93 results about "Task manager" patented technology

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.

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 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 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.

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 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.

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.

The invention provides a browser plug-in task scheduling method and device, equipment and a storage medium, and relates to the technical field of e-commerce platforms. The task scheduling method for the browser plug-in comprises the steps that under the condition that the browser plug-in is started, a task manager is initialized so as to set a single timed task, and the period of the single timed task is a preset fixed period; in response to triggering of the single timed task, obtaining a current task execution record through local storage of the browser; determining a to-be-executed task list of the browser plug-in based on the current task execution record; and sequentially executing the tasks in the to-be-executed task list. According to the embodiment of the invention, the high controllability of the task execution time sequence of the browser plug-in can be realized.

The present invention discloses a method for dynamic task management of robots, including: S1: a task manager of a scheduling management system receives tasks and places them in a task pool to await assignment; S2: a task manager dynamically calculates the assignment priority of tasks; S3: screening candidate robots that can execute tasks and calculating the movement cost of the robots executing the task with the highest assignment priority; S4: assigning the task with the highest assignment priority to the robot with the lowest movement cost; S5: the task manager sets the assigned tasks in the task pool to be running, updates the waiting time of unassigned tasks to be run, and continues to dynamically calculate the assignment priority in the task pool; S6: after a robot with an existing task obtains a new task, the robot calculates the execution priority of the task based on the initial task priority and a compensation coefficient, and reorders the tasks according to the execution priority; S7: the robot executes the task according to the reordered task queue. By dynamically allocating tasks based on multiple factors, the efficiency, rationality, and resource utilization of task allocation are improved.

The invention discloses an asynchronous tool calling method, device and equipment based on an MCP protocol and a storage medium, is applied to a client side and relates to the technical field of computers, and the asynchronous tool calling method comprises the steps that a built-in calling interceptor of the client side is used for capturing tool calling in a synchronous calling form initiated by a large language model, and the tool calling is converted into an asynchronous task execution request; sending the asynchronous task execution request to the server, so that a task manager of the server creates an asynchronous task, returns response information to the client, executes the asynchronous task by using a target asynchronous executor of the server, generates a target heartbeat signal, and monitors heartbeat information broadcasted by the server by using a preset log channel; and calling the server to determine a structured output result, and transmitting the structured output result to the large language model, so that the large language model completes asynchronous tool calling operation based on the structured output result. And network connection timeout interruption, task state uncertainty and result loss caused by long-time tasks are avoided.

The invention provides a communication system and a communication method for acquiring a user short message. The system comprises a UE / eNB simulator, a task manager, an S6a data cleaner and a distributed crawler system, the UE / eNB simulator is in interactive connection with the task manager, and the task manager is connected with the S6a data cleaner and the distributed crawler system. Based on a system structure of a mobile communication network, multi-user terminals are simulated in batches in a base station mode, and after the IMSI of the mobile phone of the target crowd is confirmed, the mobile phone short message verification code of the target user can be quickly and effectively acquired, so that a background program can be assisted to log in the network account of the target user and acquire the network track of the target crowd, and the user experience is improved. Important support is provided for investigation of network security cases; according to the user conflict detection and avoidance mode, the obtained short message of the target user is more secret, the secrecy of the operation process of obtaining the short message of the user is enhanced, and the efficiency of network security case investigation is effectively improved.

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.

The invention discloses a scheduling system task fault diagnosis and alarm system, method and device based on logs, and relates to the technical field of log management.The system comprises a task manager, a task executor, Logstash and Elasticsearch, the task manager is used for achieving creation, distribution, management and alarm of tasks, and the task executor is used for executing the tasks; and performing log generation according to a life cycle from creation of the task to completion of distribution, and the task executor is used for executing a service corresponding to the task, checking a log and generating the log according to a life cycle from the beginning of execution to the end of execution of the task. According to the invention, the reliability and stability of the task scheduling system can be improved, and the fault of the task scheduling system can be quickly diagnosed and alarmed.

The invention provides an image task automatic deployment method and device, a medium and a program product, and relates to the technical field of image processing.The method comprises the steps that a user configures target image content through a front-end interaction interface; sending the target image content to a task manager, and deploying an image task according to the target image content through a preset task base and a plurality of image processing units; running the image task to obtain a target image; wherein the task base comprises a conventional task base and a free task base; the conventional task base acquires video image frames according to a preset acquisition rule, processes the video image frames by adopting image processing algorithms corresponding to respective image tasks in sequence according to the sequence of the image processing units, and outputs a target image; and the free task base is used for controlling acquisition and processing of video image frames by the image processing unit and outputting a target image. The problem that the image task is difficult to automatically deploy according to the target image content configured by the user in the prior art is solved.

The invention belongs to the technical field of computer simulation, and discloses a behavior assembly method and system based on a finite-state machine, and the method comprises the steps: carrying out the aggregation and assembly of a pre-constructed physical model and a pre-constructed behavior model into a standardized equipment entity, and forming an extensible markup language of the equipment entity, deploying the equipment entity at a preset initial position, filling parameters of the combined behavior model on an editing interface of the task manager, generating an executable simulation scenario file, and loading a physical model and a behavior model of the equipment entity through the task manager according to the simulation scenario file; and if the combined behavior exists, starting the finite-state machine, loading and analyzing the extensible markup language in the database, continuously monitoring the state and input, and carrying out state switching according to a state transition rule until the task is completed. According to the method, the cross-platform deployment capability is further improved by introducing a standardized interface definition and a dynamic loading mechanism of the behavior module.

The present invention discloses a general software layered architecture for distributed formation robots, comprising: a physical link layer, which is responsible for providing different communication modes to the upper layer and completing communication driver configuration; a communication management layer, which includes a formation communication management module and a hardware communication management module, which are used to manage the communication between the robot and different nodes and implement node management, clock synchronization, and data verification functions; a data exchange layer, which includes a network protocol encapsulation module, a hardware data module, and a hardware protocol encapsulation and parsing module, which are responsible for data encapsulation and parsing, and complete data exchange with the algorithm task layer; a task layer, which includes a task manager and a task pool, which are used to develop formation control algorithms and implement the operation and switching of formation tasks. The present invention reduces the difficulty of expanding the formation robot by modifying the communication cycle of the formation robot, increasing or decreasing the number of robots within a certain range, and can be widely used in the field of robot formation control technology.