85 results about "Total delay" patented technology

The invention discloses a production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention relates to the technical field of chip design, and provides a critical path screening method based on a chip time sequence report, which comprises the following steps: collecting chip time sequence report data, screening out first N critical paths from the data based on time sequence index data, and establishing a path ID-delay type-scene three-dimensional data model; and calculating the delay ratio of the unit delay to the interconnection line delay in each critical path. Performing single-type delay, type relevance and RC condition influence comparison on different scenes to obtain delay characteristic data. The invention discloses a system for executing the method, which can avoid full data redundancy and improve the analysis efficiency by screening the first N critical paths. And the delay change rule in different scenes is quantified. The delay characteristic data reflects the actual influence of the key path delay on the time sequence, so that a designer can quickly position the delay bottleneck of the key path, and a basis and a reference are provided for the targeted optimization of the total delay of the chip in the later period.

The invention discloses a resource intelligent joint optimization method based on deep reinforcement learning, and the method comprises the following steps: 1, building a communication network topology, and detecting the data flow in the network topology; 2, randomly generating a task flow, and simulating analysis and sending of tasks in the network topology; 3, the switch receives the task and searches n simple paths of the task; 4, setting constraint conditions, and screening candidate paths from the n simple paths; step 5, taking the minimum total time delay as a target, selecting an optimal path from the candidate paths, and distributing optimal computer resources and communication resources for the optimal path; and step 6, issuing the communication resources allocated to each link of the optimal path to a switch to realize communication resource allocation. According to the method provided by the invention, more concurrent tasks can be effectively accommodated on the premise of ensuring the task delay requirement, so that the resource utilization rate is maximized.

The invention provides a scene port entering and leaving aircraft joint optimization scheduling method. The method comprises the following steps: aiming at aircrafts which take off and land on a plurality of parallel runways in an airport, establishing a mixed integer programming model for a scene port entering and leaving aircraft IASP; setting a target function and a constraint condition of an IASTP problem by taking minimization of the total delay duration of the port entering and leaving aircrafts as a target; and based on the constraint conditions, solving a target function of the IASTP problem by adopting a heuristic algorithm based on VNS, and obtaining a comprehensive scheduling scheme of runway and sliding path distribution, take-off and landing sequence and time of the port entering and leaving aircraft. According to the method, actual operation constraints of various scenes are considered, runway distribution and sorting, taxiing path selection and taxiway scheduling of the port entering and leaving aircrafts, and a deducing sorting scheduling scheme of the port leaving aircrafts are planned and optimized as a whole, the take-off and landing delay time of the port entering and leaving aircrafts is shortened as much as possible, the hub airport operation efficiency is improved, and the airport landing time is shortened. And decision support is provided for efficient scheduling of aircrafts.

The invention discloses an aircraft remote control delay compensation response method and system, and the method comprises the steps: obtaining airborne data, and constructing a multi-source data set; calculating bidirectional transmission delay based on a bidirectional delay estimation algorithm, and calculating initial total delay; the initial total delay is dynamically calibrated by using a self-adaptive delay calibration algorithm, the confidence coefficient is evaluated, and the delay precision is improved; deducing a flight state in a delay time period by adopting a delay deduction hybrid model, and dynamically adjusting parameters along with load; according to the deduction result and the original instruction, generating a compensation instruction containing constraints by using a model prediction control algorithm; and executing the instruction after onboard verification, comparing the actual state with the expected state, and feeding back a deviation value to optimize the weight of the next-round model so as to form a closed-loop lifting mechanism. According to the method, the problems of incomplete delay composition analysis, poor environmental adaptability, lack of constraint collaboration of compensation instructions, no closed-loop optimization mechanism and the like in the existing aircraft remote control delay compensation are solved.

