16results about How to "Improve task processing efficiency" patented technology

The invention provides video processing equipment and a video processing method. The video processing device comprises a multi-core processor, a task scheduling module, a visual processing unit and a cooperative control module, and the multi-core processor is used for sending a task queue corresponding to a video stream to the task scheduling module; the task queue comprises a plurality of to-be-processed tasks and real-time levels corresponding to the tasks; the cooperative control module is used for monitoring the states of the visual processing unit and the multi-core processor to obtain state information and sending the state information to the task scheduling module; the task scheduling module is used for dynamically distributing each task to a target processing component corresponding to each task according to the real-time level and state information corresponding to each task, and each target processing component comprises a multi-core processor and a visual processing unit for processing; the target processing component is used for processing each task to obtain an output result corresponding to the video stream; and the multi-core processor is used for outputting an output result corresponding to the video stream.

The application discloses a media task resource quota calculation method and device, a storage medium and a computer device, and relates to the technical field of media task resource quota calculation. The method comprises the following steps: in response to a media task creation request, extracting multimedia task parameters of a media task, wherein the multimedia task parameters comprise a service type and a service attribute; if the service attribute is contained in a service-specific configuration list corresponding to the service type, determining a weight value corresponding to the media task based on the service-specific configuration list; if the service attribute is not contained in the service-specific configuration list corresponding to the service type, querying a configuration item matched with a resolution parameter of the media task in a resolution configuration list according to the video resolution parameter corresponding to the media task, and determining the weight value corresponding to the media task based on the configuration item; and determining a resource quota value according to the weight value corresponding to the media task, so as to perform server resource scheduling based on the resource quota value.

The application discloses an AGV task compiling and script executing method based on an RDS scheduling system, and relates to the technical field of AGV scheduling. The method comprises the following steps: a task compiling unit and a script configuration unit are built in the RDS scheduling system, and a visual interface and a script language are provided as two operation modes; a running script containing a task allocation sub-script, a driving control sub-script and an exception handling sub-script is compiled through the script configuration unit; the compiled task and the running script are subjected to syntax checking and parameter matching checking, and are stored in a task database after the checking; the RDS scheduling system calls the task and the corresponding running script in the task database, generates executable instructions and sends the executable instructions to a target AGV, the AGV analyzes the task logic according to the running script, completes the task execution and feeds back the execution state. The application can realize flexible configuration of AGV tasks, high adaptation of scripts and tasks and efficient execution of complex tasks, and improves the adaptability, execution efficiency and reliability of the scheduling system.

This invention provides a multi-task learning resource optimization method based on the lottery hypothesis, belonging to the field of multi-task learning and neural network optimization technology. This method employs a sparse expert hybrid approach based on the lottery hypothesis to effectively prune neural network model parameters, thereby significantly reducing the computational cost and storage overhead of the neural network model. The sparse expert network structure retains high sensitivity to key task information. The introduction of a softmax-based router and mask matrix allows each task to select and activate suitable expert network components based on the features of its input data. Cross-training and iterative amplitude pruning optimize the omnipotent expert matrix, further enhancing the model's generalization ability in handling complex tasks and different data distributions. Task-specific layers independently process and optimize the features of each task, reducing mutual interference between tasks and improving the accuracy of feature extraction and the efficiency of task processing.

The application provides a body intelligent robot control method and device, a terminal and a storage medium, and relates to the technical field of robot autonomous task planning and motion control. The method comprises the following steps: collecting multi-modal prompt information of a robot to be controlled; based on a large-scale pre-training language model, multi-modal prompt information and an atomic skill library suitable for long sequence whole body linkage motion of the robot, generating a corresponding chain reasoning text when the robot to be controlled executes a task description, and decomposing the task description into an atomic skill calling sequence according to the chain reasoning text, wherein the atomic skill calling sequence comprises a plurality of atomic skills in calling order; after each atomic skill is called and activated one by one in the calling order, the robot to be controlled is controlled to execute each atomic skill in the calling order. The application can realize seamless connection of whole body motion and local operation of the robot, so that the robot can efficiently complete a long sequence task in a complex environment.

