A weather data intelligent analysis method for new energy

Abstract

The application belongs to the technical field of new energy meteorological monitoring, and discloses a meteorological data intelligent analysis method for new energy. The method comprises the following steps: based on the fusion of global reanalysis data sets and regional high-resolution observation data sets, meteorological element data of a target region is extracted, and a new energy meteorological analysis large model is constructed; according to the current meteorological element data, the new energy meteorological analysis large model is used for meteorological data prediction; multi-dimensional feature information of each monitoring region is collected, and the meteorological prediction data of each monitoring region are combined to generate a power generation feasibility evaluation result corresponding to each wind field monitoring region, water field monitoring region and light field monitoring region, and power generation feasibility is judged, and then abnormal power generation new energy type direction and key power generation new energy type direction are further identified. The accuracy and timeliness of the prediction are significantly improved, the power generation feasibility can be comprehensively and accurately evaluated, potential risks can be detected in advance, and the stability of power generation and the safety of equipment are effectively guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy weather monitoring, and relates to a new energy-oriented weather data intelligent analysis method. BACKGROUND

[0002] New energy power production process is highly dependent on weather data prediction, and weather prediction can provide information about key weather parameters such as wind speed, wind direction, sunshine time, and cloud thickness, which are crucial for accurately predicting new energy production management. Due to the complexity and variability of weather conditions, how to obtain fine and high-resolution weather data, especially accurate prediction in the short term, is one of the main challenges faced by new energy power generation processing prediction. Therefore, the research based on new energy-oriented weather data intelligent analysis is of great significance.

[0003] There are also technical solutions for new energy weather monitoring in the prior art, for example, a new energy weather big model construction method, device and power prediction method of Chinese invention patent application with publication number CN118332937B, which includes: constructing a new energy weather big model training data set based on ERA5 data; constructing a graph neural network big model with wind and light node attention and dynamic graph structure; inputting the constructed input data set and output data set into the graph neural network big model, and obtaining a new energy weather big model through training; obtaining field station new energy weather element prediction data through the new energy weather big model, and inputting the weather-power post-correction model to obtain predicted power.

[0004] In addition, a new energy long-term power prediction method and system for power weather data intelligent migration of Chinese invention patent application with publication number CN116227249B, which includes: obtaining historical power generation data of a target power station, determining a first power generation prediction result of the target power station in a prediction time period according to the historical power generation data of the target power station; matching similar power stations of the target power station in the power station set according to the target weather characteristics corresponding to the target power station; obtaining historical power generation data of the similar power stations, determining a second power generation prediction result of the target power station in the prediction time period according to the historical power generation data of the similar power stations; and determining a target power generation prediction result of the target power station according to the first power generation prediction result and the second power generation prediction result.

[0005] The above two schemes although put forward some solutions for new energy weather monitoring, but still have certain limitations: on the one hand, the existing technical solutions usually use a single data set obtained by conventional general meteorological factor mechanism research for meteorological prediction analysis and processing, for example, the above-mentioned Chinese invention patent application of new energy weather large model construction method, device and power prediction method, which constructs a new energy weather large model training data set based on ERA5 data, the data source is single, and the difference with the new energy weather forecast demand is large, which reduces the accuracy of meteorological data analysis and prediction.

[0006] On the other hand, the existing technical solutions usually do not comprehensively analyze the meteorological prediction situation, weather influencing factors and terrain influencing factors when further processing meteorological data, for example, in the above two invention patent applications, the field station new energy weather element prediction data and the target power generation prediction result are simple meteorological data prediction results. This analysis method will lead to the lack of comprehensiveness in the meteorological data processing process, and cannot accurately reflect the real meteorological environment.

[0007] In addition, the predicted power and the target power generation prediction result analysis in the above two invention patent applications are not new energy related operation parameters directly obtained based on the data processing results, and lack of quantitative analysis and processing of the risk degree of power generation, so that the risk assessment result lacks usability and practicality, and the effect of system use is reduced. SUMMARY

[0008] In view of this, in order to solve the problems proposed in the above background art, a new energy-oriented meteorological data intelligent analysis method is proposed.

[0009] The purpose of the present application can be achieved by the following technical scheme: a new energy-oriented meteorological data intelligent analysis method, comprising: S1, extracting two-dimensional distribution information of a target area to divide the monitoring area into several monitoring areas, and generating a unique code, each monitoring area corresponds to different new energy types, including wind field, water field and light field.

[0010] S2, based on the fusion of global reanalysis data set and regional high-resolution observation data set, extract the meteorological element data of the target area, and further construct a new energy weather analysis large model, and collect the current meteorological element data of each monitoring area.

[0011] S3, according to the current meteorological element data, use the new energy weather analysis large model to predict the meteorological data, and obtain the meteorological prediction data of each monitoring area in the preset prediction period.

[0012] S4, collect multi-dimensional feature information of each monitoring area, and then generate power generation feasibility evaluation results corresponding to each wind field monitoring area, water field monitoring area and light field monitoring area in a preset prediction period in combination with meteorological prediction data of each monitoring area.

[0013] S5, judging the power generation feasibility of each monitoring area in a preset prediction period according to the power generation feasibility evaluation results, and further identifying specific abnormal power generation new energy type direction and key power generation new energy type direction.

[0014] Compared with the prior art, the beneficial effects of the present application are as follows: (1) The present application extracts meteorological element data of the target area based on the global reanalysis data set and the regional high-resolution observation data set, which makes the data more complete, effectively reduces the error and uncertainty of a single data source, and enhances the reliability of the data.

