84 results about "Task mapping" patented technology

The invention discloses an adaptive task division and decision joint optimization-based cloud edge computing resource collaborative scheduling method for a complex manufacturing system. The method comprises the following steps of 1, constructing a hierarchical cloud-edge collaborative computing network model; constructing a multi-objective optimization model, and defining an objective function and constraint conditions; 2, dynamically predicting and calculating a resource state through a resource sensing module based on an LSTM neural network, and generating a node resource prediction matrix; 3, dividing a calculation task generated by the manufacturing system into fine-granularity, medium-granularity and coarse-granularity subtask sets by adopting a multi-granularity subtask division algorithm (MSPA), and mapping the subtasks to corresponding calculation nodes; 4, constructing a task unloading decision model based on the D3QN, and dynamically selecting unloading nodes and an execution sequence of the subtasks in combination with a multi-objective optimization reward function; and 5, iteratively optimizing parameters of the D3QN model through a target network updating mechanism and a self-adaptive exploration strategy to realize real-time dynamic adjustment of a task scheduling decision.

The invention provides a robot control method based on Model-Based and RL, and relates to the technical field of automatic control. According to the method, a high-level task atlas model and a predefined task action set are constructed, a target task is mapped into a state node, graph search is carried out in combination with a current world state, an optimal task action path is planned, and a first action instruction is output. And calling a baseline strategy in the simulation environment to generate a control instruction, executing feasibility verification, compensating the deviation between the simulation and the real environment through the error prediction model, correcting the real control instruction, and then driving the robot to execute. And dynamically updating the success probability weight of state transition in the atlas based on the execution result, and feeding back the executed state for subsequent decision. According to the method, the reliability, the adaptability and the execution safety of migrating a simulation strategy to a real robot are improved, and closed-loop optimization and continuous self-adaption of a complex task are realized.

The invention relates to the technical field of task coordination execution, and discloses a multi-task intelligent coordination execution method and system based on a health care accompanying robot, and the method comprises the following steps: 1, collecting multi-modal perception data, and carrying out the integration to generate a multi-modal fusion feature vector; 2, identifying to obtain a target event object, and clustering to obtain a situation-emotion cluster; step 3, performing task mapping on the target event object, extracting a time urgency degree and a historical success rate, and performing weighted fusion to obtain a task priority index; 4, acquiring the occupation amount of each task on robot resources, and calculating a resource cooperation index between the tasks; 5, determining an optimal task subset through an optimization algorithm, and generating a planning path and an action sequence instruction; and step 6, when the deviation between the planned path and the actual execution path exceeds a preset deviation threshold value, triggering rescheduling. According to the invention, efficient, accurate and reliable multi-task execution of the robot in a dynamic environment is realized.

The invention relates to the technical field of digital twinning, in particular to a digital twinning task scheduling system and a digital twinning task scheduling method oriented to multi-machine cooperation, which realize intelligent mapping between resources and tasks by introducing a topological resource space model. Comprising a topological resource space builder, a task analysis and modeling device, a task resource mapping decision-making device, a global monitoring and optimizing unit and a data fusion processor, the topological resource space builder builds a topological space model of a resource and task mapping relation, and the task analysis and modeling device extracts feature information and a dependency relation of a digital twin task; a task resource mapping decision maker generates a task scheduling decision based on a topological space model and feature information, a global monitoring and optimizing unit dynamically adjusts connectivity parameters of the topological space model according to execution information, a scheduling strategy is optimized, the resource utilization rate is increased, and a data fusion processor merges execution result data and improves the task scheduling efficiency. And through multi-dimensional task feature analysis and intelligent decomposition combination, complex tasks are flexibly dealt with, and the execution efficiency is improved.