The invention discloses an air-space-ground integrated network cooperative computing service optimization method based on adversarial learning, and belongs to the technical field of artificial intelligence and wireless communication in an air-space-ground integrated network. The method comprises the following steps: (1) in an edge mobile computing communication system in the sky-ground integrated network, proposing a plurality of allocation modes of computing tasks; (2) obtaining total time delay and energy consumption experienced by the wireless equipment in different distribution modes; (3) proposing an objective function and a constraint condition, and forming an optimization problem based on the objective function and the constraint condition; and (4) finding out an optimal result through a plurality of iterations. According to the invention, the total delay of the system is minimized through the space-air-ground integrated network cooperative computing service optimization method; the genetic algorithm driven by the generative adversarial network is provided, the nonlinear high-complexity problem can be solved more efficiently and accurately, the generative adversarial network can learn features and distribution in the data, the generated solution is closer to the actual situation, and the actual problem can be better solved.

The invention provides a neural network storage and calculation resource optimal distribution method and system based on an FPGA, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a parallel factor and the delay of each layer of a neural network, and the calculation resource consumption of the parallel factor and the FPGA, and building an interlayer calculation resource relation model; based on the inter-layer computing resource relation model, obtaining an optimal parallel factor of each layer of the distributed neural network through a computing resource distribution method; analyzing the delay, parallelism and memory resource consumption of each module in the neural network layer, and establishing an in-layer memory resource relation model; and based on the inter-layer computing resource relation model, the intra-layer memory resource relation model and the allocated optimal parallel factor of each layer of the neural network, realizing balance optimization of the intra-layer memory resources and the delay. According to the method, the degree of parallelism of each layer is reasonably distributed, data flow blocking is reduced on the premise of minimum resource occupation, and the effect of minimum total delay is achieved by taking the consistency of module delays in each layer and the consistency of delays between layers as targets.

The invention provides an energy storage equipment phase synchronization control method and an energy storage system, relates to the technical field of energy storage equipment, and aims to solve the problem of phase tracking lag of slave machines caused by communication link delay when multiple machines are connected in parallel. The energy storage device phase synchronization control method comprises the steps that a first synchronization frame is received from a first energy storage device, and the first synchronization frame comprises a first voltage phase; a second synchronization frame is received from the first energy storage device, the second synchronization frame comprises a first time difference, and the first time difference is the difference value between the sending completion time and the sending start time of the first synchronization frame; calculating a second time difference based on a first reception time of the first synchronization frame and a second reception time of the second synchronization frame; and performing phase synchronization based on the first voltage phase, the first time difference and the second time difference. According to the phase synchronization control method, the communication processing total delay of the whole link is actively calculated, then the real-time phase of the host at the current moment is dynamically predicted, and finally accurate synchronization without lag errors is achieved.

For example, to enable optimal delay compensation adapted to a change in delay time to be performed. An information processing apparatus includes a control unit that performs processing of: predicting a total amount of delay that changes according to a state of a reproduction system including a sensor unit that senses a position of an observation target and a reproduction unit that reproduces a reproduction signal generated on the basis of sensing data of the sensor unit, the total amount of delay possibly occurring between sensing by the sensor unit and reproduction by the reproduction unit; and compensating for a delay of the predicted total amount of delay and then generating the reproduction signal.

The invention provides an internet of things task processing method and system, and the method comprises the steps: an edge management node measures the total delay of a task data flow on each edge calculation node according to a sampling period; judging the size relationship between the total delay and a third threshold value and a fourth threshold value, wherein the third threshold value is smaller than the fourth threshold value; under the condition that the total delay is smaller than the third threshold value, an existing task data flow is kept; when the total delay is larger than or equal to the third threshold value and smaller than a fourth threshold value, task data flow selection operation is executed; and under the condition that the total delay is greater than or equal to the fourth threshold value, executing a reset operation of the edge node. Through the steps, the edge nodes are suitable for processing data streams of different tasks, and on the other hand, the edge nodes are designed to support reconfiguration, so that the operation requirements of various tasks are met.