[0015] (2) The present application constructs a new energy meteorological analysis large model based on the global reanalysis data set and the regional high-resolution observation data set to extract meteorological element data of the target area, and performs meteorological prediction based on the model, which significantly improves the accuracy and timeliness of the prediction, and takes into account the large-scale meteorological trend and regional subtle meteorological changes.

[0016] (3) The present application considers the influence of meteorological prediction results, environmental factors and abnormal weather indexes when generating power generation feasibility evaluation results, which can comprehensively and accurately evaluate the power generation feasibility, can detect potential risks in advance, and can ensure the stability of power generation and the safety of equipment.

[0017] (4) The present application judges the power generation feasibility of each monitoring area in a preset prediction period according to the power generation feasibility evaluation results, which can provide energy enterprises with a clear prediction of the power generation status of each region in the future period in advance, help them to reasonably plan production plans; by accurately positioning the high feasibility area, concentrating resources to improve power generation efficiency and reduce operating costs; for low feasibility areas, early warning and countermeasures can be developed to enhance power generation stability and safety. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0019] Figure 1 The method steps of the present application are illustrated in the schematic diagram.

[0020] Figure 2 The present application provides a wind field power generation feasibility judgment flow chart.

[0021] Figure 3 A water field power generation feasibility judgment flowchart is provided for the present application.

[0022] Figure 4 A light field power generation feasibility judgment flowchart is provided for the present application.

[0023] Figure 5 A block diagram of a computer system for implementing some embodiments of the present application.

[0024] Reference signs: 100 - computer system, 110 - memory, 120 - processor, 130 - bus, 140 - input / output interface, 150 - network interface, 160 - storage interface. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0026] Please refer to Figure 1 As shown in the figure, the present application provides a meteorological data intelligent analysis method for new energy, which includes: S1, extracting the two-dimensional distribution information of the target area to divide the monitoring area to obtain a plurality of monitoring areas, and generating a unique code, each monitoring area corresponds to a different new energy type, including wind field, water field and light field.

[0027] It should be noted that the two-dimensional distribution information of the target area is extracted based on the design or drawing of the target area.

[0028] It should be noted that the specific way of dividing the monitoring area is: based on the two-dimensional distribution information of the target area, the wind field distribution area, the water field distribution area and the light field distribution area of the target area are divided according to the new energy type, and then further divided into a plurality of wind field monitoring areas, water field monitoring areas and light field monitoring areas based on the equal-area grid division method. It should be noted that in practice, the target area may only have one or two of the wind field, water field and light field. Considering the universality of the method technology, the target area in the present application contains the wind field, water field and light field, and the wind field, water field and light field are divided into a plurality of wind field monitoring areas, water field monitoring areas and light field monitoring areas.

[0029] It should be noted that the wind field refers to a place for wind power generation, which can be a photovoltaic power plant, and the water field refers to a place for hydroelectric power generation, which can be a hydroelectric power dam. The wind field refers to a place for wind power generation, which can be a wind power plant.

[0030] S2, based on the global reanalysis data set and the regional high-resolution observation data set, the meteorological element data of the target area is extracted, and then a new energy meteorological analysis large model is constructed, and the current meteorological element data of each monitoring area is collected.

[0031] It should be noted that the global reanalysis data set refers to a global consistent meteorological and climate data set generated by combining historical observation data and numerical weather prediction model using data assimilation technology. It provides long time series, high spatio-temporal resolution global meteorological variables, and is widely used in climate research, weather forecasting, environmental monitoring and other fields. The global reanalysis data set in the present application refers to ERA5.

[0032] It should be noted that the regional high-resolution observation data set refers to a specific geographical region, which is obtained by high-density observation network or high-precision remote sensing technology, and has high spatio-temporal resolution of meteorological, environmental or geographical data. It is usually obtained by comprehensive collection of ground observation stations, satellite remote sensing and radar observation technology. The regional high-resolution observation data set in the present application refers to the Chinese regional high-resolution data set.

[0033] It should be noted that the reason for extracting the meteorological element data of the target area based on the global reanalysis data set and the regional high-resolution observation data set is: on the one hand, the global reanalysis data set provides widely spatio-temporal coverage meteorological data, and the regional high-resolution observation data set focuses on regional details, the combination of the two can make up for the limitations of a single data set, so that the extracted data is more comprehensive and accurate, and the meteorological element characteristics can be completely presented. On the other hand, the fusion data provides more abundant information for constructing a new energy meteorological analysis large model, which helps the model to learn complex meteorological rules and improve the accuracy of meteorological data prediction, so as to more reliably evaluate the new energy power generation feasibility and provide strong support for the development of new energy industry.

[0034] In a preferred embodiment of the present application, the specific way of constructing the new energy meteorological analysis large model is as follows: extracting the key meteorological element data in the target power generation area after the global reanalysis data set and the regional high-resolution observation data set are fused, using neural network to simulate the evolution process of meteorological element data with time and space, and obtaining the new energy meteorological analysis large model.

[0035] It should be noted that the reason for building the new energy weather analysis big model is that the new energy weather analysis big model can accurately evaluate the new energy power generation condition. The power generation efficiency of new energy is greatly affected by weather conditions. Wind field power generation depends on wind speed and wind direction, water field depends on rainfall, and light field is related to solar irradiance. By fusing global reanalysis data set and regional high-resolution observation data set to extract meteorological element data, a large model is built to simulate the spatio-temporal evolution of meteorological elements. This can predict the meteorological data of each monitoring area, evaluate the power generation feasibility combined with multi-dimensional feature information, identify abnormal and key power generation areas, help new energy enterprises to reasonably plan power generation, improve power generation efficiency, reduce the risk caused by weather factors, and promote the stable development of new energy industry.

