2051 results about "Task management" patented technology

The invention provides an integrated logistics management system for cargo transportation. The integrated logistics management system comprises a multi-mode collaborative perception module, a semantic reasoning module, a scene adaptation decision module, an intelligent execution module and a logistics trust chain module. The multi-mode collaborative sensing module collects logistics full-scene state data through a sensor network; the semantic reasoning module is used for constructing a knowledge graph based on a graph attention network and realizing unified modeling of multi-source information; the scene adaptation decision module is combined with a pulse coupled neural network (PCNN) and a swarm intelligence collaboration mechanism to complete routing optimization; the intelligent execution module adopts a hierarchical task management and dynamic adjustment mechanism, and adjusts a task scheme according to execution abnormity and credit evaluation; the logistics trust chain module constructs a logistics trust system through a hierarchical consensus mechanism, a credit scoring model and a trust propagation and early warning mechanism, and realizes risk prevention and control of the whole process. The system solves the technical problems of a traditional logistics system in the aspects of data integration, dynamic supervision, decision support, collaborative scheduling, safety traceability and the like.

The invention relates to the technical field of computers, and discloses a distributed task scheduling method and system for heterogeneous tasks based on cloud native, and a medium, and the method comprises the steps: a user submits a task through a task management module, and the task carries description information; the system obtains hardware states and task load information of all nodes in a cluster in real time through a resource monitoring module, a scheduling optimization module analyzes task characteristics according to task types and selects an optimal scheduling strategy in combination with a current resource state, and a task distribution module distributes tasks to optimal computing nodes and starts task execution. In the task execution process, the system continuously monitors the task state, and dynamically adjusts resource allocation or executes preemptive scheduling when necessary. According to the method, efficient management and scheduling of heterogeneous computing resources are realized by optimizing a scheduling strategy and a resource allocation mechanism, and the overall performance and the resource utilization rate of the system are improved.

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.

An innovative system for integrating generative artificial intelligence (Al) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced Al techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an Al-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface. The system also features a cross-departmental health monitoring and notification mechanism, alongside an intent¬ based interaction system. This solution enhances organizational efficiency, improves decision¬ making, and provides unprecedented levels of automation and insight in managing complex SaaS ecosystems.

The invention belongs to the technical field of computing power scheduling, and particularly relates to a computing power scheduling method in a distributed computing environment. According to the task scheduling method and system, the task needing to be processed at present is obtained through the task management system, the task is divided into the real-time task set, the priority task set and the common task set, and the related tasks are scheduled by adopting the corresponding scheduling strategy based on the set corresponding to the current task and the priority in the set. Resource scheduling and allocation in a distributed computing environment are realized; according to the method, through the steps of task classification, real-time resource monitoring, resource demand estimation, priority ranking, reasonable scheduling strategy, load calculation, resource migration and the like, the technical problem of computing resource scheduling and allocation in a distributed environment is effectively solved, timely processing of high priority is ensured, the system load is balanced, and the system performance is improved. And efficient utilization of resources and stable operation of the system are realized, so that the performance and reliability of the whole distributed computing environment are improved.

The invention relates to the technical field of stacking, in particular to a stacking-based distributed scheduling system and a path planning method thereof, comprising a distributed scheduling architecture, an autonomous task allocation module, a cooperative work module and a load balancing module, the distributed scheduling architecture comprises a task management layer capable of dynamically adjusting a task allocation strategy according to real-time order data and a scheduling layer composed of a plurality of scheduling nodes, and each scheduling node can monitor the state and task progress of the stacker in real time and dynamically adjust task allocation and path planning according to a monitoring result. The stackers execute according to task allocation and path planning instructions of a scheduling layer, the autonomous task allocation module configured to each stacker can independently decide task paths and execution sequences according to task priorities, stacker positions and current states, so that the stackers can quickly respond to task changes, and dependence on a central scheduling system is reduced.

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 field of resource scheduling, in particular to a cloud-side collaborative computing power dynamic distribution system, which comprises a task management module, a cloud-side collaborative computing power dynamic distribution module, a cloud-side collaborative computing power dynamic distribution module and a cloud-side collaborative computing power dynamic distribution module, the edge information management module is used for collecting information of edge equipment at an edge end and collecting calculation basic information and resource utilization rate of the edge equipment by issuing a calculation power test standard task; the computing power collaboration module is used for acquiring data of the edge information management module and distributing the tasks to the corresponding edge ends according to the computing power requirements of the tasks and the residual available computing power of the edge devices; the calculation feedback module is used for acquiring the isomerism and task calculation type of the edge equipment through the cloud edge communication module; the parameter dynamic optimization module sets a weight coefficient according to the heterogeneity of the edge end and the task calculation type, and the cloud edge collaborative computing power dynamic distribution system flexibly, efficiently and reliably carries out data distribution and optimizes a cloud edge collaborative scheduling distribution strategy.

