Fan hail suppression control method, device, equipment, medium and product

Abstract

The invention discloses a hail suppression control method, device and equipment for a fan, a medium and a product. The method comprises the following steps: in response to a hail suppression control request for a target fan, determining a target meteorological radar according to a wind power plant area to which the target fan belongs, and performing data acquisition based on the target meteorological radar to obtain a networking image of radar echoes; preprocessing and converting the networking image to obtain a target format file, and analyzing the target format file to obtain target meteorological data corresponding to the networking image; and according to the target meteorological data and a preset evaluation threshold value, determining whether hail influence exists at the point location of the target fan, and if yes, sending early warning information to a fan energy management platform to instruct the fan energy management platform to perform hail suppression control based on a preset hail suppression strategy. According to the technical scheme of the invention, automatic and accurate recognition and rapid early warning of hail can be realized, subjectivity and misjudgment of manual analysis are overcome, and economic loss caused by fan maintenance is reduced.

Description

Technical Field

[0001] This invention relates to the field of wind farm technology, and in particular to a wind turbine hail control method, device, equipment, medium and product. Background Technology

[0002] For wind farms, hail, as solid precipitation during severe convective weather, primarily damages the leading edge of wind turbine blades through the violent collision and impact between high-speed rotating blades and hail particles. The blade tip has the highest linear velocity, currently reaching 100 m / s or higher in mainstream models, making hail and other particle impacts the dominant factor in leading-edge erosion. The high local stress generated during impact far exceeds the adhesion and substrate strength of conventional coatings, leading to coating peeling, fiber breakage, and interlayer delamination. The damage intensifies significantly from the blade root to the tip. Existing blade leading-edge protection materials and processes are insufficient to effectively withstand such extreme impacts, easily causing aerodynamic performance degradation, power loss, and unplanned downtime, severely restricting the economic efficiency and reliability of wind farms throughout their entire lifecycle.

[0003] Therefore, how to solve the shortcomings of existing technologies in which wind turbines cannot respond to weather warnings in a timely manner, and to achieve automated, accurate identification and rapid warning of hail, overcoming the subjectivity and misjudgment of manual analysis, is an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method, device, equipment, medium, and product for controlling hail in wind turbines, so as to achieve automated, accurate identification and rapid early warning of hail, overcome the subjectivity and misjudgment of manual analysis, and reduce the economic losses caused by wind turbine maintenance.

[0005] According to one aspect of the present invention, a wind turbine hail prevention control method is provided, comprising:

[0006] In response to the hail control request for the target wind turbine, the target weather radar is determined according to the wind farm area to which the target wind turbine belongs, and data is collected based on the target weather radar to obtain a network image of the radar echo;

[0007] The network images are preprocessed and converted to obtain target format files, and the target meteorological data corresponding to the network images are obtained by parsing the target format files.

[0008] Based on the target meteorological data and preset assessment thresholds, determine whether the target wind turbine location is affected by hail. If so, send the warning information to the wind turbine energy management platform to instruct the platform to implement hail control based on the preset hail prevention strategy.

[0009] According to another aspect of the present invention, a wind turbine hail prevention control device is provided, comprising:

[0010] The determination module is used to respond to the hail control request of the target wind turbine, determine the target weather radar according to the wind farm area to which the target wind turbine belongs, and collect data based on the target weather radar to obtain the network image of the radar echo;

[0011] The parsing module is used to preprocess and convert the network images to obtain target format files, and to obtain the target meteorological data corresponding to the network images by parsing the target format files.

[0012] The control module is used to determine whether there is hail impact at the location of the target wind turbine based on the target meteorological data and preset evaluation thresholds. If so, it sends the warning information to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail prevention control based on the preset hail prevention strategy.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the wind turbine hail prevention control method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the wind turbine hail prevention control method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program that, when executed by a processor, implements the wind turbine hail prevention control method of any embodiment of the present invention.

