Fan blade infrared monitoring system and method based on unmanned aerial vehicle inspection

Abstract

The invention discloses a fan blade infrared monitoring system and method based on unmanned aerial vehicle inspection, relates to the technical field of image recognition, and solves the problem that in the prior art, an infrared image processing mode of a fan blade is insufficient to adapt to blade temperature changes caused by various environments. According to the method, the infrared thermal images of the fan blades are obtained through the infrared camera carried by the unmanned aerial vehicle, the highest temperature and the lowest temperature of each infrared thermal image are calculated through temperature inversion, then the temperature difference of the fan blades is obtained, and in addition, the multiple infrared thermal images of the fan blade numbers in the same area are obtained; and calculating the highest temperature and the lowest temperature of each infrared thermal image, constructing a temperature analysis set and calculating an overall standard deviation so as to judge the dispersion degree of the temperature in the region, and finally establishing a health evaluation coefficient based on factors such as the temperature difference and the dispersion condition so as to provide quantitative data reference for managers and decision makers. And subsequent management and decision-making work are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to a wind turbine blade infrared monitoring system and method based on unmanned aerial vehicle inspection. Background Art

[0002] Drones play a vital role in wind turbine blade inspection. As wind turbine blades are often exposed to extreme weather and corrosive environments, they are the most vulnerable part of the wind power generation system. Therefore, regular inspection and maintenance of blades are essential for the stable operation of wind farms. The use of drones has greatly improved the efficiency of blade inspection and reduced the risk of personnel working at heights. Equipped with high-resolution cameras and advanced sensors, they can conduct comprehensive inspections of blades at different distances and viewing angles, thereby improving the accuracy and automation of inspection work.

[0003] At present, the processing of infrared images of wind turbine blades obtained by UAVs is mostly based on infrared images to determine whether the temperature inside the UAV is abnormal, such as identifying the part with the highest temperature and checking whether the highest temperature exceeds the set threshold, and then judging whether there is abnormal temperature rise in the wind turbine blades. Although this method can identify some abnormal temperature rise phenomena, it does not analyze the temperature uniformity of the wind turbine blades themselves, nor does it combine the temperature of the wind turbine blades with the geographical environment, which are important factors affecting the temperature of the wind turbine blades, and thus affect the accuracy of the judgment. In other words, the current processing method of infrared images of wind turbine blades does not combine the environment or the overall temperature of the wind turbine blades for analysis, and is not enough to adapt to the problem of blade temperature changes caused by various environments.

[0004] In view of this, there is a need for an infrared monitoring system and method for wind turbine blades based on drone inspection. Summary of the invention

[0005] In view of the problem that the infrared image processing method of fan blades in the prior art is not sufficient to adapt to the temperature changes of blades caused by various environments, the present invention provides a fan blade infrared monitoring system and method based on drone inspection, which can obtain infrared thermal imaging of fan blades based on drones, and then obtain the temperature difference of fan blades based on infrared images. In addition, different fan blades in the same area are analyzed as a whole, the standard deviation of blade temperature in the area is obtained, and the temperature dispersion in the area is obtained. Finally, a health evaluation coefficient is established based on factors such as temperature difference and dispersion, which provides quantitative data reference for managers and decision makers, facilitating subsequent management and decision-making. The specific technical scheme is as follows:

[0006] A wind turbine blade infrared monitoring system based on drone inspection, comprising:

[0007] An image acquisition unit, comprising a drone, an infrared camera and an image storage module carried on the drone, wherein the infrared camera is used to acquire a plurality of infrared thermal images of the wind turbine blades and store them in the image storage module;

[0008] An image recognition unit, connected to the image acquisition unit, comprising a temperature difference recognition module, wherein the temperature difference recognition module is used to recognize the temperature difference in infrared thermal imaging based on temperature inversion;

[0009] The evaluation and management unit is used to evaluate the health factor of the fan blades. The calculation formula is as follows:

[0010]

[0011] Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, Year is the service life, N t is the number of failures within the set period, δ 1 , δ 2 , δ 4 They are the weight coefficients of temperature difference, standard deviation, service life and number of failures respectively.

