131 results about "Task manager" patented technology

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.

A method for managing a data stream processing pipeline includes: monitoring, by an orchestrator, data stream ingestion of a streaming storage system (SSS) to obtain data stream metrics; analyzing, by the orchestrator, the data stream metrics based on a user-defined scaling policy; making, based on the analyzing and by the orchestrator, a first determination that task manager scaling is required; making, by the orchestrator, a second determination that the data stream ingestion is increased, in which the second determination indicates an increase in a number of parallel stream segments associated with a data stream, in which a segment store hosted by the SSS manages the parallel stream segments; and to increase a stream processing system's compute capability and by the orchestrator, initiating scaling of a number of the plurality of task managers to support the increase in the number of the parallel stream segments.

An automation system controls an automated production process of an industrial plant. The industrial plant includes a plurality of equipment for performing the production process. The automation system includes an augmented reality system including augmented reality glasses, executable program logic and a database system including a task manager configured to store a list of processes associated with respective equipment, where each process includes one or more tasks to be performed with respect to the equipment. The executable program logic is configured to receive from the database system the list of processes for display to a user of the AR glasses, receive a selection of a displayed process assigned to the user, send the selected process to the database system, and receive from the database system additional data corresponding to the selected process.

A task manager assigns configurations to task nodes, where the task nodes perform tasks in accordance with the configurations assigned to them. The task manager creates a record comprising a new configuration assigned to a task node, along with an identifier of that task node. The task manager adds these created records to a shared task configuration data structure. A task node accesses the shared task configuration data structure to look for new records with its identifier. The task node obtains a new record from the shared task configuration data structure, and analyzes the obtained new record to determine its assigned new configuration. The task node configures itself in accordance with the new configuration, without restarting the individual task node.

An integrated well construction system for constructing a well includes equipment controllers which control drilling equipment, and a process controller operatively connected to the equipment controllers. The process controller includes a drilling control module which defines an operational drilling scheme and a task manager module. The operational drilling scheme has a first part to control the drilling equipment, a second part with manual tasks and / or automated tasks to be performed, and a relation between an execution of control of the drilling equipment via the first plan and of manual tasks and / or the automated tasks via the second plan. The task manager module has a user interface, a task refinement component which updates the second part, and a user interaction device which provides a manual addition of new manual tasks and / or new automated tasks, and a removal or amendment of at least one of the manual tasks and / or the automated tasks.

The invention relates to the technical field of artificial intelligence and edge computing, in particular to a hybrid expert model distributed computing system based on an edge computing technology. Comprising a central server, an edge device network, a data privacy protection module, an automatic model decomposition module, a performance feedback and adaptive learning module, a fault detection and self-healing module and a task priority scheduling module. The central server comprises a self-adaptive gating network, a task manager and a model manager; the self-adaptive gating network is used for receiving an input task, extracting task characteristics, intelligently identifying task data distribution and routing the task to the expert sub-model matched with the task according to an identification result; according to the method, the tasks are dynamically scheduled according to the computing power and the load condition of the edge device, the model updating and synchronization mechanism is imperfect, the consistency of the model in the distributed environment is ensured, the computing cost is remarkably reduced, and the dependence on special hardware facilities is reduced by utilizing idle edge device computing resources.

The present application relates to the technical field of computing power networks, in particular to a self-correcting computing power measurement method, system and apparatus. The method comprises: monitoring performance data of an application, analyzing per-transaction resource consumption metrics in the temporal dimension, determining trough periods of metric values as idle hours, and determining peak periods of the metric values as busy hours; randomly selecting one idle hour and one busy hour every day / per week, respectively triggering a measurement task manager to carry out a test, and outputting an idle period, a busy period, an idle hour measurement value and a busy hour measurement value of the system every day / per week; and if a difference of the per-transaction resource consumption metric values of the same period in two consecutive weeks is greater than a preset warning value, re-triggering the measurement task manager for carrying out a test, and outputting a new computing power measurement value. The present invention outputs a temporal-dimension computing power measurement result, comprising the idle period, the busy period, the idle hour measurement value and the busy hour measurement value of the system, thereby more accurately reflecting changes of the computing power along with time.

A method and system provide the ability to manage an orchard. Sensor data that represents a first state of the orchard is captured via one or more sensors. The sensor data is captured as the one or more sensors are traveling through the orchard. An almanac is maintained. The almanac provides a state library of sequential states of a representative orchard and a task library for one or more tasks to be performed to transition between the sequential states. A task manager queries the almanac to identify a first task of the one or more tasks and allocates the first task to one or more robots that perform the first task.