The invention relates to the technical field of multi-agent systems, in particular to a task matching method and system based on a multi-agent system, a master agent is used for analyzing a request of a user, identifying a plurality of corresponding sub-target tasks according to the request, and establishing task mapping with other slave agents according to the identified sub-target tasks; and the slave agent performs corresponding processing according to the relationship between the sub-target tasks, and stores a corresponding processing result to a local knowledge base. According to the method, the newly added task requests are collected to expand and supplement the primary knowledge graph, the expanded knowledge graph is subjected to element analysis, so that the incidence relation between the elements is recognized, then whether the task requests needing to be processed conform to the incidence relation or not is judged, the time dependence relation of the task requests is further judged, and the task requests are processed according to the time dependence relation. And the master agent allocates different slave agents to perform task processing according to different results which are possibly obtained, so that the corresponding slave agents can be allocated more accurately.

The invention provides a single instruction stream multi-fiber programming model and a construction method thereof. The method comprises the following steps: constructing a task, thread and fiber three-level concurrent unit; different code logic units are configured for each task, and one task is mapped to a plurality of threads; mapping the threads and processor cores one by one, and mapping one thread to a plurality of threads; the threads within the thread are caused to share all registers on the processor core. According to the single-instruction-stream multi-fiber-thread programming model and the construction method thereof, the programming model is constructed based on the fiber threads, concurrent execution of asynchronous data streams is achieved, the resource utilization rate is effectively increased, and redundant calculation is eliminated.

A self-optimizing and self-programming computing system (SOSPCS) design framework that achieves both programmability and flexibility and exploits computing heterogeneity [e.g., CPUs, GPUs, and hardware accelerators (HWAs)] is provided. First, at compile time, a task pool consisting of hybrid tasks with different processing element (PE) affinities according to target applications is formed. Tasks preferred to be executed on GPUs or accelerators are detected from target applications by neural networks. Tasks suitable to run on CPUs are formed by community detection to minimize data movement overhead. Next, a distributed reinforcement learning-based approach is used at runtime to allow agents to map the tasks onto the network-on-chip-based heterogeneous PEs by learning an optimal policy based on Q values in the environment.

The invention relates to the technical field of resource scheduling, in particular to an AI-assisted multi-factory collaborative production scheduling system which comprises a path filing module, a task sorting module, a strategy switching module, a node evaluation module and a task mapping module. According to the invention, the device sharing node is identified through the transverse connection of the device and the path node, the resource cross interference degree is effectively mastered, the task priority is accurately adjusted, the coordination of resource allocation and task execution is optimized, and the flexible adjustment capability in the task execution process is improved; node resource state switching and fluctuation changes are dynamically monitored, abnormal node load and processing time features are accurately extracted, a path replacement strategy is finely judged in combination with equipment shutdown time sequence analysis and resource residual feature classification, and the flexibility and adaptability in multi-factory collaborative production scheduling are enhanced. Equipment faults and state fluctuation are pre-judged, and a path strategy is adjusted, so that the risk of task delay caused by production rhythm fluctuation and resource interference is effectively reduced.

The invention provides an index query method and device, electronic equipment and a medium. The method comprises the steps that input information of a user is acquired; matching the input information with a sub-query task in a preset query task mapping table to obtain at least one matched sub-query task; obtaining a parameter template corresponding to at least one sub-query task, and normalizing query parameters of the sub-query task based on the parameter template; calling a query interface of the at least one sub-query task to obtain normalized query parameters of the at least one sub-query task so as to output a query result of each sub-query task; the query task mapping table comprises a plurality of sub-query tasks, and each sub-query task corresponds to one parameter template and one query interface. The technical problem that an existing large model query strategy is high in calculation cost and long in calculation time is solved.

The invention relates to the technical field of software, in particular to an automatic construction method based on dependency topology analysis and dynamic orchestration, which comprises the following steps of: reading and analyzing a predefined configuration file to construct a service dependency relationship graph; wherein nodes in the service dependency relationship graph represent services, and directed edges represent dependency relationships among the services. According to the method, the predefined configuration file is read and analyzed to construct the service dependency relationship graph, so that automation of dependency management is realized, and manual identification errors are avoided; through dynamic mapping of services and construction tasks, the problem of conversion from a logic architecture to physical execution is solved; based on collaborative optimization of topological sorting and a priority strategy, the construction efficiency and the resource utilization rate are improved, and time consumed by a critical path is shortened. Therefore, full-link automation from dependency identification and task mapping to intelligent arrangement is completed, and the efficiency bottleneck and error risk caused by manual intervention are effectively solved.

