1020 results about "Linear programming" patented technology

The invention belongs to the technical field of resource scheduling, and particularly discloses a resource scheduling method and system based on reinforcement learning, and the method comprises the steps: describing the dependency and conflict relation between tasks through constructing a task causal relation graph which can be dynamically updated; constructing a state space and an action space based on a current task state and a causal relationship, training an intelligent agent by adopting a reinforcement learning model in combination with a multi-target reward function, and generating a scheduling and resource allocation strategy; linear programming and heuristic joint resource allocation are carried out under strategy guidance, and task priorities and causal relationships are dynamically adjusted in combination with task states; by collecting execution data and analyzing strategy deviation, a model structure and parameters are further adjusted, and a complete closed-loop optimization process is formed. According to the method, task priority dynamic adjustment, resource allocation strategy self-adaptive updating and scheduling process closed-loop optimization are realized, and the method is suitable for complex project management scenes under multi-task and multi-resource constraints.

The invention discloses a renewable energy power generation power prediction and power dispatching method and system, and the method comprises the steps: collecting the historical power generation data and real-time meteorological data of renewable energy power generation, carrying out the linear interpolation of the historical power generation data and the real-time meteorological data, and carrying out the missing value filling and box plot anomaly detection, obtaining a normalized training data set; constructing a hybrid prediction model by using the normalized training data set and adopting a neural symbol acceleration technology with time logic constraints, extracting medium and long term space time features, and generating a renewable energy power generation power prediction result; and according to the renewable energy power generation power prediction result and the system constraint condition, adopting a linear one-dimensional projection constrained distribution robust control method to formulate a scheduling strategy, and utilizing the scheduling strategy to solve an optimal scheduling scheme through mixed integer linear programming. According to the method, the renewable energy power generation power prediction precision and the power dispatching robustness are remarkably improved.

The invention provides a hybrid parallel and dynamic scheduling method of a hybrid expert model based on 3D near-memory processing. The method comprises the following steps: establishing a joint performance analysis model; performing off-line linear programming optimization expert distribution; performing Bayesian optimization on physical node mapping; performing online reasoning; carrying out online dynamic priority detection; and an expert pre-broadcast and communication-friendly lexical element distribution strategy with optimal efficiency is provided. According to the method, node balancing optimization is realized through offline linear programming, and the problem of load imbalance of 3D NMP calculation is remarkably improved; in combination with a Bayesian optimization mapping strategy of link balance, the communication speed-up ratio is increased, and NoC link congestion is reduced; a dynamic scheduling strategy adapts to dynamic changes of expert activation in real-time reasoning through calculation load prediction and a pre-broadcast mechanism. Through cooperation of the offline automatic hybrid parallel mapping algorithm and the online dynamic scheduling strategy, the calculation load and the communication overhead are effectively balanced, and the reasoning efficiency of the hybrid expert model MoE on the 3D near-memory processing architecture is remarkably improved.

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak / valley periods, and timing of peak / valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention discloses an automatic business travel itinerary optimization method, and relates to the technical field of intelligent itinerary planning, and the method comprises the steps: integrating the multi-source heterogeneous data of enterprise policies, personal preferences and real-time traffic through a federated learning framework, and achieving the cross-domain knowledge sharing; the method comprises the following steps: constructing a staged optimization engine by adopting an attention mechanism to dynamically balance cost, time, comfort and sustainability targets: in the first stage, modularly disassembling a travel through sparse constraint linear programming, and quickly generating a Pareto frontier candidate set; in the secondary stage, on the basis of a multi-agent reinforcement learning framework, complex interaction is simulated through a Markov decision process, and strategy iteration is driven through a special reward function for quantifying a comfort index; in order to cope with real-time disturbance, event-driven edge computing nodes are deployed, flight delay and traffic jam emergencies are responded in real time, an incremental topology updating algorithm is triggered, and only affected sub-modules are reconstructed to reduce computing complexity. According to the invention, the bottleneck of dynamic adjustment efficiency and multi-target balance capability is solved.