[0012] Preferably, the temperature recognition module obtains the temperature difference in the following manner:

[0013] Acquire infrared thermal imaging in an image storage module;

[0014] Obtain a mapping table or conversion formula between grayscale value and temperature provided by the manufacturer of the infrared camera;

[0015] Based on the conversion formula or mapping table, obtain the temperature set of several pictures with the same fan blade number at the same timestamp, and obtain the maximum temperature by finding the maximum value: T max =max(T); Similarly, the lowest temperature is obtained by finding the minimum value: T min =min(T);

[0016] The corresponding timestamp-fan blade number-temperature difference is obtained, where the temperature difference is calculated as follows:

[0017] ΔT=T max -T min

[0018] Where ΔT is the temperature difference, T max is the maximum temperature, T min is the lowest temperature.

[0019] Preferably, the image recognition unit also includes a uniformity recognition module, which is used to obtain several infrared thermal images of each wind turbine blade number in the same area, and then uniformly calculate the maximum temperature and the minimum temperature of each infrared thermal image, construct a temperature analysis set, and calculate the overall standard deviation based on the temperature analysis set to judge the degree of discreteness of the temperature in the area.

[0020] Preferably, the influencing factor of the health coefficient of the fan blade also includes a standard deviation reflecting the degree of dispersion, and the calculation formula is as follows:

[0021]

[0022] Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, σ is the standard deviation of the area where the blade is located, Year is the service life, and N is the t is the number of failures within the set period, δ 1 , δ 2 , δ 3 , δ 4 They are the weight coefficients of temperature difference, standard deviation, service life and number of failures respectively.

[0023] Preferably, the uniformity recognition module performs the following operations:

[0024] Divide different areas based on geographical locations and obtain all infrared thermal imaging data in the same area;

[0025] Obtain a mapping table or conversion formula between grayscale value and temperature provided by the manufacturer of the infrared camera;

[0026] Based on the conversion formula or mapping table, obtain the temperature set of several pictures of the same fan blade number i at the same timestamp, and obtain the maximum temperature by finding the maximum value: T max (i) = max(T); Similarly, the lowest temperature is obtained by finding the minimum value: T min (i) = min(T);

[0027] Calculate the population standard deviation using the following formula:

[0028]

[0029] Where T(i) is the set of the highest temperature and the lowest temperature of the fan blade number i, that is, T(i) = [T max (i), T min (i)], N is the total number of fan blade numbers in the region, and μ is the overall average, i.e., the average of the maximum and minimum temperatures of all fan blades in the region.

[0030] Preferably, the image recognition unit further comprises an alarm module, wherein a temperature difference threshold θ is set in the alarm module. 温度差 And the alarm threshold value of the standard deviation θ 标准差 , by collecting infrared thermal images of the motor blades in real time, we try to obtain the temperature difference of the motor blades and compare it with the threshold value θ of the temperature difference in real time. 温度差 When the temperature difference of the motor blades is greater than the set threshold, the first alarm signal is issued; when the temperature standard deviation in the area is greater than the temperature difference threshold θ 温度差 When the second alarm signal is issued.

[0031] Preferably, it also includes a terminal feedback unit, which is used to receive the first alarm signal and the second alarm signal, and go to the site to perform troubleshooting and corresponding processing based on different alarm signals, and feedback the troubleshooting situation and response processing situation to the evaluation and management unit; the troubleshooting situation is to confirm that the alarm signal is correct and to confirm that the alarm signal is wrong, and the response processing situation is that the fault has been processed and the fault has not been processed.

[0032] A method for infrared monitoring of wind turbine blades based on drone inspection includes the following steps:

[0033] Through the infrared camera carried by the drone, several infrared thermal images of the wind turbine blades are obtained and stored in the image storage module. The data storage method is: timestamp-geographic location-wind turbine blade number-several images of the wind turbine blade under the current number corresponding to the timestamp. Each infrared thermal image contains the corresponding timestamp label and wind turbine blade number label.