[0036] It should be noted that the present application extracts the meteorological element data of the target area based on the fusion of global reanalysis data set and regional high-resolution observation data set, so that the data is more complete, the error and uncertainty of a single data source are effectively reduced, and the data reliability is enhanced.

[0037] S3, using the new energy weather analysis big model to predict the weather data according to the current meteorological element data, and obtaining the weather prediction data of each monitoring area in a preset prediction period.

[0038] In a preferred embodiment of the present application, the specific content of the weather prediction data of each monitoring area is as follows: the monitoring areas can be divided into wind field monitoring areas, water field monitoring areas and light field monitoring areas according to the type of new energy, wherein the weather prediction data of the wind field monitoring area is wind speed and wind direction, the weather prediction data of the water field monitoring area is rainfall, and the weather prediction data of the light field monitoring area is solar irradiance.

[0039] It should be noted that the current meteorological element data of each monitoring area collected is input into the new energy weather analysis big model which has been built. The model performs operation according to the previously learned evolution law of meteorological elements, and outputs the weather prediction data of each monitoring area in a preset prediction period, wherein the wind field monitoring area obtains wind speed data, the water field monitoring area obtains rainfall data, and the light field monitoring area obtains solar irradiance data.

[0040] It should be further noted that the present application extracts the meteorological element data of the target area based on the fusion of global reanalysis data set and regional high-resolution observation data set to build the new energy weather analysis big model, and performs weather prediction based on the model, which significantly improves the accuracy and timeliness of the prediction, and takes into account the large-scale weather trend and regional subtle weather changes.

[0041] S4, collecting multi-dimensional feature information of each monitoring area, and then generating power generation feasibility evaluation results corresponding to each wind field monitoring area, water field monitoring area and light field monitoring area in a preset prediction period in combination with the weather prediction data of each monitoring area.

[0042] In a preferred embodiment of the present application, the specific method for collecting multi-dimensional feature information of each monitoring area is as follows: a high-definition camera is used to obtain a distribution image of the target area, the positions of each obstacle in each wind field monitoring area are located based on the distribution image, each obstacle is numbered, and the height of each obstacle, the distance between each obstacle and each wind turbine, and the hub height of each wind turbine are obtained by using image processing software.

[0043] It should be noted that the obstacles can be natural obstacles and non-natural obstacles, wherein the natural obstacles can be mountains and tall trees, and the non-natural obstacles can be buildings and communication towers.

[0044] The distribution image of the target area obtained by using a high-definition camera is extracted, the flow direction of each water body in each water field monitoring area is obtained, and each monitoring area through which each water body flows is obtained as each water quantity collection influencing area of each water field monitoring area, and the influence distance of each water quantity collection influencing area relative to each water field monitoring area is obtained.

[0045] It should be noted that the influence distance of each water quantity collection influencing area relative to each water field monitoring area specifically refers to the horizontal distance between the center point of each water quantity collection influencing area and the center point of each water field monitoring area.

[0046] The meteorological prediction result obtained by using a new energy meteorological analysis large model to predict meteorological data according to the current meteorological element data is extracted, a preset prediction period is divided into a plurality of monitoring periods based on an equal interval time length, the time length of each monitoring period remains consistent, each monitoring period is numbered, and the rainfall of each monitoring period of each water field monitoring area and the rainfall of each monitoring period of each water quantity collection influencing area within the preset prediction period are statistically obtained.

[0047] It should be noted that the interval time length is set according to the following: first, the change characteristics of meteorological data, if the meteorological elements such as rainfall change frequently, a shorter interval time length needs to be set to accurately capture the changes; second, the accuracy requirement of the power generation feasibility evaluation, if a more detailed evaluation of the water field power generation feasibility is expected, a shorter interval can provide more detailed rainfall data; third, data processing cost and efficiency, although a longer interval can reduce the amount of data processing, it will lose information, and a shorter interval will increase the processing burden, so the appropriate time length needs to be determined by balancing the two. Exemplarily, the interval time length is 20 minutes. Correspondingly, the preset prediction period is 6 hours.

[0048] The cloud thickness of each light field monitoring area within the preset prediction period is obtained according to the meteorological prediction result, and then the cloud thickness quantification relationship is quantified based on the preset cloud thickness quantification relationship to obtain the cloud layer influence coefficient of each light field monitoring area.

[0049] It should be noted that the cloud thickness quantification relationship is shown in Table 1.

[0050] Table 1. Quantitative Relationship of Cloud Thickness

[0051]

[0052] In a preferred embodiment of the present invention, the specific analysis method for generating the power generation feasibility evaluation results corresponding to each wind field monitoring area, water field monitoring area and solar field monitoring area within a preset prediction period is as follows: Specifically, the power generation feasibility evaluation results corresponding to the wind field, water field and solar field need to construct the power generation feasibility evaluation index of each wind field monitoring area, the power generation feasibility evaluation index of each water field monitoring area and the power generation feasibility evaluation index of each solar field monitoring area.

[0053] The specific analysis method for the power generation feasibility evaluation index of each wind farm monitoring area is as follows: extract the wind speed of each wind farm monitoring area, denoted as... ,in Indicates the number of the wind field monitoring area. , This indicates the number of areas monitored by the wind farm.

[0054] Extract the height of each obstacle, the distance between each obstacle and each wind turbine, and the hub height of each wind turbine for each wind farm monitoring area, and record them as follows: , , ,in Indicates the number of the obstacle. , Indicates the number of obstacles. This indicates the fan's serial number. , This indicates the number of wind turbines.

