption are provided, and thus a system which unifies the asynchronous task, the timing task and the workflow is achieved.

The invention provides a task assistance method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining a task implementation process needed for completing an omics task through a coarse-grained planning group; traversing each process step in the task implementation process in sequence, and executing the following operations: determining specific implementation steps required for completing each process step through a fine-grained programming group; and executing the specific implementation steps through the action execution group to obtain a target omics result after the specific implementation steps are completed. According to the task assistance method and device, the electronic equipment and the storage medium provided by the invention, a method for carrying out omics research by combining a multi-agent system based on a large language model and bioinformatics research is provided, and an agent group grouping management mode is creatively designed; the interactive information redundancy and meaningless dialogue between the intelligent agents can be reduced, efficient and accurate information flow is ensured, and the efficiency and accuracy of omics research between the intelligent agents and the intelligent agents are effectively improved.

The invention provides a React component fine-grained state response system and method based on Proxy and dependency injection and application of the React component fine-grained state response system and method, and relates to the field of computer front-end development. The system comprises a centralized observable state storage module for storing a global sharing state, a dependency automatic tracking and registering module integrated in a set interceptor of Proxy, a state change accurate notification and target update triggering module integrated in a set interceptor, and a component dependency injection and connection module. The system is based on Proxy automatic dependence tracking, and can be accurate to the specific attribute path level of a state object. Only when the minimum state unit which the component actually depends on changes, the component can be rendered again, unnecessary DOM operation is reduced to the maximum extent, and the performance in a frequent updating scene is improved.

The invention provides a tool calling method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the steps of obtaining target task information, inputting the target task information into a large language model for analysis, and generating a task planning response; a tool calling strategy is determined based on the task planning response, and the process of determining the tool calling strategy comprises the steps of judging whether an external tool is called or not and determining the type of the tool; under the condition that an external tool needs to be called, utilizing a first cue word template to guide the large language model to extract parameter information of a target tool from the target task information; and calling the target tool based on the parameter information to obtain first task response information returned by the target tool.

The invention belongs to the technical field of artificial intelligence, and provides an automatic contract auditing system and method based on multi-agent collaboration, and the method comprises the steps: configuring contract auditing description information, and generating a contract auditing request; receiving a contract auditing request, performing semantic analysis on contract auditing description information in response to the contract auditing request, and extracting key auditing elements; calling a pre-trained intention recognition agent in an agent library, and carrying out keyword, semantic and logic multi-level matching on the key auditing elements and a pre-constructed auditing rule library in sequence by utilizing the intention recognition agent to obtain an auditing intention; and based on the attribute of the auditing intention, matching, searching and calling a corresponding pre-trained agent from an agent library, and carrying out collaborative auditing on the contract content by utilizing the called agent to obtain an auditing result. According to the method, the contract can be automatically and accurately audited based on multi-agent collaboration.

The embodiment of the invention discloses a method and a system for realizing enterprise collaborative decision-making based on an AI algorithm and a government-enterprise API (Application Program Interface). The method comprises the following steps: receiving input data interacted by using a natural language; converting the input data into standard format data; determining a service type related to the input data by using an AI model; the service type comprises public service and enterprise application service; the public service and the enterprise application service are both associated with one or more subtasks; determining a thinking chain providing the service; the sub-tasks contained in different thinking chains are different, or the execution sequences of the sub-tasks of different thinking chains are different, or the execution proportions of the sub-tasks contained in the thinking chains are different; planning an optimal thinking chain; the optimal thinking chain corresponds to an optimal execution path; the optimal execution path comprises one or more execution nodes which are arranged in order; and calling the API according to the optimal execution path, so that the called API provides the service required by the user based on the standard format data.

The invention discloses a large language model LLM-based hierarchical workflow automatic generation method. The method comprises the following steps: firstly, processing natural language task description input by a user through a pre-trained large language model to obtain a structured task component; then, related workflow modes and optimal practices are retrieved from a pre-constructed domain knowledge base based on the structured task component, and the workflow modes and the optimal practices are fused to form knowledge-enhanced workflow description; based on the knowledge-enhanced workflow description, automatically converting the knowledge-enhanced workflow description into a standardized directed acyclic graph (DAG) through the steps of node extraction, type classification, dependency analysis, structure verification and the like; and finally, performing quality evaluation and iterative optimization on the generated DAG, and finally realizing end-to-end automatic generation from a natural language to an executable workflow. The technical problems that an existing workflow generation method based on template matching seriously depends on a preset template, is insufficient in flexibility and is difficult to adapt to complex and changeable user requirements can be solved.

The invention provides an aerodynamic task processing method and device based on multiple agents, and relates to the technical field of aircraft simulation design, and the method comprises the steps: obtaining a task target and working condition parameters inputted by a user; based on the capability of each agent group in the multi-agent system, generating a plurality of sub-tasks based on the task target; distributing the plurality of sub-tasks to corresponding agent groups in the multi-agent system for task processing to obtain a calculation result corresponding to the task target; the multi-agent system comprises a first agent group, a second agent group and a third agent group. According to the multi-agent-based aerodynamic task processing method and device provided by the invention, the role of a person in a traditional CFD normal form is replaced by the agents, automatic execution of the whole process of the CFD task is realized, and the processing efficiency of the aerodynamic task is greatly improved.

The invention provides a component configuration method which can be applied to the technical field of cloud computing and the technical field of big data. The method comprises the steps of obtaining configuration information used for configuring a target interface component from a server side based on an interface request; packaging the interface request into an asynchronous data acquisition object, and storing the asynchronous data acquisition object as a global shared instance; registering a callback processing function based on the global shared instance for the target interface component; and responding to the configuration information returned by the server side and triggering the callback processing function, and executing component rendering on the target interface component based on the configuration information.

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 provides a front-end logic execution system based on a canvas flow, and the system comprises an editor end which comprises an item switching module which is used for providing an item menu tree and a visual page; the page building module is used for storing a menu component; the action flow arrangement module is used for connecting the menu components according to the running sequence and configuring execution conditions, preposed actions, delay events and loop events to form a low-code data packet; the running device end comprises a data packet analysis module which is used for loading a low-code data packet and restoring the low-code data packet into JSON (JavaScript Object Notation) configuration; the rendering module is used for converting the JSON configuration into HTML (Hypertext Markup Language) elements and JS (JavaScript) codes and rendering the HTML elements and the JS codes to a browser And the event execution module is used for executing each action after the browser component is triggered. The method has the beneficial effects that the generation convenience of the business logic execution link can be improved, the technical threshold of front-end business logic development is reduced, and the flow visualization of the whole link generation and execution links is realized.

The invention relates to the technical field of data security, in particular to a data desensitization method and system based on combination of an MCP protocol and rule driving. The method comprises the following steps: loading a service rule through an MCP Server and registering a desensitization tool as a standardized tool; user query is received through the MCP Client, and user intention is analyzed by the AI agent; the AI agent dynamically calls a standardized tool to form a desensitization task chain according to a user intention and a business rule; and executing the desensitization task chain to process the target data, wherein the calling process comprises dynamic strategy adjustment based on context information. The system comprises an MCP service layer, a desensitization tool library module and an intelligent decision and integration layer. Through MCP protocol standardization integration, service rule driving and AI agent decision making, dynamic weight distribution and probabilistic algorithm selection of a desensitization strategy are achieved, the flexibility, the intelligent level and the system integration efficiency of data desensitization are effectively improved, and meanwhile the safety and compliance guarantee capacity is enhanced.