12results about How to "Improve scheduling accuracy" patented technology

The invention discloses a cloud edge hierarchical collaborative information system integrated resource scheduling system. The system comprises an acquisition module. Data preliminary cleaning and feature extraction are locally executed through the edge end, only key feature data are uploaded, network transmission quantity is reduced, cloud computing pressure is relieved, real-time tasks can respond quickly, task timeout caused by transmission delay is avoided, feature vectors of the edge end and the cloud end are dynamically analyzed based on a scheduling decision model of reinforcement learning, and the real-time performance of the edge end is improved. Task requirements and resource states are intelligently matched, the scheduling accuracy is improved, model parameters are dynamically updated through task execution state data (such as duration and resource utilization rate deviation), the model is adaptively optimized along with environment changes (such as edge end hardware aging and cloud load fluctuation), the efficient scheduling capacity is kept for a long time, and the scheduling efficiency is improved. Layered processing and intelligent scheduling avoid edge end overload or cloud end resource idling, resource allocation of a CPU, a memory and the like is optimized, resource waste is reduced, and the system collaboration efficiency is integrally improved.

The embodiment of the invention relates to a processing method and device for setting a traffic scheduling mark based on SDN traffic prediction. The method comprises the following steps: confirming an exchange layer topological graph of a hardware exchange layer; configuring a flow collection library for each switching node, continuously sampling the flow of each node, and sampling data based on the flow collection library; constructing a traffic prediction model, and regularly constructing a latest data set based on all traffic collection libraries to train the model; and using the traffic prediction model to predict the traffic of the exchange node set in the future period to obtain a node prediction set, and identifying whether traffic scheduling needs to be started based on the node prediction set to obtain a corresponding traffic scheduling mark and a scheduling node set. According to the invention, the risk of network congestion can be reduced, and the scheduling precision, efficiency and timeliness of traffic scheduling can be effectively improved.

This invention discloses an intelligent optimization method for energy management based on a swarm algorithm, comprising the following steps: S1, collecting multi-source time-series data and adjustable equipment parameters from the energy management system to generate a prediction sequence, constituting a scheduling space; S2, calculating a risk index sequence to form a risk map; S3, constructing a mapping structure between scheduling schemes and gap fields; S4, constructing a coupled coding structure between population individuals and gap fields to form an initial population cluster; S5, forming an updated population cluster using an improved HHO algorithm; S6, performing selection, replication, and elimination operations to form a new generation population cluster; S7, performing convergence determination, updating the risk map and prediction sequence, and completing the optimization closed loop. This invention enables risk perception, dynamic adjustment, and global optimization of complex energy systems across multiple time scales, improving the economy, energy efficiency, and operational safety of the energy management process.

This invention discloses a two-tier decision-making method and system for collaborative electricity-carbon trading among building producers and consumers. The method constructs a two-tier optimization framework for collaborative electricity-carbon trading based on the functional responsibilities of upper-level aggregators and the carbon trading positioning of lower-level building producers and consumers. It categorizes individual buildings according to differentiated carbon emission compliance requirements and calculates CCER mutual recognition amounts based on the principle of emission reduction equivalence and the amount of green electricity consumed in each scheduling period. Through dynamic application and allocation by aggregators, a dynamic carbon emission reduction mutual recognition mechanism is constructed. Combining the framework and mutual recognition mechanism, a two-tier objective function and constraints including building thermal balance are constructed, forming a two-tier nonlinear optimization model. This model is then transformed into a single-tier mixed-integer linear programming model through Boolean variable constraint relaxation, Lagrangian function construction, KKT condition transformation, and strong duality principle and Big M method linearization to obtain the decision results. This invention solves the problems of missing differentiated carbon compliance paths for building producers and consumers and poor adaptability of the electricity-carbon collaborative model in existing technologies.

This application provides a method and system for scheduling nest resources for emergency inspection tasks. The method includes: obtaining the target location of the emergency inspection task; retrieving nest resources based on the target location to obtain a candidate nest set; obtaining the status information of each candidate nest in the candidate nest set, the status information indicating whether the nest is in a planned occupancy state; for each candidate nest in the candidate nest set, if the status information indicates that the candidate nest is in a planned occupancy state, obtaining the task attribute of the task to be executed corresponding to the candidate nest; evaluating the releaseability of the candidate nest based on the task attribute to obtain a releaseability score; and determining the target nest from the candidate nest set based on the status information of each candidate nest and all releaseability scores. This method improves scheduling accuracy, enhances emergency response capabilities, optimizes resource utilization, and improves scheduling stability, achieving efficient and precise scheduling of nest resources.

