Methods, apparatus, and electronic equipment for determining wind shear index

Abstract

This application discloses a method, apparatus, and electronic device for determining wind shear index, belonging to the field of wind power generation technology. The method includes: generating N wind measurement datasets from wind measurement data acquired by a wind measuring device; inputting the wind measurement data from the N wind measurement datasets into an atmospheric stability classification model including P stability classification sub-models to obtain P atmospheric stability classifications output by the atmospheric stability classification model, each atmospheric stability classification including M atmospheric stability values, where P and M are integers greater than 1; determining a target atmospheric stability classification among the P atmospheric stability classifications that meets preset atmospheric stability classification evaluation conditions; and determining a first wind shear index under the target atmospheric stability based on the wind measurement data in the subset of wind measurement data under the target atmospheric stability in the target atmospheric stability classification.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, specifically to a method, apparatus, and electronic device for determining the wind shear index. Background Technology

[0002] In response to the increasing demand for power systems that rely on wind energy resources, in addition to continuously increasing the capacity of wind turbine generators, raising the hub height of wind turbine generators to obtain wind energy resources at higher altitudes is also one way to increase development capacity.

[0003] In wind energy resource assessment, as the hub height of wind turbines continues to increase, the cost of increasing the height of meteorological towers leads to an exponential increase. The cost-effectiveness of directly measuring wind speed at hub height using meteorological towers is becoming increasingly less attractive. Therefore, there is a growing demand for using wind shear to estimate wind speed at hub height. This involves calculating the wind shear index based on data from lower-level meteorological towers and then inputting this index into a wind speed estimation model to output the wind speed at hub height. The accuracy of the estimated wind speed at hub height in wind energy resource assessment is closely related to the accuracy of the wind shear index calculated from meteorological tower data. Therefore, exploring how to improve the accuracy of the wind shear index calculated from meteorological tower data has become one of the key technical problems that urgently need to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and electronic device for determining the wind shear index, which can improve the accuracy of the wind shear index calculated from anemometer data. Furthermore, it can improve the accuracy of wind parameter calculations at the hub height of wind turbine generators.

[0005] In a first aspect, embodiments of this application provide a method for determining the wind shear index, including:

[0006] The wind measurement data obtained by the wind measurement equipment is used to generate N wind measurement datasets, where N is an integer greater than 1;

[0007] The wind measurement data from the N wind measurement datasets are input into an atmospheric stability classification model that includes P stability classification sub-models, resulting in P atmospheric stability classifications output by the atmospheric stability classification model. Each atmospheric stability classification includes M atmospheric stability values, where P and M are both integers greater than 1.

[0008] Among the P atmospheric stability classifications, a target atmospheric stability classification that satisfies the preset atmospheric stability classification evaluation conditions is determined.

[0009] Based on the wind measurement data in the subset of wind measurement data under the target atmospheric stability in the target atmospheric stability classification, the first wind shear index under the target atmospheric stability is determined.

[0010] Secondly, embodiments of this application provide a device for determining the wind shear index, comprising:

[0011] The dataset generation module is used to generate N wind measurement datasets from the wind measurement data obtained by the wind measurement equipment, where N is an integer greater than 1;

[0012] An atmospheric stability classification module is used to input the wind measurement data of the N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models. Different atmospheric stability classifications are output by different stability classification sub-models, where P and M are both integers greater than 1.

[0013] The evaluation module is used to determine the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions among the P atmospheric stability classifications.

[0014] The first index determination module is used to determine the first wind shear index under the target atmospheric stability based on the wind measurement data in the wind measurement data subset under the target atmospheric stability in the target atmospheric stability classification.

[0015] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0016] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0017] In this embodiment, N wind measurement datasets are generated from the wind measurement data obtained by the wind measuring device. These N datasets are then input into P stability classification sub-models to obtain P atmospheric stability classifications. From these P classifications, a target atmospheric stability classification that meets preset atmospheric stability classification evaluation conditions is determined. By calculating a subset of wind measurement data for each atmospheric stability level within the target atmospheric stability classification, the first wind shear index for each atmospheric stability level within that target atmospheric stability classification is determined. In this way, multiple atmospheric stability classifications can be obtained simultaneously from the wind measurement data obtained by the wind measuring device through multiple stability classification sub-models. The wind shear index is then determined by selecting the atmospheric stability classification that meets the atmospheric stability classification evaluation conditions, thereby effectively improving the accuracy of the determined wind shear index. Furthermore, this improves the accuracy of wind parameter calculations at the hub height of the wind turbine generator. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an embodiment of the method for determining the wind shear index provided in this application;

[0019] Figure 2 This is another flowchart illustrating an embodiment of the method for determining the wind shear index provided in this application;

[0020] Figure 3 This is a schematic diagram of a portion of the process in an embodiment of the method for determining the wind shear index provided in this application;

[0021] Figure 4 This is a schematic diagram of an embodiment of the wind shear index determination device provided in this application;

[0022] Figure 5 This is another structural schematic diagram of an embodiment of the wind shear index determination device provided in this application;

[0023] Figure 6 This is a schematic diagram of an embodiment of the computing device provided in this application. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] The method for determining the wind shear index provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0027] Please see Figure 1 This is a flowchart illustrating an embodiment of the method for determining the wind shear index provided in this application. This method for determining the wind shear index is applied to electronic devices, such as servers. Figure 1 As shown, the method for determining the wind shear index includes at least the following steps 101 to 104.

[0028] Step 101: Generate N wind measurement datasets from the wind measurement data obtained by the wind measurement equipment, where N is an integer greater than 1.

[0029] Step 102: Input the wind measurement data of N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models to obtain P atmospheric stability classifications output by the atmospheric stability classification model. Each atmospheric stability classification includes M atmospheric stability values, where P and M are both integers greater than 1.

[0030] Specifically, different atmospheric stability classifications can be output by different stability classification sub-models.

[0031] Specifically, in each atmospheric stability classification, the wind measurement data in the corresponding wind measurement dataset can form at least two subsets of wind measurement data under different atmospheric stability conditions.

[0032] Step 103: Among the P atmospheric stability classifications, determine the target atmospheric stability classification that satisfies the preset atmospheric stability classification evaluation conditions.

[0033] Step 104: Based on the wind measurement data in the subset of wind measurement data under the target atmospheric stability in the target atmospheric stability classification, determine the first wind shear index under the target atmospheric stability.

[0034] In this embodiment, N wind measurement datasets are generated from the wind measurement data obtained by the wind measuring device. These N datasets are then input into P stability classification sub-models to obtain P atmospheric stability classifications. From these P classifications, a target atmospheric stability classification that meets preset atmospheric stability classification evaluation conditions is determined. By calculating a subset of wind measurement data for each atmospheric stability level within the target atmospheric stability classification, the first wind shear index for each atmospheric stability level within that target atmospheric stability classification is determined. In this way, multiple atmospheric stability classifications can be obtained simultaneously from the wind measurement data obtained by the wind measuring device through multiple stability classification sub-models. The wind shear index is then determined by selecting the atmospheric stability classification that meets the atmospheric stability classification evaluation conditions, thereby effectively improving the accuracy of the determined wind shear index. Furthermore, this improves the accuracy of wind parameter calculations at the hub height of the wind turbine generator.

[0035] In step 101 above, the electronic device can acquire wind measurement data through the wind measurement device and generate N wind measurement datasets from the wind measurement data acquired by the wind measurement device.

