25results about How to "Shorten completion time" patented technology

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention belongs to the field of intelligent manufacturing, and provides an intelligent recommendation method and system based on a shared robot, and the method comprises the steps: receiving a picture and a processing demand provided by a user; determining the processing attribute of the target workpiece according to the picture; determining a processing sub-task according to the processing attribute; determining an optimal shared robot combination according to the processing subtasks and the processing requirements; and displaying the optimal shared robot combination to the user.

The invention discloses a multi-substream parallel forwarding scheduling method for a novel intelligent computing center network. The whole communication forwarding process is divided into three stages of data stream segmentation and distribution, parallel transmission and receiving end sorting and recombination. The cooperation of a data stream segmentation distribution strategy of a sending end and an out-of-order data packet sorting and recombination operation of a receiving end is comprehensively considered, an optimal distribution ratio and a corresponding optimal forwarding path are determined, and the optimal distribution ratio and the corresponding optimal forwarding path are mapped to a physical network to complete parallel transmission of multiple substreams. According to the method, the problem of failure of a traditional network scheduling method caused by new characteristics of small flow quantity and large single flow bandwidth of distributed training service data flow of an AI large model in a novel intelligent calculation center is solved.

The invention discloses a heterogeneous multi-core task scheduling method based on a whale optimization algorithm. The heterogeneous multi-core task scheduling method comprises the following steps: step 1, task information acquisition and model establishment; firstly, to-be-scheduled task information needs to be obtained, and the task information comprises task types, calculation amount, memory requirements and communication overhead; then, according to the architecture characteristics of the heterogeneous multi-core processor, establishing a task scheduling system model; 2, the number of white whale individuals is initialized and corresponds to a task scheduling scheme; 3, solving an individual with the highest fitness based on a white whale optimization algorithm, and taking the individual as an optimal solution; and 4, determining a final task scheduling scheme according to the optimal solution, wherein the final task scheduling scheme comprises a processor to which each task is allocated and an execution sequence. 5, the scheduling scheme is applied to the heterogeneous multi-core system, and task scheduling is executed. According to the method, the task scheduling efficiency is remarkably improved, and the overall task completion time is shortened.

The invention discloses a GPU sharing scheduling method and device supporting heterogeneous job scale, and the method comprises the steps: carrying out the interference portrait analysis of a newly submitted job, obtaining the interference feature representation of the job, and adding the interference feature representation into a job queue; collecting cluster operation information and job information; determining a job scheduling interval; on the basis of the job runtime information, the job priority is updated, and the jobs are sorted through a maximum sharing efficiency priority method; traversing jobs in the job queue, and determining jobs to be run and jobs to be preempted; calling an optimal packaging strategy and job co-located interference prediction model to determine a placement position of a to-be-operated job; placing jobs and updating a cluster state; and monitoring a new job submission condition and updating a scheduling interval, if a new job is submitted, skipping to carry out interference portrait analysis on the newly submitted job, otherwise, skipping to the step of determining the job scheduling interval. According to the method, the job completion time and the queuing time can be effectively reduced, and the resource scheduling efficiency of the GPU cluster is remarkably improved.

The present application relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, and particularly relates to an optimization method for solving flexible job shop scheduling with adjusted resource constraints. The method comprises the following steps: initializing parameters and randomly generating an initial population; using a crossover operator and a mutation operator in sequence to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population in ascending order of maximum completion time to form an elite population, and updating the elite population through problem-specific local search; judging whether the evolution condition is met, and if yes, executing mathematical evolution based on CP, and outputting a final solution when the running time reaches the total running time. The present application has the positive effects of reducing resource waiting time, improving machine utilization, and improving the resource utilization efficiency and scheduling performance of the entire workshop production.

The present invention discloses a flow group scheduling method in an optoelectronic hybrid data center network, and belongs to the field of network transmission technology. The method first divides the input traffic into flow groups according to business semantics, and then inputs the network topology structure. Secondly, based on the flow group information, the order of the flow groups is solved in combination with the characteristics of the optical circuit switch and the electrical packet switch port bandwidth to obtain an ordered flow group sequence. Finally, the flow groups and the sub-flows contained therein are scheduled according to the flow group sequence to optimize the transmission of the traffic in the optoelectronic hybrid network structure, reduce the completion time of the flow group transmission, and achieve the purpose of comprehensive optimization of the overall transmission. The present invention can optimize the scheduling and transmission process of the traffic in the optoelectronic hybrid structure, and can be applied to the scenarios of integrated services in data centers, etc.