The invention discloses a dynamic flexible workshop scheduling method based on an event-driven DDQN algorithm. The method is used for solving the problem of flexible order workshop dynamic scheduling under the condition that a new order is inserted. The method comprises the following steps: firstly, considering the condition of random insertion of a new order, establishing a dynamic FJSP mathematical model taking minimization of total tardiness as an optimization target, converting the dynamic FJSP mathematical model into a multi-stage decision process based on event driving, and carrying out Markov decision process modeling of a scheduling problem; then seven key state characteristics such as machine utilization rate are designed, five composite scheduling rules are designed to form an action space, and a reward function is designed according to the variable quantity of part of the state characteristics; in addition, a soft epsilon greedy behavior strategy is designed according to the problem scale, and exploration and utilization are reasonably balanced. Through simulation research on a plurality of production scheduling examples, the result shows that the method can effectively cope with a dynamic event of random insertion of a new order, the total delay time is remarkably shortened, and good performance is shown in the aspects of convergence speed and solving efficiency.

The invention relates to the technical field of intelligent manufacturing, and provides a steel member production scheduling optimization method and device and a computer readable storage medium, and the method is realized through the following steps: a user sets a scheduling date range and screens orders; dynamically adjusting the branch priority and the machine capacity; setting optimization weights of the total tardiness time and the total delay rate; a simulated annealing algorithm is adopted to globally optimize a steel structure function unit machining sequence, a greedy algorithm is combined to allocate production line resources for component groups of the same process, and a process-level sub-algorithm is nested to solve the time sequence coupling problem of part and main body machining. The problems of neat delivery, cross-production-line resource scheduling and the like in steel member production are innovatively solved.

A method for edge cache replacement based on deep reinforcement learning is provided, including: constructing a system network model; obtaining a caching state of an MEC server in the system network model; calculating a total delay for all users to obtain cached contents based on a total delay for a single user to obtain a cached content; constructing an optimization objective function based on the total delay for all users to obtain the cached contents; formulating a Markov decision process according to the optimization objective function; and replacing the cached content of the MEC server using a dynamic content replacement algorithm based on deep reinforcement learning according to the Markov decision process. According to a condition of a cache space of the MEC server, the system network model can obtain a minimum average delay for users to obtain contents and find a most suitable cache replacement policy.

The present application belongs to the technical field of mobile edge computing microservice, and particularly relates to an edge computing microservice deployment and task scheduling method based on microcell architecture in a 5G and Internet of Vehicles scenario.The method comprises the following steps: firstly, describing an edge computing network structure based on microcells in a 5G scenario; on this basis, systematically modeling the microservice deployment and task scheduling problem, with the optimization target being to minimize the delay of all users, which is mainly manifested as processing data upload delay; then, decomposing and completely solving the sub-problems to transform the original problem into a linear integer programming problem; and using an L2Box-ADMM algorithm to solve the linear integer programming problem.Test results show that, compared with other microservice deployment and task scheduling algorithms, the algorithm of the present application can find a microservice deployment and task scheduling strategy that satisfies the resource constraint condition and has smaller total delay, and can reduce the gap with the optimal solution by 35%.

A method may include: generating round-trip pulses at one or more nodes of a multidrop network, respective ones of the round-trip pulses selectively traversing one or more of: internal circuitry of the one or more nodes, or a physical medium between a first node and a second node of the one or more nodes; determining a total delay and an internal delay of the one or more nodes at least partially based on a measurement window error and measured round trips of the round-trip pulses within a predetermined measurement window; and determining a distance between the first node and second node at least partially based on the measured round trips of the round-trip pulses and the internal delay of at least one of the first node or the second node.

Techniques related to accommodating multiple delay targets in a distributed antenna system are described. The distributed antenna system is configured to determine at least one equalized delay value for a plurality of remote units based on each of a plurality of delay targets. The distributed antenna system also controls signals transmission times of at least one node based on the at least one delay value so that downlink radio frequency signals are transmitted from each of the remote units with a total delay that satisfies each of the delay targets.