A network security script arrangement method based on LLM enhanced RL comprises the following steps: 1) LLM dynamically expands a to-be-selected strategy atom set based on a security threat scene: firstly, performing semantic similarity retrieval on input security threat intelligence by using a vector library, and matching an optimal defense strategy atom in a knowledge library; the context understanding capability of the LLM is then utilized to implement semantic understanding and reasoning on the candidate atom set, generating new policy atoms complementary to the defense. 2) RL refers to LLM atomic action preference to optimize a script generation strategy, and self-adaptive security arrangement of a complex network environment is realized: firstly, a security script graph model based on strategy atoms is adopted, and multiple constraints corresponding to nodes and edges in the graph model are abstracted on the basis of security arrangement definition; and then, a composite reward function is adopted to model a linear programming function for the multi-constrained security script arrangement problem, so that multi-dimensional optimization of defense efficiency and execution cost is realized.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention provides a distributed FA cooperative control method for edge computing nodes of a distribution network terminal, and the method comprises the steps: synchronously collecting high-frequency electrical and partial discharge signals through an edge computing terminal, carrying out the multi-dimensional processing, and forming a fault feature vector; each edge node monitors a fault in real time by using a lightweight algorithm, the fault is broadcasted to an adjacent node after being subjected to Hilbert-Huang transform feature enhancement, accurate positioning is realized by adopting multi-node cross validation, and edge cloud collaboration is linked during complex topology. And after the fault is positioned, starting dual verification, sending a tripping instruction through an encrypted GOOSE protocol, searching an optimal recovery path based on a mixed integer linear programming model, and preferentially utilizing a distributed power supply to reversely supply power. A control strategy is dynamically optimized through federated learning and an MADDPG algorithm, and a digital twinborn model is trained to improve adaptability. According to the invention, the problems of slow response, low positioning precision and the like of the traditional centralized FA are effectively solved, and the fault processing efficiency, reliability and intelligent level of the distribution network are remarkably improved.

The invention discloses an operation optimization method, system and equipment for cooperative carbon reduction of an intelligent rail line network, and relates to the technical field of energy-saving scheduling and low-carbon operation of urban public transport. A refined section-level energy consumption and carbon emission quantitative model is constructed based on dynamic characteristics of intelligent rail vehicles and carbon emission factors of a power grid; a deep learning network is used for fusing multi-source data to carry out cross-line short-time passenger flow prediction, real-time passenger flow demands, energy consumption and carbon emission indexes and intersection signal phase time window constraints are uniformly incorporated into a multi-target collaborative optimization framework, and solving is carried out through mixed integer linear programming in a rolling vision field. According to the method, combined optimization and dynamic closed-loop control of departure intervals, section operation speeds and road right strategies are realized, so that the total operation energy consumption and carbon emission of the intelligent rail line network are remarkably reduced on the premise of ensuring passenger flow transportation requirements and service quality, and the line network level energy-saving and carbon-reducing operation targets are achieved.

The invention relates to the technical field of computer vision and satellite video processing, in particular to a satellite video dense vehicle tracking method and system. The method comprises the following steps: constructing a satellite video dense vehicle data set VDD-VEH containing a motion vector label, and providing supervision information for space-time modeling; designing a motion position map (MPG), mapping a target space position and a motion flow into a three-dimensional space-time diagram structure, fusing space-time consistency, appearance features and detection confidence by using a multi-feature edge weight (MFEW) strategy, and quantifying node association strength; global optimal trajectory association is realized by adopting integral linear programming (ILP), abnormal trajectories are eliminated by combining a trajectory optimization module (TRM), trajectory fractures are repaired, and long-time-sequence tracking stability is enhanced. The method is remarkably superior to the prior art in indexes such as MOTA and IDF1, the identity switching frequency is reduced by 55%, and the method is suitable for intelligent traffic monitoring and remote sensing video analysis and has high precision and cross-scene generalization ability.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention relates to the technical field of charging station regulation and control, and discloses a charging station regulation and control method and system considering ordered charging and demand response. The method comprises the steps of processing uncertainty of vehicle arrival time and user departure time through a demand prediction model, constructing a scheduling optimization model to maximize charging station income, and comprehensively considering charging income, V2G discharge income, electricity purchase cost and user waiting penalty. After a power grid demand response signal is received, two-stage decoupling optimization is carried out, the first stage is to optimize a charging pile shutdown strategy, and the second stage is to optimize a vehicle scheduling scheme. The time uncertainty is adaptively adjusted through an affine decision rule, a mixed integer linear programming solver is utilized to obtain an optimal regulation and control instruction of the charging station, and robust scheduling execution is realized. According to the method, the problem that the charging station cannot realize multi-target collaborative optimization scheduling under double challenges of demand prediction uncertainty and power grid demand response is solved, and the robustness and the economic benefit of a charging station scheduling scheme are improved.

