39 results about "Wind resource assessment" patented technology

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention relates to the technical field of wind energy resource assessment, and discloses a wind resource assessment method based on a millimeter wave wind finding radar. The method comprises the steps of collecting wind field data of a target area through a millimeter wave wind measurement radar and performing preprocessing to generate a standardized data set; inputting the data into a prediction model to obtain a wind energy distribution characteristic graph; generating a parameter set by adopting threshold segmentation based on the feature map, and dynamically constructing an evaluation function for simulation evaluation; synchronously collecting actual wind energy data, and calculating a deviation matrix between the actual wind energy data and a predicted value to quantify an evaluation error; performing parameter correction on the prediction model by using the error to obtain an optimized model; and finally, regenerating a feature map by adopting the optimization model and completing resource evaluation. According to the method, through online model correction and dynamic evaluation function construction, closed-loop optimization and data driving in the evaluation process are realized, and the accuracy of an evaluation result and the adaptability to a specific site are improved.

The invention discloses a wind resource assessment method based on multi-source data fusion, a medium and a system. The method comprises the following steps: S1, acquiring project basic information and multi-source data of a wind power project; the project basic information comprises a project name, a geographic position and topographic information; the multi-source data comprises anemometer tower data, mesoscale data and CFD data; s2, preprocessing the anemometer tower data to generate corrected anemometer data; s3, performing multi-source data fusion on the corrected wind measurement data, mesoscale data and CFD data, and performing wind resource parameter analysis to obtain a machine site wind resource evaluation result; s4, performing reduction and uncertainty analysis based on the wind resource evaluation result of the machine site, and calculating the generating capacity; and S5, generating a wind resource assessment report according to the power generation amount calculation result and the project basic information. According to the method, the wind resource state of each machine site in the wind field can be evaluated more accurately.

The invention discloses a quantitative evaluation method for generating capacity of a wind turbine generator in a wind-light same field, and relates to the technical field of wind energy resource evaluation and wind-light collaborative optimization. The method comprises the following steps: collecting multi-dimensional meteorological, terrain and photovoltaic array geometric parameter data of a wind-light same-field area, and constructing a three-dimensional digital model; establishing a photovoltaic array-surface roughness dynamic response model based on a weighted fusion algorithm and a turbulent kinetic energy redistribution coefficient; a data interface module is developed, and real-time butt joint between the dynamic roughness model and an on-site monitoring system and between CFD numerical simulation and wind resource evaluation software is achieved; a wind-solar coupling characteristic model is established by combining a fluid mechanics theory and a machine learning algorithm, and the influence of photovoltaic array arrangement on wind speed distribution, turbulence intensity and wind turbine generator power output is quantified; and determining fan arrangement and photovoltaic array parameters through model verification and multi-objective optimization. The method can accurately reflect the dynamic evolution of the surface roughness, and is suitable for the resource evaluation and collaborative design of the same-field project of wind and light.

The invention discloses a wind measurement data correction method based on flow field simulation, and the method comprises the following steps: obtaining the current topographic data and historical target data of a target mountainous area; identifying key weather factors according to historical target data; constructing a quantitative relation model of the key weather factors and the surface form change; obtaining a topographic data prediction value of the target mountainous area; constructing a prediction digital elevation model according to the topographic data prediction value; obtaining corrected wind speed prediction data; comparing the corrected wind speed prediction data with the actually measured wind speed data of the field anemometer tower, and calculating the deviation; and constructing a space correction function based on the deviation, and applying the correction function to subsequent wind resource evaluation or fan layout optimization to realize dynamic correction of the wind measurement data. The fan flow field prediction method solves the problems that fan flow field prediction precision has a large error and the prediction result is not high in accuracy.

The invention relates to the cross technical field of wind resource assessment and meteorological disaster risk management, in particular to a wind resource assessment method and system considering disaster risks based on a reanalysis database, and the method comprises the steps: constructing a congelation intensity and frequency index through the fusion of an anemometer tower and reanalysis data through an information entropy weight method; obtaining a congelation risk index to divide risk grades; correcting a long-term wind speed sequence, selecting a representative year, calculating theoretical generating capacity, and correcting congelation loss to obtain actual generating capacity; finally, protection suggestions and evaluation results are output in combination with the risk level and the actual power generation amount, and comprehensive quantification of disaster risks and power generation benefits is achieved. According to the invention, the accuracy of disaster risk assessment and wind resource assessment is improved.