[0055] Using formula Analysis yielded the power generation feasibility evaluation index for each wind farm monitoring area. ,in This indicates the preset suitable operating wind speed. This represents the average horizontal distance between obstacles and wind turbines in each wind farm monitoring area. It is calculated by considering the distances between each obstacle and each wind turbine. The mean was calculated to obtain the result. This represents the average obstacle height in each wind field monitoring area, which is determined by the height of each obstacle. The mean was calculated to obtain the result. This represents the average turbine hub height in each wind farm monitoring area, which is determined by analyzing the hub height of each turbine. The mean was calculated to obtain the result. This represents the standard deviation of wind speed in each wind field monitoring area, which is calculated based on the wind speed in each monitoring area. represent the weight factors of the calibration of various influencing factors.

[0056] It should be noted that the construction idea of the power generation feasibility evaluation index analysis formula of each wind farm monitoring area is as follows: 1. Wind speed consideration: in the formula, This part reflects the influence of wind speed on power generation feasibility. Through the formula, the influence of the difference between the actual wind speed and the suitable wind speed on the power generation feasibility can be reflected. The closer the wind speed is to the suitable wind speed, the greater this value is, and the higher the power generation feasibility evaluation index is likely to be.

[0057] 2. Obstacle influence: This reflects the influence of obstacles. When the distance between the obstacle and the wind turbine is far , the height of the obstacle is low , and the hub height of the wind turbine is high , the promotion effect of this part on the power generation feasibility evaluation index is large, which is consistent with the principle that a low obstacle shielding coefficient is beneficial to power generation, reflecting the influence of the relative position relationship between the obstacle and the wind turbine on the power generation feasibility.

[0058] 3. Turbulence intensity consideration: The stability of wind speed is considered, which is specifically reflected in the influence of turbulence intensity. The smaller the standard deviation of wind speed , the smaller the turbulence intensity , the more stable the wind speed is, the smaller the negative influence on the power generation feasibility evaluation index, reflecting the effect of wind speed stability on power generation feasibility.

[0059] 4、 and are weight factors of the calibration of various influencing factors, which are used to weigh the influence degree of factors such as obstacle and wind speed stability on the power generation feasibility evaluation index, so as to comprehensively consider multiple factors and more comprehensively construct the calculation model of the power generation feasibility evaluation index.

[0060] It should be further explained that the specific calibration method of and is as follows: a large amount of wind farm historical data is collected, including wind speed, obstacle height and distance, power generation power and other data. Principal component analysis (PCA) is used for dimensionality reduction processing of the data, the principal components affecting power generation feasibility are found out, and the weight factors are determined according to the principal component contribution rate. If analysis shows that the principal component contribution rate corresponding to the obstacle related factor is 40%, and the principal component contribution rate corresponding to the wind speed stability factor is 60%, then can be set to 0.4, can be set to 0.6.

[0061] In a feasible embodiment, it is assumed that , , , , and then based on the wind field monitoring area power generation feasibility evaluation index analysis formula data simulation calculation, get the corresponding simulation results, part of the simulation results can refer to table 2.

[0062] Table 2 part of the wind field monitoring area power generation feasibility

[0063] Evaluation index analysis formula corresponding data simulation results

[0064]

[0065] It needs to be further explained that the above data simulation results: 1, the power generation feasibility evaluation index is obtained by comprehensively considering wind speed, obstacle condition and wind speed stability and other factors. The higher the index, the better the feasibility of power generation in the wind field monitoring area.

[0066] 2, the power generation feasibility evaluation index of the wind field monitoring area 2 is the highest (33.93), because its wind speed (9m / s) is close to the suitable working wind speed (8m / s), and the average obstacle height is low (8m), the combination of the average obstacle and the horizontal distance of the fan and the average fan hub height is also more favorable, so that the comprehensive calculation results in the formula are higher.

[0067] 3, the index of the wind field monitoring area 1 is the second (18.84), its wind speed is slightly lower than the suitable working wind speed, and the obstacle related parameters are not as ideal as area 2, but still has certain power generation feasibility.

[0068] 4, the power generation feasibility evaluation index of the wind field monitoring area 3 is the lowest (10.35), mainly because its wind speed (6m / s) is far from the suitable working wind speed, and the average obstacle height is high (12m), these factors combined together lead to its power generation feasibility is relatively poor.

[0069] In a preferred embodiment of the present application, the specific analysis method of the power generation feasibility evaluation index of each water field monitoring area is as follows: the rainfall and rainfall duration of each water field monitoring area are extracted, denoted as , wherein indicates the number of water field monitoring area, , indicates the number of water field monitoring area, based on historical data records, the water flow rate of each water field monitoring area at the current monitoring time is obtained, denoted as .

[0070] It should be noted that the specific method of obtaining the water flow rate of each water field monitoring area at the current monitoring time based on the historical data record is to extract the historical data record, and then take the water flow rate of each water field monitoring area at the reference time corresponding to the current monitoring period as the water flow rate of each water field monitoring area at the current monitoring time. The reference time refers to the time corresponding to the preset reference time period before the current reference time.

[0071] Extract the rainfall of each monitoring period of each water field monitoring area and the rainfall of each monitoring period of each water quantity collection influencing area, respectively denoted as 、 , wherein represents the number of monitoring periods, , represents the number of monitoring periods, represents the number of water quantity collection influencing areas, , represents the number of water quantity collection influencing areas.

[0072] Extract the influence distance of each water quantity collection influencing area relative to each water field monitoring area, denoted as .

[0073] Using the formula analysis to obtain the power generation feasibility evaluation index of each water field monitoring area , wherein represents the suitable water flow rate set in advance, represents the average rainfall of each monitoring period of each water field monitoring area, which is obtained by averaging the rainfall of each monitoring period of each water field monitoring area , represents the water quantity collection influencing correction factor set in advance, and the calculation method is , wherein represents the reference rainfall warning value set in advance, represents the reference collection influencing distance set in advance, represents the average influence distance of each water quantity collection influencing area relative to each water field monitoring area, which is obtained by averaging the influence distance of each water quantity collection influencing area relative to each water field monitoring area , represents the average value of the rainfall of each monitoring period of each water quantity collection influencing area corresponding to each water field monitoring area.