The invention relates to a dynamic routing and flow balancing method and system for a regionalized network topology, and the method comprises the steps: dividing a satellite network into a plurality of partitions, and distributing a core node for traffic scheduling and routing calculation for each partition; each core satellite node responds to each traffic demand, and bandwidth and traffic distribution in each partition are adjusted through a multi-anchor-segment routing method and a preset linear programming model; and each satellite node dynamically adjusts the route of each traffic demand through a probability forwarding scheduling method based on geometric topology according to the relative position of the satellite node and the target node. According to the method, low delay, high bandwidth utilization rate, load balance and efficient fault recovery of the satellite network are realized through network partitioning, a multi-anchor-segment routing method and probability forwarding based on geometric topology.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

The invention provides an electric power system source-load coordinated optimization scheduling method considering the flexibility of a steel production process, and relates to the technical field of electric power system scheduling, comprising the following steps: carrying out operation characteristic analysis on the production load of a steel enterprise, and respectively establishing an electric arc furnace model, an air separation system model and a steel rolling line model; constructing an adjustable capacity model under the coupling of the power supply system and the production process; based on the operation cost of the iron and steel enterprise power system, constructing an iron and steel enterprise flexibility day-ahead scheduling model; the electric arc furnace model, the air separation system model, the steel rolling line model, the adjustable capacity model and the iron and steel enterprise flexibility day-ahead scheduling model are integrated into a mixed integer linear programming scheduling model, the mixed integer linear programming scheduling model is solved through a solving tool, and an optimal scheduling scheme of system source-load coordination is obtained. The flexibility of a power system can be remarkably improved, and the problem of supply and demand imbalance caused by wind power output fluctuation is effectively relieved.

The invention discloses a particle swarm and non-linear programming hybrid optimization VSG adaptive control method and system, and relates to the technical field of virtual synchronous generator control, and the method comprises the steps: collecting a filter inductor current and capacitor voltage signal, outputting an instantaneous active power and a reactive power, constructing a small signal dynamic model of a VSG, and building a parameter mapping relation; setting a constraint range based on the parameter mapping relation in combination with a second-order oscillation modal stability criterion of the power system; constructing a hybrid optimization algorithm according to the constraint range, and outputting steady-state data; constructing a two-dimensional threshold criterion system, and cooperatively generating a voltage reference value through a speed regulator and a controller; the voltage reference value is compared with the actual output voltage, and the inverter output is driven through PI control and PWM modulation. The method has the beneficial effects that the parameter optimization precision is improved, the stability of the system is guaranteed, and the calculation efficiency of the algorithm is improved.

The invention discloses a laser cutting path intelligent optimization method and system based on deep learning, and relates to the field of laser cutting, and the method comprises the steps: employing wavelet transform and a feature pyramid network to carry out cross-scale feature fusion on multi-scale material thermal response data, and obtaining a multi-scale thermal response feature vector; inputting the multi-scale thermal response feature vector into the coupling model to obtain corrected temperature field data; based on the corrected temperature field data, a reinforcement learning framework is used for solving a self-adaptive adjustment cutting index; and based on the cutting indexes, determining an optimal cutting path by using a mixed integer linear programming model. The optimal cutting path is determined in combination with the coupling model and the double-delay depth deterministic strategy gradient algorithm, and the adaptability of laser cutting to batch differences, environment disturbance and working condition changes can be improved.

The invention relates to a new energy micro-grid multi-objective optimization control method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time output data of renewable energy sources of the micro-grid, operation parameters of energy storage equipment and load demand records, and performing time sequence analysis to obtain an energy output variation rule and a load demand curve; constructing a multi-objective function model containing economy-stability double objectives and a coupling correction term based on the law and the curve, generating an initial balance scheme through linear programming, and iteratively determining resource collaborative allocation parameters through a particle swarm optimization algorithm; the distribution parameters and the energy storage available capacity are input into the agent model, a real-time optimization decision instruction is generated, and an economic evaluation value is calculated in combination with running log data; and if the evaluation value does not reach the power grid stability threshold value, recalculating a balance scheme through a multi-objective function model, and generating a standardized equipment instruction set. According to the method, collaborative optimization of economy and stability of the micro-grid is realized, and stable operation of the micro-grid in different scenes is guaranteed.

The invention discloses a method for applying linear programming to CDN (Content Delivery Network) scheduling, which relates to the technical field of content delivery networks and comprises the steps of data preparation, strategy layer version smooth configuration, macroscopic layer and microscopic layer linear solution and online execution. Basic data are collected, cleaned and repaired, and a version change rule is set; the macroscopic layer constructs a linear programming model, and the cross-provincial bearing quota is solved with the aim of minimizing the cross-provincial cost; the micro layer takes the quota as a boundary and generates domain name class-node weight vectors in parallel; and adapting a routing request online through weighted rendezvous hashing and request features. According to the method, a dynamic cost matrix and a weight granularity control technology are integrated, the engineering problem of linear programming is solved, second-level response, approximate global optimal scheduling and accurate execution of floating-point-level weight are realized, memory overhead is reduced, smooth updating of a strategy and system stability are guaranteed, and CDN service quality and operation efficiency are improved.