The invention discloses a wind resource assessment method and system based on an optimized energy mode factor. According to the method, data preprocessing, parameter estimation, model verification and final evaluation are integrated into a standardized and repeatable process, so that the consistency and reliability of evaluation results are ensured; an energy mode factor directly related to wind energy is introduced to serve as a core target, a trustworthy region broken line algorithm is adopted to optimize parameter solution for solving, and it is ensured that the finally obtained Weibull parameter can reflect the energy characteristics of wind most truly instead of just the statistical distribution form of the wind speed; due to the improvement of the Weibull parameter estimation precision, the average wind energy power density calculated on the basis of the distribution model is closer to a true value, scientific support can be provided for wind power plant site selection, fan type selection and wind energy potential evaluation, and fine development of wind power projects is promoted.

The invention provides a wind power plant wind speed time sequence prediction method based on SCADA data correction and CFD modeling. The wind power plant wind speed time sequence prediction method comprises the steps that S1, historical SCADA data and synchronous reference wind speed data of a built wind power plant are collected and preprocessed; s2, establishing an SCADA wind speed correction model based on the preprocessed data, performing primary correction and secondary correction on the SCADA wind speed, and generating a reduced hub height wind speed time sequence; s3, constructing a CFD computational domain covering the constructed wind power plant and the planned wind power plant area, taking the reduced wind speed time sequence as a time-varying boundary condition, and obtaining a space wind speed transfer function through CFD simulation; and S4, extrapolating the reduced wind speed time sequence to each location point of the planned wind power plant by using a space wind speed transfer function, and generating high-precision wind speed time sequence prediction data. The method is especially suitable for wind resource evaluation scenes of complex fields, and overcomes the limitation that a traditional method is difficult to represent wind resource distribution of the complex fields.

The invention discloses a mountain wind power global wind resource assessment method. The method comprises the following steps: acquiring topographic data and constructing a mountain wind speed vertical gradient reasonable interval; troubleshooting and eliminating an unsuitable area, and marking a risk early warning area; deploying a high-frequency unmanned aerial vehicle cluster and an encrypted micro sensor; long-term dynamic monitoring is carried out, monitoring data are verified, and seasonal special monitoring is synchronously implemented; performing preliminary screening on the wind resource candidate point locations in the monitoring area; performing secondary screening on the wind resource primary screening point locations through wake flow interference checking and construction cost evaluation to form a plurality of candidate point locations; constructing a terrain and weather coupling interpolation model, and inputting monitoring data for interpolation to generate a high-resolution global wind resource map; turbulence influence quantification and high-altitude environment correction are carried out based on a global wind resource map, and candidate point locations are graded through a comprehensive scoring system; and determining the number of installation point locations and the optimal installation point location. According to the evaluation method, the arrangement of an anemometer tower can be canceled, and the evaluation of wind resources in each region of the project is realized.

The invention discloses a wind resource evaluation system for changing a wind power plant into a small wind power plant, and the system comprises an original data obtaining module which is used for obtaining the information of an original wind power plant, the historical operation data of the original wind power plant, and the actual measurement wind data, and generating complete wind resource data; the wind power plant information simulation module is used for constructing a multi-dimensional wind power plant model M and an expected wind power plant model M3 based on the original wind power plant information; the evaluation index determination module is used for calculating an index value of a wind resource index according to factors which need to be considered for large-to-small transformation, wherein the factors comprise positive correlation factors and negative correlation factors; the transformation evaluation module is used for carrying out priority calculation according to the index weight value; and the risk assessment module is used for performing risk assessment on the specified single fan and providing a decision scheme according to the management and maintenance difficulty, the management level and the technology. According to the technical scheme, the operation environment of the transformed wind power plant can be simulated, the fan transformation priority level is compared and selected, and the power generation amount after transformation is predicted; and predicting various risk factors in operation and use during and after the transformation of the wind power plant.