[0034] Through temperature inversion, the highest and lowest temperatures of each infrared thermal image are calculated, and then the highest and lowest temperatures at the corresponding timestamp and fan blade number are obtained, and finally the temperature difference of fan blade number i at the corresponding timestamp is obtained;

[0035] Obtain several infrared thermal images of each fan blade number in the same area, and then uniformly calculate the highest and lowest temperatures of each infrared thermal image, build a temperature analysis set, and calculate the overall standard deviation based on the temperature analysis set to judge the degree of temperature dispersion in the area;

[0036] Set the temperature difference threshold θ 温度差 And the alarm threshold value of the standard deviation θ 标准差 , by collecting infrared thermal images of the motor blades in real time, we try to obtain the temperature difference of the motor blades and compare it with the threshold value θ of the temperature difference in real time. 温度差 When the temperature difference of the motor blades is greater than the set threshold, the first alarm signal is issued; when the temperature standard deviation in the area is greater than the temperature difference threshold θ 温度差 When the second alarm signal is issued;

[0037] Receive the first alarm signal and the second alarm signal, and go to the site to perform fault troubleshooting and corresponding processing based on different alarm signals, and feedback the fault troubleshooting situation and response processing situation to the evaluation and management unit, wherein the fault troubleshooting situation is to confirm that the alarm signal is correct and to confirm that the alarm signal is wrong, and the response processing situation is to confirm that the fault has been processed and the fault has not been processed;

[0038] Calculate the health factor of the fan blades. The calculation formula for the health factor is as follows:

[0039]

[0040] Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, σ is the standard deviation of the area where the blade is located, Year is the service life, Nt is the number of failures within the set period, δ 1 , δ 2 , δ 3 , δ 4 They are the weight coefficients of temperature difference, standard deviation, service life and number of failures respectively.

[0041] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the infrared monitoring system for wind turbine blades based on drone inspection as described above.

[0042] A processor is used to run a program, wherein when the program is run, the infrared monitoring system for wind turbine blades based on drone inspection as described above is executed.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention uses an infrared camera carried by an unmanned aerial vehicle to obtain and store several infrared thermal images of fan blades, and calculates the maximum temperature and the minimum temperature of each infrared thermal image through temperature inversion; obtains the temperature difference of fan blade number i at the corresponding timestamp, and further obtains several infrared thermal images of each fan blade number in the same area, and then uniformly calculates the maximum temperature and the minimum temperature of each infrared thermal image, constructs a temperature analysis set, and calculates the overall standard deviation based on the temperature analysis set to judge the degree of temperature dispersion in the area, and finally establishes a health evaluation coefficient based on factors such as temperature difference and dispersion, so as to provide quantitative data reference for managers and decision makers, and facilitate subsequent management and decision-making work. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0046] Figure 1 It is a schematic diagram of the overall framework of the system of the present invention;

[0047] Figure 2 It is the operation flow chart of the temperature difference identification module of the present invention;

[0048] Figure 3 This is a flow chart of the operation of the uniformity recognition module of the present invention;

[0049] Figure 4 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0052] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0053] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] In one embodiment of the present invention, a wind turbine blade infrared monitoring system based on drone inspection is provided. Figure 1 As shown, including:

[0055] 1. Image acquisition unit

[0056] The image acquisition unit includes a drone, an infrared camera and an image storage module mounted on the drone. The infrared camera is used to acquire several infrared thermal images of the fan blades and store them in the image storage module. Moreover, the infrared thermal images are used to subsequently monitor whether the fan blades have internal heating or other damage. The data storage method in the image storage module is timestamp-geographic location-fan blade number-several images of the fan blade under the current number corresponding to the timestamp. In other words, each infrared thermal image contains a corresponding timestamp label and a fan blade number label.

