1019 results about "Task management" patented technology

Systems, methods, and software are disclosed herein for a system of agents for managing tasks of software applications which is guided by generative AI. In an implementation, a computing apparatus determines that a task has been assigned to an application assistant of an application. The application assistant includes multiple agents which interact with a generative AI model. The computing apparatus orchestrates the multiple agents in their interactions with the generative AI model in furtherance of completing the task and updates the contextual information of the task based on the interactions.

The invention relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

The invention provides a timed task execution optimization method and system, and aims to solve the problems of inflexible static priority scheduling, unintelligent resource allocation and the like in the prior art. According to the method, a distributed timed task intelligent scheduling framework is constructed and comprises five core components including a task management console, a scheduling decision engine, a resource monitoring agent, a task execution cluster and metadata storage. Task basic information is obtained through task feature extraction, a dynamic priority score is calculated based on decision factors such as SLA urgency, resource matching degree and service weight, and task priority ranking is achieved. And performing intelligent task allocation by adopting a BestFit algorithm, and allocating the task to the optimal execution node. The system monitors the CPU utilization rate, the memory occupation and the IO waiting time of the nodes in real time, and when the CPU utilization rate exceeds 85%, an elastic resource allocation strategy is triggered. And when the task execution time exceeds the pre-estimated duration, performing task splitting and rescheduling based on a dynamic fragmentation algorithm.

The invention belongs to the technical field of logistics conveying, particularly relates to an automatic material conveying system for a door and window processing workshop, and aims to solve the problem of low efficiency caused by insufficient automation, flexibility and intelligence of an existing system. The system comprises a data acquisition and perception module, a digital twinning module, a task management and scheduling module, an intelligent path planning and obstacle avoidance module, a multi-agent cooperative control module, an execution and feedback module, a man-machine interaction and visualization module and the like. And multi-mode collaborative operation, real-time path optimization and dynamic task allocation of the material conveying equipment are realized. The production efficiency, the flexibility and the intelligent level of a door and window processing workshop are remarkably improved.

The invention discloses an AGV (Automatic Guided Vehicle) scheduling system based on a soft bus protocol of a swan gap system and star flash wireless communication, and relates to the technical field of AGV scheduling planning, the AGV scheduling system comprises an AGV scheduling management center, and the AGV scheduling management center is in communication connection with a task management module, a communication connection management module, a global task collaborative planning module and a visual interaction module; and the task management module is used for receiving task requests, classifying, sorting and distributing tasks, and dynamically adjusting task priorities according to real-time production requirements and AGV states. According to the invention, low-delay and high-reliability communication between AGVs and between the AGVs and the scheduling management center is realized by the AGV scheduling system through a soft bus protocol of the swan Mongolian system and a star flash wireless communication technology, the communication delay is effectively reduced, the communication interruption caused by electromagnetic interference is avoided, the real-time performance of task scheduling and path planning is ensured, and the service life of the AGV scheduling system is prolonged. A solid guarantee is provided for stable operation of the AGV, and the overall response speed and reliability of the system are remarkably improved.

The present application relates to the technical field of large-scale data processing, and particularly relates to a parallel computing method and system suitable for large-scale data processing. The system comprises a task management unit, a distributed load balancing module, an elastic expansion architecture, an intelligent communication optimization module, and a resource monitoring unit, wherein the task management unit divides large-scale data into a plurality of sub-tasks by means of a task decomposer, and distributes the sub-tasks to computing nodes by means of a task scheduler and a priority distributor; the distributed load balancing module achieves global load balancing by means of a load sensing unit, a dynamic adjustment unit and a balance optimization unit; the elastic expansion architecture dynamically adjusts system resources by means of a node manager, a resource pool controller and an expansion decision-making device; the intelligent communication optimization module optimizes inter-node communication by means of a communication path planning unit, a bandwidth distribution unit and a delay compensation unit; and the resource monitoring unit monitors the system performance in real time by means of a performance collector, a state analyzer and an anomaly detector.

