44results about How to "Improve task execution efficiency" patented technology

The invention provides a construction method, a control method and a system of a flight control network, and the method comprises the steps: employing a depth deterministic strategy gradient algorithm, building an attitude control network of an unmanned aerial vehicle in a flight process based on the state information and a target flight value of the unmanned aerial vehicle in the flight process, employing a depth reinforcement learning algorithm, and carrying out the attitude control of the unmanned aerial vehicle in the flight process. Based on the state information and the information of the environment where the unmanned aerial vehicle is located, a path planning network and an intelligent avoidance network of the unmanned aerial vehicle in the flight process are built respectively; based on the attitude control network, the path planning network and the intelligent avoidance network, constructing a hierarchical deep reinforcement learning network of the unmanned aerial vehicle in the flight process; and training the hierarchical deep reinforcement learning network by adopting historical flight data of the unmanned aerial vehicle in different flight environments and a preset flight reward mechanism to obtain a flight control network of the unmanned aerial vehicle. According to the invention, the adaptability and flexibility of the unmanned aerial vehicle in the flight process can be improved.

The invention belongs to the technical field of robot path planning, discloses an autonomous robot path planning method based on deep reinforcement learning, and aims at the problem that an agent is difficult to fully sense the environment in a complex dynamic environment, the State space of SAC is expanded and adjusted to enhance comprehensive sensing of an unknown environment and more flexible omnibearing obstacle avoidance. A heuristic reward function is designed, dynamic adjustment of reward values is achieved, and therefore the learning process of the robot is optimized. In order to solve the problems that the calculation complexity of an algorithm is increased and the convergence speed is reduced in a complex environment, the sample utilization rate is optimized in combination with a prior experience playback (PER) technology and a SumTree data structure. The weight of a high-value sample is increased through the PER technology, so that an intelligent agent can learn an effective strategy more quickly in the training process; and a SumTree data structure is introduced, so that the PER efficiency is further improved.

The invention provides an unmanned aerial vehicle control link system and method, the unmanned aerial vehicle control link system comprises an unmanned aerial vehicle end control module, a ground station control module and a communication link module, and the communication link module is a multi-link redundant communication link module; the ground station control module is used for generating a control instruction and sending the control instruction to the unmanned aerial vehicle end control module through the communication link module; and the unmanned aerial vehicle end control module is used for receiving the control instruction of the ground station control module and controlling the flight and task execution of the unmanned aerial vehicle. According to the technical scheme, by adopting the multi-link redundancy design, the stability and reliability of a communication link are improved, and continuous control of the unmanned aerial vehicle in a complex environment is ensured.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a cluster unmanned aerial vehicle task dual planning method, which comprises the following steps of: constructing a layered task planning framework comprising a global planning subsystem and a local planning subsystem, and enabling the global planning subsystem to execute task subarea division and task distribution; based on the local planning subsystem, task refinement and path planning are carried out on the unmanned aerial vehicle in each task subarea divided by the global planning subsystem, and information is fed back to the global planning subsystem; based on feedback information, through a global planning subsystem, a task planning strategy of the cluster unmanned aerial vehicle is adjusted again, a layered task planning architecture is adopted to get rid of excessive dependence on a single central control node, a complex task can be decomposed into a plurality of relatively independent sub-tasks, the task planning complexity is reduced, the task calculation amount and planning time are reduced, and the task planning efficiency is improved. The resource of each unmanned aerial vehicle is fully utilized, and the resource utilization rate, the information processing capability and the decision scientificity of the cluster unmanned aerial vehicles are improved.

