1358 results about "Task management" patented technology

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

The present application discloses a task management method, system, device, and medium based on a graph structure, and relates to the field of computer technology. The method includes: decomposing a preset target task into at least two subtasks, creating a task node for each of the subtasks, and mapping the task node to a node set of a preset directed acyclic graph to obtain a task graph; updating the edge set of the task graph according to the dependency relationship between each of the task nodes to obtain a task dependency graph; topologically sorting all the task nodes in the task dependency graph, and determining the execution order corresponding to the independent task nodes and the dependent task nodes according to the topological sorting result; if there are multiple independent task nodes, executing the subtasks corresponding to the multiple independent task nodes in parallel, and executing all the dependent subtasks in sequence according to the execution order. The present application improves the processing efficiency of the task processing framework for complex tasks.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

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 provides an intelligent project management system based on supervision integration, and the system employs a cloud platform for deployment, and comprises a project management module which is used for task management, progress tracking and document sharing, so as to achieve the planning, starting, execution, monitoring and ending of a whole life cycle of a project; the data acquisition and integration module is used for collecting project data in real time and performing integration processing; the risk assessment and early warning module is used for performing risk assessment on the project data and giving out early warning when the risk exceeds a preset threshold value; the intelligent decision support module is used for providing decision suggestions for a project manager based on the data analysis result; and the visual display module is used for visually displaying information such as a project state, progress, cost and quality in forms such as charts and reports. According to the system, through an integrated management platform and an intelligent analysis tool, real-time monitoring, risk assessment, resource optimization and decision support of the full life cycle of a project are realized, so that the project management efficiency is improved, and the project risk is reduced.

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 belongs to the field of power grid control, particularly relates to an LLM Agent-based power system operation mode adaptive calculation method, and aims to solve the problem that load flow calculation of a power system operation mode is relatively difficult in the prior art. The method comprises the following steps: acquiring a task demand of a power system; the task demand is input into an LLM model, the LLM model splits the task demand into a plurality of sub-tasks, and Agents used for executing the sub-tasks are determined; determining the output of the LLM model, and allocating a plurality of subtasks to a load flow calculation Agent and a parameter adjustment Agent by calling a task management Agent; and calling the load flow calculation Agent and the parameter adjustment Agent, and executing each subtask allocated by the task management Agent. According to the method, the adaptability of the load flow calculation and adjustment strategy is improved by utilizing the LLM model, and meanwhile, the accuracy of load flow calculation is guaranteed through multi-Agent calling, so that the load flow calculation and adjustment effect on the power system in the prior art is improved.

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.

The invention relates to the technical field of database migration verification, in particular to a cross-data-platform database migration verification method and system applied to autonomous controllable transformation. The method comprises the following steps: configuring a source database and a target database in a data source management module; newly establishing a comparison task in the task management module; a comparison database is connected through a dynamic data source assembly, different databases are connected through JDBC, and comparison verification is executed after database connection parameters are configured; database comparison is carried out in the task management module, wherein the database comparison comprises data volume verification and data consistency verification; a comparison report is generated and stored in a built-in mode. The problem of data integrity in the cross-platform data migration process is solved, the system fully considers the difference between the MySQL database and the local database in character set, function and technical characteristics, data verification and comparison work after migration of the MySQL database source and various local target databases is supported, consistency verification before and after data migration is achieved, and the verification efficiency is improved. And the risk of data loss caused by data migration is avoided.

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.

The invention relates to the technical field of network optimization, in particular to a cloud-based computer network resource management system, which comprises a network bandwidth demand analysis module, a cloud platform computer network task management module, a cloud platform computer network task management module, a cloud platform computer network task management module, a cloud platform computer network task management module, a cloud platform computer network task management module and a cloud platform computer network task management module, and according to the peak value change and the distribution condition of the task bandwidth demand, marking the bandwidth demand area exceeding a preset frequency threshold value, and generating network bandwidth distribution characteristic data. According to the invention, a resource management process taking data driving, characteristic analysis and real-time prediction as cores is formed through dynamic distribution and optimization management of network resources. In the bandwidth demand analysis, the over-limit region is marked through the peak change of the time sequence data, the accuracy of resource distribution is optimized, and the problem of bandwidth waste or resource overload is effectively avoided.

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.

A task management method includes monitoring whether a key scenario that affects user experience is triggered; when the key scenario is triggered, monitoring, in user mode, a high-load task generated by an application; determining, in the user mode, a status of the high-load task, and transferring information associated with the high-load task to kernel mode; monitoring, in the kernel mode, a system global status of an electronic device, determining an execution policy of the high-load task based on the information associated with the high-load task and / or the system global status, and returning the execution policy to the user mode; and determining, in the user mode, an execution decision of the high-load task based on the status of the high-load task and / or the execution policy.