The invention discloses a park-level virtual power plant-oriented multi-microgrid hierarchical collaborative optimization scheduling method, and relates to the technical field of microgrid optimization scheduling, and the method comprises the steps: carrying out the multi-source photovoltaic data fusion and high-confidence power generation prediction; carrying out layered progressive load decomposition and demand baseline establishment; carrying out local multi-target self-optimization; real-time coordination of dynamic excitation is realized; micro-grid response and VPP polymerization clearing are carried out; and executing evaluation and model evolution. According to the method, the fundamental problem that a complete and feasible technical solution cannot be provided for the transverse aggregation type park under the condition of lack of physical conditions in the prior art, and efficient, reliable and sustainable resource aggregation cannot be realized is solved.

This paper relates to the field of refined oil resource scheduling technology, and provides a method, apparatus, equipment, and storage medium for refined oil resource scheduling. The method includes: using a simulator to simulate the scheduling scheme for the first sub-cycle of the current cycle based on the node parameters of each node, to obtain the inventory level and sub-cycle reward value of each node at the end of the first sub-cycle; using a deep reinforcement learning module to predict the target inventory level of each node at the end of the first sub-cycle of the next cycle based on the inventory level, sub-cycle reward value, and scheme penalty value of the current cycle, with the second sub-cycle of the current cycle as the first sub-cycle of the next cycle; using a mathematical programming module to determine the scheduling scheme for the next cycle based on the target inventory level and the operational constraints of each node, with the goal of minimizing scheduling costs and inventory level differences; and performing refined oil resource scheduling on each node according to the scheduling scheme of the first sub-cycle of the next cycle. The embodiments described in this paper can improve the efficiency of refined oil resource scheduling.

The application discloses a kind of based on wind and light output uncertainty power grid multi-energy complementary scheduling method and device, comprising: obtaining the actual wind and light output data and predicted wind and light output data of target power grid corresponding to multiple historical time points in historical time period, covariance matrix is constructed based on the difference between actual wind and light output data and predicted wind and light output data;Determine wind and light prediction output error based on covariance matrix, obtain multiple initial wind and light prediction output scene set, correct each initial wind and light prediction output scene set using wind and light prediction output error, obtain multiple wind and light prediction output scene set, wherein each wind and light prediction output scene set includes wind and light prediction output data of each time point in future time period;Each wind and light prediction output scene set is input into preset wind and light complementary scheduling model to select output scene set, obtain target wind and light prediction output scene set, and carry out electric energy scheduling according to target wind and light prediction output scene set.

The embodiment of the invention provides a multi-place inventory network optimization method and device, equipment and a storage medium, and relates to the technical field of display. The method comprises the following steps: acquiring demand correlation and historical data, constructing a first support set or a second support set based on the correlation, constructing at least one first uncertainty set or second uncertainty set corresponding to the support set according to the historical data, acquiring scheduling parameters of an inventory network, and constructing an inventory optimization model based on the scheduling parameters and the uncertainty sets, and reconstructing the constraint condition set as a linear constraint condition set by using the first uncertainty set, or reconstructing the constraint condition set as a second-order cone programming condition set by using the second uncertainty set, and solving the target function based on the linear constraint condition set or the second-order cone programming condition set to obtain inventory optimization data. For demand uncertainty, different support sets are constructed according to correlation driving, so that an inventory optimization model is not limited to a single constraint of a moment condition any more, the correlation is subjected to hierarchical processing, and the inventory cost is optimized.

The application provides a complex water system adaptive variable step length model predictive control real-time scheduling method, which comprises the following steps: constructing a canal pool integral time delay model, integral time delay model time domain verification, selecting an effective frequency domain detection / verification range, constructing a model predictive control framework, selecting different control step lengths for closed loop testing, determining an optimal control step length range, and designing an adaptive variable step length switching rule, thereby constructing an adaptive variable step length model predictive control method. According to the scheduling target and the canal pool state, the method can dynamically adjust and optimize the control step length, so as to realize the intelligent balance of the calculation efficiency and the scheduling accuracy, thereby effectively solving the problem of insufficient adaptability of the traditional fixed step length model predictive control method under complex working conditions.

This application relates to the field of network technology, and provides a traffic scheduling method, apparatus, electronic device, and storage medium. The method includes: acquiring historical CDN traffic data, switch historical traffic data, and metropolitan area network (MAN) traffic data of network nodes; inputting the historical CDN traffic data, switch historical traffic data, and MAN historical traffic data into a traffic prediction model to obtain the predicted CDN traffic, switch predicted traffic, and MAN predicted traffic for future moments output by the traffic prediction model; determining the inbound traffic and outbound traffic of network nodes based on the predicted CDN traffic, switch predicted traffic, and MAN predicted traffic; and performing traffic scheduling based on the inbound traffic and outbound traffic. This method effectively avoids scheduling lag, achieves high scheduling accuracy, and, by using a model for traffic prediction, eliminates reliance on the experience of scheduling personnel, improving scheduling efficiency and reducing the cost of manual operation and maintenance.