The invention provides a secondary equipment debugging intelligent auxiliary method and system based on a secondary logic model, and relates to the technical field of debugging assistance, and the method comprises the steps: collecting multi-source structured data related to secondary equipment; constructing a secondary logic model diagram for debugging assistance by taking the equipment nodes as objects and the communication relationship and the logic control relationship as connecting edges based on the multi-source structured data; obtaining a debugging task; mapping the debugging task to a corresponding node in the secondary logic model diagram through a semantic and configuration fusion matching mechanism; based on the secondary logic model diagram and the debugging task mapping result, generating a structured debugging operation template in a mode of combining template retrieval, state injection and logic verification; based on the structured debugging operation template, comparing and checking the running state data of the current debugging object equipment; and on the basis of the comparison check result and the structured debugging operation template, executing online monitoring of the link state, and assisting in realizing intelligent analysis of the debugging state.

The invention discloses a high-end memory chip asynchronous test system based on a distributed architecture, which relates to the technical field of chip testing, and comprises a state modeling module, a master control end initialization and test end access, memory chip test task pool construction, test end state analysis in combination with historical test load and historical fault information, and memory chip asynchronous test. Generating an optimized scheduling strategy; the scheduling module is used for performing task division and asynchronous scheduling on the memory chip test task pool by adopting an intelligent scheduling algorithm based on an optimized scheduling strategy to generate an asynchronous data flow queue; according to the issuing module, the test end receives the asynchronous data flow queue in an asynchronous mode to carry out protocol unpacking and task mapping, and a memory chip function test task is obtained and issued; according to the method, the test throughput rate and the resource utilization rate are improved by asynchronously sequencing and scheduling tasks, the task scheduling delay is reduced, the load of each test end is balanced, and meanwhile, the horizontal expansibility and the fault-tolerant capability are enhanced.

The invention discloses a privacy task scheduling method and device, equipment, a storage medium and a product, and relates to the technical field of computers.The privacy task scheduling method comprises the steps that after a privacy task operation request is received, the privacy task operation request is split into a plurality of privacy subtasks in sequence; mapping the privacy subtasks to corresponding task container mirror images; and scheduling the task container mirror image to different privacy computing platforms to execute the privacy task. According to the method, the private task is decomposed into the independent sub-tasks, each sub-task of the appropriate type is dispatched to the appropriate platform through the task container mirror image, the resource characteristics of each platform can be fully utilized, resource waste is avoided, the overall task execution time is shortened, and the private task processing efficiency is improved.

This invention belongs to the technical field of embedded artificial intelligence and real-time system scheduling, and more specifically, relates to an adaptive load resource scheduling method and apparatus for embedded GPUs. The method involves offline acquisition and modeling of the platform's thermal throttling behavior, constructing DNN variants with different computational loads and accuracies, and profiling them under multiple frequency and TPC configurations to obtain a latency-power mapping table. In the online phase, based on task deadline margins and current temperature / frequency states, the method looks up the variant with the highest accuracy and meeting the deadline under the current thermal conditions and selects the corresponding TPC configuration from the profiling table. If necessary, phased online pruning and low-overhead switching are implemented, and the method collaborates with system-level DVFS / task mapping to optimize energy efficiency. This method can still guarantee the real-time performance of inference tasks under passive frequency reduction conditions and significantly reduce performance fluctuations caused by thermal throttling.