The invention provides an automatic ridge line identification method and system based on hydrological analysis and density clustering, and the method comprises the steps: fusing the hydrological analysis with spatial clustering, elevation screening, morphological processing and other multi-step optimization, carrying out the automatic primary selection of ridge points through positive topography and zero convergence accumulation, and effectively removing noise and short branches through the combination of DBSCAN clustering and elevation quantile purification. A set of complete technical scheme of'preprocessing-positive terrain recognition-confluence analysis-clustering denoising-elevation screening-continuous optimization 'is constructed by utilizing a priority flood algorithm to efficiently fill pits and utilizing a skeletonization algorithm to optimize continuity, so that the adaptivity, geometric accuracy and processing efficiency of ridge line extraction are remarkably improved; a more reliable terrain skeleton data basis is provided for wind resource evaluation and fan site selection and layout, and finally the scientificity of wind power generation project planning and power generation capacity estimation is enhanced.

The invention relates to the technical field of offshore wind resource assessment, and discloses a fan missing wind speed restoration method, which comprises the following steps: acquiring adjacent wind speed characteristics of missing wind speed of a target fan and historical wind speed characteristics of adjacent fans; predicting a wind speed prediction value of the missing wind speed based on the adjacent wind speed features and the historical wind speed features; estimating a wind speed correction value of the missing wind speed according to the historical working power and historical wind speed characteristics of the adjacent fans; and performing weighted fusion calculation on the wind speed prediction value and the wind speed correction value to obtain a missing wind speed, and repairing the target fan according to the missing wind speed. According to the method, multi-dimensional prediction of the missing wind speed is achieved by fusing the time sequence wind speed characteristics of the target wind turbine and the space correlation characteristics of the adjacent wind turbines, a physical correction mechanism based on the relation between historical power and the wind speed is introduced, the predicted value is corrected by using the strong physical correlation parameter of the working power of the wind turbines, and the prediction accuracy of the wind turbines is improved. And the precision and the reliability in repairing the missing wind speed are improved.

The present application relates to the field of wind power generation technology, and discloses a cross-scale wind farm wind resource evaluation method based on a numerical model, comprising the following steps: step 1, obtaining multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme suitable for the terrain of the target area; step 3, generating annual-scale wind resource data as initial wind resource data through a WRF dynamic downscaling method; step 4, carrying out partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling a WRF model and a wind farm parameterization model; step 6, optimizing the lower boundary condition of the WRF; step 7, simulating the atmospheric flow of the target area and quantifying the wake evolution characteristics of the wind farm; step 8, constructing a wake analysis model; and step 9, formulating a coordinated capacity planning of the wind farm group. The present application can accurately predict the power generation capacity of a to-be-developed wind farm, and provide a reliable basis for scientific decision-making of the resource distribution development and planning of a million-kilowatt wind farm.

The invention discloses a wind power generation capacity prediction method, device and equipment and a storage medium, which are applied to the field of wind power generation evaluation, and the method comprises the steps: determining the gradient and curvature of a basic grid, and dividing a target region into a flat region, a transition region and an abrupt change region based on the gradient and curvature; based on a preset grid size, carrying out encryption processing on the basic grid in the mutation area, and carrying out gradient encryption processing on the basic grid in the transition area; setting an optimal performance turbulence model of each region, and performing simulation based on the optimal performance turbulence model to obtain a wind resource evaluation result of the target region; and determining a power curve of the fan, and determining a wind power generation evaluation result of the target area based on the power curve and the wind resource evaluation result. According to the method, the grids of the terrain abrupt change region are identified and encrypted, and the turbulence model with the optimal performance is set for each type of region, so that more accurate wind resource evaluation is realized, and a more accurate wind power generation capacity prediction result is obtained.

The invention relates to a machine position wind speed correction calculation method based on multi-source data of a wind power plant, and the method carries out the wind resource evaluation based on the multi-source data, such as a laser radar, an operation period wind power prediction system, and a unit SCADA, and carries out the correction through an analytic hierarchy process, thereby effectively reducing the wind resource evaluation deviation. A three-dimensional scale algorithm (a * b * c) based on wind measurement duration, data integrity and spatial distance is provided, and the reliability of different data sources is quantified; a three-dimensional scale algorithm (a * b * c) based on wind measurement duration, data integrity and spatial distance is provided, and the reliability of different data sources is quantified; compared with a traditional method, the corrected machine position wind speed evaluation deviation is reduced by more than 50%, and the evaluation deviation is remarkably reduced; reliable technical support is provided for application scenarios such as performance improvement of wind power plant units and transformation of replacing small by large, generating capacity prediction errors are reduced, and engineering applicability is high.