The invention discloses a decentralized fault-tolerant control method for a flexible joint space robot without speed feedback, comprising the following steps: S1: constructing a dynamic model of a flexible joint space robot system whose carrier posture is not controlled; S2: introducing a flexible compensator; based on a singular perturbation method, decomposing the model into a fast-changing subsystem representing joint flexible vibration and a slow-changing subsystem representing arm trajectory tracking; S3: for the fast-changing subsystem, designing a torque differential feedback controller; S4: for the slow-changing subsystem, using a speed observer to identify the joint angular velocity, and using a decentralized neural network to identify the uncertainty and fault terms of the model, and designing a decentralized fault-tolerant control method without speed feedback accordingly; the beneficial technical effects of the invention are: solving the problem that the singular perturbation method cannot be applied to a robot system with a small joint stiffness, using a decentralized neural network to reconstruct the model to improve the task execution efficiency of the space robot, and the robot arm can stably track the expected trajectory.

The embodiment of the invention relates to the technical field of robots, and discloses a robot control method, electronic equipment and a storage medium. The method comprises the steps that a query request sent by a target robot is acquired, the query request is generated when the target robot is forced to be in butt joint with a charging pile, the query request is responded, a target electric quantity range is determined according to a forced pile loading identifier, different electric quantity ranges correspond to different scheduling types, and the scheduling type corresponding to the target electric quantity range is determined as a target scheduling type; and controlling the target robot according to the target scheduling type. According to the embodiment, the situation that the target robot is forced to be in butt joint with the charging pile is marked through the forced pile-on identifier, and the target scheduling type is automatically determined according to the target electric quantity in response to triggering of the forced pile-on identifier, so that the target robot is controlled; in this way, the target robot can be flexibly dispatched by adapting to the electric quantity of the target robot under the condition that the target robot is forced to be in butt joint with the charging pile, and the task execution efficiency is improved.

The invention discloses an unmanned vehicle and an unmanned vehicle control method and device, relates to the field of intelligent mechanical control, solves the problem of low task execution efficiency caused by unreasonable control of the unmanned vehicle, and comprises the following steps: decomposing a first task instruction of a current unmanned vehicle to be operated according to a first large language model to obtain a second task instruction; the first large language model is obtained based on the initial large language model in combination with training of an underlying control interface of the unmanned vehicle; the first task instruction is a natural language instruction generated in response to user input; the second task instruction comprises a task target and a task action; if the task target is not empty, matching the task target according to the semantic navigation map to obtain a navigation target, and generating a task path plan according to the coordinates of the unmanned vehicle and the navigation target in the semantic navigation map; the semantic navigation map is generated by pre-scanning a current task scene by the unmanned vehicle; and controlling the unmanned vehicle to arrive at the navigation target and complete the task action according to the task path planning.

This specification discloses a method, device, storage medium and electronic device for clearing files. By clearing intermediate result files that meet the requirements of configuration files generated by a simulation system during task execution in a multi-threaded parallel manner, more storage space can be quickly released to improve the task execution efficiency of the simulation system.

The invention discloses an LLM Agent task execution method and device and electronic equipment. The method comprises the following steps: S1, identifying a user role; s2, task planning and interaction point configuration; s3, performing active interaction execution; and S4, dynamically adjusting the execution path. According to the method, interaction can be actively initiated at key nodes, an execution strategy is dynamically optimized, personalized interaction experience is provided, task execution efficiency is effectively improved, the risk that an execution result deviates from user expectation is reduced, and system availability and user satisfaction are improved.

The application discloses an AXI bus-based SOC chip, an electronic device and a data transmission method, and relates to the technical field of integrated circuits.The SOC chip comprises a master device, a filtering processing unit, a bus interconnection unit and a slave device.The filtering processing unit is configured to forward a read data instruction sent by the master device to the bus interconnection unit, and store key information in the read data instruction in a storage unit of a storage module based on the receiving sequence of the read data instruction.The bus interconnection unit is configured to send the read data instruction to the slave device, and send read data information returned by the slave device to the filtering processing unit.The filtering processing unit is further configured to read the key information stored in the storage unit based on the receiving sequence of the read data information, determine the validity of the read data information based on the key information and the read data information, send the read data information to the master device when it is determined that the read data information is valid, and send a notification message to the master device when it is determined that the read data information is invalid.

