663 results about "Linear programming" patented technology

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.

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 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 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 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 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 discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention relates to a virtual power plant resource flexible aggregation method and device based on linear programming projection, and the method comprises the steps: constructing a linear state equation corresponding to each to-be-aggregated resource according to the resource characteristic data of each to-be-aggregated resource, and carrying out the linear transformation; aggregating the energy use flexible feasible region of each to-be-aggregated resource, and solving a coordinate projection problem to obtain a preliminary aggregated energy use feasible region; constructing a network security constraint and embedding a solution process of the preliminary aggregation energy use feasible region to obtain an aggregation energy use feasible region considering the network security constraint; and constructing a multi-parameter planning model, and fusing an aggregation cost function into a solving process of an aggregation energy use feasible region considering network security constraints to obtain a virtual power plant resource flexibility aggregation result. Therefore, the problems that in the prior art, energy use feasible regions and adjustment cost of massive heterogeneous resources cannot be aggregated at the same time, network security constraints cannot be embedded, and high-dimensional aggregation errors are large are solved, and higher calculation efficiency and compatibility are achieved.

The invention discloses an electric vehicle charging station energy storage scheduling method based on a multi-agent system. The method comprises the following steps: obtaining and standardizing operation basic data of a plurality of new energy vehicle charging stations; setting five types of agents, defining observation variables and action space, and constructing a multi-agent system model; constructing a global collaborative scheduling network, and executing strategy evaluation and strategy generation; setting a constraint boundary, and constructing a linear programming scheduling model; constructing a training sample, and performing offline training and updating of a global collaborative scheduling network; solving a local optimal scheduling amount based on the real-time operation basic data, and analyzing to generate a control instruction; and issuing an energy storage control instruction and a computing power unit control instruction, acquiring a cooperative scheduling result to generate a final scheduling result set, and submitting the final scheduling result set to an upper-layer scheduling system. According to the method, a multi-agent collaborative scheduling system is constructed, strategy optimization and linear programming are fused, and efficient, stable and executable global collaborative scheduling of energy storage and computing power tasks of the charging station is achieved.

The invention belongs to the technical field of electricity markets and low-carbon clearing, and discloses a novel subject participated electricity spot market day-ahead two-stage clearing method under electricity-carbon coupling. The method comprises the following steps: firstly, designing a two-stage clearing mechanism comprising preliminary clearing and multi-target robust optimization; secondly, a unit carbon quota accounting and transaction cost model and a multi-energy power generator model considering marginal cost and carbon emission are constructed based on regional power grid carbon emission factors; then the above models are fused, a non-parameterized uncertain set is adopted to describe new energy fluctuation, a day-ahead two-stage clearing model based on a min-max-min three-layer robust optimization framework is constructed, and the model fuses stepped quotation and dynamic carbon emission factors; finally, the model is converted into a mixed integer linear programming problem, and Camp is adopted; and solving by a CG algorithm. According to the method, the comprehensive clearing cost can be effectively reduced, energy conservation and carbon reduction are promoted, meanwhile, the robustness of the clearing plan to deal with extreme scenes is improved, and the system operation safety is guaranteed.

The invention relates to the technical field of intelligent control of new energy power systems, in particular to a system and method for performing AGC instruction dynamic scheduling energy storage charging and discharging based on photovoltaic power generation volatility, and the system comprises a photovoltaic power generation unit, an intelligent inverter, a hybrid energy storage system composed of a super capacitor and a lithium ion battery, and an AGC scheduling control module. The photovoltaic power is predicted through an LSTM model, and an optimal charging and discharging instruction of the energy storage system is generated by adopting a mixed integer linear programming optimization algorithm and taking minimization of the power grid electricity purchasing cost, the frequency deviation and the battery health degree loss as targets. Through the multi-time scale cooperation of the transient response of the super capacitor and the steady-state scheduling of the lithium battery, the photovoltaic power fluctuation is effectively stabilized, the frequency stability of the power grid is ensured, and the service life of the energy storage battery is remarkably prolonged through a self-adaptive management strategy. The system also has multiple fault-tolerant mechanisms, supports multi-energy complementary access, and improves the reliability and economy of the system.