The application discloses a wind resource assessment method, device, equipment, medium and product, and belongs to the technical field of wind power generation. Before wind resource assessment modeling is performed, the wind condition parameters of the location of the wind measurement tower are estimated according to different parameter value combinations, combined with the characteristics of the power field, the wind condition parameter estimation results corresponding to the different parameter value combinations are obtained, the optimal parameter value combination matched with the wind field is determined according to the estimation results and the true results, and then the wind resource assessment is performed according to the optimal parameter value combination, that is, the parameter verification and screening steps are added before the wind resource assessment is performed, the parameters required for the wind resource assessment can be optimized through the steps, and then the assessment accuracy of the wind resource can be improved.

The invention discloses a wind power plant flow field simulation and wind resource evaluation method based on mid-scale and micro-scale coupling, and belongs to the technical field of wind power plants, and the method comprises the steps: taking a simulation result of a mid-scale WRF model as a boundary condition of a micro-scale CFD model, simulating the characteristics of a wind power plant flow field, capturing a terrain effect, a fan wake flow and a superimposed effect thereof, and evaluating the wind power plant flow field. Acquiring flow field data of the wind power plant based on medium and micro-scale coupling; performing preprocessing and feature extraction on the wind power plant flow field data to generate wind power plant flow field feature data; and analyzing the wind power plant flow field characteristic data based on the wind resource assessment generating capacity prediction model, and determining a wind resource assessment generating capacity prediction result. According to the method, the problem that the wind power plant planning and optimization effect is poor due to the fact that wind power plant flow field simulation and wind resource effective evaluation cannot be carried out based on medium and micro-scale coupling in the prior art is solved. Wind power plant flow field simulation and wind resource effective evaluation are carried out based on medium and micro-scale coupling, and the wind power plant planning and optimization effect can be improved.

The application discloses a wind power generation amount prediction method, device and equipment and a storage medium, and is applied to the field of wind power generation evaluation. The slope and curvature of a basic grid are determined, and a target area is divided into a flat area, a transition area and a mutation area based on the slope and curvature. The basic grid in the mutation area is subjected to encryption processing based on a preset grid size, and the basic grid in the transition area is subjected to gradual encryption processing. An optimal performance turbulence model of each area is set, a simulation is performed based on the optimal performance turbulence model, and a wind resource evaluation result of the target area is obtained. A power curve of a wind turbine is determined, and a wind power generation amount evaluation result of the target area is determined based on the power curve and the wind resource evaluation result. The application realizes more accurate wind resource evaluation by recognizing and encrypting the grid of the topographic mutation area and setting the optimal performance turbulence model for each type of area, and thus more accurate wind power generation amount prediction results are obtained.

The application provides a wind speed missing value completion method based on mode meteorological data, and comprises the following steps: based on the obtained wind speed time series of a wind farm and corresponding mode meteorological multi-element data, a multivariate time series tensor is formed to construct sample data; based on the sample data, a time series interpolation model is trained by using a weighted loss function; the measured wind speed time series with missing values and corresponding mode meteorological multi-element data are input into the time series interpolation model, the missing position is predicted and completed, and through post-processing operation, the complete wind speed time series after completion is obtained. The beneficial effects of the application are as follows: by constructing an observation missing mask, designing a reasonable training strategy, and introducing a deep learning time series interpolation model, high-credibility wind speed completion can be realized under different missing scenarios, thereby effectively improving the accuracy and reliability of wind power prediction, wind resource assessment and wind farm operation management.