The application provides a distributed computing cooperative flow routing method and system for a satellite time-varying network topology and a storage medium, and the distributed computing cooperative flow routing method comprises the following steps: step one, constructing a cooperative flow transmission time-varying graph model under a satellite inter-satellite link dynamic network; step two, a heuristic routing algorithm based on the cooperative flow transmission time-varying graph; solving the available path set through path calculation on the cooperative flow time-varying graph model, setting a scheduling rule based on traffic priority, and constantly selecting the optimal path under the current situation through the evaluation of the paths in the feasible path set. The application has the beneficial effects that: 1. the time used in satellite distributed computing is effectively reduced; and 2. while effectively reducing the cooperative flow completion time, the efficiency of satellite distributed computing is effectively improved.

The utility model discloses an overwater side propeller which comprises a driving mechanism and a connecting device which are detachably connected, the driving mechanism comprises a propeller and a hydraulic motor, the propeller is provided with a connecting shaft, and the output end of the hydraulic motor is connected to the connecting shaft of the propeller so as to be used for driving the propeller; the connecting device comprises a connecting rod and a sliding rail, the top end of the connecting rod is connected with the sliding rail, and the position of the propeller in the vertical direction is adjusted through vertical movement of the sliding rail. According to the overwater propeller, the propellers, the hydraulic motors, the connecting shafts, the connecting rods, the sliding rails, the hydraulic oil cylinders and other structures are combined, the operability of a gate bridge in the training process can be improved, collision of the gate bridge in the training process is reduced, the completion time of each subject of the gate bridge is shortened, and the training efficiency of the gate bridge is improved. And the task execution efficiency of the gate bridge is greatly improved. The structure is widely applied to the fields such as flood fighting and disaster relief.

The present invention proposes a dynamic multi-product comprehensive scheduling method with tight delivery deadlines. The method first adopts the "layer priority + short time + equipment priority" strategy to improve the parallel processing capability of the equipment; secondly, a product contribution value strategy is proposed to obtain the product contribution value according to the number of urgent processes, the maximum delivery date and the total processing time of the product, thereby determining the processing order of the product, effectively making multiple products processed on time within the delivery period; finally, a game process dynamic adjustment strategy is proposed to dynamically adjust the process according to different situations, so that the process is compactly processed, the idle time of the equipment is reduced, and the overall utilization rate of the equipment is improved.

The invention relates to the technical field of power equipment quality detection, and particularly discloses a power transmission line iron tower detection and quality management system. The system comprises a user authentication module, an authority management module, a detection plan management module, a sample information management module, a physical and chemical detection management module and a report generation module. Differentiated operation authorities are distributed to different user groups through an authority management module; creation, execution and state management of a detection plan are realized through a detection plan management module, and a sampling list and a detection entrance are automatically generated; complete sample data is forcibly recorded through the sample information management module so as to ensure data traceability; the detection data filling unit supports communication with external detection equipment so as to automatically collect and calculate data, supports multi-user collaborative editing and displays an editing state in real time; and finally, the report generation module automatically synthesizes various types of detection reports according to the template, so that the detection efficiency, the data accuracy and the management standardization are effectively improved.

The present invention provides a task distribution method and device based on Manage and Worker, which relate to the technical field of electronic digital data processing. A genetic algorithm is used to optimize the dynamic allocation of tasks to nodes, with the goal of minimizing the total task execution time and data transmission time. A real-time monitoring and feedback adjustment mechanism is deployed in a Docker Swarm environment, and a scheduling strategy can be dynamically adjusted according to actual feedback from system operation. The scheduling result can be further optimized by fine-tuning an index, so that the system always maintains the best operating efficiency when facing constantly changing workloads and resource states. A graph theory model is introduced to accurately analyze and optimize the dependencies between tasks, so that tasks with close dependencies can be scheduled to nodes with similar physical locations for execution, thereby significantly reducing communication delays and data transmission time.

The application discloses a multi-target computing power scheduling method, device, equipment, medium and program product, and relates to the technical field of computing power networks. The scheduling device of the method receives task carrying willingness scores and resource state information of a plurality of resource pool systems, and splits a computing power task sent by a task party to obtain a plurality of sub-computing power tasks; the task carrying willingness scores, the resource state information and the sub-computing power tasks are matched to obtain candidate resource pool systems, and scheduling information is sent to the candidate resource pool systems. The candidate resource pool systems determine predicted load evaluation results according to the scheduling information and real-time running data and send the predicted load evaluation results to the scheduling device; the scheduling device determines a target resource pool system of each sub-computing power task according to the predicted load evaluation results and the sub-computing power tasks, and sends a corresponding sub-computing power task to the target resource pool system, so that the target resource pool system executes the sub-computing power task. Therefore, the effect of computing power scheduling is improved.