[0036] The aforementioned wind measuring equipment can be any device capable of measuring wind force data at two different altitude levels. Specifically, the aforementioned wind measuring equipment can include two wind measuring towers installed at different altitude levels.

[0037] Furthermore, the wind measurement data acquired by the aforementioned wind measurement equipment can be massive, and each wind measurement data includes wind force data at two different altitude levels. Specifically, the aforementioned wind measurement data are obtained simultaneously from two wind measurement towers at different altitude levels, and the wind measurement tower data can include time, wind speed, wind direction, and wind direction standard deviation, etc.; the wind measurement tower data can also include at least one of temperature and heat flux, and the heat flux can be an indicator related to atmospheric thermodynamics, which can include at least one of solar radiation, cloud cover, and surface wind speed.

[0038] It should be noted that the above-mentioned generation of N wind measurement datasets from wind measurement data obtained by the wind measurement equipment can be achieved by randomly dividing the acquired wind measurement data into N wind measurement datasets, with the amount of data in each dataset being the same or similar; or, it can be achieved by dividing the wind measurement data into N wind measurement datasets according to a preset rule. For example, the division can be based on the size of each data point or the time period.

[0039] In this embodiment of the application, the wind measurement data obtained by the wind measurement device may be all the wind measurement data obtained by the wind measurement device within a certain period of time; or it may be part of the wind measurement data obtained by the wind measurement device within a certain period of time.

[0040] In some implementations, such as Figure 2As shown, step 101 above may include:

[0041] Step 1011: Filter the wind measurement data obtained by the wind measurement equipment to obtain wind measurement data whose wind speed and weather fluctuation correlation parameters meet the preset conditions. Among them, the weather fluctuation correlation parameters include at least one of temperature, turbulence intensity, wind speed, wind direction and air pressure.

[0042] Step 1012: Generate N wind measurement datasets by acquiring wind measurement data that meet preset conditions.

[0043] Based on this, by filtering the wind measurement data according to the correlation parameters of wind speed and weather fluctuations, and classifying the wind measurement data whose correlation parameters of wind speed and weather fluctuations meet the preset conditions into the above N wind measurement datasets, the influence of wind measurement data under special wind conditions on the above first wind shear index can be reduced, thereby improving the accuracy of the above first wind shear index.

[0044] Among them, the weather fluctuation correlation parameter can be any parameter that can reflect the weather fluctuation situation, including at least one of temperature (including at least one of single-layer temperature and double-layer temperature), turbulence intensity, wind speed, wind direction and air pressure.

[0045] Furthermore, the aforementioned filtering of wind measurement data can involve determining whether each piece of wind measurement data meets the aforementioned preset conditions. For example, for wind measurement data collected at various times, the electronic device can compare the wind measurement data collected at each time with a preset wind speed, and compare the difference between the wind measurement data collected at that time and the wind measurement data collected at the previous time with preset parameter values. If the wind speed is greater than or equal to the preset wind speed, and the difference in the weather fluctuation correlation parameters is less than or equal to the preset parameter values ​​(for example, the temperature difference is greater than or equal to the preset temperature, the turbulence intensity difference is greater than or equal to the preset turbulence intensity, the wind speed difference is greater than or equal to the preset wind speed, the wind direction angle difference is greater than or equal to the preset angle, and the air pressure difference is greater than or equal to the preset air pressure), then the wind measurement data is determined to meet the preset conditions; otherwise, the wind measurement data is determined not to meet the preset conditions, and so on.

[0046] Alternatively, the aforementioned filtering of wind measurement data can also be used to determine whether the wind measurement data within a time window meets certain conditions. For example, electronic devices can compare the minimum wind speed in the wind measurement data within each time window with a preset wind speed, and compare the difference between the maximum and minimum weather fluctuation correlation parameters within each time window with a preset parameter difference. If the minimum wind speed is less than or equal to the preset wind speed, and the difference is less than or equal to the preset parameter difference, then the wind measurement data is determined to meet the preset conditions; otherwise, the wind measurement data is determined not to meet the preset conditions, and so on.

[0047] In some implementations, such as Figure 3 As shown, step 101 above may include:

[0048] Based on the sector where each wind measurement data acquired by the wind measurement equipment is located, the wind measurement data in the wind measurement dataset is divided into wind measurement data in at least two sectors;

[0049] Classify the wind measurement data under the target sector into N wind measurement datasets, where the target sector is any sector among at least two sectors.

[0050] Based on this, by dividing the wind measurement data acquired by the wind measurement equipment into corresponding sectors and classifying the wind measurement data under the sectors into N wind measurement datasets, the correlation between the wind measurement data in the N wind measurement datasets is increased, thereby further improving the accuracy of the determination of the first shear index.

[0051] It should be noted that the sectors in which the wind measurement data acquired by the above-mentioned wind measurement equipment are located can be divided into wind measurement data in at least two sectors. This can be done by dividing all the wind measurement data acquired by the wind measurement equipment within a certain period of time into the corresponding sectors, or by dividing the filtered wind measurement data that meets the above-mentioned preset conditions into the corresponding sectors.

[0052] In step 102 above, after the electronic device generates the aforementioned N wind measurement datasets, it can input these N datasets into P stability classification sub-models to obtain P atmospheric stability classifications output by the P stability classification sub-models. Different atmospheric stability classifications are output by different stability classification sub-models, each atmospheric stability classification includes M atmospheric stability values, and within each atmospheric stability classification, the wind measurement data in its corresponding wind measurement dataset forms at least two subsets of wind measurement data under each atmospheric stability value.

[0053] In this embodiment of the application, each of the above P stability classification sub-models can arbitrarily classify the atmospheric stability in the area where the wind measurement tower is located based on the input wind measurement data to obtain M atmospheric stability models.

[0054] For example, the aforementioned P atmospheric stability classification models may include stability classification sub-models based on wind direction standard deviation and atmospheric stability classification models based on wind shear index, etc. Since this application does not specifically limit the various stability classification sub-models, and the process by which each stability classification sub-model classifies atmospheric stability based on input data is well known to those skilled in the art, it will not be elaborated upon here.

[0055] The M atmospheric stability values ​​mentioned above can be determined based on the aforementioned stability classification sub-model and actual needs. For example, the atmospheric stability classification can be set to include strongly unstable, unstable, weakly unstable, neutral, relatively stable, and stable (i.e., 6 atmospheric stability values, M=6); or the atmospheric stability classification can be set to include unstable, neutral, and stable (i.e., 3 atmospheric stability values, M=3), etc. This embodiment does not limit this and can be determined according to actual needs.

[0056] Furthermore, the above-mentioned input of wind measurement data from N wind measurement datasets into P stability classification sub-models to obtain P atmospheric stability classifications output by the P stability classification sub-models can be achieved by each stability classification sub-model determining the atmospheric stability of the area where the wind measurement tower is located when measuring each wind measurement data based on the input wind measurement data, and classifying each wind measurement data into a subset of wind measurement data under that atmospheric stability, forming the aforementioned P subsets of wind measurement data. That is, each subset of wind measurement data includes wind measurement data measured under the same stability.

[0057] It should be noted that the above-mentioned input of wind measurement data from N wind measurement datasets into P stability classification sub-models can be achieved by randomly inputting each wind measurement dataset into each stability classification sub-model, and the P stability classification sub-models input wind measurement data from at least one of the N wind measurement datasets. The wind measurement data input into different stability classification sub-models can be completely different or partially the same.

[0058] For example, P can be set to N, and the electronic device can randomly input N wind measurement datasets into N stability classification sub-models, with different wind measurement datasets input into different stability classification sub-models, and so on.