The invention discloses a wind resource assessment data preprocessing method and system based on multi-source data fusion, and relates to the technical field of wind energy resource assessment and wind power plant design. The preprocessing method comprises the following steps: carrying out anomaly identification and cleaning on an anemometer tower and reanalysis data; spatial matching is achieved through terrain weighted interpolation, and the two types of data are unified to the height of the fan hub in combination with a wind shear formula; establishing a linear regression model based on the overlapping time periods to carry out system deviation correction; and performing classification completion according to the length of the missing measurement time period, performing adjacent data interpolation in a short time, and performing compensation by combining corrected reanalysis data with a local trend in a long time, thereby finally generating a continuous, reliable and representative long-term wind speed sequence. The data sequence generated by the method is high in continuity, small in error and stable in distribution and is applied to a plurality of mountain wind power projects, the prediction deviation of the generating capacity is reduced to be within 6% from original 12% or above, and the reliability of engineering economy evaluation is remarkably improved.

The invention discloses a wind resource assessment report generation method and system based on an improved Self-RAG and multi-Agent technology, and solves the problem of low efficiency of multi-source heterogeneous data governance by constructing a dynamic knowledge base (including XML labeling processing of an OCR + rule engine, NER entity library construction and an incremental learning mechanism) fusing structured and unstructured data. An improved Self-RAG technology (introduction of an reflection mark to realize retrieval dynamic decision, segment-level beam search to optimize multi-text segment fusion, and verification of generation quality by a self-commenting mechanism) is adopted to construct a self-reflection retrieval enhanced generation module, and the bottleneck of traditional RAG generation reliability is broken through; a multi-Agent automatic report generation module is constructed in combination with a multi-Agent distributed collaborative architecture (an intelligent agent division and communication mechanism from a data layer to a report generation layer), full-process automation from data acquisition to report output is realized, and finally the technical breakthrough of improving data processing efficiency, improving the accuracy of generated content facts and shortening the knowledge updating response speed is achieved.

1. Name of the product in this design: Wind Resource Assessment LiDAR. 2. Application of this design: This design is used for regional wind measurement. It utilizes the Doppler frequency shift principle of laser pulses to measure key wind resource parameters such as wind speed and wind direction by detecting the frequency shift of the echo signal. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention discloses a wind speed vertical extrapolation method based on data quality perception and multi-normal form AI, and the method comprises the steps: obtaining and preprocessing multi-source data, carrying out the weighted matching of an optimal meteorological station through information entropy and terrain complexity, and quantifying the data quality to generate a routing flag bit; when the data is scarce, generating an initial estimation value by adopting a physical model and fitting a residual error to generate a correction model; when the data is sufficient, time-varying physical parameters are inverted, and a mapping relation is established through deep learning to generate a dynamic parameterized model; when the performance of a single model is insufficient, starting a meta-learner to integrate the heterogeneous model; and finally, preferentially generating a target extrapolation model on the independent test set and deploying the target extrapolation model. According to the method, the wind speed prediction precision under the complex terrain is remarkably improved, the dependence on wind measurement equipment is reduced, and a customized wind resource evaluation scheme with physical rationality and data adaptability is provided for a wind power plant.

The invention relates to the technical field of wind power generation, and provides a self-adaptive wind measurement method, device and equipment for an anemometer tower anemometer and a medium. The method comprises the following steps: acquiring real-time attitude data of an anemograph; comparing the real-time attitude data with a preset target attitude to obtain a pose deviation signal; according to the position and posture deviation signal, a position and posture adjusting mechanism connected between the anemometer tower body and the anemograph is controlled to execute motion so as to drive the anemograph to carry out position and posture adjustment; and acquiring original wind speed data measured by the anemograph, and performing coordinate transformation on the original wind speed data according to the real-time attitude data to obtain real-time wind measurement data. According to the invention, high-precision and high-stability wind speed and wind direction real-time measurement in a turbulence tower top environment is realized, and the accuracy and reliability of wind resource evaluation, fan load calculation and wind power plant operation control are remarkably improved.

