317 results about "Power model" patented technology

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

The invention discloses an equipment fault diagnosis method and system based on a large electric power model, and the method comprises the steps: collecting and fusing voltage and current waveform data and alarm log information in a short time window before and after a fault moment, achieving the context integration of a multi-dimensional feature vector through multi-level feature coding and semantic embedding, and achieving the fault diagnosis of a large electric power model. And relevant knowledge fragment retrieval is carried out in combination with a power field knowledge base. On the basis, relevant knowledge fragments, voltage-current waveform fusion feature coding vectors and fault alarm key information semantic embedding coding feature vectors are jointly embedded into a preset Prompt template and then are input into a diagnosis engine based on large model driving, so that intelligent analysis and report generation of fault types, reasons and key features are realized. Through the mode, the limitation of dependence on single data or rules traditionally is broken through, the accuracy, automation and interpretability of fault diagnosis under complex working conditions are remarkably improved, and a solid support is provided for intelligent and high-reliability equipment operation and maintenance.

A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps: step 10: obtaining IT system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model. The present invention provides a cooperative control optimization method suitable for a near-field fan wall micromodule, to resolve the technical problem that local hot spots and overcooling are prone to occur during energy-saving scheduling of a near-field fan wall micromodule.

The invention relates to the technical field of network security, and particularly discloses an edge device network threat detection method and system based on an electric power large model, and the method comprises the steps: capturing a network message sequence in real time, extracting a time sequence randomness feature and a semantic deviation feature from a time dimension and a protocol dimension, and carrying out the fusion to form a comprehensive threat feature vector; performing multi-dimensional feature analysis and time sequence modeling by adopting a lightweight electric power large model to realize millisecond-level threat assessment; establishing a multi-level response mechanism, dynamically triggering a differential protection strategy according to the threat level, and ensuring the reliability and consistency of response actions through digital signature and collaborative verification; according to the method, the complex network attack in the power edge equipment can be effectively identified, the threat detection accuracy and the system defense capability are improved, and the strict requirements of a power system on real-time performance and reliability are met.

Embodiments of the present application set forth a trajectory tracking control method for an exoskeleton robot, comprising: in a first stage, when an exoskeleton robot is not applied to a subject, determining system interference of a power system of the exoskeleton robot, and compensating an output parameter of a power model of the exoskeleton robot; in a second stage, when the exoskeleton robot is applied to a subject, isolating the system interference from a human-machine interaction torque of the second stage, and determining the human-machine interaction torque; inputting the human-machine interaction torque into a controller, and using the controller to determine an assistance of the power system of the exoskeleton robot.

The invention discloses a server power consumption monitoring method, a computer program product and a data center, and relates to the technical field of power consumption prediction.The method comprises the steps that a server power consumption model comprising a hardware basic power consumption model, a state switching loss model and a thermal hysteresis compensation power consumption model is constructed; the power of the central processing unit under the steady-state working condition, the power changed compared with the steady-state working condition under the state switching working condition and the power consumption increased due to heat accumulation can be predicted, and the sum of the three is the total power consumption of the server; compared with the prior art, the method has the advantages that power response lag caused by state switching working condition load sudden change and leakage current power consumption calculation errors caused by thermal lag are considered, the accuracy of real-time prediction of the server power is greatly improved, and the problem of inaccurate real-time power prediction of the server power model is solved.

The invention discloses a hydrogen fuel cell hybrid electric vehicle energy management method based on robust optimization, and the method comprises the following steps: building a vehicle model: building a longitudinal dynamic model of a hydrogen fuel cell hybrid electric vehicle, determining a vehicle demand power model, and building an evaluation function of instantaneous hydrogen consumption, constructing an output power model of the hydrogen fuel cell and the capacitor; performing uncertainty analysis on the hydrogen fuel cell hybrid electric vehicle, determining uncertain parameters in the vehicle model, and constructing an uncertainty set of the uncertain parameters; the method comprises the following steps: constructing an sLSTM vehicle speed prediction model through vehicle historical speed data, predicting a future driving speed, analyzing the uncertainty of the prediction model, and outputting a prediction speed error in a current prediction time domain; and according to the optimization target, constructing a target function for minimizing the hydrogen consumption, solving the target function by applying a robust optimization algorithm, and outputting a first control result of the optimal control sequence.