[0059] In some implementations, the above-mentioned inputting wind measurement data from N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models may include:

[0060] Among the P stability classification sub-models, determine the stability classification sub-model corresponding to each of the N wind measurement datasets;

[0061] The wind measurement data from each wind measurement dataset is input into the stability classification sub-model corresponding to the wind measurement dataset.

[0062] Based on this, by inputting the wind measurement data from each wind measurement dataset into the stability classification sub-model corresponding to that dataset, the data from each wind measurement dataset can be input into a suitable stability classification sub-model, thereby further improving the accuracy of the determined wind shear index. Furthermore, it can further improve the accuracy of wind parameter calculations at the hub height of wind turbine generators.

[0063] It should be noted that, among the P stability classification sub-models, determining the stability classification sub-model corresponding to each of the N wind measurement datasets can be done by determining the correspondence between each wind measurement dataset and the stability classification sub-model according to preset rules.

[0064] For example, if the electronic device has a pre-set correspondence between different stability classification sub-models and time periods, and if the above N wind measurement datasets are classified according to time periods (such as day and night), the electronic device can determine the stability classification sub-model corresponding to the time period of each wind measurement dataset as the stability classification sub-model corresponding to that wind measurement dataset.

[0065] In some implementations, step 101 above may include:

[0066] Obtain wind measurement data using wind measurement equipment;

[0067] Based on the data type of the wind measurement data acquired by the wind measurement equipment, the wind measurement data acquired by the wind measurement equipment is classified into N wind measurement datasets.

[0068] The above-mentioned inputting the wind measurement data from each wind measurement dataset into the stability classification sub-model corresponding to the wind measurement dataset can include:

[0069] The wind measurement data of the target wind measurement dataset is input into the target stability classification sub-model. The wind measurement data in the target wind measurement dataset includes data of the target data type, and the target stability classification sub-model is associated with the target data type.

[0070] Based on this, a more flexible approach is made by inputting the target wind measurement dataset, which includes data of the target data type, into the target stability classification sub-model associated with the target data type.

[0071] The data types included in the aforementioned wind measurement data may include at least one of the following: time, wind speed, wind direction, and wind direction standard deviation.

[0072] In addition, regarding the data types contained in the wind measurement data obtained by the wind measurement equipment, classifying the wind measurement data obtained by the wind measurement equipment into N wind measurement datasets can be done by classifying the wind measurement data obtained by the wind measurement equipment into N wind measurement datasets based on whether each wind measurement data contains data of the target data type.

[0073] Specifically, classifying the wind measurement data acquired by the wind measurement equipment into N wind measurement datasets based on the data type of each data set can include:

[0074] Based on whether each wind measurement data contains at least one of the parameters related to the double-layer temperature and heat flux, the wind measurement data acquired by the wind measurement equipment are classified into N wind measurement datasets.

[0075] Based on this, by checking whether the wind measurement data contains at least one of the parameters related to bilayer temperature and heat flux, the wind measurement data obtained by the wind measurement tower can be used to classify atmospheric stability by selecting the corresponding stability classification sub-model based on whether the wind measurement data contains at least one of the parameters related to bilayer temperature and heat flux.

[0076] Specifically, based on whether the wind measurement data contains at least one of the parameters related to the double-layer temperature and heat flux, the wind measurement data acquired by the wind measurement device is classified into N wind measurement datasets. This can be done by adding wind measurement data containing at least one of the parameters related to the double-layer temperature and heat flux to wind measurement dataset 1, and adding wind measurement data that does not contain the parameters related to the double-layer temperature and heat flux to wind measurement dataset 2.

[0077] Alternatively, based on whether the aforementioned wind measurement data contains at least one of the parameters related to the bilayer temperature and heat flux, the wind measurement data acquired by the wind measurement equipment can be classified into N wind measurement datasets, such as... Figure 3 As shown, the wind measurement data obtained by the aforementioned wind measurement equipment can be classified to form the following wind measurement dataset:

[0078] I. The wind measurement data includes a wind measurement dataset containing both temperature and heat flux at two layers;

[0079] II. Wind measurement data set 4, which includes double-layer temperature but does not include heat flux;

[0080] III. Wind measurement data sets that include heat flux but not bilayer temperature 5;

[0081] IV. The wind measurement data does not include double-layer temperature and heat flux, but includes single-layer temperature data set 6;

[0082] V. The wind measurement data does not include wind measurement data for temperature and heat flux.

[0083] In addition, if the electronic device classifies the wind measurement data acquired by the wind measurement device into N wind measurement datasets based on whether each wind measurement data contains at least one of the parameters related to the double-layer temperature and heat flux, the electronic device can input each wind measurement dataset into its corresponding stability classification sub-model.

[0084] Specifically, the electronic device may have a preset stability classification sub-model 1 that is associated with the aforementioned double-layer temperature and heat flux, and a preset stability classification sub-model 2 that is not associated with the aforementioned double-layer temperature and heat flux. When the electronic device classifies the wind measurement data obtained by the wind measurement device into wind measurement dataset 1 and wind measurement dataset 2, the electronic device may input the wind measurement data of wind measurement dataset 1 into stability classification sub-model 1, and input the wind measurement data of wind measurement dataset 2 into stability classification sub-model 2.

[0085] In some implementations, inputting the wind measurement data of the target wind measurement dataset into the target stability classification sub-model may include:

[0086] The wind measurement data from the first wind measurement dataset is input into the first stability classification sub-model. The wind measurement data in the first wind measurement dataset includes heat flux correlation parameters, and the first stability classification sub-model is a model that classifies stability based on these heat flux correlation parameters; or...

[0087] The wind measurement data from the second wind measurement dataset is input into the second stability classification sub-model. The wind measurement data in the second wind measurement dataset includes bilayer temperatures, and the second stability classification sub-model is a model that classifies stability based on bilayer temperatures; or...

[0088] The wind measurement data in the third wind measurement dataset is input into the third stability classification sub-model. The wind measurement data in the second wind measurement dataset does not include heat flux correlation parameters and temperature. The third stability classification sub-model is a model that classifies stability based on parameters other than heat flux correlation parameters and temperature.

[0089] Based on this, atmospheric stability can be classified using the aforementioned first, second, and third stability classification sub-models. This enriches the methods for atmospheric stability classification and strengthens the correlation between the various wind measurement datasets and the stability classification sub-models, leading to more suitable atmospheric stability classifications and further improving the accuracy of the determined first wind shear index. Furthermore, it can further improve the accuracy of wind parameter calculations at the hub height of wind turbine generators.

[0090] The first wind measurement dataset, the second wind measurement dataset, and the third wind measurement dataset mentioned above can be one or more of the above N wind measurement datasets.

[0091] For example, such as Figure 3As shown, the electronic device can input the wind measurement data of the above-mentioned wind measurement dataset 3 and wind measurement dataset 5 (i.e., the above-mentioned first wind measurement dataset includes wind measurement dataset 3 and wind measurement dataset 5) into the first stability classification sub-model; input the wind measurement data of wind measurement dataset 4 and wind measurement dataset 5 (i.e., the above-mentioned first wind measurement dataset includes wind measurement dataset 4 and wind measurement dataset 5) into the second stability classification sub-model; input the wind measurement data of wind measurement dataset 7 (i.e., the above-mentioned third wind measurement dataset includes wind measurement dataset 7) into the third stability classification sub-model. Of course, the wind measurement data of wind measurement dataset 6 can also be input into the third stability classification sub-model.