The current document is directed to an infrastructure-as-code (“IaC”) cloud-infrastructure-management service or system that allows users and upstream management systems to define and deploy infrastructure, such as virtual networks, virtual machines, load balancers, and connection topologies, within cloud-computing systems. The IaC cloud-infrastructure-management service or system includes a service frontend, a task manager, an event-processing component, and multiple Idem-service workers. The task manager manages execution of commands and requests received from the service frontend, using multiple queues, provides for prioritization of command-and-request execution by the multiple Idem-service workers, and provides for preemption of long-running executing commands and requests. The IaC cloud-infrastructure-management service or system enforces specified states of the cloud infrastructure using enforced-state identifiers and enforced-state versions supplied in state commands and enforce requests.

A control system includes a task manager that can determine capability requirements to perform a task on the target object. The task has an associated series of sub-tasks, with the sub-tasks having one or more capability requirements. The task manager selects and assigns a first sequence of sub-tasks to a first robotic machine that has a first set of capabilities and operates according to a first mode of operation. The task manager selects and assigns a second sequence of sub-tasks to a second robotic machine that has a second set of capabilities and operates according to a second mode of operation. The task manager selects the first robotic machine based on the first set of capabilities and the first mode of operation of the first robotic machine, and selects the second robotic machine based on the second set of capabilities and the second mode of operation.

The photomask plate transmission control system comprises a service-based photomask plate control module, a photomask plate transmission control module and a photomask plate transmission control module, the service manager is used for managing tasks required for executing user services and execution logic of the tasks, and the service state machine is used for acquiring the tasks to be executed from the service manager, calling a task execution interface of a photomask project module and receiving task execution results from the photomask project module; the task-based photomask project module comprises a task manager which is used for providing an action interface for the dynamic combination of the tasks of the photomask business module, and a task state machine which is used for acquiring an action to be executed from the task manager, calling the electrical equipment management module to execute the action and receiving an action execution result; and the electrical equipment management module is used for managing the electrical equipment executing the action. In this way, complex business decomposition is controlled through photomask plate transmission, a double-queue organization business model is adopted, and business issuing and response are rapidly achieved under the condition that other subsystems are not affected.

The invention discloses a robot dynamic task management method, which comprises the following steps: S1, a task manager of a scheduling management system receives a task, and places the task in a task pool to wait for distribution; s2, the task manager dynamically calculates the allocation priority of the tasks; s3, candidate robots capable of executing tasks are screened, and the moving cost of the robots for executing the tasks with the highest distribution priority is calculated; s4, distributing the task with the highest distribution priority to the robot with the lowest moving cost; s5, the task manager sets distributed tasks as running and undistributed to-be-run tasks to update waiting time in the task pool, and continues to dynamically calculate distribution priorities in the task pool, and S6, after the robot with the tasks obtains new tasks, the execution priorities of the tasks are calculated in combination with the initial task priorities and the compensation coefficients, and the tasks are distributed to the task pool. Reordering according to the execution priority; and S7, the robot executes the task according to the reordered task queue. Through multi-factor dynamic task allocation, the task allocation efficiency, the reasonability and the resource utilization rate are improved.

An operational planning system and method for optimal assignment of resources to the completion of tasks includes a combination of software modules and customizable plugins. A resource manager assesses known resources to determine which resources are assignable to a set of tasks to be completed, and a task manager determines which tasks are available for completion and prioritizes the valid tasks by resolving task dependencies. Customizable plugins determine cost factors for assigning each valid resource to each valid task, whether valid resources should continue their current task assignments and receive new assignments. Based on the assignment cost data, the customizable plugins ultimately determine an optimal assignment of valid resources to valid tasks (e.g., optimal in the sense of lowest cost and / or other desirable factors).

An innovative system for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced AI techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an AI-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface.

A system includes avionics line-replaceable units (LRUs); a mission manager including application programming interfaces (API); a processing device; and a memory storing one or more machine readable instructions causing the system to: communicatively couple the mission manager to external avionics services via a data gateway to receive updated mission conditions at the mission manager; register one of the avionics LRUs with one of the mission manager APIs; receive, at the mission manager, updated mission conditions from the external avionics services via the data gateway; provide one or more of the one or more updated mission conditions to the registered avionics LRU based on one or more of an updated mission plan and an unexpected update to the mission condition; and control one or more aircraft systems based on one or more of the updated mission plan and the unexpected update to the mission condition.

