30results about How to "Improve scheduling accuracy" patented technology

The invention discloses an optical power prediction method and system considering photovoltaic panel snow covering, and the method comprises the steps: 1, determining influence factors which cause the photovoltaic power generation power loss of a photovoltaic cell panel under a snow covering condition, and building a relation model between each influence factor and a photovoltaic power generation power loss rate; 2, building a prediction model of the photovoltaic power generation power under the snow covering condition based on a relation model between each influence factor and the loss rate of the photovoltaic power generation power; and step 3, predicting the photovoltaic power generation power of the photovoltaic cell panel under the snow covering condition based on the constructed photovoltaic power generation power prediction model. Therefore, the snow accumulation influence on the photovoltaic panel is particularly considered, multiple key influence factors of the photovoltaic power generation power loss rate under the snow covering condition are fully considered, the prediction precision is high, the power prediction accuracy of the photovoltaic power station under the snow covering environment is remarkably improved, and the power prediction accuracy of the photovoltaic power station under the snow covering condition is improved. And the risk of insufficient large-scale photovoltaic power supply caused by snow covering is effectively reduced.

The invention provides a new energy transport capacity task scheduling method and system based on an energy complementation scene, and relates to the technical field of transport capacity task scheduling, and the scheduling method comprises the steps: collecting and preprocessing the real-time data of the energy, position, energy consumption and the like of a vehicle, carrying out the vehicle endurance evaluation and energy complementation judgment according to the real-time data, according to the endurance evaluation and energy complementation judgment result, screening energy complementation sites meeting preset conditions, carrying out scheme calculation and cost calculation on the screened energy complementation sites, comparing calculation results through an intelligent scheduling decision algorithm to obtain a final scheduling scheme, sending a scheduling instruction, carrying out cycle scheduling on newly added waybills or transport capacity tasks, and carrying out scheduling on the newly added waybills or transport capacity tasks. The scheduling scheme is dynamically adjusted and updated in real time according to actual conditions so as to adapt to efficient operation of a complex scene; the scheduling flexibility is enhanced, the scheduling efficiency and economic benefits of the new energy transport capacity in the energy complementing scene can be remarkably improved, and powerful support is provided for wide application of new energy automobiles in the logistics transportation field.

The invention belongs to the technical field of comprehensive energy systems. The invention provides a preset time fully distributed integrated energy system optimization scheduling method and system, the problem of integrated energy system scheduling is solved by taking the minimum total cost as an objective function, the objective function is configured with safe operation constraints, and the safe operation constraints comprise a power balance constraint, a power upper and lower limit constraint and a slope rate constraint; and assuming that the communication topologies of the source side, the load side, the storage side and the station side are undirected connected graphs, and solving the target function based on a TBG preset time fully distributed optimization algorithm, so that the integrated energy system reaches a convergence state within a preset time, and an optimal output strategy of the integrated energy system is obtained. The method does not depend on an initial value and parameters of the system, and can solve the optimal scheduling model of the comprehensive energy system containing the source-load-storage-station and containing the equality and inequality constraints without using any global information at any preset time, so that the scheduling precision is improved.

The invention relates to the field of production scheduling and resource optimization, in particular to a flexible job shop scheduling method based on deep reinforcement learning, which is characterized by comprising the following steps: 1) constructing a flexible shop scheduling environment; (2) a flexible workshop scheduling environment is abstracted to be Disjunction Graph, and the flexible workshop scheduling environment is abstracted to be Disjunction Graph; 3) extracting state features; 4) coding the state features; 5) executing a decision based on the encoded features to obtain scheduling probability distribution; and 6) generating a scheduling plan according to the probability distribution. According to the invention, through multi-level feature extraction and fusion, accurate perception of a production environment is realized, and the accuracy and robustness of a scheduling decision are effectively improved; by utilizing a deep reinforcement learning framework, the dynamic change of the production environment can be adaptively learned, the scheduling strategy is continuously optimized, and the generalization ability is relatively high; and feature representation is enhanced through an attention mechanism, so that the model can still quickly find a global optimal solution when facing a large-scale and high-complexity FJSP problem.

The invention discloses a virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm, and the method comprises the steps: taking the operation economic cost and environmental protection cost of a virtual power plant as targets, and constructing a virtual power plant mixed environmental economy multi-target optimization scheduling model; setting a multi-dimensional dynamic constraint condition according to the power generation power of the power generation main body and the charge state of the energy storage system; performing optimization solution on the virtual power plant mixed environment economy multi-target optimization scheduling model through an improved dung beetle optimization algorithm, performing non-dominated sorting on dung beetle individuals, then calculating crowding distances of the dung beetle individuals, and adjusting dung beetle population distribution according to the crowding distances; adjusting the search radius and disturbance intensity of dung beetle individuals according to the number of iterations; utilizing a boundary control mechanism to update dung beetle individuals; iteratively screening out an optimal power dispatching scheme through an improved dung beetle optimization algorithm; the scheduling efficiency is improved by using the improved dung beetle optimization algorithm, the operation economic cost is reduced, and the support for environmental protection is enhanced.