[0092] Furthermore, the aforementioned first stability classification sub-model is a model for stability classification based on heat flux correlation parameters, and may include at least one of the Pasquill model, Pasquill-Turner model, and PS model; the aforementioned second stability classification sub-model is a model for stability classification based on bilayer temperature, and the atmospheric stability classification parameters used may include at least one of the Moning-Obuhoff length, gradient Richardson number, and total Richardson number; the aforementioned third stability classification sub-model is a model for stability classification based on parameters other than heat flux correlation parameters and bilayer temperature, and the atmospheric stability classification parameters used may include at least one of the wind direction standard deviation and wind shear index. Since this application embodiment does not specifically limit the aforementioned stability classification sub-models, and the processing procedures of the aforementioned stability classification sub-models are well known to those skilled in the art, they will not be elaborated upon here.

[0093] In some implementations, before inputting the wind measurement data from the second wind measurement dataset into the second stability classification sub-model, the following may also be included:

[0094] Obtain mesoscale data corresponding to the wind data to be processed in N wind measurement datasets. The wind data to be processed is wind data that does not include heat flux correlation parameters and bilayer temperature.

[0095] Based on the wind measurement data to be processed and the corresponding mesoscale data, processed wind measurement data containing two-layer temperature is generated, forming a fourth wind measurement dataset including the processed wind measurement data.

[0096] The above-mentioned inputting wind measurement data from the second wind measurement dataset into the second stability classification sub-model may include:

[0097] The wind measurement data from the second and fourth wind measurement datasets are input into the second stability classification sub-model.

[0098] Based on this, processed wind measurement data containing two layers of temperature can be generated using the aforementioned wind measurement data and mesoscale data, resulting in a fourth wind measurement dataset. The wind measurement data from the fourth and second wind measurement datasets are then input into the second stability classification sub-model for atmospheric stability classification, thereby making the input data richer and more comprehensive, and further improving the accuracy.

[0099] The aforementioned mesoscale data refers to wind resources and other data calculated using mesoscale numerical models and combined with wind measurement data, with typical grid accuracy at the mesoscale level. For example, mesoscale data could be MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) data, i.e., reanalysis data from NASA's (National Aeronautics and Space Administration) Global Modeling and Assimilation Office, and so on.

[0100] In addition, the above-mentioned generation of processed wind measurement data based on each wind measurement data to be processed and the corresponding mesoscale data can be achieved by determining mesoscale data whose coordinate position is the same as the coordinate position of the wind measurement equipment and whose time is the same as the time of the wind measurement data to be processed, based on the coordinate position of the wind measurement equipment and the time of the wind measurement data to be processed, and adding the temperature from the mesoscale data at the above two altitude levels to the wind measurement data to be processed, thereby generating processed wind measurement data.

[0101] For example, such as Figure 3 As shown, the electronic device can acquire the wind measurement data (i.e., the wind measurement data to be processed) in the wind measurement dataset 6 and wind measurement dataset 7, and generate processed wind measurement data including the double-layer temperature by using each wind measurement data and its corresponding mesoscale data. The processed wind measurement data generated from the wind measurement data in wind measurement dataset 6 and wind measurement dataset 7 is then input into the second stability classification sub-model.

[0102] In step 103 above, after the electronic device acquires the above P atmospheric stability classifications, the electronic device can determine whether each atmospheric stability classification meets the preset atmospheric stability classification evaluation conditions, so as to determine the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions.

[0103] The aforementioned atmospheric stability classification evaluation conditions can be pre-set conditions used to evaluate the quality of atmospheric stability classification. Specifically, the aforementioned atmospheric stability classification evaluation conditions include at least one of the following:

[0104] Whether the differences between the wind shear indices of at least one subset of wind measurement data under the above atmospheric stability classification meet the difference requirement;

[0105] Does the monotonicity of the wind shear index of the wind measurement data subsets under each stability level in the atmospheric stability classification match the monotonicity of each stability level?

[0106] Whether the distribution of wind shear index is concentrated in a subset of wind measurement data under at least one stability level in the atmospheric stability classification.

[0107] Based on this, each of the above P atmospheric stability values ​​can be evaluated using at least one of the above conditions, thus making the evaluation methods more flexible and diverse.

[0108] In this embodiment, determining whether the differences between wind shear indices of at least one subset of wind measurement data under at least one stability level in the atmospheric stability classification meet the difference requirement can be achieved by obtaining the difference between the maximum and minimum shear indices in the subset of wind measurement data under each stability level. If the difference is less than or equal to a preset difference, then the differences between the wind shear indices of the wind data subset meet the difference requirement; otherwise, the differences between the wind shear indices of the wind measurement data subset do not meet the difference requirement.

[0109] In addition, the aforementioned at least one stability can be part or all of the stability of at least two atmospheric stabilityes under the atmospheric stability classification (i.e., atmospheric stability that includes wind measurement data in the atmospheric stability classification).

[0110] For example, if the differences between the wind shear indices of all wind measurement data subsets under all stability conditions in the above atmospheric stability classification meet the difference requirements, then the atmospheric stability classification is determined to meet the preset atmospheric stability classification evaluation conditions; otherwise, the atmospheric stability classification is determined not to meet the preset atmospheric stability classification evaluation conditions.

[0111] Of course, when at least one of the above-mentioned stability is a partial stability of at least two of the above-mentioned atmospheric stability, certain conditions can be set to determine whether the above-mentioned atmospheric stability classification meets the preset atmospheric stability classification evaluation conditions based on whether the difference between the wind shear indices of the wind measurement data subset under at least one stability meets the difference requirement.

[0112] For example, if the amount of data in the wind measurement data subset under the atmospheric stability exceeds the preset amount of data, and the difference between the wind shear indices of the wind measurement data subset meets the difference requirement, then the above atmospheric stability classification is determined to meet the preset atmospheric stability classification evaluation conditions; otherwise, the atmospheric stability classification is determined not to meet the preset atmospheric stability classification evaluation conditions.

[0113] In this embodiment, since the stability parameters of each atmospheric stability under the above atmospheric stability classification are monotonically increasing or monotonically decreasing, the above electronic device can also determine whether the atmospheric stability classification meets the preset atmospheric stability classification evaluation conditions based on whether the monotonicity of the wind shear index of the wind measurement data subset under each stability in the atmospheric stability classification matches the monotonicity of each stability.

[0114] For example, the aforementioned third stability classification sub-model can use the standard deviation of wind direction for stability classification, classifying the wind measurement data of the third wind measurement dataset into a subset of wind measurement data under at least two of the following stability levels: strongly unstable, unstable, weakly unstable, neutral, relatively stable, and stable. Since the standard deviation of wind direction decreases sequentially from strongly unstable, unstable, weakly unstable, neutral, relatively stable, to stable, if the average wind shear index calculated from the subset of wind measurement data under these at least two stability levels also decreases sequentially, then the atmospheric stability classification is determined to meet the preset atmospheric stability classification evaluation conditions; otherwise, the atmospheric stability classification is determined not to meet the preset atmospheric stability classification evaluation conditions.

[0115] In this embodiment, determining whether the distribution of wind shear index of wind measurement data subsets under at least one stability in the atmospheric stability classification is concentrated can be achieved by electronic devices acquiring the wind shear index of each wind measurement data subset and then determining whether the distribution of wind shear index of each wind measurement data subset is concentrated through a preset statistical model (such as a normal distribution function).

