690 results about "Execution plan" patented technology

The invention discloses an intelligent agent automatic arrangement method and system based on a large language model, and relates to the technical field of artificial intelligence. The method comprises the following steps: decomposing a natural language instruction of a user into a structured subtask sequence by utilizing a first large language model; based on the agent portrait library, matching and allocating agents for each sub-task to generate an initial execution plan; task execution is scheduled and monitored in real time through an event-driven architecture; when abnormity is monitored, a self-adaptive adjustment mechanism is triggered, the affected plan part is re-planned, and an updating instruction is issued. According to the invention, efficient, flexible and robust multi-agent automatic arrangement is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a task scheduling execution method, device, equipment and medium based on intention recognition, and the method comprises the steps: fusing an interaction request and a dialogue history library to generate a session context, and carrying out the analysis to obtain an intention recognition result; task planning is carried out based on the intention recognition result and a knowledge base, and a task subitem set and an execution sequence are generated; matching the task subitem set with a resource directory to generate an execution resource list; scheduling the task subitem set by using the execution sequence to generate an ordered task sequence; generating an execution plan in combination with the ordered task sequence and the execution resource list; and triggering execution based on the execution plan, and aggregating output to generate an execution result and a process tracking record. According to the method, automatic connection of session analysis, task planning, resource matching, sequential scheduling and execution triggering is achieved, manual operation splitting among multiple systems is eliminated, and task processing efficiency and execution accuracy are improved.

The invention provides a data query method and system for converting a natural language into a database query language, and the method comprises the steps: analyzing the natural language input of a user through a multi-modal understanding agent on the basis of constructing a dynamic knowledge graph based on metadata, combining a historical session with a business term table, eliminating ambiguity, and generating a standardized Query, a retrieval routing agent selects a query strategy according to Query complexity, simple query directly matches a cache template, complex query traverses a knowledge graph, and related tables, fields and service constraints are returned; then, an expert committee agent generates an SQL (Structured Query Language) by adopting multi-stage collaboration, executes plan pre-evaluation, and selects a version with the highest comprehensive score; the test agent simulates and executes the SQL in the isolation environment, and verifies the grammar legality and the field permission; and finally, executing the detected SQL, and processing a result. According to the method, the accuracy of converting the natural language into the SQL (NL2SQL) in a complex database scene can be effectively improved.

The invention relates to the technical field of robot cooperative control, in particular to a humanoid robot body intelligent cooperative control system based on multi-modal sensing fusion, and the system comprises a multi-modal sensing fusion module which recognizes a target boundary position, analyzes a pressure change, and combines with a posture to extract audio features to generate an environment sensing graph; the dynamic time sequence adjustment module optimizes an action rhythm adjustment detail generation execution plan, the cross-modal behavior correction module corrects an offset optimization track generation coordination sequence, the task priority distribution module analyzes task distribution to generate an execution list, and the time sequence conflict correction module optimizes a path adjustment conflict generation coordination path. According to the method, an environment perception graph is constructed through matching and fusion of multi-source perception data, the action sequence and interval are dynamically adjusted to optimize an execution chain, trajectory offset is corrected to improve action precision, nearest response and load balancing are achieved through real-time task allocation, conflict blocking is reduced through path rearrangement and time sequence coordination, and a perception decision execution closed loop is formed; and the identification precision and the cooperation efficiency are improved.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses a business English scene knowledge graph generation and intelligent retrieval method, and relates to the technical field of natural language processing, and the method comprises the steps: constructing a document time sequence index and anaphora alignment mapping table, and registering an evidence anchor point index; extracting event units under the constraint and merging the event units into event clusters in a cross-document manner; arranging an event chain according to unified time and generating a process graph, and binding evidence anchor points to relation edges; analyzing the query into a query intention expression and a path constraint, and generating an evidence presentation sub-plan and an image-text combined execution plan; and positioning answers in the atlas, integrally returning the answers, paths and evidences, and meanwhile, forming replayable audit tracks. The method solves the problems of cross-channel time dislocation and confusion of references, and supports interpretable and auditable retrieval. The method is suitable for scenes such as mails, conference summary, instant communication and the like, guarantees locatable sources, path replay and evidence verification, and is convenient for internal control review.

