Wind power blade and control method

Abstract

The invention relates to the technical field of offshore wind power, and particularly discloses a wind power blade and a control method, and the wind power blade comprises a detection module which is used for obtaining the geographic position of the location of the wind power blade, and determining meteorological characteristic parameters according to the geographic position of the location of the wind power blade; the clustering module is used for clustering the wind power blades according to the meteorological characteristic parameters and determining a blade risk threshold value according to a clustering result of the wind power blades; the corrosion module is used for acquiring corrosion monitoring data of the wind power blade, determining a blade corrosion risk degree according to the corrosion monitoring data of the wind power blade, and correcting a blade risk threshold value according to the blade corrosion risk degree; and the control module is used for determining a blade risk grade according to the meteorological characteristic parameters of the wind power blade and the corrected blade risk threshold value, determining a blade action control parameter according to the blade risk grade, and performing action control according to the blade action control parameter. The wind turbine blade can always keep the best action, and safe operation of a wind turbine generator is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of offshore wind power, and more specifically, to a wind turbine blade and a control method. Background Art

[0002] Against the backdrop of the global energy crisis with the gradual depletion of non-renewable energy, the development of renewable energy has become an important issue that urgently needs to be solved. Among different renewable energies, wind power shows great potential due to its relatively high technical maturity, abundant availability, and relatively low environmental impact. According to its location, wind farms can be divided into onshore or offshore wind farms. With the continuous development of wind power technology and the gradual expansion of wind energy resources, wind turbines have gradually developed from early inland types to offshore. Compared with onshore wind turbines, offshore wind turbines have the advantages of rich wind resources, low turbulence intensity, large erection space, small power transmission and distribution losses, small visual impact, and low noise pollution.

[0003] Due to the relatively far location and complex environment of offshore wind turbines, the wind turbine blades are vulnerable to seawater erosion and wind force, and there are great challenges in the risk assessment and environmental perception of offshore wind turbines. The existing technology has poor monitoring effects on wind turbine blades, resulting in non-specific and untimely control of wind turbine blades, and further leading to the occurrence of blade accidents. Summary of the Invention

[0004] The present invention provides a wind turbine blade and a control method to solve the problems of non-specific and untimely control of offshore wind turbine blades in the prior art, including:

[0005] A detection module, configured to obtain the geographical location of the wind turbine blade and determine meteorological characteristic parameters according to the geographical location of the wind turbine blade;

[0006] A clustering module, configured to cluster each wind turbine blade according to the meteorological characteristic parameters and determine a blade risk threshold according to the clustering result of the wind turbine blade;

[0007] A corrosion module, configured to obtain corrosion monitoring data of the wind turbine blade, determine a blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blade, and correct the blade risk threshold according to the blade corrosion risk degree;

[0008] A control module, configured to determine a blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, determine blade action control parameters according to the blade risk level, and perform action control according to the blade action control parameters.

[0009] Further, the detection module determines meteorological characteristic parameters according to the geographical location of the wind turbine blade, including:

[0010] Determine the historical meteorological data corresponding to the geographical location based on the geographical location where the wind turbine blade is located, and the historical meteorological data includes wind direction change data and wind speed change data;

[0011] Determine the wind direction characteristic parameters according to the wind direction change data, and determine the wind speed characteristic parameters according to the wind speed change data;

[0012] Perform normalization processing on the wind direction characteristic parameters and the wind speed characteristic parameters respectively, calculate the sum value of the normalized wind direction characteristic parameters and the wind speed characteristic parameters, and obtain the meteorological characteristic parameters of the wind turbine blade.

[0013] Further, the determining the wind direction characteristic parameters according to the wind direction change data and determining the wind speed characteristic parameters according to the wind speed change data includes:

[0014] Determine the optimal wind direction angle according to the orientation of the wind turbine blade, and determine the weight values of the remaining wind direction angle ranges according to the differences between the remaining wind direction angle ranges and the optimal wind direction angle;

[0015] Obtain the duration of the wind direction change data in each wind direction angle range within a preset period, multiply the duration by the corresponding weight value, and obtain the wind direction characteristic parameters;

[0016] Draw a wind speed change curve according to the wind speed change data within a preset period, divide the wind speed change curve according to a preset sliding time window, and obtain a number of sub-wind speed change curves;

[0017] Calculate the average value of each sub-wind speed change curve, and screen out the sub-wind speed dangerous change curves with an average value greater than the preset dangerous wind speed threshold;

[0018] Count the frequency of the sub-wind speed dangerous change curves appearing in the wind speed change curve, and determine the wind speed characteristic parameters according to the frequency of the sub-wind speed dangerous change curves appearing in the wind speed change curve.