[0019] The technical solution of this invention, in response to a hail control request for a target wind turbine, determines the target weather radar based on the wind farm area to which the target wind turbine belongs, and collects data based on the target weather radar to obtain a network image of the radar echo. The network image is preprocessed and converted to obtain a target format file, and the target meteorological data corresponding to the network image is obtained by parsing the target format file. Based on the target meteorological data and a preset evaluation threshold, it is determined whether the location of the target wind turbine is affected by hail. If so, a warning is sent to the wind turbine energy management platform to instruct the platform to implement hail control based on a preset hail prevention strategy. By analyzing the network image collected by the weather radar, hail risk can be accurately identified, and hail control can be implemented in a timely manner, thereby reducing economic losses caused by wind turbine maintenance.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a wind turbine hail prevention control method provided in an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of a wind turbine hail prevention control method provided in an embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a wind turbine hail prevention control device provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," "target," "candidate," and "alternative," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the invention described herein can be practiced in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant laws and regulations.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a wind turbine hail prevention control method provided in an embodiment of the present invention. This embodiment is applicable to situations where a cloud platform analyzes networked images collected by meteorological radar to accurately identify hail risks and promptly implement hail prevention control. This method can be executed by a wind turbine hail prevention control device, which can be implemented in hardware and / or software. This wind turbine hail prevention control device can be configured in electronic equipment, such as electronic equipment equipped with a cloud platform, and executed by the cloud platform. Figure 1 As shown, the hail prevention control method for this wind turbine includes:

[0030] S101. In response to the hail control request for the target wind turbine, determine the target weather radar according to the wind farm area to which the target wind turbine belongs, and collect data based on the target weather radar to obtain a network image of radar echoes.

[0031] Here, "target wind turbine" refers to a wind turbine in a wind farm area that is targeted for detection to determine if it may be affected by hail. "Hail control request" refers to a request to analyze the risk of hail at the location of the target wind turbine and to implement hail control measures if such a risk exists. "Target weather radar" refers to a radar system within the wind farm area containing the target wind turbine that can collect meteorological data about its location. "Network imagery" refers to a set of images obtained from the echoes after the target weather radar is activated. Network imagery includes at least one of the following: basic reflectivity image, liquid vertical cumulative water content image, echo top height image, and combined reflectivity image.

[0032] For example, radar data covering a designated wind farm area can be collected by cooperating with the meteorological bureau or by web crawling to obtain a network image of radar echoes.

[0033] For example, a target-based weather radar can perform periodic data acquisition at a frequency of 6 minutes. The acquired network image products include basic reflectivity images (unit: dBZ), liquid vertical cumulative water content images (unit: mm), echo top height images (unit: km), and combined reflectivity images (unit: dBZ).

[0034] S102. Preprocess and convert the network images to obtain target format files, and obtain the target meteorological data corresponding to the network images by parsing the target format files.

[0035] The preprocessing may include at least one of the following: file verification, filtering, and registration. Conversion processing refers to converting the network images into a target format file based on a preset conversion table. The target format file may be, for example, a file based on NetCDF (Network Common Data Form, a scientific data format for array-type data). The target meteorological data includes at least one of the following: the time corresponding to the network image, target latitude and longitude, basic reflectance, vertical cumulative water content, echo top height, and combined reflectance. The time corresponding to the network image refers to the time when the network images were acquired.

[0036] Optionally, the network image is preprocessed and converted to obtain a target format file, including: performing file verification, filtering, and registration processing on the network image to achieve preprocessing of the network image and obtain the target latitude and longitude; and converting the network image into a target format file based on the target format according to the network image color mark, the target latitude and longitude, and a preset conversion table.

[0037] The preset conversion table can be a conversion table that converts pixel colors into physical values ​​(such as dBZ, mm, km), which is a mapping table between RGB (Red Green Blue) values ​​and physical quantities.

[0038] Optionally, a transformation table can be created based on the color scales of the network image, and the network image can be converted to Net CDF format according to the transformation table and the coordinate transformation in the previous step. The file structure is as follows:

[0039] "Dimensions: time, lat, lon. Variables: base_reflectivity (basic reflectivity, dBZ), composite_reflectivity (composite reflectivity, dBZ), vil (liquid water content, mm), etop (echo top height, km)."

[0040] Optionally, after determining the target format file, keywords can be extracted from the target format file based on its structural characteristics to obtain the target meteorological data corresponding to the network image.

[0041] Optionally, file verification, filtering, and registration processing are performed on the networked images to preprocess them and obtain the target latitude and longitude. This includes: aligning the networked images based on their filenames and timestamps, verifying their integrity, and removing abnormal images to perform file verification; removing isolated pixel noise using a preset filtering algorithm to perform filtering; determining the geographical range of the networked images based on their geographical boundary information, and mapping the pixel coordinates to a latitude and longitude grid using an affine transformation to obtain the target latitude and longitude. The target latitude and longitude refer to the latitude and longitude corresponding to the networked images acquired by the target weather radar. The target latitude and longitude can be obtained by identifying the networked images.