An electronic method for contextual task creation during a screen sharing session includes collecting shared screen data, searching the shared screen data for a potential task, and confirming the potential task with a user to generate a confirmed task. The electronic method can further include sending the confirmed task to a task management engine where the task can be collaboratively reviewed for progress and accuracy. The electronic method can be performed by a contextual task creation program or system that utilizes optical character recognition techniques for converting an image of text into a machine-readable text format, and natural language processing techniques for identifying keywords, entities, and actionable items within the text to provide possible task suggestions to a user.

The method comprises the following steps: firstly, acquiring environment information, capturing surrounding information at the frequency of not less than 20Hz through a laser radar and other sensors, constructing a real-time updated three-dimensional high-precision environment map by fusing data through a deep learning algorithm, analyzing task requirements, receiving instructions or determining operation types and evaluating task priorities according to preset conditions, and performing real-time task management on the environment map. Locking a key area, pre-estimating the operation duration and generating a preliminary plan, finally constructing a path planning model, customizing a reward function and matching a weight based on a reinforcement learning algorithm and setting the driving efficiency as an optimization target, and training by using massive samples, so that the model masters an optimal path strategy. According to the invention, 24-hour efficient operation, precise environment sensing, rapid path planning, energy consumption reduction, cleaning quality improvement, labor cost reduction and urban environmental sanitation intelligence assistance can be effectively realized for the sanitation vehicle according to intelligent planning.

The invention discloses an unmanned aerial vehicle multi-target task scheduling method based on a distributed cooperative game, and the method comprises the following steps: S1, defining the state information of an unmanned aerial vehicle, and generating basic data; s2, constructing a task competition game model; s3, individual task optimization is carried out, and an improved artificial protozoa optimization algorithm is adopted; s4, performing global task scheduling optimization by adopting an improved egret group optimization algorithm; s5, performing adaptive adjustment in a task execution process; and S6, completing the task scheduling method. According to the method, the resource utilization rate and global task income of the unmanned aerial vehicle are remarkably improved, and the method can be widely applied to unmanned aerial vehicle cluster task management scenes of multiple industries such as logistics distribution, disaster rescue, environment monitoring and military reconnaissance.

An AI and machine learning-based system for automating employee management and work information processing within an organizational structure, the system comprising: a central processing module configured to aggregate and pre-process data from multiple sources, including attendance records, performance logs, task management systems, and communication channels, with the central processing module normalizing and filtering the data to ensure consistency and accuracy in real-time analysis; a machine learning-based analysis unit operatively connected to the central processing module, the analysis unit comprising a natural language processing (NLP) sub-module, a sentiment analysis sub-module, and a pattern recognition sub-module, and configured to extract, analyze, and interpret both structured and unstructured data for insights into employee behavior and performance assessment, and further configured to adapt and refine models based on continuous data inputs from the central processing module; a predictive task scheduling component comprising a reinforcement learning-based model that leverages employee skill profiles, historical task completion rates, and workload patterns to dynamically distribute tasks, with the predictive task scheduling component further configured to self-optimise based on real-time feedback regarding task completion efficiency, priority changes, and schedule adjustments within the organization; a performance tracking unit configured to receive inputs from the central processing module and the machine learning-based analytics unit, wherein the performance tracking unit continuously monitors employee performance, challenges, and areas for improvement and stores these insights in individualized, encrypted employee profiles that can be accessed in real time, thus supporting data-driven performance reviews and improvement plans; an input / output interface for user interaction, the interface providing managers with interactive access to review employee metrics, task assignments, and performance feedback, and enabling employees to securely view individual performance metrics, feedback, and task details, the interface further being configured with user-level access controls based on historiographical organizational roles; a secure data storage component configured to securely store employee data, task logs, and performance metrics, where the data storage component supports both local and cloud-based storage solutions, uses encryption protocols for secure data retention, and provides access control mechanisms for authorized retrieval of stored data; a communication interface module operatively connected to the central processing module and configured with multi-protocol communication capabilities, including Wi-Fi, Ethernet, and Bluetooth, wherein the communication interface module facilitates real-time data synchronization between remote and local devices and is integrated with an AI-based anomaly detection system that flags irregularities or potential security threats in data transmissions; and an adaptive learning module operatively connected to the machine learning-based analytics unit and configured to continuously retrain machine learning models based on real-time data inputs, where the adaptive learning module uses reinforcement learning and unsupervised learning techniques to refine task recommendations, adjust performance metrics, and optimize task assignment rules based on the evolving needs of the organization.

