15 results about "Task mapping" patented technology

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.

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.

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.

This invention discloses a real-time optimization method and system for a robot control board based on TSN and a heterogeneous architecture. First, the task status, resource utilization, and communication status of the robot's heterogeneous computing units are collected to generate a system state matrix. A global resource decision model is then input to generate a collaborative optimization instruction set, including resource mapping adjustment instructions for computing tasks and traffic scheduling configuration instructions for the TSN network. Based on this instruction set, the heterogeneous computing scheduler and TSN configurator are invoked respectively to dynamically adjust the mapping relationship of computing tasks and the traffic scheduling strategy of the network, thereby generating adjusted task mapping relationships and network scheduling strategies. The adjusted strategies are then applied to run the robot control board, and state acquisition is restarted, forming a closed-loop optimization. This application achieves joint optimization of computing and network resources through global collaborative decision-making and scheduling, effectively improving the real-time determinism, resource utilization efficiency, and dynamic environment adaptability of the robot control system.

The application provides a Hawk algorithm acceleration method based on GPU multi-thread parallelism. The Hawk algorithm acceleration method based on GPU multi-thread parallelism comprises the following steps: (1) thread resource division and task mapping; (2) kernel fusion and throughput peak detection; (3) NTT / iNTT parallelization reconstruction and memory access optimization; (4) FFT / iFFT structured parallelization optimization and butterfly operator fusion. The application realizes a coarse-grained parallel butterfly operator execution mode without complex address calculation, without shared memory synchronization, and without competition between threads, greatly improving the throughput efficiency on the GPU; meanwhile, the application unifies the parallel execution framework in the forward and inverse transformations of FFT / NTT, wherein the inverse transformation only needs to use the corresponding inverse rotation factor and perform simple normalization processing at the end to complete the overall recovery.

The application discloses an optimization method of a multi-layer long short-term memory network (LSTM) based on diagonal wave front parallelization, and belongs to the technical field of deep learning neural network optimization. The method aims at the problem of limited parallelism caused by the inter-layer and inter-time step dependency relationship existing in the multi-layer LSTM network, and proposes a diagonal wave front scheduling strategy. The calculation task of the multi-layer LSTM is mapped to a two-dimensional space-time plane, and the cross-layer parallel execution is realized by identifying the calculation units without data dependency on the same diagonal line. The application adopts the persistent kernel function technology to reduce the kernel function startup overhead of the parallel computing device, and combines the fine-grained synchronization strategy to maximize the utilization of the computing resources under the premise of ensuring the calculation accuracy. Compared with the traditional layer-by-layer serial execution mode, the application can effectively improve the calculation parallelism and the utilization of the computing resources of the multi-layer LSTM, and is particularly suitable for the inference scene of the long sequence and multi-layer LSTM.

The application provides a submarine data center wind power supply coordinated scheduling optimization control method, relates to the technical field of energy supply scheduling, and comprises the following steps: taking submarine data center basic power consumption as a benchmark load, performing wind power prediction to obtain a net wind power curve; extracting a plurality of elastic computing tasks; performing task mapping to obtain an initial task sequence; performing heuristic task sorting to generate N initial scheduling solutions, each initial scheduling solution containing K scheduled elastic tasks; performing iterative optimization based on neighborhood disturbance search to obtain an optimal computing task concurrent scheduling sequence and perform coordinated scheduling of the plurality of elastic computing tasks. The application solves the technical problem in the prior art that all computing tasks are generally uniformly included in a centralized optimization model for global solution, a long calculation time is required for convergence, time lag exists between the scheduling result and the current energy state, and real-time coordination between task scheduling and wind power is reduced.

The application provides a simulation training method, system, electronic equipment, storage medium and program product, and relates to the technical field of national defense simulation training. The simulation training method comprises the following steps: obtaining standardized architecture data, analyzing the standardized architecture data to obtain system interface description view data, architecture evolution description view data, capability task mapping view data and global view data; extracting architecture constraint rules based on the system interface description view data and the architecture evolution description view data, constraining the user's scenario editing operation based on the architecture constraint rules, and generating target scenario data; based on the target scenario data and the global view data, performing simulation training and collecting simulation training process data in real time; and based on the capability task mapping view data and the simulation training process data, generating architecture evaluation results. The application can solve the problems that the existing simulation training method is not consistent with the architecture, the scene consistency is poor, and the evaluation lacks pertinence.

This invention relates to the fields of educational informatization and artificial intelligence, and discloses a real-time response and home-school collaboration system for family education, including a parent input receiving module; a scene understanding and feature extraction module; a parent education style AI recognition module; and a personalized task mapping engine, used to: receive structured teaching objective information containing at least one ability point to be cultivated; select corresponding task mapping strategies and tone templates based on the parent education style classification tags; generate a personalized family collaboration task list and execution script adapted to the parent education style; and a scheme generation and output module. This invention uses style classification tags as core control variables, directly determining the logical branches, tone templates, and optimization weights of subsequent task mapping strategies, thus forming a style-aware decision-making closed loop. It solves the core problems of disconnect between recognition and execution and superficial personalization in existing technologies.

This invention discloses a BIM construction schedule optimization system based on big data analysis, comprising: a task mapping module for generating a set of planned tasks, a set of components, and a point cloud of component models; a semantic indexing module for generating a BIM semantic constraint index; a corridor construction module for generating a temporally continuous corridor; a four-point basis construction module for generating a candidate four-point basis set; a transformation solving module for generating a candidate transformation set; an evidence ranking module for outputting the target registration transformation; and an optimization output module for generating the construction schedule optimization result. This invention improves registration accuracy, schedule recognition stability, and optimization result reliability.

The application discloses a kind of car intelligent chip dynamic computing power scheduling methods for automatic driving domain, it is related to automatic driving chip scheduling technical field, including collecting automatic driving vehicle multimodal environment perception data and vehicle ontology state data, and is formed synchronous environment state stream after preprocessing alignment, assesses road scene complexity and identifies computing power demand key elements, determines the computing resource category and intensity of each functional subtask;Real-time state snapshot is formed by obtaining chip dispensable computing power, computing unit utilization and thermal state parameters, generate global scheduling instruction set including task mapping, computing power allocation proportion and execution timing and execute, then according to task execution progress and unit load on-line fine tuning to scheduling parameter, form closed-loop control.The method can accurately match the scenario computing power demand, let computing power allocation fit chip real-time running state, realize the dynamic adaptation of heterogeneous computing unit computing power, improve the cooperativity of scheduling and task execution.