14results about How to "Improve scheduling effect" patented technology

The invention discloses an integrated energy system economic dispatching model method based on deep reinforcement learning, and the method comprises the steps: constructing a park equipment environment model, and carrying out the mathematical description of a park integrated energy system low-carbon economic dispatching problem through taking economy and carbon emission as targets; secondly, the scheduling problem is expressed as a Markov decision process framework, and a state space, an action space and a reward mechanism of the system are reasonably designed; then, a near-end strategy optimization algorithm is adopted to carry out low-carbon economic dispatching decision making, and an action noise dynamic updating strategy is provided to gradually improve the exploration capability and learning capability of a network model; according to the method, the action space, the state space, the reward mechanism, the neural network structure and the like of the intelligent agent are designed in detail, the minimization of the system operation cost is considered on the basis, and a deep reinforcement learning method is adopted for scheduling simulation. The method provided by the invention can perform optimal scheduling according to the load demand of the system, and has good scheduling performance and self-learning ability.

The invention discloses a multi-stage coordinated regulation and control method and system for a virtual power plant, and the method comprises the steps: carrying out the quantitative description of an adjustable space of various distributed devices in the virtual power plant through a unified modeling method, and building an evaluation model of real-time output and response capabilities; aiming at the problem of online coordinated scheduling between the virtual power plant and the power distribution network, a centralized-distributed hierarchical scheduling structure is adopted, and the scheduling efficiency of the system and the response capability of equipment are effectively improved through unified modeling and hierarchical optimization. According to the method, the total operation cost of the virtual power plant can be effectively reduced, the problem of comprehensive information sharing needed by traditional centralized scheduling is avoided through privacy protection design, efficient interaction and information safety between the virtual power plant and the power distribution network are achieved, the requirement for real-time economic scheduling in a complex scene can be met, and high expandability and adaptability are achieved. And meanwhile, the method accords with the development direction of optimal dispatching of the current power distribution network, and plays a crucial role in promoting energy structure transformation and accelerating renewable energy consumption.

The invention discloses an electric vehicle ordered charging and discharging control method based on an improved differential evolution algorithm. The method comprises the following steps: constructing a probability model of an electric vehicle charging behavior through a Monte Carlo method; demand analysis and electric vehicle dispatching feasibility judgment are carried out by acquiring power grid information and charging electric vehicle information; considering the power grid operation safety risk and the user credit rating, and formulating a dynamic electricity price; establishing a charging and discharging control model by taking minimization of the comprehensive cost of the user and the construction cost of the power grid as a target; and an improved differential evolution algorithm is adopted to solve the model to obtain the optimal electricity price and the optimal charging and discharging strategy, so that the operation efficiency and the economical efficiency of the power system are improved. The method effectively overcomes the defects of flexibility and cost of an existing electricity price strategy and the defects of dynamic electricity price making in the aspect of user credit, can effectively cope with power grid load fluctuation, and promotes coordinated development of the electric vehicle and the power grid.

The invention discloses a task scheduling method and device for a smart workshop, and relates to the technical field of task scheduling. Power consumption data of a target production line are acquired, and a power fluctuation abnormal value of the target production line is calculated; obtaining the production timestamps of the products on the target production line, calculating the production time deviation of the target production line, and determining the fault probability of the target production line in combination with the production time deviation and the power fluctuation abnormal value; marking the target production line as a first production line if the fault probability is greater than a preset fault threshold, otherwise, marking the target production line as a second production line; distributing the current remaining production tasks of each first production line in the workshop to each second production line; by means of the mode, production line faults can be found in time, bad production tasks can be stopped, fault influences are reduced, and product quality is ensured; and meanwhile, the unfinished production tasks are redistributed to other normal production lines, so that the production speed is ensured, the overall fault is minimized, and the workshop production task scheduling efficiency is improved.