The invention provides a partition management method. According to the method, in the task mapping process, firstly, a partition to which each data item in a partition belongs is determined, the partition has a plurality of data, and the partition and an induction task have a corresponding relation; counting the data items belonging to the same partition to obtain a data item counting result; and then a data entry statistical result is sent to the management node, so that the management node makes a decision on whether to adjust the induction task or not. According to the method disclosed by the invention, the management node can make a decision on whether to adjust the number of induction tasks or not before the mapping task performs partition copying on the data items in the fragments to obtain intermediate data. Meanwhile, due to the fact that the decision is made in advance, a certain mapping task can be immediately read by the induction task after generating the intermediate data, and the induction task does not need to start to read the data items after all other mapping tasks generate the intermediate data. Therefore, the data reading efficiency is improved.

The application discloses a heterogeneous platform approximate computing task optimization mapping method based on DVFS and DPM. Firstly, real-time tasks with correlation are modeled as an approximate computing task model, so that a task directed acyclic graph (DAG), a task correlation matrix and a six-tuple representing task characteristics can be obtained; then, a mechanism of DVFS and DPM combination is introduced based on a heterogeneous multi-core platform; a problem description of task mapping based on QoS and energy joint optimization is constructed; a variable substitution method and a Big-M reconstruction method are used to process nonlinear terms in the problem, the task mapping problem is linearized, and an optimal solution is obtained through a Gurobi solver; a task layering method and a greedy algorithm are used to design a low-complexity heuristic algorithm, and the scalability of the mapping method is improved. The method of the application adopts the mechanism of DVFS+DPM joint optimization under the premise of meeting the system real-time, energy efficiency and reliability constraints, and improves the QoS of the system.

The present disclosure provides a task mapping method, comprising: determining multiple layers of second matrices based on a first matrix, wherein each layer of the second matrix is ​​divided into at least one second sub-matrix; wherein the elements in the second matrix of the N+1th layer correspond one-to-one to multiple second sub-matrices of the Nth layer, and the second sub-matrix of the 1st layer is a sub-matrix of the first matrix; determining the operation instructions corresponding to each second sub-matrix; mapping at least one second sub-matrix in each layer of the second matrix and the operation instructions corresponding to the second sub-matrix to multiple processing cores in a many-core system, wherein each processing core corresponds to a second sub-matrix and a corresponding operation instruction, so that the processing core calculates the transpose of its corresponding second sub-matrix according to its corresponding operation instruction and stores the calculation result; wherein N is a positive integer. The present disclosure also provides a task processing method, a processing core, and an electronic device.

The invention discloses an AI interaction terminal control method and system based on a cloud platform, and belongs to the technical field of artificial intelligence. State parameter sets of a plurality of AI interaction terminals are collected and uploaded to the cloud platform; constructing a modeling engine including behavior pattern recognition and state coupling analysis based on the state parameters, and generating a multi-terminal coupling sensing model; terminal priorities are calculated according to the model, an asymmetric task mapping matrix is constructed, and a control strategy is generated; issuing a heterogeneous control instruction to the terminal and collecting execution feedback; self-learning updating of the model and the strategy is carried out in combination with the original state parameters and the feedback data; if the terminal fault is detected, task migration is carried out based on the coupling weight and the redundancy, and the control matrix is updated; and finally, cross-terminal linkage and control strategy self-evolution are realized through S-F-C closed-loop iteration. According to the invention, multi-terminal cooperative sensing, adaptive control and task robust migration are realized, and the intelligence and stability of an AI control system are improved.

