41 results about "Task manager" patented technology

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.

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.

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 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.

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.

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 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.

The invention provides an information processing method and device, electronic equipment and a medium. The method comprises the following steps: obtaining a trigger instruction, responding to the trigger instruction, displaying an interface corresponding to at least one application program in a queue form based on a multi-task manager, obtaining dynamic real-time update information of a first application program in the at least one application program, and synchronously displaying the dynamic real-time update information on an interface corresponding to the first application program.

This disclosure relates to branching operations for neural processor circuitry. A neural processor includes a neural engine for performing a convolution operation on input data corresponding to one or more tasks to generate output data. The neural processor circuitry also includes data processor circuitry coupled to the one or more neural engines. The data processor circuitry receives the output data from the neural engines and generates branching commands from the output data. The neural processor circuitry also includes a task manager coupled to the data processor circuitry. The task manager receives the branching command from the data processor circuitry. The task manager enqueues one of two or more segmented branches according to the received branching command. The two or more segmented branches are after a pre-branching task segmentation that includes a pre-branching task. The task manager transfers the task from the selected segmented branch among these segmented branches to the data processor circuitry for execution of the task.

The application discloses a task exception processing method and device, and relates to the technical field of intelligent exception analysis. The method embodiment can include: obtaining a running log corresponding to a task, and splitting the running log into multiple log blocks; matching a first exception category for the multiple log blocks from multiple exception levels included in a pre-constructed exception tree and at least one exception category included in each exception level; determining an exception reason configured for the first exception category corresponding to the multiple log blocks according to the exception reason pre-configured for each exception category; and providing the first exception category and the exception reason configured for the first exception category to a task manager, so that the task manager repairs the task according to the first exception category and the exception reason configured for the first exception category. The embodiment can intelligently analyze task exceptions through logs, and discover and process abnormal tasks in time.

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.

We provide the system. [Solution] A user input means for registering tasks, An artificial intelligence tool for evaluating and determining the priority of tasks, A means of managing the progress of tasks, Methods for assigning task managers, A means of generating and sending reminder notifications, A system that includes this.

The invention discloses an online task updating method for an embedded real-time operating system ucos-ii. The method comprises the following steps: planning resources of a processor of an embedded system; a ucos-ii operating system is transplanted to a processor, and a task manager is developed; controlling a task manager to burn the application and the public function library to an external storage area through an external interface of the processor; running the ucos-ii operating system, creating a task by a task manager, loading an application and a public function library corresponding to the task to a processor program running area, and monitoring the input of an external interface of the processor at the same time; and after the task manager receives an application updating or public function library updating instruction through the external interface of the processor, destroying the running task, deleting the application or the public function library in the external storage area, then updating the application or the public function library, and reloading the destroyed task. Online updating of the task is completed; according to the method, the task can be updated in the ucos-ii running process, and operation system codes do not need to be updated.

The invention provides a SaaS distributed scheduling computing system and method, and relates to the technical field of computers.The system comprises a client and a server which are connected with each other, and the server comprises a message manager, a message node cluster, a consumption node cluster and a task manager; according to the method, distributed scheduling and node rebalance in a centerless mode are realized through distributed communication and dynamic scheduling message packets based on a Goosip protocol, so that the problem of task termination caused by a fault of a central node is avoided, and the resource utilization rate is improved; through a dynamic on-demand loading mechanism, resource occupation is reduced, so that a large amount of memory consumption is avoided, and the calculation efficiency is improved.

The invention relates to the technical field of warehousing automation control, and discloses a distributed conveying equipment control system and method, and the system comprises a plurality of single-machine controllers which are distributed at all conveying equipment and are used for carrying out the local control of the conveying equipment and carrying out the cargo handover control of other single-machine controllers, information interaction with the network topology manager and / or the task manager is carried out; the network topology manager is in communication connection with each single-machine controller and is used for centrally managing a topological structure of a conveying equipment network and issuing a material conveying path selection decision result or algorithm to each single-machine controller according to the topological structure; and the task manager is respectively connected with each single machine controller and the network topology manager, and is used for receiving the work order task of the superior management system, issuing a task command to the corresponding conveying equipment and tracking the task execution condition. The reliability, the flexibility and the expansion capability of the conveying system can be improved, the operation and maintenance cost is reduced, and the system adapts to diversified storage scenes.

This invention discloses a vehicle-road-cloud collaborative task offloading and task queue stability control system and method, comprising: a vehicle task manager, which is used to acquire task information generated by the task vehicle at the current moment, and update the local task queue status at the current moment according to the task offloading decision at the previous moment; a vehicle task scheduler, which is used to optimize the offloading strategy according to the acquired channel power gain between the task vehicle and the server, the local task queue status, and the server task queue status at the current moment; and a server manager, which has: a resource allocation update unit, which is used to allocate computing resources to the corresponding task vehicle through the server according to the task offloading decision in the optimized offloading strategy, and transmit the updated server task queue status and computing resource allocation of the task vehicle at the current moment, as well as the channel power gain between the vehicle and the server, to the vehicle task scheduler.

The invention provides a cross-medium unmanned aerial vehicle flight control system based on a modular loose coupling architecture. The system comprises two core modules, namely a task manager and an adapter. The task manager is an independent process based on a finite-state machine and is responsible for managing and controlling a task execution process and system state transition, and the internal state of the task manager covers waiting, take-off, multiple waypoint task modes, landing and completion states. The adapter serves as an independent process and is realized by adopting an adapter mode and interface-oriented programming, high-level control logic and bottom-level specific implementation are decoupled through abstract base classes and runtime polymorphisms, and a flight control adapter, a planner adapter and a positioning adapter are included in the adapter. According to the system, various message flows are transferred and converted through an adapter, data intercommunication among positioning, planning and control modules is realized, and opening and closing of adaptive channels are allowed to be controlled through non-compilation parameter configuration. According to the invention, the modularization degree, the expandability and the cross-platform adaptation capability of the unmanned aerial vehicle control system are improved.