56 results about "Task manager" patented technology

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 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.

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.

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.

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 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.

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 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 invention provides a distributed computing system, method and device based on a hybrid cluster and a storage medium, the distributed computing system based on the hybrid cluster comprises a cluster manager used for distributing a stream computing topology configuration file to a task manager; the cluster coordinator is used for collecting the states of the task process and the task manager, sending the state of the task process to the task manager and sending the state of the task manager to the cluster manager; the task manager is used for creating a task process based on the flow calculation topology configuration file, establishing a flow calculation topology based on the task process and managing the task process based on the state of the task process; wherein the task process serves as a node of the stream computing topology, and the task process is used for executing distributed stream computing and runs in an independent process mode. According to the method, the task process is limited to run in an independent process mode, resource scrambling and logic pollution caused by multi-service task sharing processes are avoided, and the stability of the whole computing cluster is improved.

The application discloses a task-level redundant fault-tolerant system based on an RTOS kernel, which comprises a user API layer, a redundant task manager, a state synchronization engine and a fault detection and switching controller, and a shared memory partition. The user API layer is used for creating a redundant task group by calling a special API interface. The redundant task manager is used for managing the whole life cycle of the redundant task group. The state synchronization engine is used for synchronizing state data and buffering task input data. The fault detection and switching controller is used for detecting timeout, software errors and hardware abnormal faults, and switching the main task and the backup task after detecting the failure. The shared memory partition is used for synchronizing information between the main task and the backup task, and modifying the access authority when the main task and the backup task are switched by configuring the access authority by an MPU. The task-level redundant fault-tolerant system based on the RTOS kernel disclosed by the application reduces the development and maintenance cost, improves the fault identification accuracy, and guarantees the execution continuity after the main-backup switching.

The present application relates to a kind of medical data index query sentence generation method and system based on statistical index;The present application is defined by attribute to statistical index, query syntax is defined, using ANTLR4 is expressed using.g4 file to query syntax, and.g4 File is generated Java syntax analysis code, the syntax in it is converted into logic execution operator, defines executor interface specification, encapsulates into the platform executor plug-in of each platform, defines task manager, identifies and registers platform executor, calls platform executor and obtains intermediate data set IDS from platform, processes into final index data set KDS with intermediate data set IDS, corresponding statistical index attribute and logic execution operator;Through the present application, the standard generation of the query language of different platform data is realized, and the programming workload of query language is reduced.

A method for performing tasks in the autonomous system for preparing a food product, the method comprises identifying that a process is required to be performed as part of preparing the food product; converting the process to multiple tasks, wherein the tasks require different tangible devices; sending the multiple tasks to a pipeline queue configured to manage the tasks on a first-in-first-out (FIFO) basis; when a specific task of the multiple tasks reaches a beginning of the pipeline queue, sending the task to a manager queue controlled by a task manager, wherein the task manager is coupled to one of the different tangible devices; when the specific task reaches the beginning of the manager queue, sending the specific task to a relevant tangible device for execution; executing the task.

The invention is suitable for the technical field of data management, and provides a mass spectrometer data management method, a data management system and a computer program product.The method comprises the steps that when it is detected that mass spectrometer software is started, a preset software process is triggered; the software process is used for forbidding the access authority of the user to the task manager; acquiring identity information of a user; based on the identity information, determining permission information of the user on a software interface of the mass spectrometer software; and based on the authority information, managing the mass spectrometer data. By means of the software process, a user is prevented from operating a software background, the risk of misoperation is avoided, and the purity and consistency of a software environment are guaranteed. And meanwhile, the authority of the user on a software interface is determined based on the identity of the user, so that the data security of mass spectrometer data is 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. 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 neural processor includes a neural engine to perform a convolution operation on input data corresponding to one or more tasks to generate output data. The neural processor circuit also includes a data processor circuit coupled to the one or more neural engines. The data processor circuit receives the output data from the neural engine and generates a branch command from the output data. The neural processor circuit also includes a task manager coupled to the data processor circuit. The task manager receives the branch command from the data processor circuit. The task manager enqueues one of two or more segmented branches according to the received branch command. The two or more segmented branches are after a pre-branch task segment that includes a pre-branch task. The task manager transmits a task from a selected one of the segmented branches to a data processor circuit to execute the task.

