14results about How to "Shorten completion time" patented technology

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 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 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 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 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.