[0116] Similarly, the aforementioned at least one stability can be part or all of the stability of at least two atmospheric stabilityes under the atmospheric stability classification (i.e., atmospheric stability that includes wind measurement data in the atmospheric stability classification).

[0117] For example, if the wind shear index of all wind measurement data subsets under all stability conditions in the above atmospheric stability classification is concentrated, it is determined that the atmospheric stability classification meets the preset atmospheric stability classification evaluation conditions; otherwise, it is determined that the atmospheric stability classification does not meet the preset atmospheric stability classification evaluation conditions.

[0118] Of course, when at least one of the above-mentioned stability is a partial stability of the above-mentioned at least two atmospheric stability, certain conditions can be set to determine whether the above-mentioned atmospheric stability classification meets the preset atmospheric stability classification evaluation conditions based on whether the wind shear index of the wind measurement data subset under at least one stability is concentrated.

[0119] For example, if the amount of data in the wind measurement data subset under the atmospheric stability exceeds the preset amount of data, and the wind shear index of the wind measurement data subset is concentrated, then the above atmospheric stability classification is determined to meet the preset atmospheric stability classification evaluation conditions; otherwise, the atmospheric stability classification is determined not to meet the preset atmospheric stability classification evaluation conditions.

[0120] It should be noted that among the P atmospheric stability classifications mentioned above, one or more atmospheric stability classifications may satisfy the aforementioned preset atmospheric stability classification evaluation. When multiple atmospheric stability classifications satisfy the aforementioned preset atmospheric stability classification evaluation, the target atmospheric stability classification can be any one of these multiple atmospheric stability classifications; or, it can be one of the multiple atmospheric stability classifications that satisfies the preset conditions. For example, the atmospheric stability classification with the highest number of stability values ​​in the wind shear index distribution set of the included wind measurement data subset can be determined as the aforementioned target atmospheric stability classification, and so on.

[0121] Of course, among the P atmospheric stability classifications mentioned above, there may not be any atmospheric stability classifications that satisfy the aforementioned preset atmospheric stability classification evaluation. In this case, the above method may further include:

[0122] If none of the P atmospheric stability classifications meet the atmospheric stability classification evaluation conditions, the M atmospheric stability classifications are updated, and the wind measurement data of the N wind measurement datasets are re-entered into the atmospheric stability classification model, which includes P stability classification sub-models, to obtain P atmospheric stability classifications output by the atmospheric stability classification model; and, among the P atmospheric stability classifications, the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions is determined.

[0123] Therefore, if none of the P atmospheric stability classifications meet the atmospheric stability classification evaluation conditions, the aforementioned M atmospheric stability classifications are updated until a target atmospheric stability classification that meets the evaluation conditions can be determined. This ensures that a suitable atmospheric stability classification can be obtained, thereby guaranteeing the accuracy of the aforementioned first wind shear index. Furthermore, it ensures the accuracy of wind parameter calculations at the hub height of the wind turbine generator.

[0124] The aforementioned update of the M atmospheric stability values ​​can involve adjusting the stability parameters corresponding to at least two stability values. For example, when classifying atmospheric stability using wind direction standard deviation, adjusting the wind direction standard deviation corresponding to at least two atmospheric stability values—such as the wind direction standard deviations corresponding to weakly unstable and neutral atmospheric stability values ​​being greater than or equal to 12.5 and less than 17.5, and greater than or equal to 9.5 and less than 12.5, respectively—can then update the wind direction standard deviations corresponding to weakly unstable and neutral stability values ​​to be greater than or equal to 13.5 and less than 17.5, and greater than or equal to 9.5 and less than 13.5, and so on.

[0125] Furthermore, the aforementioned at least two stability measures can be some or all of the aforementioned M atmospheric stability measures. Specifically, if the wind shear index of a subset of wind measurement data under some of the aforementioned M atmospheric stability measures is not concentrated or the variability does not meet the requirements, then the stability measures where the wind shear index of the subset of wind measurement data is not concentrated or the variability does not meet the requirements can be adjusted; if the monotonicity of the wind shear index of a subset of wind measurement data under each of the aforementioned M atmospheric stability measures does not match the monotonicity of each stability measure, then all stability measures can be adjusted.

[0126] In step 104 above, after the electronic device determines the target atmospheric stability classification, the electronic device can calculate the subset of wind measurement data under each atmospheric stability in the target atmospheric stability classification, determine the first wind shear index under the atmospheric stability, so that the electronic device can estimate the wind speed at upper altitude under the corresponding atmospheric stability through the first wind shear index.

[0127] In some embodiments, the above method may further include:

[0128] Based on wind measurement data where at least one of the wind speed and weather fluctuation-related parameters does not meet the preset conditions, a second wind shear index is determined under special wind conditions.

[0129] Based on this, the second wind shear index under special wind conditions can be obtained by wind measurement data in which at least one of the wind speed and weather fluctuation related parameters does not meet the preset conditions. Thus, the wind speed at high altitudes under special wind conditions can be estimated by using the second wind shear index.

[0130] In addition, this application can also achieve the following specific beneficial effects:

[0131] (1) By filtering the data, on the one hand, the data of light winds were filtered out, which increased the stability of the overall calculation results. On the other hand, the influence of special wind conditions was filtered out, and the stability classification calculation was only performed on the stable weather process. The resulting stability screening results showed better wind shear consistency.

[0132] (2) The results of different stability calculations vary greatly and differ from the results of time-by-time shear. By introducing multiple stability calculation methods and an evaluation module for stability calculation methods, the shear classification effect is effectively guaranteed, and the shear distribution effect of stability classification is further optimized.

[0133] Please see Figure 4 This is a schematic diagram of an embodiment of the wind shear index determination device provided in this application. Figure 4 As shown, the device 400 includes:

[0134] The dataset generation module 401 is used to generate N wind measurement datasets from the wind measurement data obtained by the wind measurement equipment, where N is an integer greater than 1;

[0135] The atmospheric stability classification module 402 is used to input wind measurement data from N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models, and obtain P atmospheric stability classifications output by the atmospheric stability classification model. Each atmospheric stability classification includes M atmospheric stability values, where P and M are both integers greater than 1.

[0136] Evaluation module 403 is used to determine the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions among P atmospheric stability classifications.

[0137] The first index determination module 404 is used to determine the first wind shear index under the target atmospheric stability by using wind measurement data from the wind measurement data subset based on the target atmospheric stability in the target atmospheric stability classification.

[0138] In some implementations, the stability classification module 402 includes:

[0139] The stability classification sub-model determination unit is used to determine the stability classification sub-model corresponding to each of the N wind measurement datasets among the P stability classification sub-models.

[0140] The input unit is used to input the wind measurement data of each wind measurement dataset into the stability classification sub-model corresponding to the wind measurement dataset.

[0141] In some implementations, the dataset generation module 401 includes:

[0142] The data acquisition unit is used to acquire wind measurement data through the wind measurement equipment;

[0143] The data classification unit is used to classify the wind measurement data acquired by the wind measurement equipment into N wind measurement datasets based on the data type contained in the wind measurement data.

[0144] The input unit is specifically used for:

[0145] The wind measurement data of the target wind measurement dataset is input into the target stability classification sub-model. The wind measurement data in the target wind measurement dataset includes data of the target data type, and the target stability classification sub-model is associated with the target data type.

[0146] In some implementations, the data classification unit is specifically used for:

[0147] Based on whether each wind measurement data contains at least one of the parameters related to the double-layer temperature and heat flux, the wind measurement data acquired by the wind measurement equipment are classified into N wind measurement datasets.