The invention provides a project DFMEA cooperative processing method and system and electronic equipment, and the method comprises the steps: creating a target project DFMEA based on a DFMEA knowledge base and / or an existing project DFMEA; assigning tasks related to the DFMEA of the target project; displaying a task corresponding to the user through a DFMEA task board; displaying the function structure tree on an analysis interface; displaying knowledge data or project data corresponding to the object so that a user can select or apply the object; signing and auditing the DFMEA of the target project, and auditing whether the selection or application of the user to the object in the functional structure tree is accurate or not; and in response to the approval state, recording the approval process of the DFMEA of the target project in the target file. According to the technical scheme, the accuracy of the DFMEA analysis result can be improved, and the analysis efficiency is improved.

This invention provides a method and system for collaborative environmental monitoring and pollution source tracing using UAV swarms in complex terrain, relating to the fields of UAV environmental monitoring and pollution source tracing. The method includes: a ground control center planning layered and altitude-based tasks for the target area and sending them to each group containing master and sub-UAVs; the master UAV decomposing and encoding the sub-tasks and transmitting the results back; the ground-based optimization of flight paths being distributed; the sub-UAVs collecting pollution source data, which is then preprocessed by the master UAV and uploaded; ground-based analysis results being fed back; and the master UAV calculating the contribution value C using a source tracing model. en Once the source tracing is complete, the results are uploaded to the server, and the drone returns to base. The system includes a ground control center and at least one drone swarm; each swarm consists of a master drone and sub-drones. This invention can plan flight paths based on the geographical environment of the monitored target area, avoiding detection blind spots and conflicts, and coordinate actions based on the flight path, thus improving the convenience and accuracy of environmental monitoring and pollution source tracing.

The embodiment of the invention discloses a big data processing method and system based on cloud computing, and the method is realized based on a cloud computing platform, and the method comprises the steps: decomposing a big data processing task into a plurality of micro-tasks, and distributing the micro-tasks to distributed computing nodes; each computing node selects an adjacent node based on a hyper-local algorithm, and constructs a local task processing model only by using local data and metadata of the adjacent node dynamically selected based on a fast game mechanism, the metadata including micro-task data semantic features and task dependency relationships of the adjacent node, and the task dependency relationships of the adjacent node are calculated; semantic features are extracted through a natural language processing technology, and a task dependency relationship is determined based on input and output association of the microtasks; according to the local task processing model, independently executing the micro-task on each computing node, and dynamically adjusting the distribution of the micro-task through a point-to-point local negotiation mechanism; and aggregating the micro-task processing results of the computing nodes to generate a global data processing result. According to the method, the operation and communication overhead is reduced.

The utility model relates to the technical field of equipment coordination, and discloses a multi-equipment coordination control system, which comprises user equipment and a server, and is characterized in that the user equipment comprises main control equipment and at least one coordination equipment, and the main control equipment is connected with the coordination equipment in a wired or wireless manner; the server establishes communication connection with the main control equipment; the main control equipment comprises a first communication interface, a first processor and a first storage module; the cooperative device comprises a second communication interface and an execution module. According to the utility model, through optimizing the communication interface design of the master control device and the cloud instance, the efficient and stable client device cluster physical networking topology is constructed, the hardware module layout is innovated, and the hardware-level security protection structure is strengthened; the problems of unstable equipment connection, poor module collaboration, weak safety protection and the like in the system structure level in the prior art are solved, dynamic permission adjustment, intelligent task allocation and automatic switching after main control equipment fails are realized, and the performance and reliability of multi-equipment cooperative control are improved.