The invention relates to the technical field of cloud computing and resource scheduling, and discloses a smart education cloud platform elastic resource scheduling system based on big data, which mainly comprises a data sensing module, a data lake bin module, an intelligent prediction module, a strategy generation module, a transition control module, an elastic execution module, a dynamic evaluation module and the like. The multi-source data is integrated through the data perception and lake and warehouse module, the information island is broken, the historical load, the user behavior and the service period are fused by using the intelligent prediction module to carry out accurate demand prediction, and the conversion from passive response to active pre-configuration is realized; the system automatically generates and safely executes an optimization scheduling strategy according to a prediction result, and then forms closed-loop optimization through dynamic evaluation, so that the resource utilization efficiency and the service stability are remarkably improved; the system can effectively cope with periodic and sudden loads in a cloud platform education scene, and provides intelligent, elastic and continuously optimized resource guarantee for high-concurrency and high-availability internet data services.

This application discloses an elevator scheduling method, elevator scheduling device, and electronic device, belonging to the field of Internet of Things (IoT) technology. The method includes: receiving a scheduling request sent by the elevator terminal, the scheduling request including historical elevator weights and elevator terminal operating status information, the operating status information including user waiting information for each floor corresponding to the elevator terminal, and the historical elevator weights indicating the priority of each floor's stopping order during the last elevator scheduling; determining a target elevator weight for each floor based on the operating status information and historical elevator weights, the target elevator weight indicating the priority of the elevator terminal's stopping order on each floor; and generating scheduling information corresponding to the scheduling request based on the target elevator weights. By comprehensively processing the current operating status of the elevator terminal and the priority of each floor during the last elevator scheduling, the final scheduling information is obtained, realizing dynamic adjustment of the scheduling information.

The invention belongs to the technical field of port vehicle scheduling, and discloses a dynamic scheduling and path planning method, device and equipment for trailers in a container port, and the method comprises the steps: carrying out the denoising of original positioning data, obtaining a lane-level continuous track based on the denoised positioning data, and carrying out the positioning of the trailers in the container port; setting a sliding scanning window along the road based on a lane-level continuous track, calculating a vehicle density index and a speed loss rate index in the window, judging the congestion level of each window, finally outputting real-time congestion level information, and carrying out path optimization based on the real-time congestion level information to obtain predicted arrival time; and a scheduling instruction is generated and issued by fusing the lane-level continuous trajectory, the real-time congestion level information and the predicted arrival time so as to realize scheduling and path planning. According to the mode, through dynamic identification and prediction of the lane congestion state and batch planning of the navigation optimal path and the estimation time, intelligent issuing of the scheduling instruction is guided, the inner trailer scheduling effect is optimized, and the operation efficiency of the trailers in the harbor district is remarkably improved.

The invention relates to the technical field of optimization scheduling, in particular to a graph-based complex network optimization scheduling system, which comprises a dynamic multi-layer graph generation module for generating a multi-layer graph structure according to node states, edge states and association relationships among nodes in a complex network, reflecting dynamic change characteristics of the complex network in real time in the multi-layer graph structure, and generating a dynamic multi-layer graph structure; the multi-layer graph structure comprises a physical network layer, a logic network layer and a load layer; the non-linear relation capturing module is used for analyzing non-linear relations between nodes and edges of all layers in the multi-layer graph structure; and the cross-hierarchy network scheduling module is used for carrying out cross-hierarchy scheduling optimization based on the nonlinear analysis result of the multilayer graph structure provided by the nonlinear relation capturing module. According to the invention, the scheduling strategy can comprehensively consider different optimization targets of each level, thereby improving the utilization efficiency of the whole network resource, reducing the data transmission delay, and improving the load balance of the system.

The present disclosure relates to the technical field of electric power, and discloses an assembly scheduling method and device, electronic equipment and a storage medium. The specific scheme is: for a plurality of first assemblies, performing a plurality of times the following first test scheduling operation, obtaining the first score of each first assembly; in response to the current scheduling request for the plurality of first assemblies, determining a plurality of second assemblies from the plurality of first assemblies based on the first score of each first assembly; performing scheduling processing on each second assembly respectively to obtain the scheduling result of each second assembly; and updating the first score of each first assembly based on the scheduling result of each second assembly, wherein the updated first score of each first assembly is used to respond to the next scheduling request for the plurality of first assemblies.