The present invention discloses a dynamic task replication method, device and system in an edge computing environment. The method includes: establishing an optimization problem with the goal of minimizing regret, which is the difference between the total completion time of jobs in the edge environment and the total delay of job completion under the ideal optimal replication decision; using a task replication decision algorithm based on multi-armed bandits to solve the optimization problem, including: at the beginning of the first time slot, estimating the task computation amount w according to the task type of the task and the size of the input data t ; for each task t, calculating the lower confidence bound of the delay of replicating task t from edge cluster i to edge cluster j #imgabs0# According to #imgabs1# determine all available edge clusters, and select r t smaller available edge clusters as target edge clusters, and replicate the task to all target edge clusters for execution. The present invention applies the algorithm based on multi-armed bandits to the task replication problem of the edge computing system for the first time, and the method has excellent delay performance and service reliability.

The present invention provides a signal processing method, a signal processing device, and a signal processing program that improve signal processing accuracy while minimizing user discomfort. The signal processing method measures network delay time with other devices connected via a network, obtains an input signal, calculates the permissible upper limit of the delay time incurred in an output signal relative to the input signal due to signal processing based on the measured network delay time and the permissible total delay time, selects a signal processing method with the longest delay time below the upper limit, processes the input signal using the selected signal processing method, and transmits the processed input signal as the output signal to the other device.

The invention discloses a real-time evaluation method and device for a secondary reciprocity network, equipment and a storage medium, and relates to the technical field of automatic testing. The secondary reciprocity network comprises a central switch and at least one lower computer connected with the central switch, the lower computer comprises a controller, a backplane switch and at least one resource card which are connected with one another, the method comprises the following steps: obtaining a first delay, a second delay and a third delay, the first delay is the delay of a network card corresponding to the controller or the resource card, and the third delay is the delay of a network card corresponding to the controller or the resource card; the second delay is related delay of the switch, and the third delay is software delay; determining fourth time delay based on the first time delay and the second time delay, wherein the fourth time delay is processing time delay of the resource card; and determining the total delay of the secondary reciprocity network based on a first preset formula, the first delay, the second delay, the third delay and the fourth delay. Therefore, the problem that the real-time performance of the secondary reciprocity network architecture is not easy to evaluate is solved, and accurate evaluation of the real-time performance of the secondary reciprocity network is realized.

This invention provides a scheduling method for additive manufacturing workshops based on safety reinforcement learning, relating to the field of deep learning technology. The method includes: determining a workshop state vector S based on workshop simulation environment parameters; where T represents the current time, L represents the number of tasks being processed in the current batch, Tard represents the total delay time of all tasks, C represents the total completion time of completed tasks, and Index represents the task currently being scheduled; using the DQN algorithm, combined with the workshop state vector S and a reward function, determining the priority of tasks to be scheduled, and determining the batch of tasks to be scheduled based on the priority; using an improved A3C algorithm for machine selection, allocating the tasks to be scheduled in different batches to target machines; the improved A3C algorithm is an A3C algorithm incorporating a safety shield mechanism. By comprehensively considering task urgency, processing time, and machine load, the method dynamically optimizes production scheduling, improving scheduling efficiency and resource utilization, and achieving multi-objective balance.

The invention relates to the technical field of computers, in particular to a multi-agent cooperative communication and scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-agent system architecture; s2, designing a deep reinforcement learning framework; step S3, introducing a GOLSC algorithm; s4, designing a scheduling rule; step S41, static distribution rules; step S42, dynamically scheduling the rule; and S5, intelligent agent collaboration and optimization are carried out. According to the invention, different types of dynamic events are divided into three types, so that the GOLSC algorithm can adopt different communication link construction strategies for different types of dynamic events to generate specific links with pertinence. After a GOLSC algorithm is introduced into a task scheduling model, numerical experiments are carried out on a large number of examples in different production environments, changes of the total delay rate and the total delay time are researched, and the effectiveness, adaptability and superiority of the algorithm model in the environments are further verified.