The invention discloses a pipe production task management method and system, and belongs to the technical field of pipe production, and in the pipe production task management method and system, factors such as order priority, equipment state and raw material supply are analyzed through an intelligent algorithm, and the production schedule is automatically optimized, so that production resources are utilized more reasonably and efficiently. Resource waste can be avoided, idle time in the production process is shortened, and therefore production efficiency and the productivity utilization rate are improved. The urgency degree and the delivery date of the order are analyzed through an intelligent algorithm, and the enterprise can preferentially arrange the production of the urgent order to ensure that the customer demand is met in time. Meanwhile, production tasks are reasonably distributed according to the running state of the equipment and the raw material supply condition, and the use efficiency of the equipment and the utilization rate of raw materials are improved. The dynamic adjustment mechanism monitors the production progress in real time and automatically adjusts the production plan according to the actual situation, so that the system can rapidly adjust the production plan and distribute the tasks to other devices, and continuity and on-time delivery of the production tasks are ensured.

The invention discloses a system for completing sequence control through intelligent one-key operation, relates to the technical field of intelligent management, and automatically completes operation of a circuit breaker, an isolation switch and a grounding switch through a sequence control module according to a five-prevention rule and a real-time state; the state confirmation module doubly verifies the equipment state before and after operation, ensures that the logic is correct and deals with the exception in time; the remote control module supports omnibearing remote control of primary equipment and secondary equipment; the video linkage module assists quick response through real-time monitoring, video archiving and abnormity alarm linkage; the intelligent anti-misoperation module dynamically checks the safety of the operation instruction, locks an error instruction and provides a correction suggestion; and the unified task management module integrates complex tasks into a one-key task list, so that the efficient and coherent operation process is ensured, the operation efficiency and safety are remarkably improved, the human error risk is reduced, and a powerful guarantee is provided for the intellectualization and reliability of power equipment operation.

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 invention provides a satellite-borne computing center system, in particular to a satellite-borne computing center system with high performance and high computing power, which comprises a data interaction module, a task management module, an artificial intelligence AI module M1 and an artificial intelligence AI module M2. The data interaction module is an input and output interface for high-speed data processing; the task management module serves as a task scheduling center and distributes calculation tasks to the AI module; the artificial intelligence AI module M1 and the artificial intelligence AI module M2 adopt domestic and imported AI chips, and the artificial intelligence AI module can work in an RC mode and an EP mode, provides computing power guarantee for satellite-borne high-computing-power computing tasks, and has the characteristics of flexible expansion and agile deployment. All the modules are interconnected through a serial port management plane, a gigabit network control plane, a PCIE data plane and a power supply plane, and control and data interaction between processors are achieved.

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 provides a work plan and task management system, which belongs to the technical field of work plan tasks and comprises a portal layer, an application program interface gateway layer, a business service layer, a public service layer and a data layer. And the application program interface gateway layer realizes intelligent management of the interface through a self-adaptive division module, wherein the intelligent management comprises dependency analysis, granularity calculation, aggregation optimization and recombination execution. The business service layer focuses on work plans and task management, and realizes collaboration through flexible interface interaction. The public service layer provides basic support for user authentication, log recording, message queue, service registration, configuration management and the like. And the data layer adopts a diversified storage strategy, integrates a relational database, a cache database, a time sequence database and distributed file storage, and meets data processing requirements of different scenes. The problem that in the prior art, interface definition and calling relations between micro-services are difficult to manage in a self-adaptive mode is solved.

Disclosed herein is a system which comprises a computing device including a non-transitory computer-readable storage medium configured to store an application program; and a processor coupled to the non-transitory computer-readable storage medium and configured to control a plurality of modules to execute instructions of the application program for accessing a plurality of tasks of the system. The system further comprises a computing server system configured to: create different task queues, skill groups, and prioritizations to organize the plurality of tasks based at least upon an urgency, a complexity, or a department of each task of the plurality of the tasks, present information related to each of the plurality of tasks, queue and route the plurality of tasks, and obtain task completion information related to each of the plurality of tasks.

The invention relates to the technical field of cultivated land information management and systems, in particular to a cultivated land protection management system and method based on an informatization technology. A cultivated land protection management system based on an informatization technology comprises a storage database and a management system in communication connection with the storage database, and the management system is in communication connection with a GIS system and a mobile device through an API interface. The management system comprises a login module, a data management module, a space analysis module, a task management module, a cultivated land monitoring and early warning module and a user authority management module. The problems of low data management efficiency, insufficient informatization degree, low patrol work efficiency, lack of early warning mechanism, insufficient data sharing and authority management, lack of mobile application support and the like are solved, the efficiency and quality of cultivated land protection management are improved, and the method has important significance in protection and reasonable utilization of cultivated land resources.

The invention discloses an intelligent scheduling and transportation system for a port unmanned flat car, which belongs to the technical field of port unmanned transportation, and is technically characterized by comprising a cloud intelligent scheduling platform, an unmanned flat car transportation system and a car cloud collaboration module, the cloud intelligent scheduling platform comprises a vehicle cloud control system, an operation task management system, an automatic driving operation monitoring system, an intelligent network connection operation scheduling system and a port scheduling collaborative management system; the unmanned flat car transportation system comprises intelligent sensor hardware, global fusion situation awareness, multi-redundancy fusion positioning, high-precision motion control and anthropomorphic driving decision planning. And the vehicle cloud cooperation module comprises task instruction interaction and vehicle real-time state reporting, and has the advantages that through seamless butt joint with automatic container hoisting equipment, the whole-process unmanned operation of transportation and loading and unloading operation is realized, the port operation efficiency and the intelligent level are improved, and the intelligence and the high efficiency of the port unmanned transportation system are greatly improved.