287 results about "Task control" patented technology

The invention provides an interactive question and answer task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including interactive question and answer tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an interactive question and answer agent, and different sub-agents are used for processing different types of sub-tasks; the interactive question-answering agent is controlled to present an interactive graphical user interface corresponding to the interactive question-answering task to the user, an answer result corresponding to the received feedback answer is presented, and the interactive graphical user interface comprises a question and an interactive answer area; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on an answer result output by the interactive question and answer agent, and the associated sub-target agent is a sub-target agent which depends on the answer result and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. Through application of the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost, especially for an interactive question and answer task in which a user needs to subjectively answer a proposed question and the effect is checked according to an answer result; the interactive questioning and answering agent provides the interactive graphical interface containing the question and the interactive answering area for the user, so that the task requirement of the user can be met more quickly in an interactive answering mode, and the task solving effect is improved.

The invention provides a multi-agent cooperation-based explicit result acquisition class task processing method and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including clear result acquisition class tasks, each sub-target task is correspondingly issued to each sub-target agent at least including clear result acquisition agents, and different sub-agents are used for processing different types of sub-tasks; controlling each sub-target agent to output a sub-processing result corresponding to the subordinate sub-target task in combination with the personalized preference of the user, wherein the sub-processing result comprises a clear result which is output by the clear result acquisition agent and corresponds to the subordinate clear result acquisition class task; and summarizing the sub-processing results to obtain a target processing result corresponding to the target task. By applying the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost.

The invention provides an instant message inquiry type task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including instant information inquiry tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an instant information inquiry agent, and different sub-agents are used for processing different types of sub-tasks; the instant information inquiry agent is controlled to inquire questions from the inquiry object to obtain instant feedback, and the instant information inquiry agent supports question inquiry in a background operation mode; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on instant feedback output by the instant information inquiry agent, and the associated sub-target agent is a sub-target agent which depends on the instant feedback and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. By applying the scheme, a better comprehensive task processing effect can be achieved under the condition that the comprehensive cost is lower, and in particular, an instant information inquiry task which needs to be combined with the instant information to verify whether the actual situation meets the task requirement or not and takes the verification result as instant feedback is obtained; in addition, the instant information inquiry agent also supports question inquiry (namely asynchronous task processing) in a background operation mode, so that the user does not need to stay in a session interface all the time, and other operations of the user are not influenced.

In various examples, behavior-based mission task management for mobile autonomous machine systems and applications are provided. A mission controller may generate a mission behavior model logic framework for a mission that accounts for the actions of a set of multiple task agents that play a role in completing the mission. The mission controller may assemble a framework starting from a baseline task sequence, correlate tasks defined by the baseline task sequence with pre-defined behavior models, and customize those behavior models based on mission task customization parameters. The mission controller may provide the mission behavior model logic framework to a mission dispatch function. Individual autonomous mobile task agents may then proceed to execute their assigned portions of local task sequences in accordance with the customized behavior models distributed to them by the mission dispatch function.

In one implementation, a device may identify a type of task and its context indicated by a prompt sent by a user for input to a language model. The device may authorize the prompt for input to the language model based on the type of task and its context. The device may make a determination as to whether a response of the language model to the prompt is authorized to be returned to the user based on the response, the type of task, and its context. The device may prevent the response from being returned to the user when the determination indicates that the response is not authorized to be returned.

The invention relates to the technical field of cloud control, in particular to a cloud-based steam cabinet control system, which comprises a humidity sampling processing module, a steam state judgment module, a task stage identification module, a control instruction reconstruction module and a scheduling execution response module. According to the method, the accuracy of steam management is improved through staged recognition of the steam release state, a matching mechanism is formed in combination with the task timestamp and the staged state, the real-time adaptation capacity in the task execution process is enhanced, a dynamic comparison and queue rearrangement mode is adopted in the task instruction updating link, and the task execution efficiency is improved. Synchronous cooperation of a control instruction and a current task state is achieved, targeted optimization of the task control instruction and dynamic adjustment of a control process are achieved through a multi-dimensional state analysis and time sequence mapping means, the feedback response efficiency, task scheduling flexibility and steam release accuracy in the equipment operation process are effectively improved, and the system performance is improved. Therefore, the adaptive capacity and the control precision of the steam cabinet control in various operation states are enhanced.