This application relates to a service management method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: acquiring a renewal request initiated by a target organization for an advanced service; parsing the renewal request to determine the expected number of organizational members in the target organization who wish to have access to the service in the next cycle after the advanced service expires; if the expected number of members differs from the actual number of target members, determining the service type matched to each organizational member in the next cycle based on the expected number of members, and, upon the expiration of the advanced service, matching organizational members of the advanced service type in the next cycle and assigning access to the advanced service. The target organization includes multiple organizational members; the target members among these organizational members currently possess access to the advanced service. This method can improve work efficiency.

This application discloses a task processing method, electronic device, storage medium, and program product, relating to the field of distributed technology. The method includes: a scheduling node constructing corresponding tasks to be executed based on product data and multiple processing procedures of each product to be processed that meet preset splitting processing conditions; writing each task to be executed into a task to be executed list; and sending the task to be executed list and pre-set target scheduling parameters for splitting services to a management center node; the management center node writing the tasks to be executed from the task to be executed list into a task to be executed queue according to preset writing rules; allocating execution nodes to the tasks to be executed dequeued from the task to be executed queue based on the target scheduling parameters; and distributing the dequeued tasks to the corresponding execution nodes; and the execution nodes parsing product data and multiple processing procedures from the received tasks to be executed, and executing multiple processing procedures based on the product data, thereby improving task processing efficiency and scalability.

The embodiment of the invention provides a data processing method and system, terminal equipment, a storage medium and a program product, and the method comprises the steps: determining a plurality of first processing tasks which are used for indicating to-be-processed data and information required for processing the data; obtaining resource information of a plurality of first resources, and sorting the plurality of first resources according to the resource information to obtain a first sequence identifier of each first resource; and determining a first processing task required to be executed by each first resource according to the first sequence identifier of each first resource, and processing a plurality of first processing tasks required to be executed according to the plurality of first resources. And the data processing efficiency is improved.

The invention provides a task processing method and device, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a plurality of first tasks to be processed, task states of the first tasks and memories needed for processing the first tasks, wherein the task states are used for indicating stages where the first tasks are located; according to the plurality of task states, determining a memory resource pool corresponding to each first task, the memory resource pool being used for processing the first tasks with the same task state, and the memory resource pool comprising a memory unit for providing and executing the first tasks; determining a target memory unit in a plurality of memory units of a memory resource pool corresponding to the first task according to the memory corresponding to the first task, and processing the first task according to the target memory unit; after the first task is processed, the data in the target memory unit is emptied, and the emptied target memory unit is recycled into the corresponding memory resource pool, so that the task processing efficiency is improved.

The application discloses a kind of adaptive double time scale buoy satellite temperature perception service deployment and task scheduling method.First, facing buoy-satellite collaborative ocean edge computing network, the network model of underwater node, buoy node and low-orbit satellite collaborative processing task is constructed, and the edge node temperature model is established in combination with node computing power consumption, thermal resistance thermal capacity parameters and environmental temperature.Second, the self-attention mechanism is used to fuse service request, link availability, node temperature and short time scale operation cost, and the long time scale service deployment interval is dynamically determined.Finally, the improved DQN is used in the long time scale layer to generate temperature perception service deployment scheme, and the improved TD3 is used in the short time scale layer to obtain task scheduling scheme.The application can reduce the comprehensive system cost and node overheating risk, and improve the computing task processing efficiency of cross-domain ocean edge network.