The invention relates to the technical field of ground source heat pump control, in particular to an intelligent cooperative control system for a ground source heat pump system and building energy management. The method comprises the steps that a data collecting and preprocessing unit collects multiple types of data in real time and preprocesses the data to form system state vectors; the load and power generation prediction unit predicts future cooling and heating load and photovoltaic power generation power of the building through a machine learning regression model and a time sequence prediction model; the operation optimization unit generates an optimal operation strategy of the ground source heat pump based on a depth deterministic strategy gradient algorithm; and the multi-source energy coordinated control unit realizes multi-device coordinated control through multi-target mixed integer linear programming. Through combination of prediction data and an optimization algorithm, a dynamic matching relation between load fluctuation and energy supply is fully considered, and the problems of soil heat imbalance and unstable comprehensive energy efficiency ratio of the ground source heat pump system in a traditional fixed parameter operation mode are solved.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses a family medical care path planning and scheduling method based on deep reinforcement learning, and the method comprises the steps: obtaining HHCRSP instance data, modeling the HHCRSP instance data into a mixed integer linear programming model, carrying out the problem decomposition and sorting, and obtaining a plurality of VRP sub-problems with the types of services needed by patients as the grouping basis and the service dependency as the solving sequence; modeling the solving process of each VRP sub-problem into a Markov decision process, and solving the constructed Markov decision process according to a solving sequence through a strategy network to obtain a service path scheme and a service timetable thereof; and integrating a service path scheme obtained by solving each VRP sub-problem with a service timetable to form a planning and scheduling scheme including family medical care paths of all patients. According to the method, through key technologies such as problem decomposition, a neural network parameterization strategy, service embedding and constraint perception mask, the technical problems of the HHCRSP in the aspects of expandability, real-time performance, generalization ability and complex constraint processing are systematically solved.

A system and method are disclosed including a computer that receives a formulation of a multi-objective linear programming planning problem, the formulation including at least one variable fixed at an upper bound or a lower bound. The computer also solves the formulation for a higher-order objective and fixes the upper bound or the lower bound of at least one variable to preserve a solution of the formulation for the higher-order objective. The computer also replaces at least one variable in the formulation with a value of the upper bound or the lower bound, when the upper bound of at least one variable is fixed at the lower bound or the lower bound of at least one variable is fixed at the upper bound, and checks whether replacing at least one variable with the value of the upper bound or the lower bound divides the formulation into two independent components.

The invention discloses a precise point distribution method for intelligent patrol cameras of a transformer substation. Relates to the field of substation intelligent patrol systems. Comprising the following steps: step 1, determining an installable area and limiting conditions of the intelligent patrol camera; 2, an adjustable temporary intelligent patrol camera is accurately arranged in the installable area; 3, identifying and recording an equipment state point position which can be observed by the intelligent patrol camera at the installation point position; 4, recording all temporary intelligent patrol cameras in all installable areas and observable equipment state point location data of the temporary intelligent patrol cameras; 5, establishing an intelligent patrol camera precise point distribution mathematical model, and solving intelligent patrol camera point distribution points by taking the lowest camera installation cost as a target; 6, according to the solving content, intelligent patrol camera accurate point distribution is carried out; according to the method, the mixed integer linear programming model under the comprehensive constraint is established, the lowest cost is taken as a target, meanwhile, reliable coverage of all key point positions is ensured, the number of installed cameras can be effectively reduced, unnecessary waste is avoided, and the total cost of system construction is remarkably reduced.

The invention discloses a service scheduling method and system for a photoelectric hybrid low earth orbit satellite network, and relates to the technical field of satellite communication and network resource management.The method comprises the steps that the photoelectric hybrid low earth orbit satellite network is constructed, and topological information, node information and a to-be-scheduled service set of the photoelectric hybrid low earth orbit satellite network are obtained; establishing a mixed integer linear programming model containing node selection constraint, service scheduling sequence constraint and routing constraint by taking the minimum weighted sum of the total service completion energy consumption and the total service completion time as a target; in order to solve the problem of high model complexity, a heuristic algorithm based on simulated annealing is designed, and efficient solution is carried out by iteratively optimizing a scheduling sequence and a routing path of a service; and finally, implementing service scheduling according to the obtained optimal scheduling scheme. According to the method, heterogeneous characteristics of the photoelectric nodes and link resource conflicts are fully considered, dynamic balance of energy consumption and time delay is achieved, network energy efficiency and business service quality are remarkably improved, and the method is suitable for efficient operation of large-scale low-orbit satellite constellations.

Methods, systems, and computer-readable storage media for a software dependency update system that processes computer-readable files (e.g., source code file, dependency description file) of a software project to generate an updated dependency graph using linear programming in view of a set of quality metrics for updating dependencies of the software project.

The invention discloses a space-time cooperative scheduling method for an intelligent sky rail and an AGV in an automatic container terminal, and the method comprises the steps: determining the scheduling constraint conditions of an SMV and the AGV based on an SMV and AGV dual-cycle strategy, and constructing a model and constraint conditions which take the minimization of the completion time of all tasks as a target; using an LBBD algorithm to decompose the mixed integer linear programming model into a main problem and a sub-problem, constructing three acceleration strategies based on SMV and AGV dual-cycle strategies, embedding acceleration cut into the main problem as a constraint condition, solving the main problem and the sub-problem under the constraint condition, and generating Benders cut; embedding the Benders into the main problem, and solving again to obtain a scheduling optimization result; the scheduling decision quality is fundamentally improved, a set of scientific and efficient SMV-AGV collaborative operation method is provided for an intelligent air rail system, the equipment utilization rate can be remarkably improved, the operation completion time can be shortened, the optimal collaborative scheduling scheme can be rapidly and accurately obtained in a large-scale task scene, and the unloaded driving cost, the energy consumption cost and the operation cost are synchronously reduced.