The application belongs to the technical field of offshore wind power generation, and discloses a floating wind turbine mooring positioning method, a mooring positioning system and electronic equipment. The floating wind turbine mooring positioning method obtains the measurement data of wind speed and wind direction and the position information of wind turbine layout, then constructs a mooring system model of the wind turbine to obtain the maximum length and the minimum length of the mooring line; then the genetic algorithm is used to iteratively process multiple design parameters while keeping other parameters unchanged to obtain the optimized position coordinates of the wind turbine, thereby creating a mooring system design database, and according to the established wake model of the floating offshore wind farm, the power generation capacity of each wind turbine and the position information of the layout are calculated according to the offset of the position of each wind turbine until the position information of the layout with the minimum wake loss of the floating offshore wind farm and the maximum power generation capacity of the wind farm is found, which is beneficial to improve the output power of the wind farm and reduce the fatigue load of the wind turbine.

The invention belongs to the field of renewable energy source distribution optimization scheduling, particularly relates to an energy storage management distribution optimization scheduling method and system for renewable energy sources, and provides a scheme for solving the problems that energy storage management distribution of the renewable energy sources is inconvenient to optimize, a scheduling scheme cannot be changed in time and the distribution effect is influenced in the prior art. Comprising the following steps: S1, establishing an energy storage management distribution optimization scheduling system, the energy storage management distribution optimization scheduling system comprises an energy consumption historical data acquisition module, a data analysis module, an energy quota making module, an energy distribution scheduling module, an energy monitoring module, a data extraction module, a distribution optimization module, an opinion acquisition module, an energy exchange platform, an identity login module, an energy storage acquisition module and a monitoring period setting module. According to the invention, the energy storage management distribution of renewable energy sources can be optimized conveniently, the scheduling scheme can be changed in time, and the scheduling effect is better.

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 field of electric power, in particular to a chemical industry park adjustable load dynamic identification and prediction method based on machine learning, and the method comprises the steps: classifying electrical loads based on an application scene; acquiring real-time load and historical load data according to the classified electrical loads, and constructing a park load curve prediction model; based on the adjustable load prediction dynamic identification and prediction system and the load data, constructing an optimal scheduling model; constructing a power flow topology net rack according to the net rack information; and inputting the predicted load value output by the park load curve prediction model and the optimal regulation and control value output by the optimal scheduling model into the power flow topology grid, and outputting a regulation and control scheme. According to the scheme, the trend of the load in the park can be predicted, and the dispatching capability of the power grid can be optimized. And the difference between the actual condition of load regulation and the scheduling demand is subjected to fitting correction, so that the problem of low investment utilization rate is solved, the operation of the park is better served, and the load capacity of the power grid is ensured.

The invention discloses a heterogeneous execution body scheduling method and system oriented to unknown attack endogenous perception, and the method comprises the steps: obtaining the similarity and threat degree between heterogeneous execution bodies in a scheduling set based on the constructed heterogeneous execution bodies; respectively obtaining a similarity matrix and a threat degree matrix of the scheduling set; then obtaining an execution pool similarity index and a threat degree index, and finally obtaining a comprehensive scheduling index; and performing ascending order arrangement on the execution pool list based on the comprehensive scheduling index, and obtaining a final scheduling scheme according to the priority of the execution pools in the execution pool list in combination with a time slice strategy. According to the method, the similarity and the threat degree of the heterogeneous executive are comprehensively considered, and the two quantitative indexes of the similarity and the threat degree are adopted for comprehensive index calculation, so that the balance between the reliability and the stability is ensured; and meanwhile, a time slice strategy is integrated, so that the problem of insufficient dynamic performance of an algorithm is solved to a certain extent.