The invention discloses a real time operating system (RTOS) multi-task priority scheduling and real-time control method based on advanced RISC machines (ARM), and relates to the technical field of embedded computing and control. Comprising the following steps: S1, time slice management setting: dividing CPU time according to a preset time period to obtain time slices; s2, performing task scheduling: setting each task according to a task control block, and performing context switching and priority arbitration through an OSScheduler () function; and S3, performing exception response: performing forced interruption and exception recovery operation through a set multi-stage exception classification mechanism. Through a three-level decision model and a partition response mechanism, it can be ensured that a high-priority task can be responded in time within strict time constraints, the hard real-time requirement is met, meanwhile, CPU time is divided through a fixed time slice, a counter is updated through timer interruption, and therefore the time deviation of task execution can be reduced, and the task execution efficiency is improved. And the certainty of the system is improved.

The invention relates to the technical field of business process management, in particular to a data processing method, system and device based on business process management and a medium, and the method comprises the steps: constructing a business process model driven by an abstraction layer, and configuring a multi-protocol standardized interface; according to the business process model driven by the abstraction layer and the multi-protocol standardized interface, a process instance matched with the business scene is generated, and a task is distributed to a target client according to a task demand; managing form state circulation and node jump logic according to a task control engine; an event monitor is deployed through a process key node to monitor the process, and when the process reaches a specified node, external service calling operation is triggered to form an end-to-end automatic closed loop. Therefore, the problems of code redundancy, high maintenance cost, difficulty in quickly adapting to diversified customer requirements and the like due to the fact that business process management generally needs to be subjected to customized development for different subsystems in related technologies are solved.

The invention discloses a graphics processor, a graphics drawing method, a chip and electronic equipment, and belongs to the field of chips. The graphics processor comprises a geometric processing assembly line, and the geometric processing assembly line comprises a first task generation assembly line, a task control unit, a second task generation assembly line and a task execution unit. The task control unit is used for acquiring a first coloring result based on the received first task information in the single-pipeline mode, and outputting the first coloring result to a pipeline behind the geometric processing pipeline; and acquiring a first coloring result based on the received first task information in the double-pipeline mode, and controlling the first coloring result to enter a second task generation pipeline, and based on the received second task information, obtaining a second coloring result, and outputting the second coloring result to an assembly line behind the geometric processing assembly line. According to the geometric processing pipeline, geometric processing parts of different rendering pipelines can be combined into the same data stream.

The invention discloses a kernel task execution method, electronic equipment, a readable storage medium and a program product, and relates to the technical field of computers. The method comprises the following steps: creating at least three layers of nested mapping table structures; the first layer of mapping table stores a task identifier and a nesting relationship between the task identifier and a lower layer, and the other layers of mapping tables store control rules and corresponding nesting relationships. Target task control rule information extracted from the resource strategy file is divided into task execution control rules with the corresponding number, the task execution control rules are correspondingly stored in mapping tables of corresponding layers, and the task execution control rules serve as task execution control rule storage tables to be issued to a kernel mode; and the kernel mode performs layer-by-layer control on kernel task operation according to the task execution control rule storage table. According to the method and the device, the problem that the container scene cannot store the service requirement of the access control rule due to the fact that the eBPF Map cannot be nested in multiple layers in the related technology can be solved, and container access control can be realized in the container scene.

The embodiment of the invention provides a network live broadcast processing method and device, and the method comprises the steps: firstly obtaining a preset interaction action of a task control of an interaction task of a live broadcast room in a network live broadcast process, and responding to the preset interaction action; obtaining an interaction component of an object identifier of the virtual object configured with the interaction task, displaying the interaction component in a preset area of the live broadcast page, and when a further interaction operation for the interaction component is detected, displaying the interaction component according to an interaction instruction of any object identifier in the interaction component, and performing execution processing of the interaction task and display updating of the task control and the interaction component, thereby realizing real-time feedback of the interaction state to the user in the live broadcast process.

The invention provides a controller parameter optimization method and a task control method based on reinforcement learning, and relates to the technical field of control, and the method comprises the steps: inputting the parameters of a PID controller of a current task into a DDPG model, obtaining the parameters of a PID controller of a next task, inputting the parameters of the PID controller of the next task into the PID controller to obtain a control quantity; determining an evaluation result of the control of the PID controller based on the control quantity as a reward; taking the state, action and reward of the current time step and the state of the next time step as sample data and storing the sample data into an experience pool; and training the DDPG model based on the experience pool, and determining the target parameter output by the trained DDPG model as the optimal parameter of the PID controller. In the application stage, only the PID controller needs to output the target control quantity based on the optimal parameters, the participation of a DDPG model is not needed, and the light weight of the whole control model is realized.