The invention discloses an optimization method for an optimal short-distance takeoff strategy of a vertical / short-distance takeoff and landing aircraft, and the method comprises the steps: firstly building a vertical / short-distance takeoff and landing aircraft longitudinal dynamic model and a ground stress model which consider the dynamic state of an actuator, and augmenting the dynamic state of the actuator to an aircraft motion equation; secondly, the minimum ground clearance speed is solved through a constrained balancing algorithm, the speed serves as a take-off criterion, and a fixed-value take-off strategy is obtained through theoretical derivation and serves as a dynamic optimization initial value; the optimal short-distance takeoff problem is converted into an unconstrained nonlinear programming problem with fixed final state time, and a dynamic optimization takeoff strategy is solved in combination with a numerical optimization method; and finally, carrying out sensitivity analysis on the thrust-weight ratio, the thrust distribution ratio and the gravity center position to obtain an influence rule of each factor on the takeoff distance. The method has the advantages of being high in strategy optimization efficiency, short in takeoff distance and high in engineering guidance, the rapid starting capability of the vertical / short-distance takeoff and landing aircraft is remarkably improved, and technical support is provided for control of the short-distance takeoff and landing stage of the vertical / short-distance takeoff and landing aircraft.

The invention discloses an intelligent fire-fighting resource dynamic scheduling method based on the Internet of Things, and relates to the technical field of fire-fighting emergency intelligent scheduling, and the method comprises the steps: collecting fire-fighting global situation awareness data, synchronizing the data to a central database through an encryption protocol, and constructing a fire-fighting real-time commanding combat information system; based on a combat information system of fire-fighting real-time commanding, a combat guarantee scheme is formulated, meanwhile, fire-fighting power information, geographical and meteorological information and field real-time information are collected, and a dynamic dispatching instruction is generated; dynamically mapping the dynamic scheduling instruction to a fire-fighting resource state, generating a resource state mapping table, optimizing resource allocation through a resource priority scoring function and a linear programming model, and generating a resource scheduling scheme; performing Bayesian verification and rating processing on professional pointing information in execution of the resource scheduling scheme to generate a targeted action instruction; according to the method, the continuous evolution capability of the scheduling model is realized through a multi-agent voxel learning parameter adjustment framework of the empirical rule base.

The invention discloses an optimization solution system for a large-scale mixed integer linear programming problem, and the system is characterized in that the system comprises a problem decomposition module which is used for modeling the large-scale mixed integer linear programming problem into a bipartite graph for representation, and dividing the bipartite graph into a plurality of sub-graphs through a graph decomposition algorithm; the problem reduction module is used for obtaining a feature code of each sub-graph node, inputting the feature code into a pre-training neural network to obtain a variable prediction value, reducing the scale of the large-scale mixed integer linear programming problem through a redundancy constraint removal and coefficient priority variable fixing strategy based on the variable prediction value, and then outputting the problem; and the efficient solving module is used for solving the large-scale mixed integer linear programming problem based on the result output by the problem reduction module and weighted subgraph division. According to the method, the high-quality solution of the large-scale mixed integer linear programming problem can be quickly given, so that the high-quality feasible solution can be obtained within the acceptable time.

The invention discloses a power system operation strategy generation method and device, equipment, a medium and a product, and belongs to the technical field of power system automation control. The method comprises the following steps: establishing a cooperative operation model of a wind turbine generator and a battery energy storage system participating in power grid primary frequency modulation; deriving an analytic expression of frequency stability constraints of the power system based on the collaborative operation model, wherein the frequency stability constraints comprise a maximum frequency change rate constraint, an overshoot constraint and a static frequency deviation constraint; constructing a wind power uncertainty model based on the uncertainty budget; constructing a mixed integer linear programming scheduling model taking the minimum total operation cost as a target; and solving the mixed integer linear programming scheduling model to obtain a power system operation strategy. According to the embodiment of the invention, the frequency safety level of the power system can be remarkably improved under the wind power uncertainty.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention discloses a non-full-length beam steel bar material breaking optimization method based on a hybrid optimization algorithm, and aims to solve the problem that an existing non-full-length beam steel bar material breaking method cannot balance the steel bar utilization rate and the calculation efficiency. The method comprises the following steps: for required beam steel bars under the same beam steel bar diameter, dividing the required beam steel bars into two groups according to the length of the beam steel bars; for required beam steel bars with the length larger than 12 m, a cut excess material splicing method is adopted, an optimized mathematical model of steel bar excess materials is established, and the number of used raw material steel bars and the division length of the raw material steel bars are calculated and determined through a genetic algorithm; and for required beam steel bars with the length smaller than 12 m, directly cutting from a single raw material steel bar or combining and processing the raw material steel bars with different specifications, listing all cutting modes, calculating steel bar excess materials corresponding to the cutting modes, and minimizing the total steel bar excess materials by the integer linear programming model to obtain a steel bar broken material combination scheme.