The present invention provides a RA‑IDNC network coding method based on partial blind transmission, comprising: (1) obtaining a forward link packet loss rate and a feedback link packet loss rate of a user node and a channel capacity of the user; (2) the target user feeds back a receiving state to a base station through a feedback link, performs ML judgment on lost feedback, processes nodes according to the judgment result, and updates a feedback matrix; (3) constructs a RA‑IDNC coding relationship graph; (4) determines whether an active node set in the RA‑IDNC coding relationship graph is empty; if the active node set is empty but there are hidden nodes, resets all the hidden nodes to active nodes; (5) if the active node set is not empty, constructs a maximum weight group; (6) performs RA‑IDNC according to data packets corresponding to nodes in the maximum weight group, and goes to (2) for iterative operation until all node sets in the RA‑IDNC coding relationship graph are empty.

The utility model relates to the field of plastic bottle processing and production, in particular to a conveyor belt with a following pressing function, which comprises a pressing frame and a conveying frame, at least one pressing frame is arranged on one side of the conveying frame, each pressing frame comprises a shell, two groups of pressing arms are arranged on the shell, and driving modules for driving the pressing arms to move left and right are arranged in the shell; the driving module comprises a driving screw rod; the pressing arms are pushed left and right through the arranged driving lead screw, so that when the conveying frame conveys the bottle bodies, the single pressing arm can move along with the bottle bodies, after the pressing action is completed, resetting or acceleration can be conducted, and handle assembling can be conducted on the previous bottle body or the next bottle body; therefore, the assembling efficiency of a single pressing arm in unit time is improved, two sets of pressing arms are arranged on one independent pressing frame, the pressing work can be completed in two times, the completion time of the pressing action of the single pressing arm is shortened, and the assembling efficiency of the whole pressing frame is further improved.

The embodiment of the invention provides an on-cloud AI training task resource scheduling method, system and device and a medium, and belongs to the field of cloud computing and artificial intelligence. The method comprises the following steps: deploying a monitoring agent in a cloud environment to collect multi-dimensional resource utilization characteristics of an AI training task in real time; the multi-dimensional resource utilization features are input into a pre-constructed time series prediction model, a short-term resource demand prediction result and a long-term resource demand prediction result are output to cover the whole life cycle of the AI training task, and the time series prediction model is constructed based on a long and short-term memory network; formulating a scheduling strategy and generating a scheduling instruction according to the short-term resource demand prediction result, the long-term resource demand prediction result and the current resource state; and carrying out resource capacity expansion and contraction and task migration operation based on the scheduling instruction. Through multi-dimensional feature prediction and elastic scheduling, the cloud resource utilization rate is remarkably improved, the AI training task completion time and the operation cost are reduced, and meanwhile the system self-adaptive capacity is enhanced.

Provided in an embodiment of the present application are a flow control method, apparatus and device, and a storage medium, the method comprising: a network device receiving a plurality of data packets, the plurality of data packets comprising a first data packet and a second data packet, the first data packet carrying a first preset tag, the second data packet carrying a second preset tag, the first preset label indicates that the sender does not obtain the credit value allocated by the receiver, and the second preset label indicates that the sender has obtained the credit value allocated by the receiver; if the available length of the cache queue in the network equipment is greater than a first threshold value, writing the first data packet and the second data packet into the cache queue; and sending the data packet in the cache queue. According to the scheme, the network bandwidth can be fully utilized in an AI large model training scene, the flow completion time is shortened, and the training efficiency of the AI large model is improved.

The application discloses a kind of power energy storage system's half bridge fast charging circuit and charger, the circuit includes inverter half bridge module, transformer module, first and second rectification half bridge module, first and second energy storage control module.Inverter half bridge module input end connects DC power supply, output end connects transformer module input end.Transformer module output end connects the input end of first and second rectification half bridge module and power energy storage system negative pole, and the first end of first rectification half bridge module output end connects first and second energy storage control module.The third end of second rectification half bridge module output end connects second energy storage control module.The second end of first energy storage control module fourth end connects second energy storage control module, and third end connects power energy storage system positive pole.The second end of second energy storage control module connects power energy storage system negative pole.The circuit utilizes energy storage capacitor to adjust the rising edge and falling edge of pulse current, realizes the quick conversion between continuous current charging mode and pulse current charging mode.