Systems and methods are provided for facilitating management of interactions and training for AI (artificial intelligence) agents. Systems generate and display interfaces for training AI agents and for receiving user instructions. The systems parse user instructions to identify tasks to be performed by the AI agents. The systems cause the tasks to be split into subtasks to be performed by the AI agent. The systems also display a dialog frame that presents the user instructions along with AI agent responses that identify the subtasks. The systems also display a graph that visually identifies a processing flow of the subtasks and that dynamically updates the processing flow to reflect a status of progress for the AI agent performing the subtasks.

A wireless transmit and receive unit (WTRU) may be configured to operate as task initiator (TI). The WTRU may send a request for a trust evaluation to a task management function (TMF), including a first candidate task participant (TP) and a first task and a second candidate TP and a second task. The WTRU may include trust indicators for the first candidate TP to perform the first task and for the second candidate TP to perform the second task. The TMF may perform the trust evaluation and determine a first estimated trust index (ETI) and a second ETI. Upon receiving the ETIs, the WTRU may determine an aggregated ETI. The WTRU may determine whether to assign the first task to the first candidate TP and the second task to the second candidate TP based on the aggregated ETI.

The invention relates to an intelligent inspection control system and method. The system comprises an environment sensing module, a situation recognition module, a task management and scheduling module, a path planning module, an execution control module and a communication linkage module. The system can collect various environmental parameters of an inspection area in real time, dynamically adjust the priority of an inspection task after identifying an abnormal state, and generate an optimal path to drive a robot to execute response operation; meanwhile, abnormal information, images, videos, sensor snapshots and other contents are uploaded to a background management platform through a communication linkage module, and sound-light alarm equipment, broadcast equipment and other equipment are linked to carry out field response. The control method has task preemption and path reconstruction capabilities, and supports distributed sensing deployment and whole-process execution state monitoring. Compared with the prior art, integrated fusion of environment situation awareness, task dynamic scheduling and response control is achieved, and the adaptive capacity, the response speed and the safety guarantee level of the system to complex scenes are improved.

The invention provides a DFT test task management method and device, electronic equipment and a storage medium, and belongs to the technical field of chip design verifier.The DFT test task management method comprises the steps that a data model is constructed on the basis of a design module list and a test step list, the data model is used for indicating testability design DFT test steps of all design modules, and the test steps of all the design modules are stored in the test step list; the data model comprises a plurality of DFT test tasks, attributes of each DFT test task comprise a selection state, and the selection state comprises a selected state and a non-selected state; based on the interactive input, determining at least one DFT test task of which the selection state is selected as an executable DFT regression object; and according to the DFT regression object, driving a DFT test task to be executed. According to the method, the DFT test tasks are normalized through the data model, only the DFT test tasks need to be selected for making the test plan, efficient management of the DFT test tasks is achieved, and the regression test efficiency is improved.

The invention relates to the technical field of project management, in particular to an enterprise project management system and method oriented to multi-project collaboration. A resource scheduling module; a progress control module; a cost quantification management module; and a risk management module. According to the scheme, through a micro-service architecture and a distributed data storage technology, multi-project full-period collaborative management is realized. And each module can operate and expand independently, so that the flexibility and expandability of the system are remarkably improved. The distributed data storage can ensure the real-time synchronization and quick access of the data even under the condition that a plurality of projects run in parallel. By constructing the incidence relation matrix, the system can clearly present the relation between each task and the resource of the project, and solid data support is provided for subsequent resource allocation and task management. The optimal configuration of resources and the smooth promotion of projects are realized, and the management efficiency of enterprises and the project success rate are remarkably improved.