The invention relates to the technical field of education collaborative management, and discloses a higher vocational education collaborative management system based on a multi-source knowledge graph and an intelligent agent, and the system comprises an ontology graph module which is used for obtaining data from all business systems and generating versioned knowledge graph snapshots; the rule compiling module is used for compiling laws and regulations and standards to form a discrete constraint set; the event influence module is used for determining an influence sub-graph and screening a sub-constraint set; the task decomposition optimization module is used for decomposing tasks and selecting bid-winning execution plans; the course arrangement solving module is used for solving a course arrangement result based on the constraint model; the post matching module is used for comparing the student ability with the post demand to obtain a post qualification set; and the evidence write-back module is used for constructing an evidence directed acyclic graph, generating a fingerprint hash value and forming a new version knowledge graph snapshot. According to the invention, data unified modeling, rule structured expression, task automatic execution and whole-process traceability in higher vocational education management are realized.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention discloses a heterogeneous computing power cooperative scheduling system and method for mixed precision training, and belongs to the technical field of artificial intelligence computing. The system comprises a computational graph analysis and operator portrait module which is used for analyzing and dividing a model computational graph and extracting operator features; the heterogeneous hardware capability sensing and matching module is used for managing performance files and real-time states of heterogeneous hardware in the cluster and matching optimal execution hardware for each calculation partition; and the data flow coordination and pipeline parallel controller is used for generating a global execution plan, managing cross-device data dependence and communication and calculating overlapping optimization execution efficiency through communication. According to the method, the problem of low scheduling efficiency of mixed precision training in a heterogeneous environment is solved, automatic and accurate mapping from a calculation task to heterogeneous hardware is realized, the training speed is remarkably improved, the training cost is reduced, and the overall resource utilization rate of a cluster is improved.

The invention discloses a source network load storage route adjusting method based on an AI control strategy, and relates to the technical field of power system control, and the method comprises the steps: collecting a source side signal, a network side signal, a load side signal, a storage side signal and an exogenous signal, and generating a unified system state of confidence weight and topology embedding; the state is used for digital twinning online calibration, a feasible range and a constraint set are extracted, and a projection operator and guaranteed operation curve set is generated; generating a unified energy routing vector in the feasible range by a hierarchical strategy; performing simulation checking calculation in the digital twinning and compiling into a final instruction and execution plan table; performing closed-loop issuing and receipt checking, and performing event write-back to update guardrails and strategies; the method realizes collaboration, improves on-site consumption of new energy, reduces comprehensive cost, enhances boundary compliance and traceability, is suitable for parks, micro-grids and the like, and is convenient for deployment, operation, maintenance, expansion and upgrading.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

The invention relates to the technical field of intelligent question answering, in particular to an intelligent question answering implementation method and system based on an MCP protocol. The intelligent question and answer implementation method based on the MCP protocol comprises the following steps: receiving and analyzing an original request, actively sensing related environment information, integrating and constructing a context background, and drawing core association between indexes and dimensions and specific calling logic of a tool; in combination with recorded information of the tool registration center, a tool combination is selected, and a task execution plan is made; and scheduling and calling a tool function, performing integration, verification and deep reasoning on a returned result, and finally extracting key information related to the core demand of the user, and presenting the key information to the user. According to the intelligent question and answer implementation method and system based on the MCP protocol, real-time acquisition and fusion of multi-dimensional information are realized through a dynamic function calling mechanism, so that the intelligent question and answer system has the capabilities of environment perception, multi-round reasoning and cross-modal decision making, and the question and answer accuracy and timeliness in a complex scene are remarkably improved.

The invention provides a natural language intelligent analysis and data query instruction generation method based on a large model, which relates to the technical field of natural language processing and comprises the steps of performing semantic analysis by pre-training a large-scale language model, constructing a dynamically enhanced query semantic graph, performing semantic enhancement in combination with an external knowledge graph, and generating a data query instruction. And extracting a structured feature vector and mapping the structured feature vector into a database query syntax tree, and finally optimizing an execution plan based on deep reinforcement learning to generate a standardized query instruction. According to the method, intelligent conversion from natural language query to efficient database instructions is realized, and query precision and execution efficiency are improved.

The invention discloses an operation data processing method and system based on a compliance verification library and a risk control plan, and belongs to the technical field of data compliance and risk control, and the method comprises the steps: obtaining an execution plan of a target operation task and risk control strategy information of the execution plan, analyzing operation data items, distributing corresponding compliance verification time windows and levels, and forming an operation data verification scheduling table; according to the verification level and the business domain, a rule set is extracted from a compliance verification library, and rules comprise predefined rules, model output rules and historical violation rules; field-level matching, logic consistency verification and cross-source linkage verification are executed in the verification time window; when the verification result meets the triggering condition, generating an abnormal event, and executing response operations such as delay, recheck or interception; and finally, writing the execution state and the verification result into an abnormal log queue for risk control strategy parameter updating or re-evaluation. According to the method, the dynamic compliance verification and strategy closed-loop optimization of the operation data are realized, and the intelligent compliance control capability of the system is improved.