The invention relates to the technical field of man-machine interaction, and provides an interactive task control method and device and a storage medium, an operation process of an interactive task comprises an execution process of a first sub-task and a second sub-task, the first sub-task can be controlled to be executed by operation of a user, and the second sub-task is controlled to be executed by an intelligent agent; the method comprises the steps of obtaining first subtask evaluation data of a first subtask in task execution time when an interactive task is in a running state; determining a preset deviation corresponding to the task execution time according to reference evaluation data corresponding to the task execution time and the first sub-task evaluation data; and according to the preset deviation, adjusting the preset interaction parameter of the intelligent agent so as to improve the difficulty adjustment accuracy of the interactive task.

The invention relates to the technical field of elevator control, and discloses an elevator task control method and system based on medical logistics robot scheduling. The method comprises the steps that according to a dispatching task of a medical logistics robot, the robot position and electric quantity information of an available medical logistics robot, a dispatching task to be executed, the floor where an elevator of an available elevator is located, the running direction, whether the elevator is fully loaded or not and the floor where the elevator needs to stop are recognized, and a distribution combination is constructed; an evaluation function is constructed according to the robot path length of each scheduling task in the distribution combination and the time of taking the corresponding available elevator, and a target medical logistics robot and a target available elevator are obtained through scheduling optimization based on the evaluation function; constructing a target control signal according to the target medical logistics robot and the target available elevator; and a scheduling path of the target medical logistics robot is generated, and elevator task control is conducted on the target available elevator according to the scheduling path and the target control signal. The accuracy of elevator task control can be improved.

The embodiment of the invention provides a transport vehicle scheduling method and system, and relates to the technical field of machine control, and the method comprises the steps: generating a feeding task for a machine table when the remaining material use duration of the machine table is a first duration; controlling the target transport vehicle to run to a channel area in front of the machine table in response to the feeding task of the machine table; acquiring a second time length after the target transport vehicle arrives at the channel area in front of the machine, wherein the first time length is greater than the second time length; judging whether the target transport vehicle needs to wait or not according to the second duration; if yes, controlling the target transport vehicle to keep a waiting posture; if not, the target transport vehicle is controlled to be adjusted to be in the butt joint posture, and feeding is conducted after butt joint with the machine table is completed. According to the method, the feeding task of the machine table is generated in advance, the transport vehicle can reach the machine table in advance, more sufficient time is provided for follow-up butt-joint loading of the machine table and the transport vehicle, the time that materials exist in the machine table can be prolonged, and therefore the working efficiency of the machine table is improved.

The invention provides a computing chip, a computing device, a data processing method, a storage medium and a program product. The computing chip comprises one or more computing modules and is used for executing computing tasks; the command processor is used for issuing a first calculation task to the one or more calculation modules; the data transmission module is used for carrying out data transmission with an external computing chip in a remote memory direct access mode; the transmission control module comprises a task queue creating module used for receiving the control command and adding the first calculation task to a first task queue created by the task queue creating module according to the control command; the task scheduling module is used for responding to the fact that the one or more computing modules execute the first computing task, controlling the data transmission module to communicate with an external computing chip, and transmitting a computing result corresponding to the first computing task to the external computing chip in a remote memory direct access mode.

The present application relates to the technical field of edge computing, and, in particular, to a deep reinforcement learning-based task time optimization method in an edge computing network. The task time optimization method comprises: receiving a data acquisition task; according to the acquisition task, controlling first edge computing nodes to acquire surrounding data information, and generating a task; acquiring current operation state information of a first edge computing network; according to the operation state information, splitting the task into a first number of first tasks, and splitting the first tasks into a second number of sub-tasks having constraint relationships; according to the operation state information, on the basis of deep reinforcement learning, executing a corresponding scheduling decision on the sub-tasks; and according to the scheduling decision, issuing the sub-tasks to each of the corresponding edge computing nodes. The task execution time is optimized by means of splitting the tasks. The sub-tasks are issued to the corresponding edge computing nodes according to the scheduling decision so as to execute the plurality of sub-tasks in parallel, thereby improving task execution efficiency and increasing the utilization rate of the nodes in the edge computing network.

The invention relates to the technical field of material grabbing machine control, and discloses a material grabbing machine control method and device, electronic equipment and a storage medium. According to the method, current track information and current area information of all working areas are obtained, and work task information and current material grabbing machine information are obtained, the material grabbing machine information comprises a material grabbing machine state and a material grabbing machine type, the task type of a to-be-executed task is determined based on the work task information, a material grabbing machine with the current material grabbing machine state being a preset material grabbing machine state, the current material grabbing machine type corresponding to the task type is determined as a target material grabbing machine, and the material grabbing machine is used for grabbing materials and transporting the materials. According to the current track information, the current area information and the work task information, tasks are allocated to the target material grabbing machine, a target task of the target material grabbing machine is obtained, and the target material grabbing machine is controlled to execute the target task; the control method of the material grabbing machine is more practical, more accurate and more efficient.