[0074] It should be noted that the construction idea of the power generation feasibility evaluation index analysis formula of each water field monitoring area is as follows: 1. Suitable water flow rate factor: in the formula This part reflects the influence of water flow rate on power generation feasibility. Among them is the pre-set suitable water body flow rate, is the actual water body flow rate. This formula reflects the influence of the difference between the actual water body flow rate and the suitable flow rate on the feasibility of power generation. The closer the actual flow rate is to the suitable flow rate, the greater the value of this part, and the higher the feasibility evaluation index of power generation is likely to be, highlighting the importance of water body flow rate as a key factor for power generation in the water field.

[0075] 2. Rainfall factor: This part considers the rainfall factor. is the average rainfall of each monitoring period in each monitoring area of the water field, is the rainfall of each monitoring period. The numerator emphasizes the role of average rainfall, and the denominator comprehensively considers the mean and fluctuation of rainfall in each period. This part aims to measure the influence of rainfall and its stability on the feasibility of power generation. The greater the average rainfall and the smaller the fluctuation, the more favorable it is for power generation, and the value of this part will also increase accordingly.

[0076] 3. Water quantity collection impact correction factor: in the formula is the pre-set water quantity collection impact correction factor, which is calculated as . Among them is the reference rainfall warning value, is the reference collection impact distance, is the average value of the rainfall of each monitoring period in each water quantity collection impact area corresponding to each monitoring area of the water field, is the average impact distance of each water quantity collection impact area relative to each monitoring area of the water field. This factor considers the rainfall of the water quantity collection area and its distance from the monitoring area of the water field, and other factors that affect the feasibility of power generation, and is used to correct the evaluation index, making the evaluation more in line with the actual situation.

[0077] In a feasible embodiment, it is assumed that , , , , , Based on the data simulation calculation of the power generation feasibility evaluation index analysis formula of the water field monitoring area, the corresponding simulation calculation results are obtained, and part of the simulation results can be referred to Table 3.

[0078] Table 3 Partial Power Generation Feasibility Evaluation Index Analysis Formula of Water Field Monitoring Area

[0079] Corresponding data simulation results of the power generation feasibility evaluation index analysis formula of the water field monitoring area

[0080]

[0081] It needs to be further explained that the above data simulation results: 1. The power generation feasibility evaluation index integrates the factors such as water flow rate, rainfall and water collection effect to measure the power generation feasibility of the water field monitoring area. The higher the index, the better the power generation feasibility.

[0082] 2. The power generation feasibility evaluation index of region 2 is the highest (7.29), because the actual water flow rate (6 m / s) is close to the suitable water flow rate (5 m / s), the average rainfall (15 mm) is relatively large and the rainfall fluctuation is relatively small, and the water collection effect correction factor is positive and the value is relatively large. These factors make the power generation feasibility relatively high.

[0083] 3. The index of region 1 is the second (4.60), the average rainfall is moderate (10 mm), but the water flow rate is slightly lower than the suitable value, the water collection effect correction factor is also positive, and the overall power generation feasibility is at a medium level.

[0084] 4. The power generation feasibility evaluation index of region 3 is the lowest (2.75), mainly because the actual water flow rate (3 m / s) is far from the suitable flow rate, the average rainfall (7 mm) is small, although the water collection effect correction factor is negative to a certain extent, but the overall power generation feasibility is poor.

[0085] In a preferred embodiment of the present application, the specific analysis method of the power generation feasibility evaluation index of each light field monitoring area is as follows: the solar irradiance of each light field monitoring area is extracted, denoted as , wherein represents the number of light field monitoring areas, , represents the number of light field monitoring areas.

[0086] The cloud layer influence coefficient of each light field monitoring area is extracted, denoted as .

[0087] The formula is used to analyze the power generation feasibility evaluation index of each light field monitoring area , wherein represents a pre-set reference solar irradiance threshold.

[0088] It should be noted that the construction idea of the power generation feasibility evaluation index analysis formula of each light field monitoring area is as follows: 1. Solar irradiance factor: in the formula This part reflects the influence of solar irradiance on power generation feasibility. is the solar irradiance of each light field monitoring area, is a pre-set reference solar irradiance threshold. This formula reflects the relative relationship between the actual solar irradiance and the reference threshold. The higher the actual solar irradiance, The greater the value of the solar irradiance, the higher the power generation feasibility evaluation index may be under other conditions, highlighting the importance of this key factor for light field power generation. Because the solar irradiance directly determines how much energy the light field can obtain, it in turn affects the feasibility and efficiency of power generation.

[0089] 2. Cloud layer influencing factor: introduce cloud layer influencing coefficient is taken into account the shielding and scattering effects of the cloud layer on solar radiation. The presence of clouds can change the solar irradiance reaching the light field monitoring area, and different cloud conditions (such as thickness, density, etc.) will have different effects. Comprehensively reflects the weakening or strengthening of solar radiation by clouds (when the cloud layer has a certain reflection and scattering effect to enhance the local irradiance, it may be greater than 1; in general, the cloud layer mainly plays a weakening role, less than 1). By multiplying , the actual changes in solar irradiance due to cloud factors can be corrected, making the power generation feasibility evaluation index more in line with the actual light field power generation situation.

[0090] In a feasible embodiment, it is assumed that , , based on the data simulation calculation of the power generation feasibility evaluation index analysis formula of the light field monitoring area, the corresponding simulation results are obtained, and part of the simulation results can be referred to Table 4.