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 invention discloses a regulation and control method and system for improving the regulation and control speed and precision of an energy storage system, and the method comprises the following steps: obtaining the real-time power of commercial power through the CT sampling of an ammeter or a PCS; calculating the current mains supply power adjusting quantity based on the mains supply real-time power, and adjusting the mains supply power according to the current mains supply power adjusting quantity; and calculating a photovoltaic output power adjusting quantity based on the adjusted commercial power, and adjusting the photovoltaic output power according to the photovoltaic output power adjusting quantity. According to the invention, real-time scheduling of the PCS is realized directly according to the commercial power sampling information, and the problem of slow response caused by slow sampling time of other data is avoided; and meanwhile, the problem of PCS sampling power error caused by interference is avoided.

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.

The invention relates to a task scheduling method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a scheduling request from an interaction environment, wherein the scheduling request comprises respective task attribute information and scheduling attribute information of a plurality of scheduling tasks; determining a target task category to which each scheduling task belongs according to the scheduling attribute information; determining the scheduling priority of each scheduling task based on the target task category and the task attribute information of each scheduling task; determining a scheduling server matched with each scheduling task from a plurality of server clusters in an interaction environment based on the scheduling attribute information of each scheduling task; and according to the scheduling priority of each scheduling task, scheduling servers corresponding to the scheduling tasks are called to process the scheduling tasks. By adopting the method, the scheduling accuracy can be improved.

The invention discloses a computing network resource scheduling method, system and device, a storage medium and a program product, relates to the technical field of information, and is applied to a computing network resource scheduling system comprising a computing network operation layer. The method comprises the following steps: acquiring computing network resource scheduling information input by the computing network operation layer, the computing network resource scheduling information comprises a resource scheduling demand and resource service topology information; constructing a resource service knowledge graph according to the resource service topology information, and determining a network scheduling strategy according to the resource service knowledge graph and the resource scheduling demand; and scheduling the computing network resources according to the computing network scheduling strategy. The technical problem that the scheduling accuracy of the computing network resources is not high is solved.

The invention discloses a material handling vehicle scheduling method and device, a storage medium and a vehicle. The method comprises the steps that vehicle demand information of to-be-executed target material carrying operation is obtained, and the vehicle demand information is at least used for representing loading performance and / or lifting performance allowing execution of the target material carrying operation; in response to the matched target material handling vehicle meeting the vehicle demand information, obtaining operation point position information of to-be-executed target material handling operation; based on the operation point position information, a driving route for executing the target material carrying operation is determined; and in response to a scheduling instruction corresponding to the driving route, scheduling the target material handling vehicle to execute the target material handling operation. The technical problem of low scheduling accuracy of the material handling vehicle is solved.

The invention discloses a load control method based on an original energy consumption demand of a user. The method comprises the following steps: constructing an initial load model according to the original energy consumption demand of the user; if the energy supply gap is a conventional energy supply gap, regulating and controlling the load model by adopting a translation method or a segmentation method; if a sudden energy supply gap is formed and a power peak is formed, carrying out peak regulation and control on the load model by adopting ultra-short-term interruption; if the energy supply gap still exists, the energy supply system adopts an interruption control mechanism to terminate load energy supply meeting a user interruption condition, and the load control is ended; and a user energy consumption demand load model at the current moment is introduced into the load model of the load regulation and control, an updated load model is obtained, and next load control is started. The range and degree of load control are completely based on the original energy demand proposed by the user, the control process is on the premise of meeting the user demand, load control is carried out to a greater extent according to the controllable range of the load, and the energy consumption satisfaction of the user is considered.

The invention provides a resource scheduling method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining a first resource node matched with a resource scheduling request, wherein the resource category of the first resource node and the resource category of the resource scheduling request are a first category; for each first resource node, determining a scheduling parameter value of the first resource node based on the resource demand quantity of the resource scheduling request and the available resource quantity of the first resource node; determining a second resource node from the first resource nodes based on the scheduling parameter values of the first resource nodes; and scheduling resources for the resource scheduling request from the resources of the second resource node. According to the invention, the resource scheduling effect can be improved.

The application discloses a kind of based on edge cloud cooperation's inspection robot visual analysis task dynamic scheduling method and system, belong to inspection robot visual analysis and edge computing technical field.The method includes: real-time acquisition network state parameter and computing power resource information;Task feature vector is constructed to the visual analysis task and is extracted feature;Based on multi-objective optimization scheduling model, delay, energy consumption and accuracy index are determined optimal execution position by comprehensive evaluation;Task is distributed to corresponding node execution;Based on Q-learning reinforcement learning, scheduling weight is dynamically updated.The application can realize the optimal scheduling of visual analysis task under the dynamic change of network and computing power environment, compared with the scheduling efficiency of existing static allocation strategy is improved by more than 40%, and the comprehensive performance is significantly better than local execution, full cloud execution and static allocation scheme.