The embodiment of the invention provides a federated learning contribution evaluation method and device, and the method comprises the steps: carrying out the grouping of a plurality of edge computing devices, and obtaining a plurality of sub-federated learning sets; and for the target federated learning sub-set, aggregating model update information corresponding to each edge computing device in the target federated learning sub-set, and determining a collaborative contribution value of the target federated learning sub-set based on the performance index of the updated global model on the common test set. Through a first linear programming solver, according to the collaborative contribution values of the multiple federated learning sub-sets, obtaining the maximum loss value corresponding to all the federated learning sub-sets and optimizing the maximum loss value to obtain the minimized maximum loss value, and through a second linear programming solver, obtaining the maximum loss value corresponding to all the federated learning sub-sets; and according to the maximum loss value after all the sub federated learning sets are minimized and the reference contribution values corresponding to the plurality of edge computing devices, target contribution vectors corresponding to the plurality of edge computing devices are determined, and the contribution degree of each edge computing device in the training process is accurately quantified.

The embodiment of the invention relates to the technical field of chemical products, in particular to a scheduling optimization method, device and equipment for chemical materials, and the method comprises the steps: initializing a plurality of mark sequences corresponding to a plurality of chemical material production devices; calculating the daily output of each batch of chemical materials according to the production rate and the production duration of each batch in each brand sequence; according to the daily output and the physical property parameters of each batch of chemical materials, determining the loss index of a chemical material production device and the daily consumption and the utilization rate of the chemical materials; constructing a mixed integer linear programming model by taking maximization of the utilization rate and minimization of the loss index as targets and taking conditions that the daily consumption meets daily inventory constraints and the starting time of each batch meets time window switching constraints, the mixed integer linear programming model comprises a production rate linear term and a production time linear term which are used for representing the daily output of each batch of chemical materials; and optimizing the plurality of mark sequences according to the mixed integer linear programming model.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention relates to the technical field of aircraft path planning, and particularly provides an aircraft path planning method for a space non-cooperative target. The method comprises the following steps: constructing an aircraft dynamics model and constraint conditions, and obtaining a non-convex optimal control problem; performing discretization processing on a non-convex optimal control problem through a non-equal-interval pseudo-spectrum discrete optimization model, and converting the non-convex optimal control problem into a fixed-time nonlinear programming problem about discrete nodes; according to the method, a fixed time nonlinear programming problem is convexed to form a convex sub problem, a local optimal solution of a non-convex optimal control problem is obtained through sequence iteration, under the multi-state constraint, path planning of an aircraft approaching a space non-cooperative target is achieved, the problem of no feasible solution caused by non-accurate linearization is solved, and the method is suitable for being applied to a non-cooperative target. And the convergence rate of the algorithm is accelerated.

The invention relates to a multi-data center elastic scheduling method, which comprises the following steps of: establishing a data center polymer (DCA) to integrate the space-time regulation potential of a dispersed data center, establishing a comprehensive model covering dynamic migration of a working load, power consumption regulation and control of a DVFS (Distributed Valve File System) and collaboration of an optical storage system, and adopting a double-layer elastic scheduling architecture, energy storage charging and discharging, photovoltaic output and other hyperopia actions are optimized to improve long-term decision robustness; and the lower layer solves short view actions such as server scheduling and load distribution in real time based on mixed integer linear programming to realize instant cost optimization, so that power-computing power collaborative scheduling optimization of the data center is realized. According to the method, flexible scheduling of time delay sensitive type and time delay tolerant type workloads is combined, the cooperative effect of an energy storage system and photovoltaic power generation is utilized, the power use efficiency and economical efficiency of the data center are optimized, and dynamic participation of multiple data centers in the power market is achieved.

The invention provides a transaction and scheduling optimization method and system for a virtual power plant participating in a spot market, relates to the technical field of energy systems, and solves the problem of electricity purchase cost minimization of the virtual power plant participating in spot transaction and the problem of how to report and regulate adjustable resources in the virtual power plant containing multiple distributed energy main bodies. The method comprises the following steps: acquiring first data and second data; according to the spot transaction price, the static data, the first data and the second data, constructing an optimization target for minimizing the overall power purchase cost of the virtual power plant, and setting a comprehensive power constraint condition and an energy storage system constraint condition; and solving through a linear programming solver to obtain an optimal spot declaration strategy and a regulation and control strategy of corresponding equipment. The method is used in the virtual power plant transaction and scheduling optimization process, the declaration strategy of the electric power spot market is considered, the electric quantity of the real-time market is adjusted through adjustable resources such as adjustable equipment and energy storage equipment on the user side, and the electricity purchase cost can be reduced and the marketization risk can be controlled.