[0091] Table 4 Partial Power Generation Feasibility Evaluation Index Analysis Formula of Light Field Monitoring Area

[0092] Corresponding data simulation results of the power generation feasibility evaluation index analysis formula

[0093]

[0094] It needs to be further explained that the above data simulation results: 1. The power generation feasibility evaluation index integrates solar irradiance and cloud layer influencing coefficient to measure the power generation feasibility of the light field monitoring area. The higher the index, the better the power generation feasibility.

[0095] 2. The power generation feasibility evaluation index of region 2 is the highest (1.0125), because its solar irradiance (900 W / m 2 ) is higher than the reference solar irradiance threshold (800 ), and the cloud layer influencing coefficient (0.9) is relatively high, meaning that the weakening effect of the cloud layer on solar radiation is small. These two factors combined make the power generation feasibility higher.

[0096] 3. The index of region 4 is next (0.7), its solar irradiance (700 ) is lower than the reference threshold, although the cloud layer influencing coefficient is 0.8, but the overall power generation feasibility is at a medium level.

[0097] 4. The power generation feasibility evaluation index of region 3 is the lowest (0.525), mainly because the solar radiation (600 W / m 2 ) is relatively low, and the cloud layer influence coefficient (0.7) is also small, and the cloud layer has a large weakening effect on solar radiation, resulting in poor overall power generation feasibility.

[0098] It should be noted that the present application comprehensively considers the influence of meteorological prediction results, environmental factors and abnormal weather indexes when generating the power generation feasibility evaluation results, can comprehensively and accurately evaluate the power generation feasibility, can detect potential risks in advance, and protect the stability of power generation and equipment safety.

[0099] S5, according to the power generation feasibility evaluation result, the power generation feasibility of each monitoring area in the preset prediction period is judged, and the specific abnormal power generation new energy type direction and the key power generation new energy type direction are further identified.

[0100] In a preferred embodiment of the present application, the specific method for judging the power generation feasibility of each monitoring area in the preset prediction period is as follows: the power generation feasibility evaluation results corresponding to each wind field monitoring area, water field monitoring area and light field monitoring area are extracted, and then based on the pre-set wind field power generation feasibility evaluation threshold, water field power generation feasibility evaluation threshold and light field power generation feasibility evaluation threshold, comparative analysis is carried out respectively, to obtain the result of whether each monitoring area is allowed to carry out power generation operation in the preset prediction period.

[0101] Further, the wind field monitoring area power generation feasibility evaluation index, the water field monitoring area power generation feasibility evaluation index and the light field monitoring area power generation feasibility evaluation index are compared with the pre-set wind field power generation feasibility evaluation threshold, water field power generation feasibility evaluation threshold and light field power generation feasibility evaluation threshold respectively.

[0102] Please refer to Figure 2 , if the power generation feasibility evaluation index of a certain wind field monitoring area is greater than or equal to the wind field power generation feasibility evaluation threshold, it is judged that the wind field monitoring area is allowed to carry out power generation operation.

[0103] Please refer to Figure 3 , if the power generation feasibility evaluation index of a certain water field monitoring area is greater than or equal to the water field power generation feasibility evaluation threshold, it is judged that the water field monitoring area is allowed to carry out power generation operation.

[0104] Please refer to Figure 4 , if the power generation feasibility evaluation index of a certain light field monitoring area is greater than or equal to the light field power generation feasibility evaluation threshold, it is judged that the light field monitoring area is allowed to carry out power generation operation.

[0105] It should be noted that the setting basis of the wind field power generation feasibility evaluation threshold, the water field power generation feasibility evaluation threshold and the light field power generation feasibility evaluation threshold is: first, historical data and experience, referring to the actual operation data of wind, water and light field power generation in the past, summarizing the power generation feasibility under different conditions, combining with the industry experience to determine the threshold; second, equipment performance, considering the minimum environmental conditions required for normal operation and reaching a certain power generation efficiency of wind, water and light power generation equipment, such as the suitable wind speed range of fan, the water flow condition required by water turbine, the light intensity required by photovoltaic panel, etc.; third, economic factors, comprehensively considering the power generation cost and benefit, ensuring that the power generation above the threshold is economically feasible, balancing resource utilization and economic benefit. Exemplarily, the wind field power generation feasibility evaluation threshold is 20, the water field power generation feasibility evaluation threshold is 4, and the light field power generation feasibility evaluation threshold is 0.7.

[0106] In a preferred embodiment of the present application, the specific identification process of the abnormal power generation new energy type direction is as follows: the judgment result of whether each monitoring area is allowed to carry out power generation operation is extracted, the monitoring area which is judged to allow power generation operation is recorded as a normal monitoring area, the monitoring area which is judged to not allow power generation operation is recorded as a risk monitoring area, the number of wind field monitoring areas, water field monitoring areas and light field monitoring areas corresponding to the risk monitoring area is counted, and then comparative analysis is carried out to obtain the abnormal power generation new energy type direction.

[0107] Specifically, the number of wind field monitoring areas, water field monitoring areas and light field monitoring areas corresponding to the risk monitoring area is compared, and the largest number is selected as the abnormal power generation new energy type direction.

[0108] In a preferred embodiment of the present application, the specific identification process of the key power generation new energy type direction is as follows: the number of wind field monitoring areas, water field monitoring areas and light field monitoring areas corresponding to the normal monitoring area is counted, and then comparative analysis is carried out to obtain the key power generation new energy type direction.

[0109] Specifically, the number of wind field monitoring areas, water field monitoring areas and light field monitoring areas corresponding to the normal monitoring area is compared, and the largest number is selected as the key power generation new energy type direction.

