106 results about "Task mapping" patented technology

The invention relates to the technical field of cloud platform monitoring analysis, in particular to a cloud platform computing power resource performance monitoring and real-time scheduling optimization method, which comprises the following steps of: acquiring real-time performance data of heterogeneous computing nodes; performing multi-dimensional index fusion on the real-time performance data to generate a fusion performance index set containing time correlation features; constructing a dynamic computing power topological graph based on the fusion performance index set; generating an elastic scaling strategy set according to the dynamic computing power topological graph; mapping a to-be-allocated task to a physical node meeting the topology constraint condition; and carrying out topology conflict detection on the scheduling result through a feedback verification module, and dynamically correcting the parameter weight of the elastic scaling strategy set. According to the method, resource conflicts are reduced, the utilization efficiency of computing power resources is improved, and the system bottleneck problem caused by topology conflicts is avoided, so that the stability and the load balancing capacity of the system are enhanced.

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 task allocation and optimal scheduling method and system for a multi-robot collaborative industrial assembly line, and relates to the technical field of robots, comprising the steps of constructing a task allocation strategy network through deep reinforcement learning, encoding workpiece types and attribute information into a state vector input network, a multi-objective optimization function including total assembly time minimization, load balance degree maximization and energy consumption minimization is designed, a near-end strategy optimization algorithm is adopted to train network parameters, a robot task mapping matrix is output as a distribution scheme, and the network parameters are dynamically optimized and updated in an online learning mode. The task allocation efficiency of the multi-robot collaborative assembly production line can be remarkably improved, the dynamic optimization scheduling of the production line is realized, the system energy consumption is reduced, and the assembly efficiency is improved.

The invention discloses a dynamic production plan adjustment method in an industrial internet environment, and the method comprises the steps: constructing a task dependence graph according to the task attribute data of a production task; identifying a task state change event, and updating the task dependency graph in real time based on task nodes and edges influenced by the task state change event; a linear programming algorithm is adopted to obtain a task priority ranking result, and task priority ranking is dynamically adjusted; a distributed scheduling framework is adopted to map tasks to scheduling queues of all edge nodes, and a task distribution strategy is determined; and marking the abnormal task based on the task performance index in the task execution process, identifying the abnormal mode of the abnormal task, and optimizing the task allocation strategy based on the abnormal mode data of the abnormal task. The invention provides a dynamic, self-adaptive and efficient production plan adjustment method, which can comprehensively improve the global optimization capability, real-time performance and intelligent level of task scheduling in a dynamic production environment.

The invention relates to the technical field of operation and maintenance management, in particular to an operation and maintenance service cloud platform of a mine intelligent system, which comprises a node pressure monitoring module, a path task mapping module, a conflict density identification module, a time window adjustment module and an operation and maintenance scheduling matching module. According to the method, a node group consistent in direction is extracted through a pressure difference value and a flowing direction, a dynamic response relationship is constructed, the real-time performance and accuracy of path extraction are improved, path and task mapping is combined with a direction label, the adaptation degree of a task and a physical path is enhanced, task occupancy rate calculation supports high-density section identification, accurate avoidance of a scheduling peak is realized, and the scheduling efficiency is improved. Starting time and ending time extend to form candidate windows, task execution cycles are reconstructed according to density levels, task scheduling flexibility is enhanced, response units are screened according to reachable paths and path conflict conditions in resource allocation, and dual optimization of spatial distance and path safety is achieved; and the real-time adaptability, the path configuration reasonability and the resource calling efficiency of the scheduling scheme are improved.

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 discloses an inter-core communication method, system and device based on a heterogeneous asymmetric processor and a medium, and the method comprises the steps: carrying out the classification and feature extraction of a target task, constructing a task feature vector, and mapping the target task to a processor core; the load is detected in real time, when the real-time load exceeds a threshold value, a task migration strategy is executed through atomic operation and a breakpoint management mechanism, and the voltage and frequency of a processor core are adjusted according to a target task; a trusted execution environment is divided in a processor core, a dynamic session key is generated, the dynamic session key is transmitted through a physical isolation bus, cross-core communication data is encrypted, resources are allocated according to a three-dimensional dynamic resource matrix, and cross-core data synchronization is achieved through a two-channel redundancy transmission protocol. According to the method, the software collaboration framework of the heterogeneous asymmetric processor is optimally designed, so that the performance of the heterogeneous asymmetric processor is improved, and the method can be widely applied to the technical field of inter-core communication.

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 discloses a high-availability task distribution method, system and device, and the method comprises the steps: obtaining a to-be-distributed task; using a consistent Hash algorithm to distribute task Hash identifiers for the to-be-distributed tasks, and mapping the task Hash identifiers to a Hash ring; node hash values of all available nodes in the node cluster are mapped in the hash ring; according to the node hash values of all available nodes, searching a target node of which the node resource can process the to-be-distributed task and the node hash value is closest to the task hash identifier in the hash ring; and distributing the to-be-distributed task to the target node. All available nodes in a node cluster and tasks to be distributed are mapped to the same Hash ring by using a consistent Hash algorithm, so that the tasks can be distributed to the available nodes, the processing efficiency can be improved, the stability of overall task distribution is not influenced when any node fails, and the overall performance of task distribution is ensured.

The invention provides a GPU cluster NLP task parallelization system and method based on a particle swarm optimization, and the method comprises the steps: carrying out the analysis of an input NLP model based on a task decomposition module, dividing the NLP model into a plurality of subtasks, and collecting and recording the GPU cluster resource state based on a GPU resource management module, a plurality of sub-tasks are mapped on a GPU cluster based on a task allocation module, a plurality of independent particle swarms are constructed, task allocation is optimized by using a particle swarm algorithm based on a particle swarm optimization module, task execution is scheduled according to an optimized task allocation scheme based on a task scheduling module, and task execution is completed. And based on a result processing module, collecting and integrating sub-task calculation results on each GPU, and outputting the sub-task calculation results. According to the method, the NLP training speed is remarkably increased, the resource utilization efficiency is effectively improved, and the method is suitable for being used in a compute-intensive NLP task and particularly has a remarkable effect in a scene where GPU resources are rich and highly parallel computing is needed.

The invention provides a task processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed calculation task in a many-core system which comprises a plurality of processing cores; the calculation task is obtained in real time through calculation by a previous task processing layer of a current task processing layer in a target calculation scene, and the target calculation scene comprises a plurality of task processing layers with a data connection relationship; according to the time delay requirement and the first data size of the calculation task, the calculation task is split into multiple calculation instructions from the time dimension, a processing core set distributed for the calculation task is determined from the space dimension, and the multiple calculation instructions are used for indicating the execution logic of the calculation task; according to the calculation instruction, the calculation task is mapped to the processing core group, so that the processing core group processes the calculation task, in this way, calculation tasks of different sizes can be efficiently adapted, and the utilization rate of calculation resources is increased.

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.

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 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.

Systems and methods for creating chatbot-enabled web forms and workflows, the method comprising, mapping web forms and workflows to intents, wherein the web forms have required fields to be completed and the workflows have required tasks to be performed; mapping the required fields and the required tasks to entities for the intents that map to the web forms and the workflows; mapping utterances to complete the required fields and perform the required tasks to the intents and the entities that map to the web forms and the workflows; and creating chatbots configured to assist users to complete the required fields and perform the required tasks using the utterances, the intents and the entities that map to the web forms and the workflows.

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.