The invention provides a data processing method and device for a stream processing framework, equipment and a medium, and relates to the technical field of big data, in particular to the technical field of data warehouses. The method comprises the steps of pausing a real-time task of a stream processing framework in response to a first task control instruction, obtaining a first consumption data position corresponding to the real-time task when the real-time task is paused, and starting a first offline batch task, the first task control instruction is sent based on a real-time monitoring result of a real-time task state and consumption delay data in the streaming processing framework; after the capacity expansion operation is executed on the stream processing framework, the real-time task is started again, so that the real-time task starts to process data from a corresponding second consumption data position when the real-time task is started; wherein the first offline batch task is used for processing data between the first consumption data position and the second consumption data position. According to the invention, an integrated data warehouse of real-time stream and offline stream can be realized, and the concurrency and accuracy of data are improved.

A method for controlling a robotic manipulator according to a task comprises accepting a feedback signal including a sequence of multi-modal observations of a state of execution of the task. The multi-modal observations are processed with a neural network having a self-attention module with a hierarchically conditioned output to produce a skill of the robotic manipulator and an action conditioned on the skill. The neural network is trained in a supervised manner with demonstration data to produce a sequence of skills and a corresponding sequence of actions for the actuators of the robotic manipulator to perform the task. The method further comprises determining one or more control commands for the one or more actuators based on the produced action and submitting the one or more control commands to the one or more actuators causing a change of the state of execution of the task.

The invention relates to a matrix accelerator sharing a chip system memory and an operation method of the matrix accelerator. The matrix accelerator comprises a system memory module, a task management module, a data access module and a matrix operation module. According to the matrix accelerator, the chip system memory is directly shared through the dual-port SRAM, so that the matrix accelerator can be suitable for a navigation baseband chip with small size and low power consumption, meanwhile, when the matrix accelerator does not work, the shared memory can still be used as the chip system memory, and idle waste of memory resources is avoided. Through the task queue, a plurality of operation tasks and corresponding task control instructions can be loaded into the matrix accelerator at one time, and an interrupt signal is generated after all the operation tasks are completed to complete interaction between the CPU and the matrix accelerator, so that the efficiency of the CPU is improved to the maximum extent, and the performance of a chip system is remarkably improved.

The invention relates to an unmanned aerial vehicle cluster task dynamic recombination system and method. The system comprises a task control program, a module engine, a program API interface, a task upgrading program, an ROS2 data distribution service and a plurality of task modules. The task control program controls the starting, stopping and running state monitoring function of each module, the module engine is responsible for managing the starting, stopping and running states of the modules, the program API interface is responsible for receiving an external task upgrading request or task configuration request, the task upgrading program receives a platform external request, and the module engine is responsible for managing the starting, stopping and running states of the modules. The upgrade package information is sent to a program API interface of the task control program, the task module is a specific task processing unit, and the ROS2 data distribution service is used for realizing data interaction among the task modules. The problem that an unmanned aerial vehicle cluster system lacks the capability of performing dynamic adjustment according to actual conditions in the task execution process is solved, the flexibility, expandability and task execution efficiency of the system are remarkably improved, and effective application in a complex task environment is achieved.

The invention provides a task scheduling method for realizing equipment on an electric energy meter verification assembly line, which comprises the following steps of: determining equipment and tasks participating in scheduling according to a verification working link of an electric energy meter, and determining the workload, priority and completion time of each task; acquiring state information data of each device, after preprocessing the state information data into feature data of each device, importing the feature data into the trained multifunctional prediction model, and predicting the load capacity and fault risk of each device; calculating the load capacity of each device according to the load capacity and the fault risk of each device, and calculating the load demand of each task in combination with the workload, the priority and the completion time of each task; and constructing a target function, and solving an optimal solution to obtain a task scheduling queue in combination with the load demand of each task so as to perform intelligent scheduling task control on each device. By implementing the method, the task scheduling efficiency and accuracy are improved, the generation efficiency of the electric energy meter verification assembly line is also improved, and the verification period is shortened.