The invention discloses a water supply pipe network leakage locating method based on a lightweight gradient elevator algorithm, and belongs to the technical field of intelligent water affair and pipe network monitoring. The method comprises the following steps: integrating hydraulic modeling basic information, and constructing an initial hydraulic model by utilizing hydraulic software; actual measurement values of key node pressure and main pipe flow under different working conditions are collected and compared with simulation values to be calibrated to reach the standard, and a high-precision hydraulic model is obtained; simulating each leakage working condition in different time periods on a selected potential leakage node / pipe section by using the calibration model, collecting pressure change data of a pressure monitoring node, and performing preprocessing and data division; the data set is used for training a LightGBM model, the LightGBM model learns the relevance of node pressure in spatial topology and the dynamic change characteristics of a time sequence at the same time, a spatial position differentiation weight mechanism is introduced, and accurate positioning of leakage points and quantitative estimation of leakage coefficients are achieved. According to the method, the pipe network leakage positioning precision and efficiency are remarkably improved, and support is provided for rapid and accurate leakage detection and leakage amount evaluation.

The invention relates to the technical field of power equipment fault detection, in particular to a power equipment fault diagnosis method and system based on a large power model, and the method comprises the steps: converting the multi-modal data of a power cable in operation into a feature vector, and inputting the feature vector into a pre-trained fault diagnosis model to obtain a preliminary diagnosis result and confidence; if the confidence coefficient is not lower than the threshold value, the preliminary diagnosis result is reserved; and if the confidence coefficient is lower than a threshold value, taking the feature vector as a current potential fault feature vector to perform secondary discrimination, namely obtaining a significance index by calculating the similarity and volatility of the current potential fault feature vector and a historical potential fault feature vector, analyzing a time change trend to obtain a cumulative trend index, and performing secondary discrimination on the cumulative trend index. And comprehensively determining a potential fault index through the significance index and the cumulative trend index, and determining a final diagnosis result according to the potential fault index. According to the scheme, the diagnosis accuracy of the fault diagnosis model on low-confidence potential faults and unknown faults is improved.

The invention discloses a flexible load control method and system for a multi-split variable frequency air conditioner and a medium. The method comprises the steps that operation data are collected; building an air conditioner power model: building the air conditioner power model based on the collected operation data; and air conditioner load response: when the power instruction is received, the air conditioner load response is started, and the flexible load control strategy is executed on the multi-split variable frequency air conditioner. According to the method, load control suitable for VRV air conditioners of different types and powers is achieved, the method is suitable for rooms of different state conditions, and the later maintenance cost is saved.

The invention relates to the technical field of model parameter calibration, and provides a hydrodynamic model calibration method and system based on an improved particle swarm optimization, and the method comprises the steps: S1, obtaining the corresponding relation between the section information of a hydrodynamic model and the roughness; s2, defining a roughness parameter range and an allowable error range of each section; s3, initializing a particle swarm; s4, performing parallel scheduling hydrodynamic model calculation according to the matrix number of the particle swarm to obtain a simulation result data set; s5, performing time sequence comparison on a simulation result and an actual measurement result, and calculating an error value; s6, screening a calibration optimal solution, finding out a particle with the minimum error, and recording a corresponding roughness parameter; s7, judging whether the optimal solution is within an error range or not, if yes, directly executing the step S8, and if not, repeatedly executing the steps S4 and S5 until the optimal solution meets the condition; and S8, outputting a calibration optimal solution, and displaying a calibration result, an actual measurement result process line and optimal parameters. According to the invention, the calibration efficiency can be effectively improved.