[0148] In some implementations, the input unit is specifically used for:

[0149] The wind measurement data from the first wind measurement dataset is input into the first stability classification sub-model. The wind measurement data in the first wind measurement dataset includes heat flux correlation parameters, and the first stability classification sub-model is a model that classifies stability based on these heat flux correlation parameters; or...

[0150] The wind measurement data from the second wind measurement dataset is input into the second stability classification sub-model. The wind measurement data in the second wind measurement dataset includes bilayer temperatures, and the second stability classification sub-model is a model that classifies stability based on bilayer temperatures; or...

[0151] The wind measurement data in the third wind measurement dataset is input into the third stability classification sub-model. The wind measurement data in the second wind measurement dataset does not include heat flux correlation parameters and temperature. The third stability classification sub-model is a model that classifies stability based on parameters other than heat flux correlation parameters and temperature.

[0152] In some embodiments, the apparatus further includes:

[0153] The mesoscale data acquisition module is used to acquire mesoscale data corresponding to the wind measurement data to be processed in N wind measurement datasets. The wind measurement data to be processed is wind measurement data that does not include heat flux correlation parameters and bilayer temperature.

[0154] The wind measurement data generation module is used to generate processed wind measurement data containing two-layer temperature based on each wind measurement data to be processed and the corresponding mesoscale data, and to form a fourth wind measurement dataset including each processed wind measurement data.

[0155] The input unit can be specifically used for:

[0156] The wind measurement data from the second and fourth wind measurement datasets are input into the second stability classification sub-model.

[0157] In some implementations, the atmospheric stability classification evaluation criteria include at least one of the following:

[0158] Whether the differences between the wind shear indices of at least one subset of wind measurement data under an atmospheric stability classification meet the difference requirements;

[0159] Does the monotonicity of the wind shear index of the wind measurement data subsets under each stability level in the atmospheric stability classification match the monotonicity of each stability level?

[0160] Whether the distribution of wind shear index is concentrated in a subset of wind measurement data under at least one stability level in the atmospheric stability classification.

[0161] In some embodiments, the device 400 further includes:

[0162] The atmospheric stability update module is used to update M atmospheric stability values ​​when none of the P atmospheric stability classifications meet the atmospheric stability classification evaluation conditions, and to re-execute the input of wind measurement data from N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models, thereby obtaining P atmospheric stability classifications output by the atmospheric stability classification model; and, among the P atmospheric stability classifications, to determine the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions.

[0163] In some implementations, the dataset generation module 401 includes:

[0164] The data partitioning unit is used to divide the wind measurement data in the wind measurement dataset into wind measurement data in at least two sectors according to the sector where each wind measurement data acquired by the wind measurement equipment is located.

[0165] The data classification unit is used to classify the wind measurement data under the target sector into N wind measurement datasets, where the target sector is any one of at least two sectors.

[0166] In some implementations, such as Figure 5 As shown, the dataset generation module 401 includes:

[0167] The data filtering unit 4011 is used to filter the wind measurement data acquired by the wind measurement equipment to obtain wind measurement data whose wind speed and weather fluctuation correlation parameters meet the preset conditions. The weather fluctuation correlation parameters include at least one of temperature, turbulence intensity, wind direction, wind speed and air pressure.

[0168] The dataset generation unit 4012 is used to generate N wind measurement datasets by acquiring wind measurement data that meets preset conditions.

[0169] In some embodiments, the device 400 further includes:

[0170] The second index determination module is used to determine the second wind shear index under special wind conditions based on wind measurement data where at least one of the wind speed and weather fluctuation-related parameters does not meet the preset conditions.

[0171] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments of the method, and the effects that each implementation can achieve have also been described in detail in the embodiments of the method, and will not be elaborated here.

[0172] Based on the same inventive concept, this disclosure also provides a computing device, specifically combined with... Figure 6 Please provide a detailed explanation.

[0173] Figure 6 This is a schematic diagram of an embodiment of the computing device provided in this application.

[0174] like Figure 6 As shown, the computing device 600 is an exemplary hardware architecture diagram of a computing device capable of implementing the wind shear index determination method and wind shear index determination apparatus according to the embodiments of this disclosure. The computing device 600 may refer to the electronic equipment in the embodiments of this disclosure.

[0175] The computing device 600 may include a processor 601 and a memory 602 storing computer program instructions.

[0176] Specifically, each of the aforementioned processors may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0177] The aforementioned memories may include mass storage for information or instructions. For example, and not limitingly, memories may include hard disk drives (HDDs), floppy disk drives, flash memory, optical disks, magneto-optical disks, magnetic tape, or universal serial bus (USB) drives, or combinations of two or more of these. Where appropriate, memories may include removable or non-removable (or fixed) media. Where appropriate, memories may be internal or external to the integrated gateway device. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or combinations of two or more of these.

[0178] Processor 601 performs the following steps by reading and executing computer program instructions stored in memory 602:

[0179] Processor 601 executes the wind measurement data obtained through the wind measurement device and generates N wind measurement datasets, where N is an integer greater than 1;

[0180] Wind measurement data from N wind measurement datasets are input into an atmospheric stability classification model that includes P stability classification sub-models, resulting in P atmospheric stability classifications output by the atmospheric stability classification model. Each atmospheric stability classification includes M atmospheric stability values, where P and M are both integers greater than 1.

[0181] Among the P atmospheric stability classifications, a target atmospheric stability classification that satisfies the preset atmospheric stability classification evaluation conditions is determined.

[0182] Based on the wind measurement data in the subset of wind measurement data under the target atmospheric stability in the target atmospheric stability classification, the first wind shear index under the target atmospheric stability is determined.

[0183] In some implementations, processor 601 executes, among P stability classification sub-models, to determine the stability classification sub-model corresponding to each of the N wind measurement datasets;

[0184] The wind measurement data of each of the aforementioned wind measurement datasets are input into the stability classification sub-model corresponding to the wind measurement dataset.

[0185] In some implementations, the processor 601 performs the task of acquiring wind measurement data through the wind measurement device;

[0186] Based on the data type of the wind measurement data acquired by the wind measurement device, the wind measurement data acquired by the wind measurement device is classified into N wind measurement datasets.

[0187] The wind measurement data of the target wind measurement dataset is input into the target stability classification sub-model, wherein the wind measurement data in the target wind measurement dataset includes data of the target data type, and the target stability classification sub-model is associated with the target data type.

[0188] In some implementations, the processor 601 performs an action to classify the wind measurement data acquired by the wind measurement device into N wind measurement datasets based on whether each of the wind measurement data contains at least one of the bilayer temperature and heat flux correlation parameters.

[0189] In some implementations, processor 601 executes the process of inputting wind measurement data from a first wind measurement dataset into a first stability classification sub-model, wherein the wind measurement data in the first wind measurement dataset includes the heat flux correlation parameters, and the first stability classification sub-model is a model that classifies stability based on the heat flux correlation parameters; or,

[0190] The wind measurement data in the second wind measurement dataset is input into the second stability classification sub-model, wherein the wind measurement data in the second wind measurement dataset includes the bilayer temperature, and the second stability classification sub-model is a model that classifies stability based on the bilayer temperature; or

[0191] The wind measurement data in the third wind measurement dataset is input into the third stability classification sub-model. The wind measurement data in the second wind measurement dataset does not include the heat flux correlation parameter and temperature. The third stability classification sub-model is a model that classifies stability based on parameters other than the heat flux correlation parameter and temperature.