[0042] Optionally, the filenames of each network image can be arranged and aligned in chronological order based on the timestamp of the network image, and the integrity of each network image can be verified based on a preset image recognition technology. If an incomplete network image is detected, the network image is deleted from the group of images.

[0043] Optionally, algorithms such as median filtering can be used to remove isolated pixel noise in order to filter the networked image.

[0044] Optionally, the geographic range in the image metadata can be extracted by using information such as the regional boundaries in the network image. Then, affine transformation can be used to map the pixel coordinates to a latitude and longitude grid. Finally, image processing libraries such as OpenCV can be used to remove background noise, color bars, map boundaries and other irrelevant labels, thereby extracting effective meteorological information.

[0045] S103. Based on the target meteorological data and the preset assessment threshold, determine whether there is hail impact at the location of the target wind turbine. If so, send the warning information to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy.

[0046] The preset assessment threshold refers to the pre-set thresholds for assessing whether the basic reflectance, vertical cumulative water content, echo top height, and combined reflectance in the target meteorological data are excessive. Hail prevention strategies include adding anti-hail coatings or strengthening blade materials, or controlling the turbine hub speed and blade angle using a pitch control system.

[0047] Optionally, hail protection can be achieved by adding anti-hail coatings or strengthening the blade materials, which can effectively improve the impact resistance of the target wind turbine.

[0048] Optionally, based on the target meteorological data and preset assessment thresholds, determine whether the location of the target wind turbine is affected by hail, including: determining the basic reflectivity, vertical cumulative liquid water content, echo top height, and combined reflectivity in the target meteorological data; and determining whether the location of the target wind turbine is affected by hail based on the correlation between the basic reflectivity, vertical cumulative liquid water content, echo top height, combined reflectivity, and preset assessment thresholds.

[0049] The evaluation thresholds for basic reflectivity and combined reflectivity can be set to 60 dBz, and the evaluation threshold for echo top height can be set to 3.5 g / m². 3 The threshold for assessing the vertical cumulative water content of liquid can be set to 50-60 kg / m².

[0050] Optionally, based on the target meteorological data and the preset evaluation threshold, it can be first determined whether there is hail impact at the target latitude and longitude corresponding to the network image. If so, it can be further determined whether the distance between the target latitude and longitude and the wind turbine latitude and longitude is less than the preset distance threshold (such as 1km). If so, it can be determined that there is hail impact at the location of the target wind turbine.

[0051] For example, based on the target meteorological data and the preset evaluation threshold, it can be first determined whether there is a strong echo area of ​​≥60dBZ in the combined reflectance and basic reflectance. If so, it can be further determined whether the vertical cumulative liquid water content has increased abnormally. If it is greater than the evaluation threshold corresponding to the vertical cumulative liquid water content, it can be further determined by combining the echo top height (>10 km) and vertical profile to determine whether the cloud thickness and low temperature area meet the conditions for hail growth. If so, it can be determined that there is hail influence at the target latitude and longitude position corresponding to the network image.

[0052] Optionally, after determining whether the target wind turbine location is affected by hail, the method further includes: if no hail is detected at the target wind turbine location, the hail path is extrapolated using the optical flow method to determine whether hail will affect the location within a preset future time period; if hail is determined to affect the location within a preset future time period, the warning information and the hail impact time are sent to the wind turbine energy management platform to instruct the wind turbine energy management platform to perform hail control based on the preset hail prevention strategy.

[0053] For example, if the target weather radar collects a set of network images every six minutes, it can continue to collect images for 30 minutes when it detects that there is no hail impact at the location of the target wind turbine. By combining the five sets of network images in the first 30 minutes with the optical flow method, it can predict whether there is a risk of hail at the relevant wind turbine location in the next 30 minutes.

[0054] It's important to note that optical flow is an extrapolation method that estimates the movement of each pixel in an image—how pixels move from one frame to the next. For example, given five products from the first 30 minutes, we can compare the intensity and extent of the same hailstone (i.e., pixels meeting a threshold) within each product, then calculate its movement speed and intensity changes. Assuming the hailstones will change with the same speed and intensity over the next 30 minutes, we can obtain a predicted path. Comparing this predicted path with the latitude and longitude of the target wind turbine determines the presence of hail risk.

[0055] Optionally, the warning information is sent to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy, including: sending the warning information to the wind turbine energy management platform through the communication server corresponding to the wind farm area to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy.

[0056] For example, information issued by the cloud platform of the present invention can be transmitted through a preset reverse isolation device and an intranet firewall to ensure data security and real-time control.