[0019] Further, the clustering module clusters each wind turbine blade according to the meteorological characteristic parameters, including:

[0020] Establish a meteorological characteristic data set according to the meteorological characteristic parameters of each wind turbine blade, and randomly select k initial clustering centers of the meteorological characteristic data set;

[0021] Calculate the Euclidean distance from the meteorological characteristic parameters in the meteorological characteristic data set to the initial clustering centers, and divide each meteorological characteristic parameter into the corresponding clustering cluster according to the Euclidean distance from the meteorological characteristic parameters in the meteorological characteristic data set to the initial clustering centers;

[0022] Calculate the average value of the meteorological characteristic parameters within each clustering cluster, and re-determine the clustering centers according to the average value of the meteorological characteristic parameters within each clustering cluster;

[0023] Repeat the above steps iteratively until the cluster centers no longer change or the number of iterations reaches a preset iteration threshold, and obtain k final clusters.

[0024] Further, the clustering module determines the blade risk threshold according to the clustering results of the wind turbine blades, including:

[0025] Determine the cluster centers of the corresponding clusters of each wind turbine blade according to the clustering results of the wind turbine blades, and calculate the blade risk thresholds of each wind turbine blade based on the risk threshold calculation formula according to the cluster centers of the corresponding clusters of each wind turbine blade. The risk threshold calculation formula is specifically:

[0026]

[0027] where T is the blade risk threshold, T α is the initial blade risk threshold, W α is the preset standard meteorological characteristic parameter, W is the cluster center of the corresponding cluster of the wind turbine blade, R is the preset range coefficient, and exp is the natural exponential function.

[0028] Further, the corrosion module determines the blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blades, including:

[0029] Determine the change situation of the corrosion degree of the wind turbine blade according to the corrosion monitoring data of the wind turbine blade, and draw a corrosion degree change curve according to the change situation of the corrosion degree of the wind turbine blade;

[0030] Perform curve fitting on the corrosion degree change curve to obtain a corrosion degree prediction curve, determine the time required for the blade corrosion degree to reach the preset dangerous corrosion degree according to the corrosion degree prediction curve, and determine the blade corrosion risk degree according to the time required for the blade corrosion degree to reach the preset dangerous corrosion degree.

[0031] Further, the corrosion module corrects the blade risk threshold according to the blade corrosion risk degree, including:

[0032] Obtain the preset allowable corrosion time, calculate the ratio of the blade corrosion risk degree to the preset allowable corrosion time, and obtain the risk threshold correction coefficient;

[0033] Multiply the risk threshold correction coefficient by the corresponding risk threshold to obtain the corrected blade risk threshold.

[0034] Further, the control module determines the blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, including:

[0035] Calculate the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold, and determine whether the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than the first preset threshold;

[0036] If the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than the first preset threshold, the preset first level is set as the blade risk level;

[0037] If the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is less than or equal to the first preset threshold, it is judged whether the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than the second preset threshold;

[0038] If the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than the second preset threshold, the preset second level is set as the blade risk level;

[0039] If the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is less than or equal to the second preset threshold, the preset third level is set as the blade risk level.

[0040] Furthermore, the control module determines the blade action control parameters according to the blade risk level, including:

[0041] Obtain historical blade action data, and determine the historical blade risk level and the corresponding blade actions according to the historical blade action data;

[0042] Establish a training sample set according to the historical blade risk level and the corresponding blade actions, establish an initial blade action control model according to the training sample set and train the initial blade action control model to obtain a trained blade action control model;

[0043] Input the blade risk level of the current wind turbine blade into the trained blade action control model to obtain the corresponding blade action control parameters.