The application discloses a task scheduling method and device based on a distributed large language model, terminal equipment and a storage medium. The method comprises the following steps: obtaining communication data of each device node in a multi-layer network of the distributed large language model, a to-be-scheduled task, a task feature of the to-be-scheduled task, and real-time monitoring data of each device node; then, according to the communication data, the to-be-scheduled task, the task feature of the to-be-scheduled task, and the real-time monitoring data of each device node, performing model segmentation and task scheduling on the multi-layer network with the minimum total delay of the multi-layer network as the target, obtaining a target device node corresponding to each to-be-scheduled task, and distributing each to-be-scheduled task to the corresponding target device node for scheduling. Through implementation of the application, resource management of the multi-layer network in the large language model can be realized, the performance of the network is improved, and the delay is reduced.

The present application discloses a method for correcting pulse distortion in a multi-level phase-shifted all-digital transmitter, which relates to the field of all-digital transmitters. The method comprises: determining a relationship between a total delay error and a phase-shift control word; determining an integer multiple error relationship between the total delay error and the phase-shift control word based on the relationship between the total delay error and the phase-shift control word; calculating a pre-processed phase-shift control word based on the integer multiple error relationship between the total delay error and the phase-shift control word; calculating a compensation control word based on the total duty cycle variation error and the pre-processed phase-shift control word; controlling the delay of a carrier signal based on the compensation control word to obtain a carrier signal with a duty cycle variation error; generating a correction carrier signal based on the carrier signal with the duty cycle variation error; delaying the correction carrier signal based on the pre-processed phase-shift control word, and amplifying and synthesizing the output signal. The present application can implement adaptive correction processing for pulse waveform distortion and improve the linear performance of ADTx.

The invention relates to the field of signal processing, in particular to a signal processing method, a chip and electronic equipment, the electronic equipment comprises at least one subsystem, and the method comprises the following steps: inputting a first to-be-processed signal into the at least one subsystem according to a preset sequence to obtain a first output signal; obtaining a total delay time based on the delay time of the at least one subsystem; performing delay processing on the first to-be-processed signal based on the total delay time to obtain a second to-be-processed signal, wherein the second to-be-processed signal comprises a plurality of sampling points; zero input detection is carried out on the second to-be-processed signal; judging amplitudes of a plurality of sampling points of the second to-be-processed signal based on the zero input detection result; and if the amplitudes of the plurality of sampling points of the second to-be-processed signal are smaller than the first amplitude threshold and the number of the plurality of sampling points is greater than or equal to the first threshold, reducing the amplitude of the first output signal to obtain a final output signal. Therefore, the system stability is improved, limit cycle processing is performed only once, and the limit cycle processing efficiency is improved.

The invention discloses an order type task scheduling optimization method and system, and relates to the technical field of task scheduling and control optimization, and the method comprises the steps: constructing an order type task scheduling control model; by constructing a dual-objective function of total delay time minimization and overall execution efficiency maximization, global optimization of task resource allocation is achieved, it is clear that task execution time resources do not exceed the maximum processing duration, and the situation that due to excessive optimization, resources are occupied by a single task for a long time, and the task execution efficiency is improved is prevented. Two objective functions of total delay time and average processing efficiency are constructed, the total delay time objective function can reflect the influence of the task queuing length on the execution efficiency, a penalty term is introduced into a scheme violating constraints, the evaluation weight of the scheme is reduced, it is ensured that the generated scheduling scheme meets both the optimization objective and the actual constraint condition, and the scheduling efficiency is improved. The frontier solution set is generated through iterative solution, conversion of task planning from static rules to dynamic optimization is achieved, and an efficient and reliable solution is provided for an intelligent task management system.

The embodiment of the invention relates to an information processing method and device, equipment and a medium. The method comprises the steps of obtaining first task information of a to-be-executed data production task and a planned execution moment of the data production task; generating first estimation information corresponding to the data production task based on the first task information; the first pre-estimation information at least comprises a first pre-estimation duration required by the target cluster to execute the data production task; obtaining target reference information, and estimating a total delay duration corresponding to the data production task based on the target reference information; the target reference information comprises one or more of resource information of the target cluster, second task information of an upstream task on which the data production task depends, and fault information of the target cluster; and determining an estimated completion moment of the data production task based on the planned execution moment, the first estimated duration and the total delay duration. According to the embodiment of the invention, the accuracy of the estimated completion time of the finally obtained data production task can be fully guaranteed.