The invention provides a reservoir scheduling rule extraction method based on a rolling simulation strategy, which comprises the following steps: an initial water level Z0 of a reservoir scheduling period is given, and the scheduling period t is equal to 0; the initial water level Zt and the reservoir inflow It are used as characteristic factors, the final water level Zt + 1 is used as a target value, a model Mt is trained, and Mt represents the scheduling rule of the reservoir in the tth scheduling time period; inputting the initial water level Zt and the reservoir flow It into the model Mt to obtain a decision water level Kt + 1; judging whether the decision-making water level Kt + 1 violates the reservoir dispatching constraint or not, and if yes, correcting the decision-making water level Kt + 1; setting the decision water level Kt + 1 as the initial water level Zt + 1 of the next scheduling time period, and letting t = t + 1; and judging whether the scheduling time period t meets an end condition, if so, outputting a final scheduling rule, and if not, circularly executing the steps 2-6. The practicability of the scheduling rule and the power generation benefit of the reservoir system are improved.

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 invention discloses an intelligent scheduling method and system for distributed computing power scheduling strategy optimization, and relates to the technical field of intelligent scheduling. The method comprises the following steps: self-checking distributed computing power nodes; carrying out distributed computing power first evaluation analysis on the distributed computing power nodes; performing first intelligent scheduling according to the first comparative analysis result; carrying out distributed computing power second evaluation analysis on the distributed computing power nodes; performing second intelligent scheduling according to the second comparative analysis result; performing third evaluation analysis on the distributed computing power of the distributed computing power nodes; and performing third intelligent scheduling according to the third comparative analysis result. Through self-inspection, first evaluation analysis, second evaluation analysis and third evaluation analysis and corresponding hierarchical optimization scheduling, the effect of improving the accuracy of distributed computing power resource scheduling is achieved, and the problem that in the prior art, the accuracy of distributed computing power resource scheduling corresponding to distributed power resources is insufficient is solved.

The invention provides a virtual power plant dispatching system. The virtual power plant scheduling system comprises: an integrated scheduling platform, which is adopted to perform unified management and scheduling on various distributed energy resources in a virtual power plant, and realizes real-time monitoring and coordinated scheduling on various resources through an integrated console; the integrated scheduling platform system processes and predicts historical data and real-time data by introducing an intelligent scheduling algorithm of artificial intelligence and through a machine learning and data analysis technology. According to the virtual power plant scheduling system provided by the invention, through an integrated scheduling platform, an intelligent scheduling algorithm, a real-time monitoring and feedback mechanism, multi-level decision support, an efficient communication network and a user-friendly operation interface, the scheduling precision, the response speed and the system stability of the virtual power plant can be remarkably improved.

The application relates to the field of power system automation, and specifically discloses a micro-grid hierarchical rolling collaborative optimization scheduling method under an extreme weather environment on a plateau. The method realizes accurate perception and prediction of weather mutations by constructing a multi-source weather monitoring network and an attention mechanism neural network system; realizes global optimization and local response collaboration by adopting a hierarchical scheduling architecture at the system level, the micro-grid level and the device level; establishes a multi-objective optimization function considering economic, reliable and resilient targets, and introduces robust constraints such as a plateau attenuation coefficient and temperature compensation; uses an improved multi-agent deep deterministic policy gradient algorithm for rolling solution, and triggers an emergency mechanism when a mutation is identified; and realizes model adaptive iteration based on operation data through federated learning. The application can significantly improve the accuracy of weather mutation identification and the real-time performance of scheduling, enhance the resilience and power supply guarantee rate of the micro-grid on the plateau, and ensure the safe operation of key loads under extreme environments.

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.

The invention discloses a micro-grid multi-energy integrated scheduling system, which belongs to the technical field of micro-grid control and comprises a data acquisition module, a data association module, a data cleaning module, a model development module, an energy integrated scheduling module and a scheduling execution module. According to the method, the influence of the use loss rate of various devices in the micro-grid on energy output, demand and storage is considered, and the prediction precision is improved, namely the scheduling precision is improved.

The present invention discloses a method and system for predicting light power considering snow-covered photovoltaic panels, including: step one, determining the influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and establishing a relationship model between each influencing factor and the photovoltaic power loss rate; step two, establishing a prediction model for photovoltaic power under snow-covered conditions based on the relationship model between each influencing factor and the photovoltaic power loss rate; step three, based on the constructed photovoltaic power prediction model, predicting the photovoltaic power of photovoltaic panels under snow-covered conditions. It can be seen that the present invention particularly considers the influence of snow accumulation on photovoltaic panels, and fully considers the various key influencing factors of photovoltaic power loss rate under snow-covered conditions, with high prediction accuracy, thereby significantly improving the power prediction accuracy of photovoltaic power stations in snow-covered environments, and effectively reducing the risk of large-scale photovoltaic power supply shortages caused by snow.

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.