[0057] 2. Image Recognition Unit

[0058] The image recognition unit is connected to the image acquisition unit to acquire the image stored in the storage module, and includes a temperature difference recognition module, a uniformity recognition module and an alarm module. The following further describes these three modules.

[0059] The temperature difference recognition module calculates the highest and lowest temperatures of each infrared thermal image through temperature inversion, and then obtains the highest and lowest temperatures at the corresponding timestamp and fan blade number, and finally obtains the temperature difference of fan blade number i at the corresponding timestamp. Figure 2 As shown, the temperature difference recognition module performs the following operations:

[0060] Step 1: Acquire infrared thermal imaging in the image storage module;

[0061] Step 2: Obtain a mapping table or conversion formula between grayscale value and temperature provided by the manufacturer of the infrared camera. The conversion formula includes a linear relationship, as shown below:

[0062] T=a·G+b

[0063] Where T is temperature (degrees Celsius), G is grayscale value (0-255), and a and b are calibration parameters;

[0064] Step 3: Based on the conversion formula or mapping table, obtain the temperature set of several pictures with the same fan blade number at the same timestamp, and obtain the maximum temperature by finding the maximum value: T max =max(T); Similarly, the lowest temperature is obtained by finding the minimum value: T min =min(T);

[0065] Step 4: Return the corresponding timestamp-fan blade number-temperature difference, where the temperature difference is calculated as follows:

[0066] ΔT=T max -T min

[0067] Where ΔT is the temperature difference, Tmax is the maximum temperature, T min is the lowest temperature.

[0068] Since different geographical locations will bring different environmental temperatures and humidity, temperature differences between morning and evening, altitudes and other geographical environments, the temperature changes of the fans will show strong correlation characteristics with the geographical locations. Therefore, when analyzing the temperature changes of the fans, if the fan data of all geographical locations are integrated together for analysis, the data cannot be measured under one standard due to different geographical environments. Therefore, dividing the geographical locations and classifying them based on different geographical locations will make the alarm analysis more accurate.

[0069] The uniformity recognition module is used to obtain several infrared thermal images of each fan blade number in the same area (based on geographical location), and then uniformly calculate the highest and lowest temperatures of each infrared thermal image, build a temperature analysis set, and calculate the overall standard deviation based on the temperature analysis set to judge the degree of temperature dispersion in the area. Figure 3 As shown, the uniformity recognition module performs the following operations:

[0070] Step 01: Divide different areas based on geographical locations and obtain all infrared thermal imaging data in the same area;

[0071] Step 02: Obtain the grayscale value and temperature mapping table or conversion formula provided by the manufacturer of the infrared camera. The conversion formula includes a linear relationship, as shown below:

[0072] T=a·G+b

[0073] Where T is temperature (degrees Celsius), G is grayscale value (0-255), and a and b are calibration parameters;

[0074] Step 03: Based on the conversion formula or mapping table, obtain the temperature set of several images of the same fan blade number i at the same timestamp, and obtain the maximum temperature by finding the maximum value: T max (i) = max(T); Similarly, the lowest temperature is obtained by finding the minimum value: T min (i) = min(T);

[0075] Step 04: Calculate the population standard deviation using the following formula:

[0076]

[0077] Where T(i) is the set of the highest temperature and the lowest temperature of the fan blade number i, that is, T(i) = [T max (i), T min(i)], N is the total number of fan blade numbers in the region, and μ is the overall average, i.e., the average of the maximum and minimum temperatures of all fan blades in the region.

[0078] The alarm module is provided with a temperature difference threshold θ 温度差 And the alarm threshold value of the standard deviation θ 标准差 , by collecting infrared thermal images of the motor blades in real time, we try to obtain the temperature difference of the motor blades and compare it with the threshold value θ of the temperature difference in real time. 温度差 When the temperature difference of the motor blades is greater than the set threshold, the first alarm signal is issued. In addition, by collecting infrared thermal images of the motor blades in the area and calculating the overall standard deviation of the highest and lowest temperatures of the motor blades in the area, the temperature dispersion in the area is analyzed, and then compared with the set value. When the temperature standard deviation in the area is greater than the temperature difference threshold θ 温度差 The first alarm signal and the second alarm signal carry different information. The first alarm signal carries information about the abnormal temperature of the corresponding clicked blade, and the second alarm signal carries information about the abnormal situation of the temperature dispersion degree in the area.