The invention discloses a Tube MPC-based hybrid electric vehicle layered energy management method. The method comprises the following steps of performing longitudinal dynamic modeling on a hybrid electric vehicle, determining a vehicle demand power formula, constructing an evaluation function of equivalent fuel consumption, and constructing an output power model of a vehicle engine and a battery; carrying out uncertainty analysis on the energy management system, determining uncertain parameters in the vehicle model, quantifying the uncertainty of the uncertain parameters based on probability distribution, and constructing an uncertainty set of the uncertain parameters; on the basis of a dynamic programming algorithm, under the global working condition, the minimum amount of equivalent fuel consumption serves as a target function, an optimal control sequence of the engine is output, and an SOC reference trajectory is generated; and performing tracking solution on the control variables and the state variables in the uncertainty set by applying a Tube model predictive control (Tube MPC) method to obtain an optimal control sequence under uncertain factors, and outputting a first control variable.

The invention relates to a coastal city flood multi-system coupling evaluation method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the unified projection and time resolution normalization processing of the data; constructing an upstream river network one-dimensional hydrodynamic model, an urban drainage pipe network one-dimensional hydrodynamic model and an urban two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions; coupling each model and the tide boundary; performing model calibration and error constraint on the coupled multi-dimensional hydrodynamic coupling model; constructing a composite scene, taking each group of scenes as an input boundary condition to drive the multi-dimensional hydrodynamic coupling model, and obtaining a flood evolution result; and performing risk assessment and sensitivity analysis based on a flood evolution result. According to the method, a unified river network-pipe network-earth surface-tide multi-scale coupling framework is constructed, so that the defect that a traditional single model or result superposition method is difficult to reflect multi-system interaction is effectively overcome.

The invention relates to a power transformation equipment operation and maintenance task intelligent arrangement method and system, equipment and a medium, and relates to the technical field of electric power. The method comprises the steps of obtaining real-time operation data and historical operation and maintenance data of power transformation equipment, constructing input information of a large electric power model by adopting a Prompt technology enhanced by a knowledge graph, and predicting an operation and maintenance task by utilizing the large electric power model; and generating an operation and maintenance task arrangement scheme, performing dynamic optimization of the operation and maintenance task arrangement scheme based on reinforcement learning, generating and issuing an operation and maintenance work ticket, and feeding back task execution state and equipment state information to the large electric power model in real time to optimize the operation and maintenance task arrangement scheme. Compared with the prior art, the intelligent arrangement of the operation and maintenance tasks of the power transformation equipment is realized, the operation and maintenance efficiency and the equipment reliability are improved, the human resource consumption is reduced, and the scientificity of operation and maintenance decision is enhanced.

The invention relates to the technical field of power distributed resource regulation and control, in particular to a peak regulation market electric quantity declaration method and system for distributed resources, electronic equipment and a medium. The method comprises the following steps: firstly, acquiring real-time operation data and peak regulation market policy data of load side distributed resources; constructing a double-constraint generation power model according to the real-time operation data and peak regulation market policy data; constructing a distributed resource aggregation model based on the double-constraint power generation power model; extracting an unsteady variable from the resource aggregation state and the electric quantity declaration reference value; according to the unsteady variables, an interval optimization logic solution is adopted to obtain an optimal electric quantity declaration strategy; and finally generating an electric quantity declaration scheme participating in the peak regulation market. Through a double-constraint generation power model and interval optimization logic, the problem of insufficient peak regulation market income optimization caused by neglecting adjustable load characteristics and wind and light abandoning punishment in the prior art is solved, and the adaptability and reliability of an electric quantity declaration strategy in a complex market environment are improved.