The invention discloses a large model corpus data interaction system and method based on a trusted execution environment, a terminal and a medium. The system comprises a data provider, a data demander and a trusted data space service platform. And the data provider is used for encrypting the corpus data, and performing preprocessing, directory sorting and packaging on the corpus data in the data provider connector to obtain the data capsule. And the data demander is used for submitting a calculation task in the data demander connector and obtaining corpus data of the data capsule for calculation after authorization. And the trusted data space service platform is used for storing the encrypted corpus data, controlling access of a data user, scheduling a calculation task, managing a key and generating an audit log. Through trusted execution environment hardware isolation and full-link encryption, it is ensured that corpus data only exists in a plaintext form in the trusted execution environment in the full life cycle from access to destruction, and unauthorized access and leakage are completely eradicated.

The invention relates to the technical field of industrial vehicle intelligence, in particular to an intelligent flexible forklift system based on multi-mode sensing and self-adaptive path planning. The intelligent forklift comprises an intelligent forklift body, a vehicle-mounted integrated control system, a multi-mode sensing and navigation module, a self-adaptive decision-making and path planning module, a cooperative communication and task management module and a cloud scheduling and management platform, and the intelligent forklift body is provided with a pallet fork mechanism with a 3D camera and a force sensor; the vehicle-mounted integrated control system serves as a core to achieve multi-module data interaction and instruction output, the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion, the self-adaptive decision-making and path planning module completes path planning, obstacle avoidance and pre-verification, the cooperative communication and task management module achieves multi-vehicle cooperation and task disassembly, and the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion. And the cloud scheduling and management platform realizes global monitoring and management. According to the method, the adaptability to complex scenes and the operation safety are improved, and accurate forking and flexible path planning are realized.

The invention discloses a task disassembling method based on a large model, and aims to automatically disassemble requirements input by a user into a series of independent and accurate minimum sub-tasks so as to support high efficiency and intelligence of task execution. Different from a traditional natural language processing method, the method adopts a large model technology to intelligently analyze and optimize input demand content, so that task description is clearer, and operability is achieved. The method comprises the following steps: firstly, deeply analyzing and adjusting a demand by utilizing the understanding ability of a large model to make the demand accord with a logic structure of task execution; then required core steps and logic levels are extracted through a multi-level task decomposition algorithm, and clear sub-task chains are generated through step-by-step decomposition; by means of the adaptability and dynamic reasoning ability of the large model technology, the decomposition efficiency and accuracy of complex tasks are remarkably improved, the burden of a user in the aspects of task planning and execution is relieved, and efficient and automatic development of intelligent task management is promoted.

The invention provides a collaborative data migration scheduling method based on topology awareness and deep reinforcement learning, and the method comprises the steps: initializing system configuration, constructing a weighted graph comprising a source server, a working machine, a target storage and network equipment, carrying out the embedded processing of a graph structure through a graph neural network, and generating a node and a path representation vector; in combination with source data, a working machine and a target storage state, constructing a system state vector, inputting the system state vector into a deep reinforcement learning agent for decision making, and outputting a composite action including data block selection, working machine selection, path indexing and migration rate suggestion; the network control module converts path information into flow rules, the flow rules are issued and executed through the software defined network controller, the task management module continuously monitors the migration state and processes failed data blocks, and the integrity and non-repeatability of migration tasks are ensured. According to the method, the defects of an existing data migration system in the aspects of task allocation, path selection, resource control and dynamic adaptability can be overcome.

The invention discloses an iRCS robot cluster scheduling management system, and relates to the technical field of the Internet of Things, and the system comprises a task distribution module which is used for distributing a specific task for each robot according to a task demand and robot capability; the communication coordination module is used for establishing a stable data transmission channel; the path planning module is used for generating an optimal motion path of the robot; the state monitoring module is used for acquiring and analyzing the running state and the task execution progress of the robot; the resource scheduling module is used for dynamically distributing computing resources, energy resources and task priorities in the system; the fault processing module is used for detecting the abnormal state of the robot and triggering a corresponding repairing or replacing strategy; the data storage module is used for storing task related data, robot operation logs and system operation records; and the user interaction module is used for providing visual display of a task management interface and a system running state for a user. The method is suitable for multi-field scenes, and shows remarkable technical advantages and wide application value.