The embodiment of the invention provides a software development method based on artificial intelligence and related equipment. In the embodiment of the invention, an AI software development method and mode are provided, and a user and a technical design document generation agent cooperate to complete a technical design document of a programming task; and the programming agent autonomously performs task planning based on the technical design document, and autonomously executes the programming task based on the planned execution plan. In at least one of the technical design stage or the task execution stage of the programming task, a man-machine cooperation interface is provided, and the man-machine cooperation interface is used for a user to participate in the software development process for the programming task. Therefore, the software development efficiency and the code quality are greatly improved through division cooperation and man-machine cooperation interaction among the intelligent agents.

The invention belongs to the technical field of electric power safety operation and robot control, and provides an electric power body intelligent agent interaction method and system fused with a work ticket constraint mechanism, and the technical scheme is that an executable constraint set is generated by a structured electric power inspection task work ticket, a field model and a rated table; constructing a robot capability vector, and matching the robot capability vector with the executable constraint set to determine a robot capable of executing the task; solving the constructed multi-objective function by taking the executable constraint set as a hard constraint to obtain an execution plan of the robot capable of executing the task; multi-disturbance simulation is conducted on the execution plan in the in-station twin environment, the minimum safety margin and the violation probability are obtained based on the simulation result, the minimum safety margin and the violation probability are compared with the corresponding threshold values respectively, after the minimum safety margin and the violation probability pass through the threshold values, the robot executes the task, and otherwise the executable constraint set is regenerated or the execution plan is planned again. And the action of each step of the electric power tool body intelligent agent is restrained by the bill.

The invention belongs to the technical field of robot task planning, and particularly relates to a robot closed-loop task planning method and system based on a double-track re-planning strategy. Comprises: using a large language model to generate an action sequence based on a to-be-executed task; before the action of each step is actually executed, the action executability of the action of the current step is judged; after each step of action is actually executed, action execution success judgment is carried out on the current step of action; when it is judged that the action cannot be executed or execution of the current-step action fails, a replanning decision maker based on a large language model is triggered, selection is conducted between two replanning strategies of plan reconstruction and task replacement, a plan reconstruction or task replacement result is executed, and robot closed-loop task planning is achieved. According to the method, planning flexibility and environment constraint perception are considered, invalid attempts can be reduced, and the success rate and robustness of long-time tasks in a complex scene are improved.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of actions to be performed by the computing system at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of actions to be performed by the computing system at a second level of granularity, the second level of granularity being finer than the first level of granularity.

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.

Conditions are identified in a telecommunications network. Based on the condition, a data store is accessed to identify an associated executable plan for responding to the detected anomaly. The executable plan is generated by an AI agent based on a structured operator-readable document comprising operator-executable procedures. The executable plan comprises a series of operations that are executable by an execution component.

The invention discloses an intelligent agent planning method, system and device based on context awareness and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that firstly, user input is analyzed, structured task requirements are generated, and meanwhile, external tool function attributes and historical interaction data are collected; according to the method, an ordered execution plan is generated in combination with task requirements and tool attributes, subsequent plans are optimized by utilizing historical interaction contexts, in an execution stage, parameters required by a target function can be accurately extracted from the task requirements and context information, corresponding tools are called to complete a task, and finally, an execution result is fed back for display, so that the execution efficiency is improved. And performing execution control based on the function calling state and the result. According to the method, more intelligent task planning and execution are realized through context sensing, and the method is particularly suitable for complex task scenes needing multi-step and multi-tool cooperation.

The invention discloses a shoe sole production management optimization method and system based on order demands, and belongs to the technical field of production management, and the method specifically comprises the steps: carrying out the order priority dynamic judgment according to the production complexity corresponding to a shoe sole specification parameter and the emergency degree corresponding to a delivery period in an order information file, and generating a priority sequence; calling the equipment state, raw material inventory and capacity data in the production system, pre-distributing production resources for each order according to the priority sequence, synchronously checking the feasibility of a pre-distribution scheme, and judging whether the pre-distribution scheme has resource conflicts or not; if the resource conflict exists in the pre-allocation scheme, triggering the constructed conflict automatic coordination rule, and generating a conflict-free allocation scheme by adjusting a production batch sequence or splitting batch production; and according to the conflict-free distribution scheme, generating a sole production execution plan corresponding to each order in combination with each requirement in the order information file, and determining starting and ending time and an operation standard of each production link.

The invention discloses a network scanning task intelligent segmentation and load balancing method and system. The method comprises the following steps: identifying abnormal fragments by executing integrity verification on a scanning task, analyzing a task dependency relationship to determine a divisible position, and generating a segmentation scheme in combination with granularity constraint; node resource states are collected to calculate load indexes, and scheduling weights are hierarchically configured according to load levels to establish parallel scheduling channels; constructing a node cooperation group based on load difference, formulating an inter-group circulation rule to form a cooperation scheduling space, fragmenting tasks according to node capacity, and generating a parallel execution plan; monitoring the execution progress to identify overstocked nodes, diagnosing bottleneck types and planning a dredging path to execute task migration; the resource time sequence data is collected to identify the low-load time period, and the task is distributed to the optimal time slot to be executed, so that the task segmentation reasonability and the node load balancing degree are improved, and the distributed scanning execution efficiency is improved.