The invention discloses a flood storage and detention area flood diversion process simulation method and device based on a hydrodynamic model, and the method comprises the steps: obtaining the landform, river section, dike engineering parameters, and real-time hydrological and meteorological data of a flood storage and detention area, constructing a basic parameter library, and storing the basic parameter library in a data input module; constructing a hydrodynamic model calculation module by taking a hydrodynamic equation as a theoretical basis on the basis of the basic database, and combining a finite element volume method; receiving a numerical calculation result, analyzing real-time parameters through a dynamic simulation module so as to match actual hydrological parameters, and performing comparison under multiple scenes; and transmitting the hydrological parameters and the comparison data to a result output module, and outputting key parameters of the flood diversion process and a risk assessment result. The method has the advantages that high-frequency coupling of a hydrodynamic model, multi-source dynamic data, a cross-domain model and an engineering control system is achieved, and the problems of data lag, model isolation and control disjunction in traditional simulation are solved.

The invention provides a power system frequency modulation optimization method in extreme weather. The method comprises the following steps: establishing a wind power model and a photovoltaic power model; constructing a typical extreme weather scene by adopting an improved fuzzy C-means clustering algorithm; establishing a frequency modulation optimization model taking the quasi-steady-state frequency deviation, the system regulation time and the comprehensive cost of the generator set as objective functions; constructing constraint conditions; and solving the optimization model based on a multi-target fox-monkey optimization algorithm. According to the invention, the improved fuzzy C-means clustering algorithm is adopted to construct a typical extreme weather scene, and the sample membership degree is evaluated through the comprehensive distance composed of the Euclidean distance and the included angle cosine distance, so that the accuracy of scene construction is improved; the frequency safety and economy of the system are comprehensively considered, and comprehensive optimization of various performance indexes is realized; a multi-target fox-monkey optimization algorithm is adopted for solving, and the algorithm is prevented from falling into local optimum; the accuracy of system frequency stability evaluation is improved, and a more reliable guarantee is provided for safe operation of the system in extreme weather.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.

The invention provides a distributed scheduling method for railway traction stations and peripheral micro-energy grids, and belongs to the technical field of energy supply of micro-energy grid groups containing the railway traction stations, and the method comprises the steps: building a micro-energy grid sharing mutual aid grid structure containing the railway traction stations, obtaining a power model of each railway traction station, and optimally configuring energy storage equipment, obtaining operation constraints of the energy storage equipment; constructing an energy sharing benefit function by taking each micro-energy network agent as a game main body and taking individual benefit maximization as a target, setting constraint conditions, and establishing an energy sharing non-cooperative game model; and solving the energy sharing non-cooperative game model by adopting an alternating direction multiplier algorithm to obtain a distributed energy transaction behavior result and a price result. According to the method, distributed solution is carried out on the micro-energy network energy sharing non-cooperative game model based on the alternating direction multiplier method, original centralized optimization is converted into coupling optimization among a plurality of micro-energy network agents through interactive variables, the efficiency of the whole iteration process is guaranteed, and the privacy of each energy network is protected.

The application provides a highway charging load prediction method and control system based on traffic flow power. Through a second-by-second specific power model of different electric vehicle types on the highway, factors such as road slope, vehicle load, electric vehicle braking recovery characteristics, instantaneous speed and acceleration can be considered to achieve more accurate energy consumption modeling. By establishing a specific power operation mode distribution library of different vehicle types, using a shock wave model and a cellular transmission model to solve, quantifying the congestion propagation process, dynamically correcting the specific power calculation parameters, only the road section parameters and average speed are needed to calculate the energy consumption under complex road conditions. The driver's range anxiety is quantified as a dynamic variable, and charging decision rules are established for different scenarios. The charging probability is dynamically corrected based on the queue length, weather, and driver type, and an electric vehicle charging time prediction model is established based on different service area charging scenarios.