The invention discloses a system and a method for performing flash disk redundant array computing management based on a GPU (Graphics Processing Unit). The system comprises an application layer, a host control layer, a GPU computing layer, a storage layer and a monitoring and optimizing layer. An application layer receives an external request through an API, and a task manager decomposes a file into an executable task corresponding to RAID fragmentation data; a host control layer coordinates task distribution of a GPU cluster and an NVMe solid state disk array through a task scheduler, an NVMe driver manages storage equipment, and a GPU direct connection port supports direct data transmission of a calculation layer and a storage layer; a GPU cluster of the GPU calculation layer comprises a hierarchical thread pool, a self-adaptive fragmentation module, a parallel verification assembly line and a video memory management module which are used for respectively realizing thread management, dynamic fragmentation adjustment, RAID verification calculation and video memory distribution; the storage layer stores fragments and redundant data by using an NVMe array; and the monitoring and optimization layer realizes system state monitoring and scheduling optimization through performance monitoring and AI prediction. According to the invention, the data fragmentation, verification and storage efficiency can be improved.

Methods for assisting one or more users in organizing content-items, such as location information (e.g., URLs) for online information resources (e.g., webpages) and clips taken from information resources, accessed via content-access software, such as one or more web browsers, using a content item-bundle primitive that allows users to create, build, manipulate, and / or populate their own content-item bundles according to their information investigation and collection desires / needs. In some embodiments, the methods include automatically bundling content items into suggested content-item bundles based on learned relationships among various content items. In some embodiments, the methods can be implemented to provide bundle-based task managers that allow users to not only organize their content items but also define tasks and / or projects rooted in the content-item-bundle primitive. Further embodiments are disclosed, as is software for executing disclosed methods.

The invention provides a wharf seaside operation deduction method and system based on a multi-agent system, is applied to the technical field of port wharf operation management, abstracts wharf physical equipment into agents packaged with attribute, state and behavior logic by constructing a multi-agent collaborative deduction system based on actual wharf resource configuration, and improves the deduction efficiency of the wharf seaside operation. The task manager intelligent body analyzes ship operation tasks and dispatches the tasks according to an area matching rule, the quay crane intelligent body, the field crane intelligent body and the container truck intelligent body receive the tasks through asynchronous communication, make decisions autonomously based on own resource states, and determine a task execution time sequence in combination with a dynamic action time prediction model so as to propel and deduce a logic time axis; the container truck intelligent body responds to an instruction to simulate container circulation between a quay crane and a storage yard, key instructions and state data are written into a database in real time in the deduction process, a traceable data track is formed, real-time monitoring and post multi-dimensional index analysis are supported, and distributed, dynamic and data-driven high-fidelity deduction of a seaside operation process is achieved.

A neural processing device and a method for managing tasks thereof are provided. The neural processing device includes a neural core configured to perform a task and generate a completion signal for completion of the task, a core global configured to transfer task information for the task to the neural core and receive the completion signal of the task from the neural core, and a task manager configured to generate and transmit the task information to the core global, receive the completion signal from the core global, generate a completion report, and transmit the completion report.

Embodiments relate to streaming convolution operations in a neural processor circuit that includes a neural engine circuit and a neural task manager. The neural task manager obtains multiple task descriptors and multiple subtask descriptors. Each task descriptor identifies a respective set of the convolution operations of a respective layer of a set of layers. Each subtask descriptor identifies a corresponding task descriptor and a subset of the convolution operations on a portion of a layer of the set of layers identified by the corresponding task descriptor. The neural processor circuit configures the neural engine circuit for execution of the subset of the convolution operations using the corresponding task descriptor. The neural engine circuit performs the subset of the convolution operations to generate output data that correspond to input data of another subset of the convolution operations identified by another subtask descriptor from the list of subtask descriptors.

A system and method for executing tasks by executing an artificial neural network through heterogeneous resources is disclosed. The system includes: a model analyzer that receives an artificial neural network model and outputs subgraph information generated based on the artificial neural network model including at least one subgraph; a detector that outputs perception information about the plurality of heterogeneous resources; and a task manager that outputs a first request signal for executing a task for each layer to a first resource among the plurality of heterogeneous resources based on the subgraph information and the perception information, and outputs a second request signal for executing a task for each depth direction to a second resource among the plurality of heterogeneous resources.

The invention relates to a method for managing a fleet of unmanned vehicles (5) by means of a computer system (1) comprising a supervisor (2) connected to a user interface (3) and a mission manager (4) that controls the movements of the vehicles. The invention also relates to a computer system (1) implementing such a method. Figure for the abstract: 1

This application provides a method, device, and storage medium for dimension table joins in Flink, relating to the field of big data technology. The method identifies a target subtask among multiple subtasks of a unified dimension table join operator in the current task manager. After controlling the target subtask to send a query request to an external database and obtain the dimension table data sent by the external database, the method controls the target subtask to load the dimension table data into a static dimension table object set in the metadata space of the task manager. This avoids putting significant operational pressure on the external database in a short period. The method controls the target subtask and multiple other subtasks to perform dimension table join operations based on the obtained raw data and the dimension table data in the static dimension table object, allowing all subtasks in the same task manager to share the same dimension table data, thereby saving physical memory resources.