The present application relates to the technical field of stereoscopic storage automation, in particular to a stereoscopic storage multi-axis linkage intelligent goods shelf position dynamic allocation and scheduling system, which comprises a task analysis module, an environment perception module, a goods position recommendation module, a scheduling optimization module and a plan execution module. The task analysis module receives and analyzes various storage operation instructions in real time. The environment perception module dynamically perceives the real-time state of the whole warehouse area. The goods position recommendation module generates multiple candidate goods position sets for the goods to be stored according to the goods attributes and storage requirements, in combination with the real-time state of the whole warehouse area, by using an improved collaborative filtering algorithm based on the correlation of goods storage characteristics. The scheduling optimization module evaluates each candidate set according to the preset optimization target, selects the final goods position, and generates a detailed storage execution plan containing the collaborative operation path of multiple tunnel stacker cranes. The plan execution module schedules the equipment execution plan and monitors and updates the state in real time.

The invention discloses a cloud computing task scheduling algorithm and system based on two-stage adaptive search. The cloud computing task scheduling algorithm comprises a task receiving module, a virtual machine management module, a scheduling model building module, a two-stage adaptive scheduling algorithm module and a result output module. The scheduling algorithm module completes population aggregation through a preference perception distance strategy, and when the population centroid variance reaches a threshold value, second-stage search is carried out by adopting a preference region classification strategy; meanwhile, a double-layer coding mode and a task balance mapping strategy are combined to generate a filial generation population, and multi-target collaborative optimization is achieved. The method is superior to an existing algorithm in indexes such as completion time, lease cost, energy consumption and load balancing index under the medium and large-scale cloud environment, interests of multiple parties can be effectively balanced, and a new scheme is provided for task scheduling in the complex cloud environment.

The invention discloses a data processing method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring a to-be-transmitted file list, and detecting a storage performance index of a storage system for storing the to-be-transmitted file list and a network bandwidth of a transmission environment; according to the total data volume, the storage performance index and the network bandwidth of the to-be-transmitted file list, determining the number of target fragments used for performing fragment processing on the file; extracting a plurality of to-be-transmitted files with the data volume smaller than a first preset threshold value from the to-be-transmitted file list, and combining the plurality of to-be-transmitted files into a logic block with the preset data volume in the to-be-transmitted file list to obtain a target to-be-transmitted file list; and according to the target fragment number, performing fragment processing on the target to-be-transmitted file list to obtain a target fragment. The technical problems of low transmission efficiency and too long task time consumption caused by the fact that the fragmentation scale cannot be dynamically adjusted according to network bandwidth fluctuation and storage system performance in the prior art are solved.

The present application relates to embodied intelligent robot technical field, specifically to embodied intelligent robot skill runtime data analysis and scheduling method. The method comprises: a skill data receiving module receives skill data packets of external data sources and completes format verification and integrity check; a multi-level semantic analysis engine carries out three-level progressive analysis of syntax layer, semantic layer and intention layer on the skill data packets, and generates executable action sequences; a real-time scheduling controller combines the current state of the robot and environmental information to prioritize and time slice allocate the action sequences, and generates a scheduling scheme; an adaptive optimization module continuously iterates scheduling strategy parameters based on historical execution data and real-time feedback. Through the technical framework of multi-level semantic analysis and dynamic scheduling cooperation, the present application solves the problems of information loss, response delay and insufficient adaptability in skill transfer process, and significantly improves the precision and task completion efficiency of robot skill execution in complex dynamic environment.

The invention discloses a semiconductor scheduling method based on dynamic priority and reinforcement learning decision, relates to the technical field of semiconductor manufacturing scheduling, and aims to solve the problems of low utilization rate, delivery delay and poor dynamic adaptability of traditional scheduling equipment. The method comprises the following steps: fusing equipment space topology and process dependence, and generating a machine family low-dimensional coding vector; constructing a 13-dimensional dynamic feature vector, standardizing the 13-dimensional dynamic feature vector, inputting the standardized 13-dimensional dynamic feature vector into a strategy network consisting of a multi-head attention network and a feedforward network, and outputting a real-time priority; a reinforcement learning framework is constructed based on event driving, and the network is optimized through a three-level machine distribution rule, a negative penalty reward function and a natural evolution strategy. According to the invention, equipment load balancing and scheduling intelligent adaptation are realized, the equipment utilization rate is effectively improved, the wafer batch tardiness and completion time are reduced, and the method is suitable for a complex dynamic semiconductor manufacturing scene.