[0044] To achieve the above object, the present invention also provides a wind turbine blade control method, which is applied to the above-mentioned wind turbine blade, including:

[0045] Obtain the geographical location of the place where the wind turbine blade is located, and determine the meteorological characteristic parameters according to the geographical location of the place where the wind turbine blade is located;

[0046] Cluster each wind turbine blade according to the meteorological characteristic parameters, and determine the blade risk threshold according to the clustering result of the wind turbine blade;

[0047] Obtain the corrosion monitoring data of the wind turbine blade, determine the blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blade, and correct the blade risk threshold according to the blade corrosion risk degree;

[0048] Determine the blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, determine the blade action control parameters according to the blade risk level, and perform action control according to the blade action control parameters.

[0049] The beneficial effects of the present invention are as follows:

[0050] By applying the above technical solutions, the present invention clusters each wind turbine blade according to the meteorological characteristic parameters of the geographical location where each wind turbine blade is located, and combines the blade corrosion monitoring data to accurately evaluate the blade risk level of each wind turbine blade. At the same time, the blade action control parameters are determined through the blade risk level, so that the wind turbine blade always maintains the best action and ensures the safe operation of the wind turbine unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 FIG. shows the overall structure diagram of a wind turbine blade proposed in an embodiment of the present invention;

[0053] Figure 2 FIG. shows the schematic flow chart of a control method for a wind turbine blade proposed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0055] The terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, "a plurality" means two or more.

[0056] The embodiments of the present application provide a wind turbine blade, as Figure 1 shown, including:

[0057] A detection module, configured to obtain the geographical location where the wind turbine blade is located and determine meteorological characteristic parameters according to the geographical location where the wind turbine blade is located; a clustering module, configured to cluster each wind turbine blade according to the meteorological characteristic parameters and determine a blade risk threshold according to the clustering result of the wind turbine blade; a corrosion module, configured to obtain the corrosion monitoring data of the wind turbine blade, determine the blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blade, and correct the blade risk threshold according to the blade corrosion risk degree; a control module, configured to determine the blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, determine the blade action control parameters according to the blade risk level, and perform action control according to the blade action control parameters.

[0058] In some embodiments of the present application, the detection module determines the meteorological characteristic parameters according to the geographical location where the wind turbine blade is located, including: determining the historical meteorological data corresponding to the geographical location according to the geographical location where the wind turbine blade is located, the historical meteorological data including wind direction change data and wind speed change data; determining the wind direction characteristic parameters according to the wind direction change data, and determining the wind speed characteristic parameters according to the wind speed change data; respectively performing normalization processing on the wind direction characteristic parameters and the wind speed characteristic parameters, and calculating the sum value of the normalized wind direction characteristic parameters and the wind speed characteristic parameters to obtain the meteorological characteristic parameters of the wind turbine blade.

[0059] In some embodiments of the present application, the determining the wind direction characteristic parameters according to the wind direction change data and determining the wind speed characteristic parameters according to the wind speed change data includes: determining the optimal wind direction angle according to the orientation of the wind turbine blade, and determining the weight value of the remaining wind direction angle ranges according to the difference between the remaining wind direction angle ranges and the optimal wind direction angle; obtaining the duration of the wind direction change data in each wind direction angle range within a preset period, multiplying the duration by the corresponding weight value to obtain the wind direction characteristic parameters; drawing a wind speed change curve according to the wind speed change data within a preset period, dividing the wind speed change curve according to a preset sliding time window to obtain a plurality of sub-wind speed change curves; calculating the average value of each sub-wind speed change curve, and screening out the sub-wind speed dangerous change curves with an average value greater than a preset dangerous wind speed threshold; counting the frequency of the sub-wind speed dangerous change curves appearing in the wind speed change curve, and determining the wind speed characteristic parameters according to the frequency of the sub-wind speed dangerous change curves appearing in the wind speed change curve.

[0060] In this embodiment, the weight value of the remaining wind direction angle ranges is determined according to the difference between the remaining wind direction angle ranges and the optimal wind direction angle. The larger the difference, the higher the allocated weight value.

