Unmanned aerial vehicle inspection system and method applied to offshore wind turbine

Abstract

The invention discloses an unmanned aerial vehicle inspection system and method applied to an offshore wind turbine, and relates to the technical field of unmanned aerial vehicle inspection, and the inspection method comprises the following steps: constructing an inspection data management system to store monitoring data and photos collected by an unmanned aerial vehicle in each inspection process, and generating a corresponding inspection record; based on the monitoring data stored in any inspection record, risk assessment and wear assessment are carried out on the operation state of the fan; randomly selecting one inspection record, analyzing the monitoring data about the environment stored in the inspection record, and extracting the feature factors to obtain the influence degree proportion of each feature factor; setting a performance influence proportion for two evaluation results of any inspection record to obtain a comprehensive risk value of any inspection record; the meteorological information of the position where the fan is located is obtained in real time, the running state change of the fan is predicted, and whether a temporary inspection alarm is generated or not is judged.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle inspection, and in particular to an unmanned aerial vehicle inspection system and method applied to offshore wind turbines. Background Art

[0002] Offshore wind turbines are a type of wind power generation equipment specially designed for use in marine environments. Compared with onshore wind turbines, offshore wind turbines have higher power generation efficiency and larger power generation capacity. The demand for offshore wind power operation and inspection is becoming increasingly urgent. Maintaining wind turbines in a marine environment using traditional manual inspection methods will result in long wind turbine downtime and serious power generation losses. In addition, the usage fees and labor costs of each operation and maintenance vessel going out to sea will also account for a large part of the operation and maintenance costs of offshore wind farms.

[0003] Nowadays, unmanned systems represented by drones are increasingly used in the inspection of offshore wind turbine blades and underwater wind turbine pile foundations. They can help save a lot of manpower and material resources, and can also improve inspection efficiency to a certain extent. However, the use of drones for inspections will be affected by environmental factors and the drone equipment itself. Since drones cannot conduct long-term inspections, inspection cycles are normally established. However, the environment on the sea is relatively complex, and it is easy for wind turbines to malfunction outside the inspection cycle, thus affecting normal power generation operations. Summary of the invention

[0004] The object of the present invention is to provide a drone inspection system and method for offshore wind turbines to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a drone inspection method for offshore wind turbines, the inspection method comprising the following steps:

[0006] Step S100: Monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine, take photos of the wind turbine by drone during each inspection, and receive monitoring data monitored by the monitoring equipment; build an inspection data management system to store the monitoring data and photos collected by the drone during each inspection, and generate a corresponding inspection record;

[0007] Step S200: Based on the monitoring data stored in any inspection record, a risk assessment is performed on the operating state of the fan; the operating state presented in the photo of the inspection record is obtained, and the wear assessment of the fan is performed based on the difference between the inspection record and the adjacent inspection record;

[0008] Step S300: randomly select an inspection record, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operation status and wear of the fan; compare the difference in evaluation results between the inspection record and the adjacent inspection records to obtain the influence of each characteristic factor on the two evaluation results;

[0009] Step S400: Based on the influence ratio of each characteristic factor, set the performance impact ratio for the two evaluation results of any inspection record, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether there is an abnormality in the wind turbine; obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

[0010] Furthermore, step S100 includes the following steps:

[0011] Step S101: When the drone inspects the wind turbine, the monitoring data stored in all monitoring devices are received. The monitoring data contains environmental data and wind turbine performance data. When the drone completes the reception, each monitoring device restarts the monitoring and overwrites the historical monitoring data. Each inspection only acquires the monitoring data that has not been collected for a period of time, because the previous data has lost its validity. Overwriting the invalid data can also reduce the data storage burden of the monitoring device.

[0012] Step S102: pre-set the inspection route and several shooting points of the drone. When the drone flies to any shooting point, it takes several photos of various parts of the wind turbine. When the drone returns, the received monitoring data and several photos are stored in the inspection data management system, and an inspection record is generated.