[0110] It should be noted that the present application can provide a clear prediction of the power generation condition of each area in the future period for energy enterprises in advance according to the power generation feasibility evaluation result of each monitoring area in the preset prediction period, help them to reasonably plan production plan; by accurately positioning the high feasibility area, concentrating resources to improve power generation efficiency and reduce operation cost; for low feasibility area, early warning and countermeasures can be made to enhance the stability and safety of power generation.

[0111] Please refer to Figure 5A block diagram of a computer system for implementing some embodiments of the present disclosure is shown. The computer system 100 can be in the form of a general-purpose computing device. The computer system 100 includes a memory 110, a processor 120, and a bus 130 connecting the different system components.

[0112] The memory 110 may, for example, include system memory, non-volatile storage media, and the like. The system memory may, for example, store an operating system, application programs, a BootLoader, and other programs, and the like. The system memory can include volatile storage media, such as random access memory (RAM) and / or cache memory. The non-volatile storage media may, for example, store instructions to perform at least one of the vehicle body torsional stiffness improvement methods. The non-volatile storage media includes, but is not limited to, magnetic storage media, optical storage media, flash memory, and the like.

[0113] The processor 120 can be implemented with a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, and the like discrete hardware components. Accordingly, the new energy weather analysis large model construction, weather data prediction, power generation feasibility evaluation result generation, and power generation feasibility judgment can be implemented by a central processing unit (CPU) running instructions in the memory to perform the corresponding steps, or by a dedicated circuit to perform the corresponding steps.

[0114] The bus 930 can use any of a variety of bus structures. For example, the bus structure includes, but is not limited to, an industry standard architecture (ISA) bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus.

[0115] The computer system 100 can also include an input / output interface 140, a network interface 150, a storage interface 160, and the like. These interfaces 140, 150, 160, and the memory 110 and the processor 120 can be connected through the bus 130. The input / output interface 140 can provide a connection interface for display, mouse, keyboard, and the like input / output devices. The network interface 150 provides a connection interface for various networking devices. The storage interface 160 provides a connection interface for external storage devices such as floppy disks, U disks, SD cards, and the like.

[0116] The flowcharts or block diagrams of the method according to the embodiments of the present application describe various aspects of the present application. It should be understood that each block of the flowchart or block diagram, and combinations of blocks, can be implemented by computer readable program instructions.

[0117] The above merely illustrates and describes the concept of the present application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or adopt similar ways to replace, as long as the modifications or supplements do not deviate from the concept of the present application or exceed the defined scope of the present application, and should belong to the protection scope of the present application.

