3613results 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.

Disclosed herein are system, method and / or computer program product embodiments, and / or combinations and sub-combinations thereof for providing a platform for developing an application that interacts with a set of IoT devices. An embodiment provides a source code editor that enables a user to generate source code of the application and an application builder that generates an executable version of the application based at least on the generated source code. Generating the source code includes defining an input or output of the application as an event that is mappable to a first set of IoT devices in a first network of IoT devices when the application is installed thereto and to a second set of IoT devices in a second network of IoT devices when the application is installed thereto, the first set of IoT devices including at least one device type not included in the second set of IoT devices.

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 provides a method and device for an intelligent computing center to provide computing power resources through computing power packages, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, determining a computing power package corresponding to a to-be-processed computing power task under the condition that the computing power resources need to be called, the computing power task is a task needing to call computing power resource processing, and the computing power package is used for metering the computing power of the intelligent computing center according to the calculated amount; s2, computing power resources are called based on an application program interface corresponding to the computing power package, the application program interface comprises a code program corresponding to operation for executing the computing power task, and the computing power resources called by the application program interface are matched with the operation environment of the computing power task. According to the method, the scheduling efficiency of the computing power resources can be improved, the computing power is metered according to the calculated amount through the computing power package, and wide application of the general computing power is achieved.

The invention relates to a business process processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a business process processing request; the business process processing request carries a business variable and an operation instruction; according to the operation instruction, executing a node task in a to-be-executed business instance corresponding to the business process processing request; the to-be-executed service instance is generated after an initial service instance executes at least one node task; the initial service instance is created through a pre-configured process engine; obtaining an execution path of the to-be-executed service instance according to the service variable; the execution path is used for determining the circulation trend of the to-be-executed service instance. By adopting the method, the execution efficiency can be improved.

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.

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

The invention discloses a large-model-driven Web task automatic execution method and system, and relates to the field of artificial intelligence. According to the method, Web page content is perceived and analyzed through a large model, a state-action graph is constructed and maintained, a task sequence library is stored and managed, and semantic matching and automatic task execution are carried out. According to the method, the problems of high manual intervention requirement and low task processing efficiency in traditional Web task automation are solved, and the technical effects of remarkably improving the automation level, enhancing the intelligent decision-making capability, perfecting a knowledge memory mechanism and efficiently executing are achieved.

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 relates to a peripheral adaptation system based on a dynamic plug-in loading mechanism, and belongs to the technical field of plug-in management. The system comprises a dynamic plug-in loading management unit, a peripheral adaptation management unit and a front-end management unit, the dynamic plug-in loading management unit is used for managing the loading and hot plug functions of the plug-in and realizing the loading, isolation and unloading of the plug-in when the system runs; the peripheral adaptation management unit is used for communicating with a peripheral, and analyzing and identifying a special protocol and an interface of the peripheral through the intelligent adapter; and the front-end management unit is used for designing an API (Application Program Interface) for a front-end application to call, and performing authority control on sensitive operation through the security access module. Dynamic loading and flexible support of various peripherals are realized, compatibility of the peripherals is optimized, and maintainability and expansibility of the system are improved.

The invention is suitable for the field of data management, and provides a front-end AI dialogue data management system and method, and the system comprises an asynchronous data flow management module; a state management module; a streaming data control module; a Markdown rendering engine is adopted; the beneficial effects of the invention are that the asynchronous data flow management module realizes high-efficiency synchronization of front-end and rear-end data, significantly reduces the data processing delay, and the state management module ensures the consistency of a user interface and rear-end data, optimizes the user experience, and improves the user experience. The built-in advanced Markdown rendering engine supports various self-defined grammars, so that the output content of the AI can be flexibly subjected to personalized rendering, modular design is adopted, customization and expansion can be performed according to service requirements, and the method is suitable for different application scenes.

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.

According to the automatic drive program generation method based on machine learning provided by the embodiment of the invention, the accuracy of feature extraction is improved by introducing machine learning and natural language processing technologies, and better cross-platform compatibility is realized. Meanwhile, quality control in the code generation process is enhanced, and it is ensured that the generated drive program can run stably and reliably; according to the method, hardware features can be deeply understood, an accurate protocol conversion mechanism is established, and the quality of a driving program is improved through intelligent code generation and optimization strategies.

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 provides a method and a device for calling a service API (Application Program Interface), which are applied to a general application programming interface API framework to realize calling of service APIs of different authorization types. The method comprises the steps that an API information function entity receives an identifier and an authorization type of a service API from an API release function entity, and the API information function entity sends the identifier and the authorization type of the service API to an API caller. Therefore, when an API caller calls the service API, the calling method of the service API can be determined according to the authorization type corresponding to the service API, so that the safety performance is 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 embodiment of the invention provides a byte code generation method and device and a machine code generation method and device, and the byte code generation method comprises the steps that a target source code is obtained, first processing is applied to the target source code, and the first processing comprises the steps that a target statement used for integer operation in the target source code is replaced with a preset target function; according to the first processed target source code, a target byte code is generated, the target byte code comprises a byte code instruction generated according to a preset function, and the byte code instruction indicates that integer operation corresponding to the target statement is executed through a preset machine instruction sequence and overflow check for the integer operation is executed.

The invention provides an asynchronous-to-synchronous method and device, equipment, a storage medium and a product, and the method comprises the steps: responding to a first call of a first thread to an asynchronous interface, generating a first call identifier corresponding to the first call, responding to successful call of the first call, blocking the first thread, and generating a second call identifier corresponding to the first call; the first thread is used for executing synchronous processing logic; when the asynchronous interface stores a first asynchronous processing result to a buffer storage library, a second thread is triggered to obtain the first asynchronous processing result in the buffer storage library according to the first call identifier, and the first asynchronous processing result comprises an asynchronous processing result of the asynchronous interface on the first call; and the second thread sends the first asynchronous processing result to the first thread to wake up the first thread to continue execution. According to the method provided by the invention, excessive consumption of system resources and system crash risks in a high-concurrency scene can be avoided, and the stability of the system is 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.

A task processing method and apparatus, and a device, a storage medium and a program product, which are applied to task processing scenarios such as artificial intelligence, big data and cloud computing. The task processing method comprises: on the basis of task prompt information corresponding to a target task in a service scenario, generating a system interaction parameter corresponding to the target task, wherein the task prompt information is used for describing the target task in the form of natural language, and the system interaction parameter is used for describing the target task in the form of a machine instruction; calling a task system of the target task, and processing the system interaction parameter; and on the basis of output information of the task system of the target task regarding the system interaction parameter, determining a processing result corresponding to the target task.

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 invention provides a large model reasoning and calculation enhancement system, method, device and medium, and belongs to the technical field of artificial intelligence, the system comprises a user interaction module for obtaining and processing a natural language question input by a user, and returning a calculation result to the user; the reasoning and calculating module is used for converting the processed problem into a standard Prolog code by using the adjusted large language model; the Prolog code integrity verification module is used for carrying out integrity verification on the converted Prolog code; and the Prolog interpreter module is used for analyzing and executing the Prolog code which passes the verification, and then providing a calculation result to the user interaction module. According to the method, the natural language understanding ability of the large language model is combined with the precise calculation ability of Prolog, so that the limitation of the large language model in the aspects of mathematical calculation and logical reasoning is effectively solved, and the accuracy and reliability of a reasoning result are 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.