A computer-implemented method, apparatus and system is provided for robotic task completion from natural language requests. The method may include: receiving a natural language command, processing the natural language command with a generative large language model to extract an intent and associated context, creating a three-dimensional (3D) open-vocabulary semantic scene graph of the environment, associating the scene graph with the intent and associated context, creating, based at least in part on the scene graph, an execution plan comprising a sequence of actions to complete the natural language command, and generating executable code or tool calls corresponding to one or more actions in the sequence of actions, and controlling one or more robotic manipulators and / or actuators to perform one or more actions of the sequence of actions based on the execution plan.

The invention relates to the technical field of data processing, and particularly provides a double-engine-based SQL (Structured Query Language) generation method, system and device and a medium, and the method comprises the following steps: receiving a natural language query input by a user; analyzing the natural language query by using an intention understanding engine in combination with a dialogue context and a business knowledge graph to generate a structured query intention representation; utilizing an SQL generation and optimization engine to generate at least one candidate SQL statement based on the structured query intention representation and a vectorization database Schema library; performing execution plan analysis on the at least one candidate SQL statement, and performing performance evaluation and optimization rewriting based on an analysis result to generate a final high-performance SQL statement; and executing the final high-performance SQL statement and returning a query result. According to the method, the query success rate and the user experience in a complex scene are greatly improved.

Provided are a method and device for parsing a database query statement, and a storage medium. A query statement for a database table to be queried is obtained, and an empty database table corresponding to the database table to be queried is created; the query statement is executed in the empty database table, and a parser installed in a database is called to parse the query statement to obtain a syntax tree, and an execution plan is generated based on the syntax tree for execution in the empty database table; and the syntax tree is obtained from the parser in an execution process of the query statement, and a syntax tree serialization result is generated and output as a parsing result of the query statement.

The invention discloses a dynamic operator-oriented incremental compilation optimization method and system, belongs to the technical field of deep learning, and aims to solve the technical problem of how to provide a fine-grained, reusable and extensible compilation strategy for dynamic operator-oriented incremental compilation optimization. Comprising the following steps: extracting attributes of a dynamic operator and constructing a dynamic operator hash signature according to the extracted attributes; dividing the calculation graph into a static sub-graph and a dynamic sub-graph, establishing a mapping table of a dynamic operator hash signature and a compiling kernel, and caching the mapping table in an incremental compiling cache; solving and instantiating the dynamic sub-graph through a dynamic specifying engine, generating a specialized sub-graph, querying whether a matched compiling result exists in an incremental cache or not, and updating the incremental cache; and for the static sub-graph which is not changed, directly multiplexing the last compiling result of the static sub-graph, executing cross-sub-graph fusion optimization work between the dynamic sub-graph and the static sub-graph which are subjected to specialized processing, and submitting a fusion execution plan to a runtime engine.

The embodiment of the invention provides a vehicle intelligent agent control method and device, electronic equipment, a vehicle and a medium, and the method comprises the steps that a central intelligent agent receives task analysis information sent by a cabin intelligent agent, and the task analysis information is obtained by carrying out semantic analysis on an intelligent agent cooperative control task after the cabin intelligent agent receives the intelligent agent cooperative control task; splitting the agent cooperative control task into a plurality of sub-tasks according to the task analysis information, allocating an execution agent to each sub-task, and generating a task execution plan; the execution intelligent agent comprises an intelligent driving intelligent agent and a cabin intelligent agent; and sending a task execution request corresponding to each sub-task to each execution agent according to the task execution plan, so that each execution agent executes the corresponding sub-task according to the task execution request. According to the invention, the cabin intelligent agent, the intelligent driving intelligent agent and the central intelligent agent cooperatively control the user intention of the task around the intelligent agents and cooperatively execute the task, so that the task execution efficiency and effect are improved.

A system allows multiple users to interact with the system to perform data analysis. The system allows users to specify data analytics requests using high-level natural language requests. The system generates an execution plan based on natural language requests received from a particular user and executes the execution plan. The system detects a failure in executing the execution plan and determines whether to request help from another entity. The system may change the state of execution of the execution plan to a waiting state in which the system blocks execution until it receives an approval to proceed from an entity. The system receives a request to modify the execution plan from the entity and modifies the execution plan based on the request. The system iteratively performs the modification of the execution plan until an acceptable execution plan is generated.