The invention discloses a wind power plant representative year correction optimization method and system considering disaster characteristics, and relates to the technical field of wind resource assessment. The optimization method comprises the steps of obtaining wind measurement data and reanalysis data of a wind measurement tower of a wind power plant, constructing a seasonal wind speed correction model, and performing segmented correction on a wind speed sequence of each year in the reanalysis data to obtain a 30-year seasonal corrected wind speed data set; then, candidate years with the annual average wind speed deviation rate smaller than 5% are screened out; extracting disaster parameters corresponding to the candidate years, and calculating a disaster parameter deviation rate of each candidate year; and in combination with the wind speed deviation rate and the disaster parameter deviation rate, performing quantitative evaluation on the candidate years by adopting a weighted comprehensive scoring model, and selecting the year with the highest comprehensive score as a final representative year. According to the method, screening indexes are set for dominant disaster differentiation in different regions, the adaptability in different wind power plants is enhanced, and the regional pertinence of evaluation is improved.

The application discloses a kind of wind resource evaluation system for wind farm to big change small transformation, comprising: original data acquisition module: for obtaining original wind farm information, original wind farm historical operation data and measured wind data, generate complete wind resource data;Wind farm information simulation module: for constructing multidimensional wind farm model M and expected wind farm model M3 based on original wind farm information;Evaluation index determination module: for calculating the index value of wind resource index according to the factors considered in big change small transformation, factors include positive correlation factors and negative correlation factors;Transformation evaluation module: for priority calculation according to index weight value;Risk assessment module: for risk assessment to specified wind turbine, according to management and maintenance difficulty, management level and technical decision scheme are provided.According to the above technical scheme, the running environment of the transformed wind farm can be simulated, the priority level of wind turbine transformation is selected, and the power generation after transformation is predicted;Predict various risk factors during and after the operation of wind farm transformation.

1. The name of the design product: the main body of wind radar. 2. The use of the design product: the main body and the whole can be used as a ground-based wind radar, which is used for wind resource assessment, micro-siting and review, wind field performance assessment, wind power prediction, etc. in outdoor environment. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention discloses a mountain region typical weather wind speed distribution field construction method using clustering analysis. The method comprises the following steps: step 1, obtaining multi-source data; step 2, data preprocessing; step 3, feature engineering and feature vector construction; step 4, weather type automatic clustering identification based on DBSCAN is carried out; 5, weather type interpretation and definition; step 6, modeling a three-dimensional wind speed distribution field under each weather type; step 7, verifying and outputting a wind field; according to the method, systematic deviation caused by generalization processing of a traditional method is effectively avoided in the aspect of prediction precision; the response speed is improved in the aspect of calculation efficiency, resource consumption is reduced, and the wind resource evaluation and operation and maintenance process is more efficient; and the engineering applicability and the popularization value are improved in the aspect of application practicability.

This application discloses a data processing method, apparatus, equipment, and readable storage medium for directional calculation in wind resource assessment. The method includes: calculating the convergence rate of the current iteration step based on the target physical quantity within the previous first preset iteration step and the previous first preset iteration step from the current iteration step; determining whether it is stable convergence based on the convergence rate within the next second preset iteration step; if not, obtaining the target physical quantity within the tail end and an integer multiple of the change period, and performing spectrum analysis; if the maximum amplitude of the obtained waveform is within a preset range, determining whether it is fluctuating convergence based on the waveform; if so, obtaining the directional calculation result based on the target physical quantity within the change period. The technical solution disclosed in this application uses the target physical quantity for convergence rate calculation and judgment, improving the efficiency and robustness of convergence judgment, and obtaining the directional calculation result based on the target physical quantity within the complete change period under fluctuating convergence, thereby improving the accuracy and stability of the directional result.

The invention relates to a wind resource assessment technology, discloses an atmospheric mode wind resource assessment method supporting visual parameter configuration, and solves the problems that in current wind resource assessment, the process is split, the data management efficiency is low, and result display and application are disjointed, so that efficient and accurate assessment requirements are difficult to meet. According to the scheme, the method comprises the steps that firstly, calculation parameters input by a user are obtained through a front-end visual interface, and a mode configuration file which can be directly used for atmospheric mode calling is generated; thirdly, calling an atmospheric mode based on the mode configuration file to execute numerical simulation, and generating meteorological element data containing wind speed and wind direction in combination with the preprocessed external reanalysis data; then, converting the meteorological element data from a potential height coordinate to a terrain height coordinate, and generating a map file and a time sequence file; and finally, according to the atlas file and the time sequence file, in a front-end visual interface, displaying a meteorological element atlas and a time sequence chart in the visual interface.