[0192] In some implementations, the processor 601 performs the acquisition of mesoscale data corresponding to the wind measurement data to be processed in the N wind measurement datasets, wherein the wind measurement data to be processed is wind measurement data excluding the heat flux correlation parameter and the bilayer temperature.

[0193] Based on the wind measurement data to be processed and the mesoscale data corresponding to the wind measurement data to be processed, processed wind measurement data containing double-layer temperature is generated, and a fourth wind measurement dataset including the processed wind measurement data is formed.

[0194] The wind measurement data from the second wind measurement dataset and the fourth wind measurement dataset are input into the second stability classification sub-model.

[0195] In some implementations, the atmospheric stability classification evaluation criteria include at least one of the following:

[0196] Whether the differences between the wind shear indices of at least one subset of wind measurement data under the atmospheric stability classification meet the difference requirements;

[0197] Whether the monotonicity of the wind shear index of the wind measurement data subsets under each stability level in the atmospheric stability classification matches the monotonicity of each stability level; and

[0198] Whether the distribution of wind shear index of at least one subset of wind measurement data under the atmospheric stability classification is concentrated.

[0199] In some embodiments, the processor 601, when none of the P atmospheric stability classifications meet the atmospheric stability classification evaluation conditions, updates the M atmospheric stability values ​​and re-executes the input of the wind measurement data of the N wind measurement datasets into an atmospheric stability classification model including P stability classification sub-models, to obtain P atmospheric stability classifications output by the atmospheric stability classification model; and, among the P atmospheric stability classifications, determines a target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions.

[0200] In some implementations, the processor 601 executes a process to divide the wind measurement data in the wind measurement dataset into wind measurement data in at least two sectors, based on the sector where each wind measurement data acquired by the wind measurement device is located.

[0201] The wind measurement data under the target sector are classified into N wind measurement datasets, where the target sector is any one of the at least two sectors.

[0202] In some embodiments, the processor 601 performs filtering on the wind measurement data acquired by the wind measurement device to obtain wind measurement data whose wind speed and weather fluctuation correlation parameters meet preset conditions, wherein the weather fluctuation correlation parameters include at least one of temperature, turbulence intensity, wind direction, wind speed and air pressure.

[0203] By acquiring wind measurement data that meets the preset conditions, N wind measurement datasets are generated.

[0204] In some implementations, processor 601 executes wind measurement data based on wind speed and weather fluctuation-related parameters that do not meet the preset conditions, and determines a second wind shear index under special wind conditions.

[0205] Regarding the computing device in the above embodiments, the specific manner in which the processor performs operations has been described in detail in the embodiments of the relevant method, and the effects that each implementation can achieve have also been described in detail in the embodiments of the above method, and will not be elaborated here.

[0206] This disclosure also provides a computer storage medium storing computer-executable instructions for implementing the method for determining the wind shear index as described in this disclosure.

[0207] The processor mentioned above is the processor in the computing device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0208] This disclosure also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method for determining the wind shear index as described in the first aspect.

[0209] The computer program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0210] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable transaction data statistical device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable transaction data statistical device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0211] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable transaction data statistical device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction means, which is implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0212] These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0213] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0214] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining the wind shear index, characterized in that, include: The wind measurement data obtained by the wind measurement equipment is used to generate N wind measurement datasets, where N is an integer greater than 1; The wind measurement data from the N wind measurement datasets are input into an atmospheric stability classification model that includes P stability classification sub-models to obtain P atmospheric stability classifications output by the atmospheric stability classification model. Each atmospheric stability classification includes M atmospheric stability values, where P and M are both integers greater than 1. Among the P atmospheric stability classifications, a target atmospheric stability classification that satisfies the preset atmospheric stability classification evaluation conditions is determined. Based on the wind measurement data in the subset of wind measurement data under the target atmospheric stability in the target atmospheric stability classification, the first wind shear index under the target atmospheric stability is determined. The subset of wind measurement data is the dataset under the target atmospheric stability formed by wind measurement data in the wind measurement dataset corresponding to the target atmospheric stability classification. The process involves acquiring wind measurement data using wind measurement equipment and generating N wind measurement datasets, including: Obtain wind measurement data using wind measurement equipment; Based on the data type of the wind measurement data acquired by the wind measurement device, the wind measurement data acquired by the wind measurement device is classified into N wind measurement datasets.

2. The method according to claim 1, characterized in that, The step of inputting the wind measurement data from the N wind measurement datasets into P stability classification sub-models includes: Among the P stability classification sub-models, determine the stability classification sub-model corresponding to each of the N wind measurement datasets; The wind measurement data of each of the aforementioned wind measurement datasets are input into the stability classification sub-model corresponding to the wind measurement dataset.

3. The method according to claim 2, characterized in that, The step of inputting the wind measurement data of each of the wind measurement datasets into the stability classification sub-model corresponding to the wind measurement dataset includes: The wind measurement data of the target wind measurement dataset is input into the target stability classification sub-model, wherein the wind measurement data in the target wind measurement dataset includes data of the target data type, and the target stability classification sub-model is associated with the target data type.

4. The method according to claim 1, characterized in that, Based on the data type of the wind measurement data acquired by the wind measurement device, the wind measurement data acquired by the wind measurement device is classified into N wind measurement datasets, including: Based on whether each of the wind measurement data contains at least one of the parameters related to double-layer temperature and heat flux, the wind measurement data acquired by the wind measurement device is classified into N wind measurement datasets.

5. The method according to claim 4, characterized in that, The step of inputting the wind measurement data from the N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models includes: The wind measurement data in the first wind measurement dataset is input into the first stability classification sub-model, wherein the wind measurement data in the first wind measurement dataset includes the heat flux correlation parameters, and the first stability classification sub-model is a model that classifies stability based on the heat flux correlation parameters; or... The wind measurement data in the second wind measurement dataset is input into the second stability classification sub-model, wherein the wind measurement data in the second wind measurement dataset includes the bilayer temperature, and the second stability classification sub-model is a model that classifies stability based on the bilayer temperature; or The wind measurement data in the third wind measurement dataset is input into the third stability classification sub-model. The wind measurement data in the third wind measurement dataset does not include the heat flux correlation parameter and temperature. The third stability classification sub-model is a model that classifies stability based on parameters other than the heat flux correlation parameter and temperature.

6. The method according to claim 5, characterized in that, Before inputting the wind measurement data from the second wind measurement dataset into the second stability classification sub-model, the process also includes: Obtain mesoscale data corresponding to the wind measurement data to be processed in the N wind measurement datasets, wherein the wind measurement data to be processed is wind measurement data excluding the heat flux correlation parameter and the bilayer temperature; Based on the wind measurement data to be processed and the mesoscale data corresponding to the wind measurement data to be processed, processed wind measurement data containing double-layer temperature is generated, and a fourth wind measurement dataset including the processed wind measurement data is formed. The step of inputting the wind measurement data from the second wind measurement dataset into the second stability classification sub-model includes: The wind measurement data from the second wind measurement dataset and the fourth wind measurement dataset are input into the second stability classification sub-model.

7. The method according to claim 1, characterized in that, The atmospheric stability classification and evaluation criteria include at least one of the following: Whether the differences between the wind shear indices of at least one subset of wind measurement data under the atmospheric stability classification meet the difference requirements; Whether the monotonicity of the wind shear index of the wind measurement data subsets under each stability in the atmospheric stability classification matches the monotonicity of each stability. as well as Whether the distribution of wind shear index of at least one subset of wind measurement data under the atmospheric stability classification is concentrated.