[0079] In addition, in a preferred embodiment of the present invention, the specific calculation of temperature can also use Planck formula to convert the sensor radiation intensity value corresponding to the image pixel into the corresponding brightness temperature value. The formula is as follows:

[0080]

[0081] Among them, T sensor is the brightness temperature value (unit K), DN is the spectral radiation value obtained after image preprocessing, K 1 and K 2 Is a constant.

[0082] 3. Terminal processing and feedback unit

[0083] The terminal processing unit is used to receive the first alarm signal and the second alarm signal, and go to the site to perform troubleshooting and corresponding processing based on different alarm signals, and feed back the troubleshooting situation and response processing situation to the evaluation and management unit.

[0084] The fault troubleshooting conditions include confirming that the alarm signal is correct and confirming that the alarm signal is wrong, and the response processing conditions include whether the fault has been processed and whether the fault has not been processed.

[0085] IV. Evaluation and Management Unit

[0086] The evaluation and management unit is used for the health factor of the fan blades. The calculation formula of the health factor is as follows:

[0087]

[0088] Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, σ is the standard deviation of the area where the blade is located, Year is the service life, Nt is the number of failures within the set period, δ 1 , δ 2 , δ 3 , δ 4 They are the weight coefficients of temperature difference, standard deviation, service life and number of failures respectively.

[0089] In one embodiment of the present invention, a method for infrared monitoring of wind turbine blades based on drone inspection is provided. Figure 4 As shown, the following steps are included:

[0090] S1: Using an infrared camera mounted on a drone, obtain several infrared thermal images of the wind turbine blades and store them in an image storage module. The data storage method is: timestamp-geographic location-wind turbine blade number-several images of the wind turbine blades under the current number corresponding to the timestamp. Each infrared thermal image contains a corresponding timestamp label and a wind turbine blade number label.

[0091] S2: Calculate the highest and lowest temperatures of each infrared thermal image through temperature inversion, and then obtain the highest and lowest temperatures at the corresponding timestamp and fan blade number, and finally obtain the temperature difference of the fan blade number i at the corresponding timestamp;

[0092] S3: Obtain several infrared thermal images of each fan blade number in the same area, and then uniformly calculate the maximum temperature and the minimum temperature of each infrared thermal image, construct a temperature analysis set, and calculate the overall standard deviation based on the temperature analysis set to judge the degree of temperature dispersion in the area;

[0093] S4: Set the temperature difference threshold θ 温度差 And the alarm threshold value of the standard deviation θ 标准差 , by collecting infrared thermal images of the motor blades in real time, we try to obtain the temperature difference of the motor blades and compare it with the threshold value θ of the temperature difference in real time. 温度差 When the temperature difference of the motor blades is greater than the set threshold, the first alarm signal is issued; when the temperature standard deviation in the area is greater than the temperature difference threshold θ 温度差 When the second alarm signal is issued;

[0094] S5: receiving the first alarm signal and the second alarm signal, and going to the site to perform troubleshooting and corresponding processing based on different alarm signals, and feeding back the troubleshooting situation and the response processing situation, wherein the troubleshooting situation is confirmation that the alarm signal is correct and confirmation that the alarm signal is wrong, and the response processing situation is whether the fault has been processed and whether the fault has not been processed;

[0095] S6: Calculate the health factor of the fan blades. The calculation formula of the health factor is as follows:

[0096]

[0097] Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, σ is the standard deviation of the area where the blade is located, Year is the service life, Nt is the number of failures within the set period, δ 1 , δ 2 , δ 3 , δ 4 are the weight coefficients of temperature difference, standard deviation, service life and number of failures.