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

The invention relates to the technical field of task management, and discloses a laboratory automatic data processing and management method, system, equipment and medium, and the method comprises the steps: obtaining the equipment information and resource information of a laboratory; and establishing an equipment simulation model of the laboratory. A task instruction is collected, and the task instruction is disassembled into atomic tasks. And arranging the execution sequence of the atomic tasks according to the inherent sequence constraint of the task instruction, the maximum resource utilization rate, the minimum scheduling frequency and the comprehensive analysis result of the optimal working time of the equipment. And performing task execution by using the execution sequence of the atomic tasks. Idle and faults of equipment are effectively avoided, resource waste is reduced, and automation and accuracy of experimental operation are improved. Besides, by analyzing the working time of the equipment and the validity period of the consumables, the energy consumption and the operation cost can be reduced while the smooth proceeding of the experiment is ensured, and the automatic management level of the laboratory is further improved.

The invention discloses a task dynamic decomposition management system and method based on an AI framework, and relates to the technical field of task management, and the method comprises the steps: dividing task parameters into a plurality of dimension features, carrying out the fusion of the multi-dimension features through a space-time attention network, and constructing a three-dimensional feature tensor; recursively decomposing the three-dimensional feature tensor through an adaptive secondary screening algorithm, and outputting a decoupled modal set; predicting a resource load and quantifying a modal demand level based on a long short-term memory network, constructing a bipartite graph matching model, and adaptively allocating resource instances through dynamic matching; time performance and resource consumption data of task execution are collected in real time, a modal deviation value is calculated, if the modal deviation value exceeds a deviation threshold value, dynamic re-decomposition is triggered, resources are reallocated based on an updated modal set, and dynamic capture, efficient decomposition and accurate resource allocation of task features are achieved; therefore, the task execution efficiency and the resource utilization rate are improved.

The embodiment of the invention provides a data transmission method and device and a storage medium. In the embodiment of the invention, a new direct memory access interface is provided, in implementation, an agent module is provided for network card equipment, and a task management queue and a memory management queue are provided between the agent module and an application as a direct memory access channel between the agent module and the application; in the data transmission process of the two ends, on one hand, the two ends can adopt a bypass kernel and zero copy technology to improve the data transmission efficiency based on the respective task management queues, and on the other hand, the two ends actively implement memory management for data storage based on the respective memory management queues, and interaction of memory management with the opposite end is not needed; the interactive operation of the two ends in the data transmission process can be reduced, and the transmission delay can be further reduced on the basis of achieving direct memory access.

The invention is suitable for the technical field of robots, and provides a task management method and system, a robot and a computer program product, and the method comprises the steps: obtaining task information of a to-be-executed task; the task information is input into the large model for task analysis, and a conflict prediction result is obtained; starting an adaptive algorithm to generate a conflict solution according to a conflict prediction result and a robot state; adjusting a task execution plan according to the conflict solution, executing the task according to the adjusted task execution plan, utilizing a large model to deeply analyze the task, predicting a possible conflict task, and dynamically generating an optimal conflict solution of the conflict task through an adaptive algorithm. The task is executed based on the optimized task execution sequence and the resource allocation scheme in the conflict solution, so that the occurrence of task conflicts can be effectively reduced, the task execution efficiency is improved, the resource waste and execution delay caused by the task conflicts are reduced, and the reliability and stability of a task scheduling system are improved.

Provided are an information processing apparatus, an information processing method, and a program that enable task design using a chatbot application via natural language and a generative AI that collaborates with storage. **Solution**: An information processing apparatus that performs task design using a chatbot application via natural language and a generative AI, the apparatus including: a storage provided outside or inside the generative AI to collaborate with the generative AI for the task design; and a predetermined task management system in which task information designed by the task design is stored.

Systems and methods for orchestrating events in distributed DevOps apparatus leveraging generative AI are disclosed to automate and streamline software development and deployment in distributed DevOps environments using Generative Adversarial Networks (GANs) and other AI techniques. The method involves interpreting UML diagrams, design documents, or the like with generative AI and computer vision to create DevOps tasks, integrating with various DevOps tools for task management, and deploying generated rules for automated event execution. Metadata is generated and processed in order to facilitate AI analysis. The systems and methods reduce manual intervention, increase efficiency, and improve accuracy, scalability, security, and compliance in DevOps workflows.