This invention discloses an industrial agent reasoning framework and method based on physical first-principle constraints, belonging to the field of industrial intelligence technology. The framework includes a data access layer, a task parsing layer, a candidate solution generation layer, a physical constraint verification layer, a conflict rollback and replanning layer, an execution decision layer, and a knowledge accumulation layer. The physical constraint verification layer uses at least one physical first-principle model as a pre-constraint hard constraint gating before the candidate decision sequence enters the execution decision layer. It performs parameter mapping, boundary condition solving, consistency calculation, residual determination, and executability determination on the physical computation variables obtained from the structured reasoning task mapping, and outputs structured conflict information when the verification fails. The conflict rollback and replanning layer performs targeted rollback and replanning based on the violation type, violation node level, and source of missing boundary conditions in the structured conflict information. The execution decision layer only outputs execution-type output results for candidate decision sequences that have obtained the pass flag. The knowledge accumulation layer writes the structured conflict information and the corrected pass results into a rule base, template base, or historical case base to serve as filtering conditions, initial parameter values, tool sorting criteria, or threshold setting criteria during subsequent candidate generation. This invention can improve the physical feasibility, reliability, interpretability, and auditability of industrial agent decision-making results.

The present invention relates to the technical field of task coordination and execution, and discloses a multi-task intelligent coordination and execution method and system based on a health care accompanying robot, comprising the following steps: Step 1, collecting multimodal perception data and integrating it to generate a multimodal fusion feature vector; Step 2, identifying the target event object and clustering it to obtain a context-emotion cluster; Step 3, performing task mapping on the target event object, extracting the time urgency and historical success rate, and weighted fusion to obtain a task priority index; Step 4, obtaining the robot resource usage of each task and calculating the inter-task resource coordination index; Step 5, determining the optimal task subset through an optimization algorithm, and generating a planned path and action sequence instructions; Step 6, triggering rescheduling when the deviation between the planned path and the actual execution path exceeds a preset deviation threshold. The present invention realizes efficient, accurate, and reliable multi-task execution of the robot in a dynamic environment.

The invention relates to the technical field of computing power scheduling, and discloses a computing power intermediate station resource layer full-link cooperative scheduling method oriented to multiple service scenes. According to the method, a resource request carrying a service scene label is received, the requirements for GPU computing power, a memory and storage IO are analyzed, and a standardized resource descriptor is generated. Meanwhile, states of all physical nodes are dynamically collected, a global resource view is constructed, resource descriptors are matched with the view, priorities are distributed in combination with service scene labels, and a resource reservation mechanism is triggered. And mapping the task to a target node according to a matching result, and executing GPU computing power division, memory allocation and network bandwidth reservation. Strategies are dynamically adjusted in the scheduling process, and efficient utilization of resources is ensured.

The application provides a method and system for single-step debugging in a data processing flow, and belongs to the field of data processing. The method comprises the following steps: receiving a single-step debugging start instruction, mapping the whole flow to a flow instance by analyzing the content of the data processing flow, and mapping the tasks being executed or having been executed in the flow to task instances; executing the current task instance, updating the task instance state and attribute assignment after the execution is completed; interrupting the flow instance after the execution of the task instance is completed; when the returned task instance information is received, the execution state and input and output data of the task instance are visually displayed according to the task instance state; when the next step running instruction is received, the flow instance identifier is input, the next task to be executed of the flow instance is queried, and the task instance is encapsulated and run, and the above steps are repeated to complete the whole flow debugging. The application can realize flow interruption and recovery, real-time visualization of intermediate data, greatly improve the debugging efficiency, and help to quickly locate errors.

The application discloses a quantum computing task mapping method and a quantum computer operating system, idle quantum bits in a quantum chip are dynamically partitioned in real time according to actual requirements of a quantum computing task to be executed, a quantum bit topology structure obtained can be matched with the quantum computing task to be executed, no waiting time is generated in the matching, and the matching degree is high, so that the resource utilization rate of the quantum chip is greatly improved, and the execution timeliness of the quantum computing task in a program waiting queue is effectively improved; by using the mapping method, each quantum computing task in the program waiting queue can quickly find an optimal partition area on idle quantum bits of a required quantum chip in a quantum chip cluster of the system to perform mapping.