The embodiment of the invention provides a task scheduling method and device, electronic equipment and a storage medium, belongs to the technical field of artificial intelligence, and is suitable for the field of financial science and technology and the field of intelligent medical treatment. The method comprises the steps of obtaining a target task type of a robot, test calculation amount data of a test task and execution performance data of an original actuator; and constructing an initial mapping relation based on the target task type, the test calculation amount data and the execution performance data. And selecting a reference identifier from the original identifiers based on the initial mapping relationship and the current task type. And obtaining a reference actuator according to the reference identifier, obtaining current load data of the reference actuator, and matching current calculation amount data of the current task type. And updating the initial mapping relation according to the execution performance data, the current load data and the current calculation amount data to obtain a target mapping relation. And performing task scheduling based on the target mapping relationship. The task execution efficiency of the robot can be improved.

The application discloses a domain control-based inspection VLA long task control scheduling method and device, relates to the technical field of robots and automation control, and comprises the following steps: analyzing a domain control instruction to obtain task decomposition data; resource matching is performed in combination with a resource state in a domain to generate resource allocation data; conflict resolution is performed on the resource allocation data to generate a task scheduling sequence; resource demand prediction and deviation calculation are performed based on the scheduling sequence to obtain dynamic adjustment data; and control output data is generated according to the dynamic adjustment data and real-time state feedback to drive execution of an inspection VLA long task. In the foregoing manner, the method realizes automatic decomposition of a complex long task, optimal matching of resources, real-time conflict resolution and dynamic scheduling adjustment, and improves the task execution efficiency, resource utilization rate and overall reliability of an inspection system in a multi-domain collaborative environment.

The application provides a task resource pool construction method for a large-scale satellite cluster, belongs to the technical field of aerospace, and is characterized in that, on the basis of abstractly and quantitatively representing required resources of a task and residual resources of each node, a node receiving a task request is taken as a center, communication is performed layer by layer outward, resource information of a participating node is collected, and when the total number of resources of the participating nodes meets the task demand, the search outward is stopped to complete the search of the task resource pool member nodes; the method can make the large-scale satellite cluster have the rapid response capability to deal with sudden tasks, strengthens the autonomous decision-making capability in orbit, and has a wide application prospect.

The invention relates to the technical field of data processing, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task file exception re-reading method, device and equipment and a medium. Generating a to-be-executed task sub-file corresponding to the to-be-executed task main file according to the task configuration parameters; reading a reading progress value and a read data volume corresponding to the to-be-executed task main file, and monitoring a task data reading state of the to-be-executed task main file according to the reading progress value and the read data volume; when the task data reading state is reading failure, reading the to-be-executed task sub-file, and executing the to-be-executed task sub-file based on a reading result of the to-be-executed task sub-file to obtain a sub-file task execution state; identifying a reading abnormal factor of the main file of the task to be executed, and extracting a file configuration parameter of the reading abnormal factor; and synchronously updating the sub-file task execution state and the file configuration parameters to the to-be-executed task main file. According to the method, the task file exception rereading efficiency can be improved.

This application discloses a control method, apparatus, self-moving device, storage medium, and program product for a self-moving device. The self-moving device is equipped with a robotic arm that acquires first position information of a target shoe to be grasped and first grasping position of the target shoe. Based on the first position information and first grasping position, the robotic arm of the self-moving device is controlled to grasp the target shoe. This application embodiment achieves the grasping of footwear objects by acquiring the first position information and first grasping position of the target shoe and controlling the robotic arm to grasp the target shoe based on this information. By using a robotic arm to grasp the target shoe, this application embodiment ensures that the self-moving device autonomously completes the grasping of footwear objects, thereby improving the task execution efficiency and the versatility of the self-moving device.

This invention provides a task execution method, apparatus, electronic device, and storage medium, relating to the field of embodied intelligence technology. The method includes: determining redundant feature components from the first task reference information based on the feature sources of feature components and the importance values ​​of each feature source, wherein the importance value of each feature source represents the magnitude of the influence of the feature component of that feature source on the successful output of the robot's expected action by the first VLA model; performing redundancy removal processing on the redundant feature components in the first task reference information to obtain processed first task reference information; inputting the processed first task reference information into the first VLA model to obtain the predicted action of the robot output by the first VLA model; and controlling the robot to execute the task based on the predicted action. Applying the solution provided by this invention can improve the robot's motion control frequency, thereby improving task execution performance.