The invention provides a tire crane automatic control system and method based on embedded edge calculation, and relates to the technical field of port automation, the tire crane automatic control system comprises a sensing and detecting layer used for collecting motion states and operation environment information of a cart, a trolley and a lifting appliance of a tire crane in real time; the core control layer is used for processing sensor data in real time and generating a control instruction; the action execution layer comprises a PLC gateway, a main control PLC and a driving motor, the PLC gateway communicates with the core control layer and is used for receiving a control instruction, and the main control PLC drives the motor to execute corresponding actions of a cart, a trolley and a lifting appliance; and the task management layer comprises an intelligent remote control console, is in communication connection with the core control layer, and is used for issuing the operation task instruction to the core control layer and receiving the system state data from the core control layer. Unmanned operation of the tire crane can be achieved, and a complete technical base is provided for port automation.

The invention discloses an electric power field operation management system, which relates to the technical field of electric power and comprises the following process modules: S1, planning; s1-1, an operation plan and task management module; s1-2, a power industry core combined scale block; s2, preparing; s2-1, a personnel and qualification management module; s2-2, a tool and material management module; s2-3, a risk assessment and scheme management module; s3, executing; s3-1, a field execution and monitoring module; s3-2, an emergency processing module; s4, checking and accepting; s4-1, an acceptance check and filing module; s4-2, a performance and data analysis module; s4-3, a system management module; and S5, summarizing. According to the electric power field operation management system, through systematicness and integrity of emergency plan management, a process covers full life cycle management of an emergency plan from formulation, auditing, issuing to updating, and meanwhile, links such as emergency response starting, disposal, resource management, information communication and recovery summarization are included; and a complete closed loop of prevention, preparation, response and recovery is formed.

A task management system detects condition(s) are satisfied with respect to task record(s), and triggers action(s) at least in part by substituting information from the task record(s) into placeholder(s) of user-modifiable template(s) to prompt large language model(s) for generating narrative(s) about how the condition(s) were satisfied. The triggered action(s) retrieve information from identified task record(s), substitute value(s) from the record(s) into the placeholder(s) of the user-modifiable template(s), and trigger(s) call(s) to large language model(s) to generate task completion narrative(s) or other narrative(s) for the task record(s). The narrative(s) may be stored in narrative field(s) of the record(s) for use in displaying the narrative(s) on a user interface, email notification, or other message.

The invention discloses a LoRaWAN-based mining equipment intelligent management system and method, and relates to the technical field of equipment intelligent management, and the system comprises a transport vehicle data collection module, a transport vehicle data processing module, a health index and task completion degree analysis module, a matching degree analysis module, a priority analysis module, and a transport vehicle task management module. The method comprises the following steps: collecting operation data, health data and task historical data of a transport vehicle, carrying out preprocessing and feature extraction on the data, generating standardized feature data, calculating a health index and task completion degree of the transport vehicle, establishing a matching degree model to analyze the matching degree of the transport vehicle to a target task, generating a priority, and according to a priority score, determining the task completion degree of the transport vehicle. According to the method, the vehicle task completion capability is clearly reflected through multi-dimensional data fusion and intelligent model construction, and the efficiency loss caused by blind scheduling is effectively reduced.

The embodiment of the invention relates to the field of artificial intelligence, and discloses an agent collaborative execution system based on a task dialogue link, and electronic equipment. An agent management station; the task scheduling and execution module is used for generating a corresponding dialogue node when task scheduling is triggered each time in the process of executing the periodic task by the intelligent agent, and a plurality of dialogue nodes corresponding to the periodic task form a dialogue link according to a time sequence; the agent access and collaboration interface module is used for realizing interaction between the agent collaboration execution system and the agent; the agent workbench comprises a task management and execution viewing module which is used for responding to the periodic task execution viewing request and displaying the execution process of the periodic task in the form of a dialogue link. A dialogue link is formed by orderly connecting dialogue nodes, so that the execution process of the periodic task has a uniform and displayable data organization form, and backtracking, auditing and multiplexing of the execution process are facilitated.