[0057] Optionally, the cloud platform can transmit early warning information to the wind farm's three-zone communication server via the internet. During transmission, encryption technology is used to ensure data security and prevent unauthorized external access. The wind farm's three-zone communication server receives and parses the instructions, then transmits them to the tiered energy management platform through the three-zone reverse isolation device and internal network firewall.

[0058] Optionally, if the hail prevention strategy involves controlling the turbine hub speed and blade angle through a pitch system, then the pitch system can be used to adjust the turbine's power and energy utilization in real time based on changes in the turbine hub speed and blade angle, thereby optimizing the turbine's operating efficiency and achieving hail prevention.

[0059] Optionally, after hail control measures are implemented, the status of the target wind turbine can be monitored. If no hail warning is detected or a no-hail command is manually issued by relevant personnel, the cloud platform can issue a recovery command to restore the target wind turbine to its normal operating state. During the recovery process, all hail control operation steps and results can be automatically recorded for subsequent analysis and optimization. By monitoring the recovery process, the reliability and response speed of the cloud platform can be further improved, ensuring that the target wind turbine can quickly return to normal operation after the hail risk has passed.

[0060] The technical solution of this invention, in response to a hail control request for a target wind turbine, determines the target weather radar based on the wind farm area to which the target wind turbine belongs, and collects data based on the target weather radar to obtain a network image of the radar echo. The network image is preprocessed and converted to obtain a target format file, and the target meteorological data corresponding to the network image is obtained by parsing the target format file. Based on the target meteorological data and a preset evaluation threshold, it is determined whether the location of the target wind turbine is affected by hail. If so, a warning is sent to the wind turbine energy management platform to instruct the platform to implement hail control based on a preset hail prevention strategy. By analyzing the network image collected by the weather radar, hail risk can be accurately identified, and hail control can be implemented in a timely manner, thereby reducing economic losses caused by wind turbine maintenance.

[0061] Example 2

[0062] Figure 2 This is a flowchart of a wind turbine hail prevention control method provided in an embodiment of the present invention; based on the above embodiments, this embodiment provides a preferred example of analyzing networked images collected by meteorological radar to accurately identify hail risk and promptly implement hail prevention control, such as... Figure 2 As shown, the method includes the following steps:

[0063] Optionally, the cloud platform can acquire meteorological lidar data through the data acquisition module to obtain networked images, and then perform file verification, filtering and registration processing on the image product data (i.e., networked images) through the data preprocessing module, and then perform data conversion, that is, output NC format files (i.e., Net CDF format target format files) based on color marks, pixel conversion values ​​and latitude and longitude.

[0064] Optionally, the hail identification module can determine whether the wind turbine location is affected by hail based on thresholds such as combined reflectance and VIL (Vertical Integrated Liquid Water) density, combined with the latitude and longitude information of the wind turbine in the wind farm.

[0065] Optionally, if it is determined that the wind turbine location is affected by hail, the cloud platform can issue an early warning and predict the impact time. If it is determined that the wind turbine location is not affected by hail, the hail path can be extrapolated using the optical flow method to determine whether the hail will affect the wind turbine at a preset time in the future. If not, the process ends. If so, the cloud platform can issue an early warning and predict the impact time.

[0066] Optionally, after the cloud platform issues an early warning and the estimated impact time, the information reaches the site's communication server, is transmitted to the energy management platform, and commands are issued through the energy management platform for hail control, such as adjusting the wind turbine hub speed and blade angle. Through cloud commands, the wind farm's energy management platform, and the pitch control system, the wind turbine hub speed and blade angle can be automatically adjusted, achieving effective control over blade damage, avoiding damage to the blades under hail impact, and reducing equipment losses.

[0067] It should be noted that this invention achieves automated hail identification by accessing meteorological radar echo data and monitoring the reflectivity data of the radar echoes, eliminating the need for manual intervention. This overcomes the problems of strong subjectivity and high misidentification rate in existing technologies, improving the accuracy and reliability of hail identification. Simultaneously, by utilizing the real-time monitoring capabilities of meteorological radar, it can promptly capture changes in hail weather conditions, enabling rapid early warning of hail disasters and effectively improving the timeliness of weather warnings. In summary, this invention simplifies hail prevention operations, improves work efficiency, and reduces manual workload by using hail warnings based on meteorological radar echo data combined with automated operation of wind turbine control programs.