The present invention provides a semantic positioning method, device and robot based on probabilistic ontology, the method comprising: based on the position context information of the robot's operating object and human common sense, performing probabilistic ontology knowledge expression and constructing a multi-entity Bayesian network model; based on the entity Bayesian network model, performing multiple instantiations, combining into a target situation Bayesian network, and inferring a first probability value; in the case where the robot has target historical task data, inferring a second probability value based on the target historical task data and the target situation Bayesian network; based on the first probability value and the second probability value, using a weight factor for calculation, obtaining the positioning result of the robot's operating object, so that the robot performs the task according to the positioning result of the operating object and records the historical task data. The semantic positioning method, device and robot based on probabilistic ontology provided by the present invention can improve the accuracy of semantic positioning.

The application discloses a kind of distributed network scanning method and system based on multiple cloud nodes, by collecting the network state and load information of each scanning node, available domain is divided and dynamic scheduling loop is constructed;Reorganize scanning path and identify network blind area through bidirectional connectivity detection, build effective scanning coverage domain;Delay distribution is analyzed to determine low delay window, and the optimal scanning route is planned and task allocation strategy is formulated;Task is disassembled into core detection sequence and edge detection sequence, and hot standby node cluster is constructed for edge task;Abnormal monitoring is carried out to core task, and when node is disconnected, hot standby node is drafted to form takeover link node, and collaborative scanning grid is formed;According to collaborative scanning grid, node feedback topology is built, time delay calibration is carried out to generate distributed scanning instruction, and the execution efficiency and fault tolerance of scanning task in multi-cloud environment are improved.

The utility model discloses unmanned aerial vehicle task load replacement trolley, including trolley garage and electromagnet trolley, and trolley garage is equipped with an opening, and the inside bottom surface of trolley garage is equipped with guide recess and rack, and electromagnet trolley bottom is equipped with guide block, and guide block is inserted in guide recess, and electromagnet trolley is equipped with drive motor and gear inside, and drive motor drives gear rotation, and gear is engaged with rack and drives electromagnet trolley overall translation. Advantages lie in, through control electromagnet trolley on-off electricity, realize the adsorption and release of battery load, can quickly complete the loading and unloading and removal of battery load, and electromagnet trolley cooperates load tray and its track use, and this module structure is compact, and occupies small, can realize the quick disassembly and replacement of task load, significantly shorten the residence time of unmanned aerial vehicle in the nest, improve task execution efficiency.

The invention discloses a charging pile component inventory management method based on a Harris eagle optimization algorithm, and the method comprises the following steps: building an inventory management model, and defining a target function which aims at minimizing the cost; initializing parameters of the Harlins eagle optimization algorithm; generating an initial population; evaluating the population; updating the population, wherein an updating strategy comprises an exploration stage and a utilization stage; and terminating the algorithm and outputting an optimal solution and a fitness value. According to the charging pile part inventory management method based on the Harris eagle optimization algorithm, the Harris eagle optimization algorithm is applied to a power grid charging pile part management process, so that the power grid charging pile part management process algorithm can adapt to a complex working environment, and the task execution efficiency is improved; and risks of conflicts, waste and the like are reduced to the greatest extent, so that greater benefits and safety are brought to integrated and automatic power grid charging pile part management work.

The present invention provides a method and device for executing a water conservancy one map application task driven by a large model, the method comprising: mining out tasks with independent business meanings and establishing a task library according to the user historical behavior log of the water conservancy one map system; using a large language model to generate a text representation of each task according to the meta-task knowledge block of each meta-task in each task, converting the text representation into a vector representation, and storing it in a vector index of the task library; using the vector index to retrieve multiple candidate tasks from the task library; using a large language model to determine a task to be executed according to the user's question content; using a large language model to determine the task name and input parameters of the first meta-task according to the meta-task knowledge block of the first meta-task in the task to be executed, so as to execute all meta-tasks in the task to be executed. The present invention can realize the understanding of the natural language requirements input by the user, and perform accurate task generation and execution.