[0061] In some embodiments of the present application, the clustering module clusters each wind turbine blade according to meteorological characteristic parameters, including: establishing a meteorological characteristic data set based on the meteorological characteristic parameters of each wind turbine blade, and randomly selecting k initial clustering centers of the meteorological characteristic data set; calculating the Euclidean distance from the meteorological characteristic parameters in the meteorological characteristic data set to the initial clustering centers, and dividing each meteorological characteristic parameter into the corresponding clustering cluster according to the Euclidean distance from the meteorological characteristic parameters in the meteorological characteristic data set to the initial clustering centers; calculating the average value of the meteorological characteristic parameters within each clustering cluster, and re-determining the clustering centers according to the average value of the meteorological characteristic parameters within each clustering cluster; repeating the above steps iteratively until the clustering centers no longer change or the number of iterations reaches a preset iteration threshold, and obtaining k final clustering clusters.

[0062] In some embodiments of the present application, the clustering module determines the blade risk threshold according to the clustering result of the wind turbine blades, including: determining the clustering centers of the corresponding clustering clusters of each wind turbine blade according to the clustering result of the wind turbine blades, and calculating the blade risk threshold of each wind turbine blade based on the risk threshold calculation formula according to the clustering centers of the corresponding clustering clusters of each wind turbine blade. The risk threshold calculation formula is specifically as follows,

[0063]

[0064] where T is the blade risk threshold, T α is the initial blade risk threshold, W α is the preset standard meteorological characteristic parameter, W is the clustering center of the corresponding clustering cluster of the wind turbine blade, R is the preset range coefficient, and exp is the natural exponential function.

[0065] In some embodiments of the present application, the corrosion module determines the blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blade, including: determining the change situation of the corrosion degree of the wind turbine blade according to the corrosion monitoring data of the wind turbine blade, and drawing a corrosion degree change curve according to the change situation of the corrosion degree of the wind turbine blade; performing curve fitting on the corrosion degree change curve to obtain a corrosion degree prediction curve, determining the time required for the blade corrosion degree to reach a preset dangerous corrosion degree according to the corrosion degree prediction curve, and determining the blade corrosion risk degree according to the time required for the blade corrosion degree to reach a preset dangerous corrosion degree.

[0066] In this embodiment, the corrosion monitoring data of the wind turbine blade is collected by a blade corrosion monitoring sensor.

[0067] In some embodiments of the present application, the corrosion module corrects the blade risk threshold according to the blade corrosion risk degree, including: obtaining a preset allowable corrosion time, calculating the ratio of the blade corrosion risk degree to the preset allowable corrosion time to obtain a risk threshold correction coefficient; multiplying the risk threshold correction coefficient by the corresponding risk threshold to obtain a corrected blade risk threshold.

[0068] In some embodiments of the present application, the control module determines the blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, including: calculating the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold, and determining whether the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than a first preset threshold; if the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than the first preset threshold, setting the preset first level as the blade risk level; if the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is less than or equal to the first preset threshold, determining whether the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than a second preset threshold; if the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is greater than the second preset threshold, setting the preset second level as the blade risk level; if the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold is less than or equal to the second preset threshold, setting the preset third level as the blade risk level.

[0069] In this embodiment, the blade risk level of the wind turbine blade is determined by the difference between the meteorological characteristic parameters of the wind turbine blade and the blade risk threshold. The larger the difference, the higher the corresponding blade risk level ranking and the greater the blade risk.

[0070] In some embodiments of the present application, the control module determines the blade action control parameters according to the blade risk level, including: obtaining historical blade action data, determining the historical blade risk level and the corresponding blade actions according to the historical blade action data; establishing a training sample set according to the historical blade risk level and the corresponding blade actions, establishing an initial blade action control model according to the training sample set and training the initial blade action control model to obtain a trained blade action control model; inputting the blade risk level of the current wind turbine blade into the trained blade action control model to obtain the corresponding blade action control parameters.