The invention discloses a distributed task scheduling method and device, and belongs to the technical field of distributed task scheduling, and the method comprises the steps: obtaining a current load parameter, a first task label and preset task processing time consumption, and sending a task pulling request signal to a task pool under the condition of judging that the task pool is in an idle state, sending the task to be processed to a task pool to enable the task pool to feed back second task labels of all the tasks to be processed, task estimated load parameters and a task preset timeout time threshold value, and then for each task to be processed, when it is determined that the current task to be processed is a target task which can be currently processed by itself, pulling the target task from the task pool; and finally, processing the target task, and feeding back a processing result to the task pool after the target task is successfully processed. By implementing the method and the device, the problems that the CPU is easy to idle, the overall resource utilization rate is reduced and the task processing efficiency is low due to the adoption of a scheduling mechanism based on an active task pushing mode in the prior art can be solved.

The embodiment of the invention discloses a robot control method, a robot control system and a computer readable storage medium. The robot control method is applied to the robot control system, and the robot control system comprises a control end, a cloud end and an execution end. The method comprises the following steps: a control end obtains interaction data of a user and unifies the interaction data into a task instruction; task information is obtained according to the task instruction, and the task instruction and the task information are sent to the cloud; the cloud performs task splitting analysis based on the task instruction and the task information to generate a corresponding composite task planning set; issuing the composite task planning set to an execution end; and the execution end generates an execution instruction according to the composite task planning set and drives the robot to complete corresponding actions. Therefore, generalization is improved through interaction between the execution end and the user, the cloud end completes control planning and execution end control of the task execution end, the problem of long-view composite tasks is solved, and optimal resource configuration is achieved.

The invention provides a multi-agent task scheduling method, device and equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a natural language request input by a user; performing structured task decomposition on the natural language request to generate a task graph containing a plurality of task nodes and dependency relationships thereof; matching a target execution agent corresponding to each task node from a preset agent registration center according to the required capability label of each task node in the task graph; scheduling each target execution agent to execute a corresponding sub-task, and obtaining a structured execution result of each task node; and generating a task execution response corresponding to the natural language request according to the structured execution results of all the task nodes. According to the invention, the adaptability and reliability of multi-agent cooperative processing of complex tasks can be improved.

This application discloses an electronic device, an information processing method, and a chip, relating to the field of artificial intelligence technology. The specific technical solution is as follows: the electronic device includes a system-level artificial intelligence (AI) engine integrated within the operating system of the electronic device; wherein, the system-level AI engine is used to invoke hardware components and / or system services of the electronic device based on user intent information to execute a target task.

This invention relates to a resource scheduling method and related equipment for a hybrid computing cluster. The method includes: acquiring computing tasks and extracting task characteristic parameters; estimating the quantum computing speedup ratio based on the task characteristic parameters; if the quantum computing speedup ratio is greater than a threshold, allocating the computing task to a quantum computing unit for execution; acquiring the physical state parameters of each quantum computing unit to calculate a quantum resource health score; dividing the computing task based on coherence time constraints according to the quantum resource health score and a decoherence time window, generating multiple quantum subtasks; before the execution of the quantum subtasks, if it is determined that the estimated execution time of any quantum subtask exceeds the corresponding decoherence time window, or the quantum resource health score is lower than the score threshold, triggering an automatic rollback mechanism to schedule the corresponding quantum subtask to a classical computing unit for computation. This solves the problems of poor resource scheduling and low task processing efficiency in related technologies.

The invention relates to the technical field of artificial intelligence and knowledge graph fusion application, in particular to a large model information processing method based on a knowledge graph. The process comprises the following steps: in a task analysis stage, embedding structured Schema information of a knowledge graph into a prompt template, and guiding a large language model to output a structured task object conforming to a predefined task model under a field constraint condition; in the information processing process, a field-level format verification and enumeration consistency judgment mechanism is introduced, and legality verification and exception labeling are carried out on task fields; in a knowledge graph calling stage, based on a mapping relationship between a task field and a graph structure, automatically constructing a query statement conforming to a graph Schema constraint and executing query; in the result output stage, the query results are classified and sorted, semantic feedback information is generated, and interpretable output of the results is achieved. The standardization, the automation degree and the engineering availability of satellite task information processing are improved.