The invention discloses a wind, light and green power coupled pumping unit well group operation collaborative optimization method. The method comprises the following steps: acquiring field data of a multi-energy micro-grid pumping unit well group; establishing a wind and light output uncertainty model, an energy storage battery model, a pumping unit power model and bidirectional AC / DC converter equipment; based on each model, establishing a multi-objective optimization scheduling model for the intermittent pumping operation of the multi-energy micro-grid pumping unit well group; an NSGA-II method is adopted to solve the established multi-objective optimization scheduling model, and a Pareto optimal scheduling scheme is obtained; and carrying out comprehensive evaluation and decision analysis based on an entropy weight TOPSIS method, and selecting an optimal scheme from a Pareto optimal scheduling scheme set obtained by NSGA-II. According to the multi-energy micro-grid rod-pumped well group off-peak interval pumping operation multi-objective optimization method based on NSGA-II and entropy weight TOPSIS, the power utilization requirement of the rod-pumped well group can be met, the working system of rod-pumped well group interval pumping operation is optimized, the economical efficiency and stability of the operation of a rod-pumped well interval pumping system under a wind-solar storage micro-grid are ensured, and the green power consumption capability of the system is enhanced.

The invention discloses a flood risk prediction method and system based on adaptive Bayesian model averaging, and the method comprises the steps: building a plurality of basic hydrodynamic models, and enabling the multiple of an upstream flow hydrograph and the multiple of roughness in the input data of different hydrodynamic models to be different; constructing the plurality of basic hydrodynamic models into a hydrodynamic model set, and performing iterative optimization on the hydrodynamic model set to obtain output data of the hydrodynamic model set; in the output data of the hydrodynamic model set, performing double correction on the output data of a single hydrodynamic model to form a hydrodynamic model after double correction; step 3, constructing a probability prediction framework, averaging the probability prediction framework by adopting a Bayesian model, and obtaining an optimal Bayesian model average structure; and applying the selected optimal Bayesian model average structure, and operating an EM algorithm to calculate the final downstream water level and flow predicted value of the flood. The accuracy of the prediction result can be improved.

The invention discloses a sliding mode prediction energy management method for a series hybrid electric propulsion system, and belongs to the field of energy management. According to the method, firstly, a series hybrid electric propulsion system power model is established, the power flow direction in the system is analyzed, an energy management system is designed, the working principle of all parts of the hybrid electric propulsion system and the mutual relation are clarified, and the hybrid electric propulsion system energy management optimization control method is provided. Optimal distribution of the output power of the engine and the battery is achieved, and it is guaranteed that the fuel consumption of the aircraft is minimized under various constraint conditions. Therefore, on the premise that all related parts of the hybrid electric propulsion system are normal and operate safely, reasonable distribution between the output power of the engine and the output power of the battery is ensured, the fuel consumption of an aircraft is minimized, the overall energy efficiency is improved, and energy optimization management of the hybrid electric propulsion system is achieved.

The invention discloses a power system cascading failure chain modeling and blocking method, device and system considering source-load double-end uncertainty, and a medium. The method comprises the steps of constructing source-load uncertainty input distribution (wind power introduces Weibull distribution based on a wind speed segmented power model, photovoltaic introduces Beta distribution based on an irradiance output model, and a load side introduces a normal distribution fluctuation model); an MSFF method is adopted to construct a unified source-load stochastic power flow model, and a multi-scene power flow state set is obtained; introducing a power flow out-of-limit criterion and a hidden fault probability function, building a cascading fault propagation model fusing wind-solar load uncertainty, and dynamically simulating fault chain evolution; and formulating a staged collaborative blocking strategy (cutting off non-critical load emergency control, quickly responding a battery energy storage system and dynamically adjusting a critical line protection constant value) to realize real-time fault suppression. Simulation shows that the method is suitable for power system security defense in a high-proportion new energy penetration scene, and has good engineering adaptability and dynamic control performance.

The invention discloses a dynamic power management method for an all-electric ship hybrid energy storage system. The method comprises the following steps: S1, establishing a direct-current voltage dynamic model and a ship hybrid energy storage system model based on a ship micro-grid structure of a ship; s2, establishing a multi-working-condition propulsion power model based on the movement mechanism of the ship; s3, establishing a joint SOC-SOP estimation model based on the nonlinear relationship among the state of charge, the battery power characteristics and the ambient temperature; s4, on the basis of the direct-current voltage dynamic model, the ship hybrid energy storage system model and the multi-working-condition propulsion power model, establishing a dynamic power distribution model; and S5, estimating an available power range of the battery through a combined SOC-SOP estimation model, inputting the available power range into the dynamic power distribution model, obtaining a dynamic power distribution strategy, and realizing dynamic power distribution of the ship hybrid energy storage system. According to the method, the dynamic power management of the hybrid energy storage system of the all-electric ship under the uncertain navigation condition is optimized, and the stability and the economical efficiency of a ship power system are improved.