8. The method according to claim 1, characterized in that, Before determining the first wind shear index under the target atmospheric stability based on the wind measurement data subset under the target atmospheric stability in the target atmospheric stability classification, the method further includes: If none of the P atmospheric stability classifications meet the atmospheric stability classification evaluation conditions, the M atmospheric stability classifications are updated, and the wind measurement data of the N wind measurement datasets are re-inputted into the P stability classification sub-models to obtain the P atmospheric stability classifications output by the P stability classification sub-models; and, among the P atmospheric stability classifications, the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions is determined.

9. The method according to claim 1, characterized in that, The wind measurement data acquired through the wind measurement equipment generates N wind measurement datasets, including: Based on the sector where each wind measurement data acquired by the wind measurement device is located, the wind measurement data in the wind measurement dataset is divided into wind measurement data in at least two of the sectors; The wind measurement data under the target sector are classified into N wind measurement datasets, where the target sector is any one of the at least two sectors.

10. The method according to claim 1, characterized in that, The wind measurement data acquired by the wind measurement equipment generates N wind measurement datasets, including: The wind measurement data acquired by the wind measurement device is filtered to obtain wind measurement data whose wind speed and weather fluctuation correlation parameters meet preset conditions. The weather fluctuation correlation parameters include at least one of temperature, turbulence intensity, wind speed, wind direction and air pressure. By acquiring wind measurement data that meets the preset conditions, N wind measurement datasets are generated.

11. The method according to claim 10, characterized in that, The method further includes: Based on wind measurement data where at least one of the wind speed and weather fluctuation-related parameters does not meet the preset conditions, a second wind shear index is determined under special wind conditions.

12. A device for determining wind shear index, characterized in that, include: The dataset generation module is used to generate N wind measurement datasets from the wind measurement data obtained by the wind measurement equipment, where N is an integer greater than 1. An atmospheric stability classification module is used to input the wind measurement data of the N wind measurement datasets into an atmospheric stability classification model that includes P stability classification sub-models, and to obtain P atmospheric stability classifications output by the atmospheric stability classification model. Each atmospheric stability classification includes M atmospheric stability values, where P and M are both integers greater than 1. The evaluation module is used to determine the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions among the P atmospheric stability classifications. The first index determination module is used to determine the first wind shear index under the target atmospheric stability based on the wind measurement data in the wind measurement data subset under the target atmospheric stability in the target atmospheric stability classification. The wind measurement data subset is the dataset under the target atmospheric stability formed by the wind measurement data in the wind measurement dataset corresponding to the target atmospheric stability classification. The dataset generation module includes: The data acquisition unit is used to acquire wind measurement data through the wind measurement equipment; The data classification unit is used to classify the wind measurement data acquired by the wind measurement device into N wind measurement datasets based on the data type contained in the wind measurement data acquired by the wind measurement device.

13. The apparatus according to claim 12, characterized in that, The stability classification module includes: The stability classification sub-model determination unit is used to determine the stability classification sub-model corresponding to each of the N wind measurement datasets among the P stability classification sub-models. The input unit is used to input the wind measurement data of each of the wind measurement datasets into the stability classification sub-model corresponding to the wind measurement dataset.

14. The apparatus according to claim 13, characterized in that, The input unit is specifically used for: The wind measurement data of the target wind measurement dataset is input into the target stability classification sub-model, wherein the wind measurement data in the target wind measurement dataset includes data of the target data type, and the target stability classification sub-model is associated with the target data type.

15. The apparatus according to claim 13, characterized in that, The data classification unit is specifically used for: Based on whether each of the wind measurement data contains at least one of the parameters related to double-layer temperature and heat flux, the wind measurement data acquired by the wind measurement device is classified into N wind measurement datasets.

16. The apparatus according to claim 15, characterized in that, The input unit is specifically used for: The wind measurement data in the first wind measurement dataset is input into the first stability classification sub-model, wherein the wind measurement data in the first wind measurement dataset includes the heat flux correlation parameters, and the first stability classification sub-model is a model that classifies stability based on the heat flux correlation parameters; or... The wind measurement data in the second wind measurement dataset is input into the second stability classification sub-model, wherein the wind measurement data in the second wind measurement dataset includes the bilayer temperature, and the second stability classification sub-model is a model that classifies stability based on the bilayer temperature; or The wind measurement data in the third wind measurement dataset is input into the third stability classification sub-model. The wind measurement data in the third wind measurement dataset does not include the heat flux correlation parameter and temperature. The third stability classification sub-model is a model that classifies stability based on parameters other than the heat flux correlation parameter and temperature.

17. The apparatus according to claim 16, characterized in that, Also includes: The mesoscale data acquisition module is used to acquire mesoscale data corresponding to the wind measurement data to be processed in the N wind measurement datasets, wherein the wind measurement data to be processed is wind measurement data excluding the heat flux correlation parameter and the bilayer temperature. The wind measurement data generation module is used to generate processed wind measurement data containing two-layer temperature based on each of the wind measurement data to be processed and the mesoscale data corresponding to the wind measurement data to be processed, and to form a fourth wind measurement dataset including each of the processed wind measurement data. The input unit is specifically used for: The wind measurement data from the second wind measurement dataset and the fourth wind measurement dataset are input into the second stability classification sub-model.

18. The apparatus according to claim 12, characterized in that, The atmospheric stability classification and evaluation criteria include at least one of the following: Whether the differences between the wind shear indices of at least one subset of wind measurement data under the atmospheric stability classification meet the difference requirements; Whether the monotonicity of the wind shear index of the wind measurement data subsets under each stability in the atmospheric stability classification matches the monotonicity of each stability. as well as Whether the distribution of wind shear index of at least one subset of wind measurement data under the atmospheric stability classification is concentrated.

19. The apparatus according to claim 12, characterized in that, Also includes: An atmospheric stability update module is used to update the M atmospheric stability values ​​when none of the P atmospheric stability classifications meet the atmospheric stability classification evaluation conditions, and to re-execute the input of wind measurement data from the N wind measurement datasets into an atmospheric stability classification model including P stability classification sub-models to obtain P atmospheric stability classifications output by the atmospheric stability classification model; and to determine the target atmospheric stability classification that meets the preset atmospheric stability classification evaluation conditions among the P atmospheric stability classifications.

20. The apparatus according to claim 12, characterized in that, The dataset generation module includes: The data partitioning unit is used to partition the wind measurement data in the wind measurement dataset into wind measurement data in at least two sectors according to the sector where each wind measurement data acquired by the wind measurement device is located. A data classification unit is used to classify the wind measurement data under the target sector into N wind measurement datasets, wherein the target sector is any one of the at least two sectors.

21. The apparatus according to claim 12, characterized in that, The dataset generation module includes: The data filtering unit is used to filter the wind measurement data acquired by the wind measurement device to obtain wind measurement data whose wind speed and weather fluctuation correlation parameters meet preset conditions. The weather fluctuation correlation parameters include at least one of temperature, turbulence intensity, wind speed, wind direction and air pressure. The dataset generation unit is used to generate N wind measurement datasets by acquiring wind measurement data that meets the preset conditions.

22. The apparatus according to claim 21, characterized in that, Also includes: The second index determination module is used to determine the second wind shear index under special wind conditions based on wind measurement data where at least one of the wind speed and weather fluctuation-related parameters does not meet the preset conditions.

23. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a program or instructions stored in the memory and running on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method for determining the wind shear index as described in any one of claims 1 to 11.

24. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method for determining the wind shear index as described in any one of claims 1 to 11.

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