Techniques are described for enabling replication-aware resource management and task management in a cloud infrastructure for cross-region replication. In some embodiments, each replication job is associated with a set of replication-related information. In certain embodiments, the replication-aware resource management allocates resources, using a combination of various resource allocation schemes, to a fleet of replicators to allow the fleet to select replication jobs in a job queue, and perform resource scaling based on monitored performance metrics reported by the fleet. In some embodiments, the replication-aware task management enables replication job selection based on the set of replication-related information to optimize the performance of all cross-region replications in the region.

The invention discloses a method and device for improving printer task management efficiency and a storage medium, and relates to the technical field of printer task management.The method comprises the steps that firstly, printing task parameters and printer real-time data are obtained, and task priorities are calculated through a task priority evaluation formula; dynamically allocating hardware and software resources according to the priority, the real-time data and a rule containing a time slice allocation formula; the states of tasks and equipment are monitored in real time, and an exception handling formula and a conflict coordination formula are used for scheduling when exceptions or conflicts occur; and finally, remaining time is calculated through a printing progress formula and a time prediction algorithm and is fed back to an interactive interface, equipment comprises a data acquisition module, a priority evaluation module and the like, a data storage and model optimization module is further arranged to optimize formula parameters, and the scheme improves task management efficiency and prediction accuracy and optimizes user experience.

The invention discloses a multi-intelligent-vehicle material collaborative distribution method and system, and the method comprises the steps: enabling task management equipment to respond to an emergency task distribution request, obtaining a distribution task set and task information based on the emergency task distribution request, and transmitting the task information to material management equipment and all intelligent vehicles, the method comprises the steps of constructing a task matching graph based on material configuration information, task information and a task matching degree, obtaining to-be-distributed task nodes and available vehicle nodes in the task matching graph, performing node splitting on the to-be-distributed task nodes to obtain a plurality of sub-task nodes, constructing a task matching matrix based on the sub-task nodes and the available vehicle nodes, and performing task matching on the sub-task nodes and the available vehicle nodes. And determining an emergency task distribution strategy based on the task matching matrix, and sending the emergency task distribution strategy to each intelligent vehicle, so that each intelligent vehicle carries out emergency material distribution based on the emergency task distribution strategy, thereby realizing accurate distribution of tasks, improving cooperative distribution capability of the intelligent vehicles, and ensuring timeliness of material distribution.

In the data acquisition system, a simulation login service module receives a token refreshing request of a task management center module, simulates user login operation, obtains an effective login token and provides identity authentication for data acquisition; the task management center module initiates a token refreshing request, configures acquisition task parameters, pushes the acquisition task parameters to the task scheduling center module, monitors results of the acquisition tasks and optimizes the acquisition task parameters; the task scheduling center module receives the task data pushed by the task management center module, distributes acquisition tasks to an executor based on load balancing and a priority strategy, monitors the state of the executor for executing the acquisition tasks and records task scheduling information; the executor and the acquisition script module execute an acquisition task, and when an effective token is obtained for identity authentication, the acquired data are analyzed according to rules and packaged into structured result data; and the data storage service module receives result data of the actuator, and the result data is buffered and written into a database to realize data persistence.

The invention provides an online car-hailing driver qualification dynamic verification system and method based on multi-source information fusion. Comprising a multi-source information acquisition module used for receiving related data submitted by a driver end and performing consistency verification by using a person-certificate consistency detection algorithm to generate a first verification result; the data consistency comparison module is used for carrying out cross-certificate comparison and generating a second verification result; the automatic auditing and decision-making module is used for outputting a driver qualification auditing conclusion and a conclusion identifier according to a preset city auditing rule base and the second verification result; the training task management module is used for matching and distributing a training course task for the target driver account according to the conclusion identifier, and receiving a training completion state; and the vehicle authority control module is used for opening or keeping limitation on the order receiving authority of the target driver account according to the conclusion identifier and the training completion state, and dynamically adjusting the order receiving authority. According to the invention, rapid auditing, automatic training distribution, data linkage and real-time dynamic compliance management and control can be realized.