[0098] In summary, the present invention uses an infrared camera carried by a drone to obtain and store several infrared thermal images of wind turbine blades, and calculates the maximum temperature and the minimum temperature of each infrared thermal image through temperature inversion; obtains the temperature difference of wind turbine blade number i at the corresponding timestamp, and in addition, obtains several infrared thermal images of each wind turbine blade number in the same area, and then uniformly calculates the maximum temperature and the minimum temperature of each infrared thermal image, constructs a temperature analysis set, and calculates the overall standard deviation based on the temperature analysis set to judge the degree of temperature dispersion in the area, and finally establishes a health evaluation coefficient based on factors such as temperature difference and dispersion, to provide quantitative data reference for managers and decision makers, and facilitate subsequent management and decision-making.

[0099] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0100] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0101] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A wind turbine blade infrared monitoring system based on drone inspection, characterized in that: include: An image acquisition unit, comprising a drone, an infrared camera and an image storage module carried on the drone, wherein the infrared camera is used to acquire a plurality of infrared thermal images of the wind turbine blades and store them in the image storage module; An image recognition unit, connected to the image acquisition unit, comprising a temperature difference recognition module, wherein the temperature difference recognition module is used to recognize the temperature difference in infrared thermal imaging based on temperature inversion; The evaluation and management unit is used to evaluate the health factor of the fan blades. The calculation formula is as follows: Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, Year is the service life, N t is the number of failures within the set period, and δ1, δ2, and δ4 are the weight coefficients of temperature difference, standard deviation, service life, and number of failures, respectively.

2. The infrared monitoring system for wind turbine blades based on drone inspection according to claim 1 is characterized in that: The temperature recognition module obtains the temperature difference in the following way: Acquire infrared thermal imaging in an image storage module; Obtain a mapping table or conversion formula between grayscale value and temperature provided by the manufacturer of the infrared camera; Based on the conversion formula or mapping table, obtain the temperature set of several pictures with the same fan blade number at the same timestamp, and obtain the maximum temperature by finding the maximum value: T max =max(T); Similarly, the lowest temperature is obtained by finding the minimum value: T min =min(T); The corresponding timestamp-fan blade number-temperature difference is obtained, where the temperature difference is calculated as follows: ΔT=T max -T min Where ΔT is the temperature difference, T max is the maximum temperature, T min is the lowest temperature.

3. The infrared monitoring system for wind turbine blades based on drone inspection according to claim 1 is characterized in that: The image recognition unit also includes a uniformity recognition module, which is used to obtain several infrared thermal images of each fan blade number in the same area, and then uniformly calculate the maximum temperature and the minimum temperature of each infrared thermal image, construct a temperature analysis set, and calculate the overall standard deviation based on the temperature analysis set to judge the degree of discreteness of the temperature in the area.

4. The infrared monitoring system for wind turbine blades based on drone inspection according to claim 3 is characterized in that: The influencing factors of the health coefficient of the fan blade also include the standard deviation reflecting the degree of dispersion, and the calculation formula is as follows: Where Z is the health factor, ΔT is the temperature difference of the fan blade at the current moment, σ is the standard deviation of the area where the blade is located, Year is the service life, and N is the t is the number of failures within the set period, and δ1, δ2, δ3, and δ4 are the weight coefficients of temperature difference, standard deviation, service life, and number of failures, respectively.

5. The infrared monitoring system for wind turbine blades based on drone inspection according to claim 1 is characterized in that: The uniformity recognition module performs the following operations: Divide different areas based on geographical locations and obtain all infrared thermal imaging data in the same area; Obtain a mapping table or conversion formula between grayscale value and temperature provided by the manufacturer of the infrared camera; Based on the conversion formula or mapping table, obtain the temperature set of several pictures of the same fan blade number i at the same timestamp, and obtain the maximum temperature by finding the maximum value: T max (i) = max(T); Similarly, the lowest temperature is obtained by finding the minimum value: T min (i) = min(T); Calculate the population standard deviation using the following formula: Where T(i) is the set of the highest temperature and the lowest temperature of the fan blade number i, that is, T(i) = [T max (i), T min (i)], N is the total number of fan blade numbers in the region, and μ is the overall average, i.e., the average of the maximum and minimum temperatures of all fan blades in the region.