[0071] Based on the same technical concept, as Figure 2 shown, the present invention also provides a wind turbine blade control method, which is applied to the above-mentioned wind turbine blade, including:

[0072] S101, obtaining the geographical location of the place where the wind turbine blade is located, and determining the meteorological characteristic parameters according to the geographical location of the place where the wind turbine blade is located;

[0073] S102, clustering each wind turbine blade according to the meteorological characteristic parameters, and determining the blade risk threshold according to the clustering result of the wind turbine blade;

[0074] S103, obtaining the corrosion monitoring data of the wind turbine blade, determining the blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blade, and correcting the blade risk threshold according to the blade corrosion risk degree;

[0075] S104. Determine the blade risk level based on the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, determine the blade action control parameters according to the blade risk level, and perform action control according to the blade action control parameters.

[0076] By applying the above technical solutions, the present invention includes a detection module for obtaining the geographical location of the wind turbine blade and determining meteorological characteristic parameters based on the geographical location of the wind turbine blade; a clustering module for clustering each wind turbine blade according to the meteorological characteristic parameters and determining the blade risk threshold according to the clustering result of the wind turbine blade; a corrosion module for obtaining the corrosion monitoring data of the wind turbine blade, determining the blade corrosion risk degree according to the corrosion monitoring data of the wind turbine blade, and correcting the blade risk threshold according to the blade corrosion risk degree; and a control module for determining the blade risk level based on the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, determining the blade action control parameters according to the blade risk level, and performing action control according to the blade action control parameters. The present invention can keep the wind turbine blade in the best action state all the time and ensure the safe operation of the wind turbine unit.

[0077] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (such as a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A wind turbine blade, characterized in that: include: A detection module is used to obtain the geographical location of the wind turbine blades and determine the meteorological characteristic parameters according to the geographical location of the wind turbine blades; A clustering module is used to cluster each wind turbine blade according to meteorological characteristic parameters, and determine the blade risk threshold according to the clustering results of the wind turbine blades; A corrosion module is used to obtain the corrosion monitoring data of the wind turbine blades, determine the corrosion risk of the blades according to the corrosion monitoring data of the wind turbine blades, and modify the blade risk threshold according to the corrosion risk of the blades; The control module is used to determine the blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, determine the blade action control parameters according to the blade risk level, and perform action control according to the blade action control parameters.

2. The wind turbine blade according to claim 1, characterized in that: The detection module determines the meteorological characteristic parameters according to the geographical location of the wind turbine blades, including: Determine historical meteorological data corresponding to the geographical location according to the geographical location of the wind turbine blade, wherein the historical meteorological data includes wind direction change data and wind speed change data; Determine wind direction characteristic parameters according to wind direction change data, and determine wind speed characteristic parameters according to wind speed change data; The wind direction characteristic parameters and the wind speed characteristic parameters are normalized respectively, and the sum of the normalized wind direction characteristic parameters and the wind speed characteristic parameters is calculated to obtain the meteorological characteristic parameters of the wind turbine blades.

3. The wind turbine blade according to claim 2, characterized in that: Determining the wind direction characteristic parameter according to the wind direction change data, and determining the wind speed characteristic parameter according to the wind speed change data, comprises: Determine the optimal wind direction angle according to the orientation of the wind turbine blades, and determine the weight values ​​of the remaining wind direction angle ranges according to the difference between the remaining wind direction angle ranges and the optimal wind direction angle; Obtain the duration of wind direction change data in each wind direction angle range within a preset period, multiply the duration by the corresponding weight value, and obtain the wind direction characteristic parameter; Draw a wind speed change curve according to the wind speed change data within a preset period, and divide the wind speed change curve according to a preset sliding time window to obtain a plurality of sub-wind speed change curves; Calculate the average value of each sub-wind speed change curve, and select the sub-wind speed dangerous change curve whose average value is greater than the preset dangerous wind speed threshold; The frequency of occurrence of the sub-wind speed danger change curve in the wind speed change curve is counted, and the wind speed characteristic parameter is determined according to the frequency of occurrence of the sub-wind speed danger change curve in the wind speed change curve.