The invention relates to the technical field of computers and data processing, discloses a streaming transmission and low-delay decoding method based on a cloud game, and aims to solve the problems of high transmission delay, low coding and decoding efficiency and split interaction response in the prior art. The method comprises the following steps: extracting dynamic characteristics of a game picture, dynamically adjusting a coding rate and a resolution ratio through a reinforcement learning model based on network state parameters and the dynamic characteristics, generating a current frame prediction result by using an AI prediction model, and preferentially transmitting a key frame patch, decoding is accelerated through a special hardware decoding chip, asynchronous rapid decoding is adopted for key frame patches, and frames and user input events are decoded synchronously to control the end-to-end total delay to be smaller than or equal to 50 ms. According to the technical scheme, the loss rate and the average transmission delay of the key picture area can be remarkably reduced, the decoding delay is reduced, it is ensured that the total delay is stabilized within 50 ms, and the interaction synchronism and the operation response accuracy of the high-speed dynamic game are improved.

The invention provides a flight delay recovery method and device, computer equipment and a storage medium, and belongs to the field of aviation operation and maintaining.The method comprises the steps that the minimum total delay economic loss, the total delay duration and the delay duration standard deviation serve as optimization objectives; establishing an optimization model by taking aircraft resource allocation, unit task connection, passenger travel guarantee, ground guarantee resource collaboration and airport time slot capacity limitation as constraint conditions; a Harris eagle optimization algorithm is used as a basic algorithm, and a dynamic balance pool mechanism and a transverse crossing mechanism are introduced for improvement; and carrying out optimization solution on the optimization model by utilizing an improved Harris eagle optimization algorithm to generate a flight recovery scheme. In this way, a mathematical model which takes a plurality of key indexes as optimization targets and comprehensively considers multi-dimensional operation rule constraints is established, and complex scenes and requirements of flight recovery can be described more comprehensively and accurately.

The invention relates to the technical field of file data synchronous conversion, and discloses a file data synchronous conversion method and device, equipment and a storage medium. By obtaining a file data synchronous demand list, the file data conversion processing capacity of each synchronous database is considered; the method comprises the following steps of: optimizing and solving a conversion execution strategy of a file data synchronization demand list by taking the shortest total delay duration when a plurality of file data synchronization contents are executed as a target, and executing a conversion processing synchronous database by reasonably distributing each file data synchronization content; and the efficiency of executing file data synchronous conversion among a plurality of database systems is improved. Meanwhile, the conversion execution strategy is dynamically updated by monitoring the difference between the actual execution time period and the standard execution time period of the file data synchronization action, and the possible data volume change of the synchronous database is considered, so that the environmental adaptability of the file data synchronization conversion process is improved; the superiority and accuracy of the overall operation performance of the system can be obviously improved.

The invention discloses a material cross-domain distribution method and device, and the method comprises the steps: building a material cross-domain distribution multi-target optimization model under a comprehensive constraint condition with the minimum material cross-domain distribution total delay time as a first optimization target and the minimum scheduling cost from a material support point to a material demand point as a second optimization target; mapping the material cross-domain distribution multi-objective optimization model by using a Logistic chaotic function to generate an initialized population, and fusing a variable neighborhood search operator into an MOEA / D algorithm to obtain an optimized MOEA / D algorithm; solving the material cross-domain distribution multi-target optimization model based on an optimized MOEA / D algorithm to obtain a material cross-domain distribution multi-target Pareto frontier solution set; therefore, the material cross-domain distribution scheme is determined based on the preset selection criterion according to the Pareto frontier solution set, the task reliability of mixed material cross-domain distribution in the whole period can be effectively improved, and the cost-effectiveness ratio of mixed material cross-domain distribution in the whole period can also be improved.