The invention relates to the technical field of multi-robot cooperative control, in particular to a multi-robot cooperative control method and system based on a multi-modal large model. The method comprises the steps of obtaining multi-modal environment data and a natural language instruction containing at least one target task in response to a heterogeneous sensor, uploading the multi-modal environment data and the natural language instruction to a cloud end, and generating an analyzable task plan according to the multi-modal environment data and the natural language instruction through a cognitive loop; splitting the analyzable task plan into sub-tasks corresponding to each robot, generating a task mapping table based on the sub-tasks, and issuing the task mapping table to the robot, so as to execute the task mapping table through a control loop of the robot; the problem that an existing robot cannot perform effective cooperative operation in the process of executing the intention of a worker in time can be effectively solved.

Disclosed herein is a method for performing a robot skill based on skill uncertainty using a large language model. The method includes generating a subtask list using a large language model by receiving a target task and environment information, mapping a subtask in the subtask list into a skill embedding space through an abstract skill policy network, and performing the subtask by decoding the mapped subtask through a manipulation skill policy network.

This invention relates to an energy-saving scheduling method for inaccurate hybrid critical tasks based on a genetic algorithm, comprising the following steps: establishing an inaccurate hybrid critical task scheduling model; encoding the mapping of inaccurate hybrid critical tasks to multiple processors; formulating a fitness function to evaluate and select task mapping schemes to processors; performing crossover and mutation operations on existing task mapping schemes; and calculating the energy-efficient speed of each processor's task set. This invention, based on a genetic algorithm, seeks task mapping schemes that balance the load across multiple processors and calculates the optimal speed for calculating the energy consumption of each processor, ensuring that all tasks meet deadline requirements and reducing system energy consumption.

A data transmission method and apparatus, the method comprising: mapping M tasks of a first service to N logical channels, M and N being positive integers greater than or equal to 2; each task in the M tasks comprises one or more messages; and executing a logical channel priority (LCP), the logical channels for executing the LCP including L logical channels in the N logical channels, and L being a positive integer less than or equal to N. The method can improve the data transmission performance.

The application discloses a kind of unified impedance control method and system of redundant mechanical arm, control method includes: obtaining the state data of each joint of redundant mechanical arm and the desired pose of end effector, and calculating end actual pose;Task mapping matrix and task space impedance control quantity are obtained by solving;Zero space base vector is constructed, and corresponding zero space control quantity is calculated;Task mapping matrix and zero space base vector are fused to construct generalized mapping matrix, and task space impedance control quantity and zero space control quantity are fused to construct generalized control force vector;Generalized jacobian matrix and seven-dimensional generalized control force vector are calculated by matrix multiplication operation to obtain each joint control moment, and after limiting, it is output to servo driver.In the application, by constructing generalized space, joint torque can be directly output by once matrix multiplication, task and zero space are decoupled, calculation efficiency is improved, and high control precision and motion stability are obtained.

The application provides a robot control method based on Model-Based and RL, and relates to the technical field of automatic control.The method maps a target task into a state node by constructing a high-level task graph model and a predefined task action set, performs graph search in combination with a current world state, plans an optimal task action path, and outputs a first action instruction.In a simulation environment, a baseline strategy is called to generate a control instruction, a feasibility check is performed, a simulation and real environment deviation is compensated through an error prediction model, a real control instruction is corrected, and a robot is driven to execute.The success probability weight of state transition in the graph is dynamically updated based on the execution result, and the state after execution is fed back for subsequent decision-making.The application improves the reliability, adaptability and execution safety of simulation strategy migration to a real robot, and realizes closed-loop optimization and continuous self-adaptation of a complex task.

A computer-implemented method can receive an internal representation of a transformer model which defines one or more repeating blocks, each block including a sequence of cells, and each cell including a set of tasks of the transformer model. The method can search for a plurality of parallel schedules for partitioning devices included in a device cluster for parallel execution of the transformer model. The searching includes determining a number of model replicas, determining a number of stages that divide the one or more repeating blocks, determining a number of cell replicas for each cell in a block, and for each cell replica of a cell, generating a task mapping which maps the set of tasks included in the cell to devices partitioned into the cell replica.