4. The wind turbine blade according to claim 1, characterized in that: The clustering module clusters each wind turbine blade according to the meteorological characteristic parameters, including: A meteorological characteristic data set is established according to the meteorological characteristic parameters of each wind turbine blade, and k initial clustering centers of the meteorological characteristic data set are randomly selected; Calculate the Euclidean distance between the meteorological characteristic parameters in the meteorological characteristic data set and the initial cluster center, and divide each meteorological characteristic parameter into a corresponding cluster cluster according to the Euclidean distance between the meteorological characteristic parameters in the meteorological characteristic data set and the initial cluster center; Calculate the average value of the meteorological characteristic parameters in each cluster, and re-determine the cluster center according to the average value of the meteorological characteristic parameters in each cluster; Repeat the above steps until the cluster center no longer changes or the number of iterations reaches the preset iteration threshold, and k final clusters are obtained.

5. The wind turbine blade according to claim 4, characterized in that: The clustering module determines the blade risk threshold according to the clustering result of the wind turbine blades, including: The cluster center of each wind turbine blade corresponding to the cluster cluster is determined according to the cluster result of the wind turbine blades, and the blade risk threshold of each wind turbine blade is calculated according to the cluster center of each wind turbine blade corresponding to the cluster cluster based on the risk threshold calculation formula. The risk threshold calculation formula is specifically: Where T is the leaf risk threshold, T α is the initial leaf risk threshold, W α is the preset standard meteorological characteristic parameter, W is the cluster center of the cluster corresponding to the wind turbine blade, R is the preset range coefficient, and exp is the natural exponential function.

6. The wind turbine blade according to claim 1, characterized in that: The corrosion module determines the blade corrosion risk according to the wind turbine blade corrosion monitoring data, including: Determine the change of the corrosion degree of the wind turbine blades according to the corrosion monitoring data of the wind turbine blades, and draw a corrosion degree change curve according to the change of the corrosion degree of the wind turbine blades; The corrosion degree change curve is fitted to obtain the corrosion degree prediction curve. The time required for the blade corrosion degree to reach the preset dangerous corrosion degree is determined based on the corrosion degree prediction curve. The blade corrosion risk is determined based on the time required for the blade corrosion degree to reach the preset dangerous corrosion degree.

7. The wind turbine blade according to claim 6, characterized in that: The corrosion module corrects the blade risk threshold according to the blade corrosion risk, including: Obtaining the preset allowable corrosion time, calculating the ratio of the blade corrosion risk to the preset allowable corrosion time, and obtaining the risk threshold correction coefficient; The risk threshold correction coefficient is multiplied by the corresponding risk threshold to obtain a corrected blade risk threshold.

8. The wind turbine blade according to claim 1, characterized in that: The control module determines the blade risk level according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, including: Calculating the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold, and determining whether the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold is greater than a first preset threshold; If the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold is greater than a first preset threshold, the preset first level is set as the blade risk level; If the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold is less than or equal to the first preset threshold, then determining whether the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold is greater than a second preset threshold; If the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold is greater than the second preset threshold, the preset second level is set as the blade risk level; If the difference between the meteorological characteristic parameter of the wind turbine blade and the blade risk threshold is less than or equal to the second preset threshold, the preset third level is set as the blade risk level.

9. The wind turbine blade according to claim 8, characterized in that: The control module determines the blade action control parameters according to the blade risk level, including: Acquire historical blade motion data, and determine historical blade risk levels and corresponding blade motions based on the historical blade motion data; Establishing a training sample set according to historical blade risk levels and corresponding blade motions, establishing an initial blade motion control model according to the training sample set, and training the initial blade motion control model to obtain a trained blade motion control model; The blade risk level of the current wind turbine blade is input into the trained blade motion control model to obtain the corresponding blade motion control parameters.

10. A wind turbine blade control method, applied to a wind turbine blade as claimed in any one of claims 1 to 9, characterized in that: include: Obtaining the geographical location of the wind turbine blades, and determining meteorological characteristic parameters according to the geographical location of the wind turbine blades; Clustering each wind turbine blade according to meteorological characteristic parameters, and determining the blade risk threshold according to the clustering results of the wind turbine blades; Obtaining wind turbine blade corrosion monitoring data, determining blade corrosion risk based on the wind turbine blade corrosion monitoring data, and correcting blade risk thresholds based on the blade corrosion risk; The blade risk level is determined according to the meteorological characteristic parameters of the wind turbine blade and the corrected blade risk threshold, the blade action control parameters are determined according to the blade risk level, and action control is performed according to the blade action control parameters.

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