A method of implementing a computing service on edge nodes of a communication network, where the computing service is made from a plurality of service functions, each of which is connected to another service function by a communication routine. Service functions and routines can be deployed on edge nodes and communication between any two service functions can be performed via a routine and a privacy router, with coordination from a routine manager. Service functions and routines can be grouped into tasks, which can be coordinated with task managers. Tasks can be grouped into works, which can be coordinated with work managers. Because a privacy router can perform encryption on routine communications, any of the entities implementing the computing service can be deployed on edge nodes of a communication network, and a service manager can interface with external entities.

The embodiment of the invention discloses a file management method and device, equipment and a storage medium, and belongs to the field of file management. The method is used for a life cycle management system, and comprises the following steps: responding to a received life cycle scanning task, and performing file search by a task executor according to a storage structure of a file; based on a life cycle rule stored in the task manager, the task executor determines a target processing mode of the searched file, and the life cycle rule is used for indicating a corresponding relation between the file and a storage state; the task executor sends a first processing message to the metadata manager, wherein the first processing message comprises a target processing mode and a file identifier of the file; under the condition that the first processing message is received, the metadata manager carries out target processing on file metadata of the file, and the metadata manager sends a second processing message to the data manager; and under the condition that the second processing message is received, the data manager performs target processing on the file data of the file.

A neural processor includes neural engines for performing convolution operations on input data corresponding to one or more tasks to generate output data. The neural processor also includes a data processor circuit coupled to external system memory. The data processor circuit includes a buffer for storing the output data from the neural engines. The neural processor further includes a task manager coupled to the data processor circuit. The task manager receives a context-switch task. The context-switch task specifies a switch of the data processor circuit from handling an outgoing task to an incoming task. The task manager sends configuration data of the context-switch task to cause the data processor circuit to transmit the output data corresponding to the outgoing task from the buffer to the external system memory. The data processor circuit also fetches data corresponding to the incoming task from the external system memory to the buffer.

The invention discloses a flight scheduling system, which comprises a data processing module used for acquiring flight data from a butted flight data source and preprocessing the flight data, a flight scheduling module used for acquiring to-be-scheduled tasks from the preprocessed flight data and performing task scheduling on all the to-be-scheduled tasks, sending the task scheduling result to a corresponding task executor; the node monitoring module is used for monitoring each state node in the execution process of each flight task; the message notification module is used for receiving the early warning information generated by the node monitoring module and pushing the early warning information to a corresponding task manager; through cooperative work of all the modules, the scheduling and dispatching efficiency can be improved, manpower consumption is reduced, and the intelligence and informatization level of flight operation guarantee is effectively improved.

The present disclosure discloses a batch computing system and an associated method. The batch computing system includes a memory, a task manager and an inference computer. The memory stores a shared model parameter set common to a plurality of tasks that is generated by fine tuning a shared model and a task-specific parameter set of each task. The inference computer receives a plurality of task requests, derives a data length and a designated task of each task request, and enables the task manager to read a task-specific parameter set and a shared model parameter set corresponding to each task request. The inference computer further assigns task requests corresponding to the shared model to a plurality of computation batches, performs, in batch, the common computation of designated tasks in each batch computation, and performs task-specific computation operations of the designated tasks of each batch computation.

This invention discloses a Flink load balancing method and system based on multi-metric performance evaluation. The method includes: collecting multiple task manager node metrics and constructing a comprehensive performance evaluation model based on multiple metrics; obtaining a comprehensive performance score for each task manager node based on the comprehensive performance evaluation model based on multiple metrics and constructing an initial task allocation scheme set; obtaining a baseline performance score for each scheme based on the path utility function, and obtaining an optimal recommended scheme and a suboptimal recommended scheme based on the baseline performance score; performing virtual load loading state simulation on the optimal and suboptimal recommended schemes, and obtaining the optimal scheme based on the virtual load loading state simulation results and the baseline performance score; using the optimal scheme as a new initial task allocation scheme, repeating the above steps for iterative optimization; and rewriting the partitioning method of Flink's custom partitioner wrapper interface. This invention improves resource utilization and system throughput, and reduces computational latency.