The invention discloses an intelligent interaction method, an intelligent interaction system and a display device, the method is applied to the intelligent interaction system, the intelligent interaction system comprises a task manager, an interaction agent and a deep processing agent, and the task manager obtains an interaction task corresponding to an interaction request of a user; the task manager determines the task type of the interaction task; the task manager allocates the interaction task to a deep processing agent under the condition that the task type of the interaction task is a complex task; the deep processing agent executes the interaction task to obtain task execution information of each task execution stage; and in the interaction task execution process, the interaction agent obtains task execution information of the current task execution stage, and feeds back a response result of the interaction request in the current task execution stage to the user based on the task execution information until the interaction task is executed completely. By adopting the method and the device, the requirement of real-time interaction with the user can be met while deep analysis is performed on the complex task, and the user experience is improved.

The invention discloses a method for automatically initializing a hardware testing platform, and relates to the technical field of hardware testing. The method comprises the following steps: establishing a system library and a test tool library, arranging and scheduling an initialized test task through a task manager, binding the test task with a test script, managing uploading, editing, execution and log recording of the test script through a script manager, providing a script template and a script example, and integrating a script test environment. The correctness of the test script is verified before the test script is executed officially, the hardware test environment is connected with the hardware test platform through the equipment manager by utilizing a BMC interface, an instruction from the task manager is received, the instruction content comprises a corresponding test task and the test script, and the instruction is issued through the equipment manager to execute automatic initial operation.

The present invention discloses a method for implementing a virtual GPU based on a vectorized instruction set, which is used to construct a virtual GPU accelerator card in a CPU processor to enable virtual heterogeneous accelerated computing on a processor platform. The method includes: constructing a virtual GPU thread operating environment based on scalable vector instructions in the CPU, wherein each scalar computing unit in the vectorized computing unit serves as a parallel computing core of the virtual GPU, and each virtual GPU parallel core has an independent stack space as a local storage space; and a virtual GPU task manager creates a task block space required for the virtual GPU thread and manages the operation of the virtual GPU task. The system constructs a GPU-like parallel computing mode by utilizing the parallel execution capability of the CPU scalable vector instructions, thereby constructing a virtual GPU and providing GPU use in an environment without GPU hardware.

The invention provides a server non-perception application distributed fault-tolerant method and system based on asynchronous parallel. The method comprises the steps that calling of external data read-write in server non-perception application codes is converted into calling of idempotent data read-write API; the idempotent data read-write API converts read-write dependency into a dependency chain referenced by an object, and returns a result and a reference pointing to a log persistence result to a data storage manager; the log asynchronous scheduler requests to obtain log persistence tasks called by the storage idempotent data read-write API of the log task manager and corresponding semantic information, sorts and constructs log persistence data operation requests, and a log management result is obtained; and executing the operation request and reference in the data storage manager through an Actor model according to a log management result. The method can be applied to codes in a wider range, extra tool verification is not needed while idempotent consistency of the application is guaranteed, and better running performance and higher universality are achieved.

The invention discloses a service request processing system and method, and the system comprises a gateway, a service module, a general task manager, and an interconnection module: the gateway is used for obtaining a service request, and transmitting the service request to the service module; the service module is used for determining a first format of a first message in the service request, and sending the first message to the interconnection module when determining that the first format does not belong to a preset message format; the interconnection and intercommunication module is used for converting the first format of the first message into a second format to obtain a second message, and sending the second message to the service module; and the general task manager is used for acquiring the second message sent by the service module, performing service processing based on the second message, generating a first service file and feeding back the first service file. And interconnection and intercommunication between service systems are realized.

The present invention provides an operator operation method and device, a storage medium, and an electronic device. The method comprises: obtaining M operators to be run in a target tracking algorithm, where M is an integer greater than 1; setting some or all of the M operators in L tasks in an idle state in a task manager, where L is an integer greater than 1; calling the L tasks in the idle state to run some or all of the operators in parallel through L chip cores of a hardware manager, that is, according to the needs of the operators to be run in the target tracking algorithm, the operators to be run can be set in the tasks in the idle state in the task manager, and then the operators to be run in the tasks in the idle state can be run in parallel through the chip core of the hardware manager. The above technical solution is adopted to solve the problems in the related technology that the algorithm cannot be processed in parallel and the algorithm cannot be effectively deployed to the chip core.

1. The name of this design product: Time Task Manager (pro). 2. Purpose of this design product: used to record the completion status of planned matters or habit cultivation. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.