The application relates to the technical field of mobile robots and discloses a complex under-actuated robot full-dynamics decoupling servo control system and method, wherein the control system comprises a calibration target module, a feature sensing module, a data processing module, a position control module and a posture control module; the control method comprises the following steps: energy equations and potential energy equations are established by analyzing the force conditions of each moving component of a robot body; a robot dynamics model is established by substituting a Lagrange function into a Lagrange equation; a conversion equation representing the conversion relationship between robot control variables and self-state variables is established by analyzing the mapping relationship between the robot control variables and the self-state variables; and a control model is obtained based on the conversion equation, a control algorithm is designed, and a control signal is generated. On the basis of ensuring that the robot self-model can fully reflect the dynamics characteristics of the robot, the posture and position dynamics of the robot are decoupled, the accuracy and stability of the body control are improved, and the maneuverability and sensitivity of the robot are effectively exerted.

The invention relates to the technical field of robots, in particular to a task change processing method, a robot and a storage medium, the method is applied to the robot, and the method comprises the steps that in the process that the robot executes elevator operation, in response to a received task change request, elevator pause operation is executed based on the elevator operation, the elevator operation comprises elevator entering operation, taking operation and elevator exiting operation; changing a task list of the robot based on the task change request; and elevator operation is executed based on the changed task list. According to the method and the device, in the process of executing the elevator operation, the robot pauses execution of the current elevator operation, does not need to initialize or cancel the current task, flexibly updates the task list, and ensures the task execution continuity and high efficiency, so that the task execution efficiency is improved, and the user experience is improved.

This invention discloses a control method for an electrical appliance, comprising: acquiring a map of objects placed in a target space; the object map being generated based on map data constructed by a mobile device during its movement within the target space, the object map containing information on objects placed in different spatial areas within the target space; determining target operating parameters for the electrical appliance based on the object map; and controlling the electrical appliance to operate according to the target operating parameters to perform a target function. This invention also discloses an electrical appliance, an electrical system, and a computer-readable storage medium. This invention aims to improve the convenience of electrical appliances in realizing their functions in conjunction with the actual state of a space, and to improve task execution efficiency.

The application discloses a device resource application method and device, an electronic device and a storage medium. The method comprises the following steps: determining a single container demand quantity for a GPU resource card indicated by a task request in a current task request set; determining a whole machine card quantity of a GPU device in a device cluster; based on the whole machine card quantity and the single container demand quantity indicated by the task request, constructing a task request group in which the sum of the indicated single container demand quantities matches the whole machine card quantity, so as to apply for corresponding GPU device resources based on the task request group. The application can guarantee that the whole machine resources are applied to the device as much as possible at the task request level, thereby reducing the GPU resource cards in the form of fragments, effectively guaranteeing the device resource utilization rate and load balancing. Especially for the case where the task request is sufficient, the task execution efficiency can be improved.

The invention relates to the field of artificial intelligence, in particular to a computing power distribution and scheduling method and device, electronic equipment and a computer program product. The method comprises the following steps: performing quantification processing on computing power resources of various types of computing power nodes and computing power demands of computing tasks to generate quantification results; determining a target computing power distribution strategy at least based on the quantification result and a preset optimization model, the target computing power distribution strategy being used for indicating to distribute the computing task to a target computing power node with the highest matching degree; and based on the target computing power distribution strategy, distributing and monitoring the computing tasks. By dynamically analyzing and calculating the heterogeneity of task requirements and computing power resources and adopting an optimized allocation strategy and a scheduling mechanism, the resources are effectively utilized, and the task execution efficiency is improved.