Claims

1. A method for intelligent analysis of meteorological data for new energy, characterized in that, The method comprises the following steps: S1, extracting two-dimensional distribution information of the target area to divide the monitoring area and generate a unique code, each monitoring area corresponds to a different new energy type, including wind field, water field and light field; S2, based on the global reanalysis data set and the regional high-resolution observation data set, the meteorological element data of the target area is extracted, and a new energy meteorological analysis large model is constructed, and the current meteorological element data of each monitoring area is collected; S3, according to the current meteorological element data, the new energy meteorological analysis large model is used to predict the meteorological data, and the meteorological prediction data of each monitoring area in the preset prediction period is obtained; S4, collecting multi-dimensional feature information of each monitoring area, and then combining the meteorological prediction data of each monitoring area to generate the corresponding power generation feasibility evaluation results of each wind field monitoring area, water field monitoring area and light field monitoring area in the preset prediction period; S5, according to the power generation feasibility evaluation results, the power generation feasibility of each monitoring area in the preset prediction period is judged, and the specific abnormal power generation new energy type direction and the key power generation new energy type direction are further identified; The specific way of collecting multi-dimensional feature information of each monitoring area comprises: Using a high-definition camera to obtain a distribution image of the target area, positioning the positions of each obstacle in each wind field monitoring area based on the distribution image, numbering each obstacle, and using image processing software to obtain the height of each obstacle, the distance between each obstacle and each wind turbine, and the hub height of each wind turbine; The generation of the power generation feasibility evaluation results of each wind field monitoring area in the preset prediction period needs to construct a power generation feasibility evaluation index of each wind field monitoring area, and the specific analysis method is as follows: extract the wind speed of each wind farm monitoring area, denoted as wherein denotes the number of wind farm monitoring areas, , denotes the number of wind farm monitoring areas; Extract each wind farm monitoring area corresponding to each obstacle height, each obstacle and each wind turbine distance and each wind turbine hub height, respectively recorded as , , , wherein indicates the number of obstacles, , indicates the number of obstacles, indicates the number of wind turbines, , indicates the number of wind turbines; Using formula Analysis yielded the power generation feasibility evaluation index for each wind farm monitoring area. ,in This indicates the preset suitable operating wind speed. This represents the average horizontal distance between obstacles and wind turbines in each wind farm monitoring area. It is calculated by considering the distances between each obstacle and each wind turbine. The mean was calculated to obtain the result. This represents the average obstacle height in each wind field monitoring area, which is determined by the height of each obstacle. The mean was calculated to obtain the result. This represents the average turbine hub height in each wind farm monitoring area, which is determined by analyzing the hub height of each turbine. The mean was calculated to obtain the result. This represents the standard deviation of wind speed in each wind field monitoring area, which is calculated based on the wind speed in each monitoring area. This indicates the weighting factors that are pre-determined by comprehensively considering all influencing factors. 2.The new energy-oriented weather data intelligent analysis method of claim 1, wherein: The specific way of constructing the new energy meteorological analysis large model is as follows: Extracting the key meteorological element data in the target power generation area after the global reanalysis data set and the regional high-resolution observation data set are fused, using a neural network to simulate the evolution process of the meteorological element data with time and space, and obtaining a new energy meteorological analysis large model. 3.The new energy-oriented weather data intelligent analysis method of claim 1, wherein: The specific content of the meteorological prediction data of each monitoring area is as follows: The monitoring areas can be divided into wind field monitoring areas, water field monitoring areas and light field monitoring areas according to the new energy type, wherein the meteorological prediction data of the wind field monitoring area is wind speed and wind direction, the meteorological prediction data of the water field monitoring area is rainfall, and the meteorological prediction data of the light field monitoring area is solar irradiance. 4.The new energy-oriented weather data intelligent analysis method of claim 3, wherein: The specific way of collecting multi-dimensional feature information of each monitoring area further comprises: Extracting the distribution image of the target area using a high-definition camera, obtaining the flow direction of each water field monitoring area, and obtaining each monitoring area through which each water flow direction flows, as the water collection influence area of each water field monitoring area, and obtaining the influence distance of the water collection influence area relative to each water field monitoring area; Extract the meteorological prediction result obtained by using a new energy meteorological analysis large model according to the current meteorological element data, divide the preset prediction period into several monitoring periods based on equal interval time length, the time length of each monitoring period remains consistent, number each monitoring period, and statistically obtain the rainfall of each monitoring period of each water field monitoring area and the rainfall of each monitoring period of each water quantity collection influence area in the preset prediction period; According to the meteorological prediction result, obtain the cloud layer thickness of each light field monitoring area in the preset prediction period, and then perform cloud layer thickness quantification based on a pre-set cloud layer thickness quantification relationship to obtain the cloud layer influence coefficient of each light field monitoring area. 5.The new energy-oriented weather data intelligent analysis method of claim 4, wherein: The specific analysis method of generating the power generation feasibility evaluation result corresponding to the water field monitoring area and the light field monitoring area in the preset prediction period is as follows: Specifically, the power generation feasibility evaluation result corresponding to the water field and the light field needs to construct a power generation feasibility evaluation index of each water field monitoring area and a power generation feasibility evaluation index of each light field monitoring area. 6.The new energy-oriented weather data intelligent analysis method of claim 5, wherein: The specific analysis method of the power generation feasibility evaluation index of each water field monitoring area is as follows: extract the rainfall and rainfall duration of each water field monitoring area, denoted as wherein represents the number of water field monitoring areas, , represents the number of water field monitoring areas, and the water flow rate of each water field monitoring area at the current monitoring time based on historical data records is denoted as ; extract rainfall of each monitoring period of each monitoring area of each water field and rainfall of each monitoring period of each water quantity collection influence area corresponding to the rainfall, respectively denoted as , wherein denotes the number of monitoring period, , denotes the number of monitoring period, denotes the number of water quantity collection influence area, , denotes the number of water quantity collection influence area; The influence distance of each water quantity sink gathering influence area relative to each water field monitoring area is recorded as ; Using formula Analysis yielded the power generation feasibility evaluation index for each water field monitoring area. ,in This indicates the pre-set suitable water flow velocity. This represents the average rainfall in each monitoring area and during each monitoring period. It is calculated by analyzing the rainfall in each monitoring area and during each monitoring period. The mean was calculated to obtain the result. This refers to the pre-set correction factor for water collection impact, which is calculated as follows: ,in This indicates the pre-set reference rainfall warning value. This indicates the pre-set reference pooling influence distance. This represents the average influence distance of each water collection area relative to each water monitoring area. The mean was calculated to obtain the result. This represents the average rainfall during each monitoring period in the area affected by the convergence of water volumes in each water monitoring area. 7.The new energy-oriented weather data intelligent analysis method of claim 6, wherein: The specific analysis method of the power generation feasibility evaluation index of each light field monitoring area is as follows: extracting the solar irradiance of each light field monitoring area, denoted as wherein denotes the number of the light field monitoring area, , denotes the number of the light field monitoring area; The cloud layer influence coefficient of each light field monitoring area is extracted, denoted as ; Using the formula The power generation feasibility evaluation index of each light field monitoring area is analyzed wherein represents a pre-set reference solar irradiance threshold.

8. The intelligent analysis method for meteorological data for new energy sources as described in claim 1, characterized in that: The specific method of performing power generation feasibility judgment on each monitoring area in the preset prediction period is as follows: Extract the power generation feasibility evaluation result corresponding to each wind field monitoring area, water field monitoring area and light field monitoring area, and then perform comparative analysis based on the pre-set wind field power generation feasibility evaluation threshold, water field power generation feasibility evaluation threshold and light field power generation feasibility evaluation threshold, to obtain the result of whether each monitoring area is allowed to perform power generation operation in the preset prediction period. 9.The new energy-oriented weather data intelligent analysis method of claim 8, wherein: The specific identification process of the abnormal power generation new energy type direction is as follows: Extract the judgment result of whether each monitoring area is allowed to perform power generation operation, record the monitoring area that is allowed to perform power generation operation as a normal monitoring area, record the monitoring area that is not allowed to perform power generation operation as a risk monitoring area, statistically obtain the number of wind field monitoring areas, water field monitoring areas and light field monitoring areas corresponding to the risk monitoring area, and then perform comparative analysis to obtain the abnormal power generation new energy type direction. 10.The new energy-oriented weather data intelligent analysis method of claim 9, wherein: The specific identification process of the key power generation new energy type direction is as follows: Statistically obtain the number of wind field monitoring areas, water field monitoring areas and light field monitoring areas corresponding to the normal monitoring area, and then perform comparative analysis to obtain the key power generation new energy type direction.

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