[0068] Example 3

[0069] Figure 3 This is a structural block diagram of a wind turbine hail prevention control device provided in an embodiment of the present invention. This embodiment is applicable to situations where a cloud platform analyzes networked images collected by meteorological radar to accurately identify hail risks and promptly implement hail prevention control. The wind turbine hail prevention control device provided by the present invention can execute the wind turbine hail prevention control method provided in any embodiment of the present invention, possessing the corresponding functional modules and beneficial effects of the execution method. This wind turbine hail prevention control device can be implemented in hardware and / or software and configured in electronic devices with wind turbine hail prevention control functions, such as electronic devices configured with a cloud platform, and executed by the cloud platform. Figure 3 As shown, the wind turbine hail prevention control device may specifically include:

[0070] The determination module 301 is used to respond to the hail control request of the target wind turbine, determine the target weather radar according to the wind farm area to which the target wind turbine belongs, and collect data based on the target weather radar to obtain a network image of radar echoes.

[0071] The parsing module 302 is used to preprocess and convert the network image to obtain the target format file, and obtain the target meteorological data corresponding to the network image by parsing the target format file;

[0072] The control module 303 is used to determine whether there is hail impact at the location of the target wind turbine based on the target meteorological data and preset evaluation threshold. If so, it sends the warning information to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail prevention control based on the preset hail prevention strategy.

[0073] The technical solution of this invention, in response to a hail control request for a target wind turbine, determines the target weather radar based on the wind farm area to which the target wind turbine belongs, and collects data based on the target weather radar to obtain a network image of the radar echo. The network image is preprocessed and converted to obtain a target format file, and the target meteorological data corresponding to the network image is obtained by parsing the target format file. Based on the target meteorological data and a preset evaluation threshold, it is determined whether the location of the target wind turbine is affected by hail. If so, a warning is sent to the wind turbine energy management platform to instruct the platform to implement hail control based on a preset hail prevention strategy. By analyzing the network image collected by the weather radar, hail risk can be accurately identified, and hail control can be implemented in a timely manner, thereby reducing economic losses caused by wind turbine maintenance.

[0074] Furthermore, the target meteorological data includes at least one of the following: time, latitude and longitude, basic reflectance, vertical cumulative water content of liquid, echo top height, and combined reflectance corresponding to the network image; the analysis module 302 is specifically used for:

[0075] File verification, filtering, and registration are performed on the networked images to preprocess them and obtain the target latitude and longitude.

[0076] Based on the color scale of the network image, the target latitude and longitude, and the preset conversion table, the network image is converted into a target format file based on the target format.

[0077] Furthermore, the parsing module 302 is also used for:

[0078] Based on the filenames and timestamps of the network images, each network image is aligned and its integrity is verified. Abnormal images are removed to perform file verification processing on the network images.

[0079] Isolated pixel noise is removed based on a preset filtering algorithm to filter the network image;

[0080] Based on the geographical boundary information in the network image, the geographical range of the network image is determined, and the pixel coordinates are mapped to the latitude and longitude grid based on the affine transformation method to obtain the target latitude and longitude.

[0081] Furthermore, the control module 303 is specifically used for:

[0082] Determine the basic reflectance, vertical cumulative liquid water content, echo top height, and combined reflectance in the target meteorological data;

[0083] Based on the correlation between basic reflectivity, liquid vertical cumulative water content, echo top height, combined reflectivity, and preset evaluation threshold, it is determined whether the location of the target wind turbine is affected by hail.

[0084] Furthermore, the above-mentioned device is also used for:

[0085] If no hail is detected at the location of the target wind turbine, the hail path is extrapolated using the optical flow method to determine whether there will be hail impact in the future preset time period.

[0086] If it is determined that hail will affect the area within a preset time period in the future, the warning information and the time of hail impact will be sent to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy.

[0087] Furthermore, the hail prevention strategy is to prevent hail by adding anti-hail coatings or strengthening the blade material, or by controlling the wind turbine hub speed and blade angle through a pitch control system.

[0088] Control module 303 is also used for:

[0089] The warning information is sent to the wind turbine energy management platform through the communication server corresponding to the wind farm area, so as to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy.

[0090] Example 4

[0091] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0092] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory 12 or a random access memory 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from storage unit 18 into the random access memory 13. The random access memory 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, read-only memory 12, and random access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0093] Multiple components in electronic device 10 are connected to input / output 15, including: input unit 16, such as a keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as a disk, optical disk, etc.; and communication unit 19, such as a network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0094] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as wind turbine hail control methods.