The invention discloses a general query field aggregation method and device for multi-source heterogeneous data, electronic equipment and a storage medium, and the method comprises the steps: employing a task flow annotation configurator to carry out annotation marking for each task chain in advance, and marking the task chain as a column aggregation processing flow task chain; managing an execution chain type task set marked by the same type through a task control manager, and loading a task processor to a system cache; loading a task processor in a system cache through a chained task processor, and executing a preset task based on a task process processor interface; and receiving a plurality of chained task processors through a column data aggregation executor, constructing processor request context parameters, executing the processors in sequence, and outputting an aggregation result. By means of the method and device, on one hand, flexible arrangement of the processing sequence of the function processors is achieved, dynamic expansion of the processors is achieved, on the other hand, the processing flow of field-level aggregation is unified, and the aggregation query efficiency is improved.

The present invention discloses an enterprise human resources comprehensive management platform, which relates to the field of data management technology and includes: a resource management system determination module for determining a resource management system; a management logic chain determination module for determining a management logic chain; a task management strategy determination module for determining a task management strategy; a sensitive management mode setting module for setting a sensitive management mode for sensitive data classes; a sensitive management strategy determination module for determining a sensitive management strategy; and a pre-management task control module for executing and controlling pre-management tasks. The platform solves the technical problems of the existing enterprise human resources management, such as the difficulty in coping with complex and changing enterprise management needs, the lack of integration of multiple enterprise application systems, and the risks of sensitive data management, which lead to the inaccuracy and security of enterprise human resources management. The platform intelligently realizes the comprehensive integration and efficient use of human resources information, achieving the technical effect of improving the accuracy and security of enterprise management.

The invention discloses a multi-robot synchronous control method and system, and relates to the technical field of robot control, and the method comprises the steps: uploading a directional task to a task management center, carrying out the task interpretation and role marshalling planning through a task engine, determining an execution unit cluster, and building a task communication system based on the execution unit cluster through a 5G private network. Gathering at a task side end and deploying a dynamic management component; and based on the task communication system and the dynamic management component, the execution unit cluster is taken over, and driving control and tracking supervision are carried out by robot coding, so that the technical problems of relatively limited efficiency, flexibility and expandability of robot cluster task control and scheduling in a complex environment in the prior art are solved. And efficient, flexible and extensible task management and scheduling can be realized in a complex environment.

The invention provides a power dispatching auxiliary decision-making agent interaction method and system. The method comprises the following steps: constructing a digital mirror image model, obtaining real-time operation information of a power grid, determining a dispatching task, decomposing the dispatching task into a plurality of dispatching sub-tasks, and copying the digital mirror image model to obtain a plurality of copy digital mirror image models; judging whether the sub-scheduling task control strategy meets a preset credibility requirement or not based on an auxiliary decision verification agent; outputting a first scheduling task scheduling scheme based on the aid decision reinforcement learning agent and the aid decision scheduling agent; and outputting a second scheduling task scheduling scheme based on the aid decision reinforcement learning agent. The problems that in the prior art, data are not uniform, historical faults occur or extreme operation states are few, an intelligent agent is difficult to output a valuable scheduling control strategy, and consequently the credibility of the scheduling control strategy is low are solved. According to the method, the intelligent agent interaction environment is simulated more accurately, and the reliability of intelligent agent strategy generation is improved.

The invention relates to a Monte Carlo tree searching method and device and computer equipment. The method comprises the following steps: acquiring first state information of a root node to be searched; controlling at least one idle thread in the first thread pool, determining an extended child node based on the first state information, and generating a simulation task of the extended child node based on the second state information of the extended child node; at least one idle thread in the second thread pool is controlled, the simulation task is executed, a back propagation task is generated, and the back propagation task comprises a simulation reward value; at least one idle thread in the first thread pool is controlled to execute a back propagation task, second state information is updated according to the simulation reward value, and the at least one idle thread in the first thread pool and the at least one idle thread in the second thread pool are distributed to corresponding cores of the processor and executed by the processor in parallel; and repeatedly executing the steps until a preset search condition is met, and determining a search result based on the second state information.

A computing device displays a graphical user interface (GUI) comprising a navigator interface component. The navigator interface component comprises an interactive graphical representation of an asset. The device, responsive to detecting a predefined event, generates an alert. The device updates the navigator interface component to indicate the alert in association with the asset in the interactive graphical representation. The device receives a selection of the asset via the interactive graphical representation. The device displays, in the GUI, a second interactive graphical representation specific to the asset. The second interactive graphical representation of the asset comprises a task control element for controlling tasks specific to management of the asset. The device receives, using the task control element: a task for resolving the alert; and an association with the task and the asset. The device updates the navigator interface component to indicate the association with the task and the asset.