6. The infrared monitoring system for wind turbine blades based on drone inspection according to claim 5 is characterized in that: The image recognition unit also includes an alarm module, in which a temperature difference threshold θ is set. 温度差 And the alarm threshold value of the standard deviation θ 标准差 , by collecting infrared thermal images of the motor blades in real time, we try to obtain the temperature difference of the motor blades and compare it with the threshold value θ of the temperature difference in real time. 温度差 When the temperature difference of the motor blades is greater than the set threshold, the first alarm signal is issued; when the temperature standard deviation in the area is greater than the temperature difference threshold θ 温度差 When the second alarm signal is issued.

7. The infrared monitoring system for wind turbine blades based on drone inspection according to claim 6 is characterized in that: It also includes a terminal feedback unit, which is used to receive the first alarm signal and the second alarm signal, and go to the site to perform troubleshooting and corresponding processing based on different alarm signals, and feedback the troubleshooting situation and response processing situation to the evaluation and management unit; the troubleshooting situation is to confirm that the alarm signal is correct and to confirm that the alarm signal is wrong, and the response processing situation is that the fault has been processed and the fault has not been processed.

8. An infrared monitoring system for wind turbine blades based on drone inspection, characterized in that: The following steps are involved: Through the infrared camera carried by the drone, several infrared thermal images of the wind turbine blades are obtained and stored in the image storage module. The data storage method is: timestamp-geographic location-wind turbine blade number-several images of the wind turbine blade under the current number corresponding to the timestamp. Each infrared thermal image contains the corresponding timestamp label and wind turbine blade number label. Through temperature inversion, the highest and lowest temperatures of each infrared thermal image are calculated, and then the highest and lowest temperatures at the corresponding timestamp and fan blade number are obtained, and finally the temperature difference of fan blade number i at the corresponding timestamp is obtained; Obtain several infrared thermal images of each fan blade number in the same area, and then uniformly calculate the highest and lowest temperatures of each infrared thermal image, build a temperature analysis set, and calculate the overall standard deviation based on the temperature analysis set to judge the degree of temperature dispersion in the area; Set the temperature difference threshold θ 温度差 And the alarm threshold value of the standard deviation θ 标准差 , by collecting infrared thermal images of the motor blades in real time, we try to obtain the temperature difference of the motor blades and compare it with the threshold value θ of the temperature difference in real time. 温度差 When the temperature difference of the motor blades is greater than the set threshold, the first alarm signal is issued; when the temperature standard deviation in the area is greater than the temperature difference threshold θ 温度差 When the second alarm signal is issued; Receive the first alarm signal and the second alarm signal, and go to the site to perform fault troubleshooting and corresponding processing based on different alarm signals, and feedback the fault troubleshooting situation and response processing situation to the evaluation and management unit, wherein the fault troubleshooting situation is to confirm that the alarm signal is correct and to confirm that the alarm signal is wrong, and the response processing situation is to confirm that the fault has been processed and the fault has not been processed; Calculate the health factor of the fan blades. The calculation formula for the health factor is as follows: Where Z is the health coefficient, ΔT is the temperature difference of the fan blade at the current moment, σ is the standard deviation of the area where the blade is located, Year is the service life, Nt is the number of failures within the set period, and δ1, δ2, δ3, and δ4 are the weight coefficients of temperature difference, standard deviation, service life, and number of failures, respectively.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the wind turbine blade infrared monitoring system based on drone inspection as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the wind turbine blade infrared monitoring system based on drone inspection as described in any one of claims 1 to 7.

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