[0095] In some embodiments, the wind turbine hail prevention control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via read-only memory 12 and / or communication unit 19. When the computer program is loaded into random access memory 13 and executed by processor 11, one or more steps of the wind turbine hail prevention control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the wind turbine hail prevention control method by any other suitable means (e.g., by means of firmware).

[0096] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), system-on-a-chip (SoCs), complex programmable logic devices (PLCs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube or liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (e.g., voice input, speech input, or tactile input).

[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0101] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system to address the shortcomings of traditional physical hosts and virtual reality services, such as high management difficulty and weak business scalability.

[0102] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the wind turbine hail prevention control method of any embodiment of the present invention.

[0103] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling hail prevention in wind turbines, characterized in that, include: In response to the hail control request for the target wind turbine, the target weather radar is determined according to the wind farm area to which the target wind turbine belongs, and data is collected based on the target weather radar to obtain a network image of the radar echo; The network images are preprocessed and converted to obtain target format files, and the target meteorological data corresponding to the network images are obtained by parsing the target format files. Based on the target meteorological data and preset assessment thresholds, determine whether the target wind turbine location is affected by hail. If so, send the warning information to the wind turbine energy management platform to instruct the platform to implement hail control based on the preset hail prevention strategy.

2. The method according to claim 1, characterized in that, in, The target meteorological data includes at least one of the following: time, latitude and longitude, basic reflectance, vertical cumulative liquid water content, echo top height, and combined reflectance corresponding to the network image; The preprocessing and conversion of the network image to obtain the target format file includes: File verification, filtering, and registration are performed on the networked images to preprocess them and obtain the target latitude and longitude. Based on the color codes of the networked image, the target latitude and longitude, and the preset conversion table, the networked image is converted into a target format file based on the target format.

3. The method according to claim 2, characterized in that, The networked images undergo file verification, filtering, and registration to preprocess them and obtain the target latitude and longitude, including: Based on the filenames and timestamps of the network images, each network image is aligned and its integrity is verified. Abnormal images are removed to perform file verification processing on the network images. Isolated pixel noise is removed based on a preset filtering algorithm to filter the network image; Based on the geographical boundary information in the network image, the geographical range of the network image is determined, and the pixel coordinates are mapped to the latitude and longitude grid based on the affine transformation method to obtain the target latitude and longitude.

4. The method according to claim 1, characterized in that, Based on the target meteorological data and preset assessment thresholds, determine whether the location of the target wind turbine is affected by hail, including: Determine the basic reflectance, vertical cumulative liquid water content, echo top height, and combined reflectance in the target meteorological data; Based on the correlation between basic reflectivity, liquid vertical cumulative water content, echo top height, combined reflectivity, and preset evaluation threshold, it is determined whether the location of the target wind turbine is affected by hail.

5. The method according to claim 1, characterized in that, After determining whether the target wind turbine location is affected by hail, the following steps are also included: If no hail is detected at the location of the target wind turbine, the hail path is extrapolated using the optical flow method to determine whether there will be hail impact in the future preset time period. If it is determined that hail will affect the area within a preset time period in the future, the warning information and the time of hail impact will be sent to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy.

6. The method according to claim 1, characterized in that, in, The hail prevention strategy is to prevent hail by adding anti-hail coatings or strengthening the blade material, or by controlling the wind turbine hub speed and blade angle through a pitch control system. Accordingly, the early warning information is sent to the wind turbine energy management platform to instruct the platform to implement hail control based on a preset hail prevention strategy, including: The warning information is sent to the wind turbine energy management platform through the communication server corresponding to the wind farm area, so as to instruct the wind turbine energy management platform to carry out hail control based on the preset hail prevention strategy.

7. A wind turbine hail prevention control device, characterized in that, include: The determination module is used to respond to the hail control request of the target wind turbine, determine the target weather radar according to the wind farm area to which the target wind turbine belongs, and collect data based on the target weather radar to obtain the network image of radar echo; The parsing module is used to preprocess and convert the network images to obtain target format files, and to obtain the target meteorological data corresponding to the network images by parsing the target format files. The control module is used to determine whether there is hail impact at the location of the target wind turbine based on the target meteorological data and preset evaluation thresholds. If so, it sends the warning information to the wind turbine energy management platform to instruct the wind turbine energy management platform to carry out hail prevention control based on the preset hail prevention strategy.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the wind turbine hail control method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the wind turbine hail control method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the wind turbine hail prevention control method according to any one of claims 1-6.

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