The invention relates to the technical field of information processing, in particular to a question number question and answer processing method based on an AI large model. By calculating the delay deviation risk highlighting representation value of each type of computing power model of the user question number questions and answers in each field, the delay deviation risk highlighting sorting sequence of each type of computing power model of the user question number questions and answers in each field is quickly determined, the efficiency of question number question and answer information processing is improved, and meanwhile, the user question number question and answer information processing efficiency is improved. The maximum delay deviation risk highlighting characterization value of different types of computing power models of users in the question number question-answering field can be accurately obtained by obtaining the user actual field of question number question-answering uploaded by the user side and the sample data information of the corresponding type of computing power model, and the accuracy of question number question-answering information processing is improved. And the information processing efficiency is improved on the premise of ensuring the reliability.

The invention provides a multi-direct-current power transmission line power optimization method and device giving consideration to supply guarantee and consumption. The method comprises the steps of carrying out initial configuration, simulating source load characteristics of a transmitting end and a receiving end, proposing a load loss probability and a renewable energy power abandoning rate index, constructing a multi-objective function for multi-loop direct current power optimization, constructing a multi-loop direct current line safety constraint, constructing a positive and negative rotation standby calling constraint, solving by a solver and outputting a result. The invention specifically provides a cross-regional multi-direct-current transmission line power model under the background of high-proportion new energy, provides a Monte Carlo method for simulating load characteristics of a transmitting end and a receiving end and fluctuation characteristics of new energy, provides a load loss probability and a renewable energy power abandoning rate index, and further realizes multi-target collaborative optimization of the multi-direct-current transmission line. The method can achieve the modeling of a power model of the cross-regional multi-DC transmission line under the background of high-proportion new energy, helps to achieve the day-ahead optimal scheduling of a multi-DC line power grid, is large in engineering application potential, will effectively support the optimal scheduling of a multi-circuit DC line, and provides a basic guarantee for the safe and economical operation of a system.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant energy storage configuration method and system, and the method comprises the steps: S1, carrying out the fuzzy simulation of an output power model of a distributed renewable energy unit through a cuckoo algorithm according to the geographic position of a virtual power plant, and simulating a typical daily power supply power curve; s2, calculating a typical daily user load demand curve; s3, calculating a typical daily energy storage charging and discharging power curve; s4, based on the simulated typical daily power supply power curve, the typical daily user load demand curve and the typical daily energy storage charging and discharging power curve, adding attenuation factors, and then simulating the income condition in the virtual power plant operation period; and S5, judging whether the current energy storage configuration meets the condition or not according to the virtual power plant income condition in the simulated operation period. According to the method, the virtual power plant is accurately designed by dynamically optimizing the energy storage configuration and combining full-analogue simulation and financial final value judgment, and the defects of blindness and risk resistance of a traditional method are overcome.

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 relates to the field of fish habitat evaluation, in particular to a spawning migration type fish full life history stage suitable habitat inversion method based on a hydrodynamic model, and the method mainly comprises the steps: (1) collecting spawning migration type fish data, hydrological data and topographic data; (2) determining life history stages of the fishes and the actual number of the fishes; (3) establishing one-dimensional hydrodynamic models of different life history stages; (3) grouping the water depth and the flow velocity of each life history stage, and calculating the number frequency of fishes in each interval; (4) constructing a habitat suitability curve of water depth and flow velocity; and (5) evaluating a suitable habitat. According to the method, the suitable habitat of the spawning migration type fish in the whole life history stage can be efficiently and accurately evaluated, and protection strategy formulation and measure implementation of aquatic organisms are facilitated.