7results about How to "Improve scheduling accuracy" patented technology

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 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 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.