The invention relates to the technical field of intelligent testing, and discloses a software test task management method and device based on an intrusive instruction knowledge base, equipment and a medium, and the method comprises the steps: analyzing an obtained demand card based on a large model technology, analyzing a semantic role of a vocabulary in the demand card through a semantic role labeling technology, and obtaining a semantic role of the vocabulary in the demand card; extracting test requirements and test points of semantic roles of vocabularies in the requirement cards; querying test information matched with the test requirements and the test points in a knowledge base of the software project, and determining the test information with the highest matching degree as target test information; in combination with the demand card and the target test information, automatically generating a test scheme of the current version based on a large model technology; and according to the generated test scheme. The method can be applied to the development of business systems such as financial science and technology, medical health, old-age care and the like, and the efficiency of the test work is remarkably improved by automatically analyzing the demand card, generating the test scheme and arranging the test task.

The invention relates to the technical field of cleaning robot task management, and discloses an LLM-based cleaning robot task management method, which comprises the steps of analyzing a user voice instruction in real time through an LLM-driven natural language interaction engine, generating a structured task description, dynamically optimizing task priorities and resource allocation in combination with multi-modal perception data, and carrying out task management on the user voice instruction. The method achieves the autonomous decision-making of environment abnormity, supports the real-time change of tasks and the closed-loop learning of historical data in the cleaning process, and forms a personalized service closed-loop mechanism. According to the method, the man-machine interaction naturalness, the task execution efficiency and the system self-adaptive capacity are remarkably improved, and the technical problems that in the traditional technology, semantic understanding is lacked, environmental perception is insufficient, resource distribution is rigid and personalized services are lacked are solved.

The invention discloses a disaster site air-ground integrated system based on communication monitoring management. The disaster site air-ground integrated system comprises a digital site commanding and dispatching system, a converged communication service system and a network management system, the digital on-site commanding and dispatching system is used for carrying out situation awareness and cooperative combat through on-site rescue service whole-process management including emergency response, element management, intelligence analysis, task management, situation display, commanding and dispatching, a rescue assistant, disaster situation archives, basic support and background management. The converged communication service system is used for accessing various heterogeneous communication systems, so that the networking capability of transverse interconnection and intercommunication, longitudinal multi-layer grading and management of decentralized domains is realized; the network management system collects, gathers, fuses and analyzes state information of each communication device in the emergency tactical internet, configures and adjusts operation parameters of the communication devices according to needs, and realizes operation monitoring, situation presentation, parameter adjustment and link on-demand construction of the communication network of the emergency rescue digital battlefield.

The invention discloses a vehicle authentication detection task analysis system, and the system comprises a task management center which builds a mapping relation among contract entries, customer detection standards and an internal standard library; the sample car parameter intelligent processing unit is used for capturing data through an API (Application Program Interface) of the Ministry of Communications, verifying the integrity and performing deviation early warning on additional recorded parameters; the sighting judgment core engine is used for analyzing the multi-dimensional similarity between the sample vehicle and historical data based on a random forest algorithm; and the abnormal process control gateway drives the dynamic approval workflow and is connected with a short message platform to push an encrypted link and an overtime alarm. The system integration message middleware synchronizes CSS platform data in real time, the report generation module dynamically fills a detection result through an XML template and calls RESTful API signature, and batch asynchronous processing of historical reports and compression downloading of digital watermarks are supported. According to the invention, the problems of low task analysis efficiency, flow splitting and difficulty in compliance management in vehicle authentication detection are solved.