[0013] Further, step S200 includes the following steps:

[0014] Step S201: Obtain the fan performance data stored in any inspection record, and divide the fan performance data into different types according to the data type; preset corresponding evaluation rules for each type of performance data to obtain the risk value presented by the fan in any type of performance data; accumulate the risk values ​​presented by various types of performance data to obtain the first characteristic risk value F1 of the fan; the first characteristic risk value is a risk assessment of the internal operating performance of the fan, and is one of the important indicators for judging whether the fan has an abnormality, which is conducive to a more reasonable calculation of the subsequent comprehensive risk value;

[0015] Step S202: extracting a number of photos stored in the inspection record, and determining the shooting point of each photo according to a preset shooting point; sorting all the inspection records according to the order in which the records are generated, obtaining a number of photos of the previous inspection record of the inspection record, and also determining the shooting point of each photo; respectively identifying the photos obtained at the same shooting point in the two inspection records, extracting the structural features presented in the photos, performing structural feature comparison on the two photos at the same shooting point, respectively identifying a number of wear areas in the two photos, and respectively obtaining the total area of ​​the wear areas presented in the two photos; because environmental factors will have a certain impact on the fan, in addition to the internal performance, it will also cause loss to the external structure, and the loss of the external structure will also affect the performance of the fan;

[0016] Step S203: respectively obtain the record generation time of the two inspection records to obtain the time interval Δt between the two inspection records; according to the formula:

[0017]

[0018] Among them, A1 is the total area of ​​the wear area shown in the photo of the previous inspection record, A2 is the total area of ​​the wear area shown in the photo of the inspection record, k is the area conversion ratio of the photo taken by the drone, and the area conversion ratio is the ratio of the actual area to the area shown in the photo; the wear coefficient W of the fan in the inspection record is calculated; the wear area at different time points is simply subtracted because the wear area will only change from nothing to something, from small to large, so simple subtraction is sufficient. The wear area can effectively and directly analyze the wear condition of the fan, and the wear coefficient reflects the degree of wear on the external structure during this period of time, providing scientific reference data for subsequent wear risk assessment;

[0019] Step S204: Identify the wear area of ​​each photo stored in the inspection record to obtain the wear area and several wear coefficients of the fan, and calculate the average value of the several wear coefficients to obtain the comprehensive wear coefficient Wav of the inspection record. e ; According to the formula:

[0020] F2=A×k×Waave

[0021] Among them, A is the wear area of ​​the fan, which is obtained by adding up the total area of ​​the wear area presented in each photo; the second characteristic risk value F2 of the fan is calculated; the second characteristic risk value represents the wear condition of the external structure of the fan. The larger the wear area or the higher the degree of wear over a period of time, the more effectively it can reflect the degree of abnormality of the fan.

[0022] Further, step S300 includes the following steps:

[0023] Step S301: Set the monitoring data about the environment in the inspection record as environmental data, sort all the inspection records according to the order in which the records are generated, and obtain the environmental data in any two adjacent inspection records respectively; divide the environmental data into several categories according to the type of environmental data, compare the same type of environmental data in the two inspection records, preset corresponding comparison rules for each type of environmental data, and obtain the difference between the two inspection records in any type of environmental data;

[0024] Step S302: Preset different safety ranges for various types of environmental data. If the difference of one type of environmental data exceeds the safety range, set the one type of environmental data as a characteristic factor.

[0025] Step S303: Set the first characteristic risk ratio of two adjacent inspection records to ΔF1 and the second characteristic risk ratio to ΔF2; obtain the number of characteristic factors between the two adjacent inspection records as m, according to the formula:

[0026]

[0027] Among them, C i is the difference amplitude of the i-th characteristic factor between two adjacent inspection records; the influence degree of the i-th characteristic factor on the operation status of the fan is calculated as α i And the influence degree on the wear of the fan β i , the influence of the i-th characteristic factor on the two evaluation results is P i =|α i | / (|α i |+|β i |) and Q i =|β i | / (|α i |+|β i |); Various environmental factors will have a certain impact on the internal operating performance and external structure of the wind turbine. For example, wind speed will affect the power generation efficiency of the wind turbine and cause wear on the external structure; however, the degree of impact on the two is different, so they need to be calculated separately. Due to the different degrees of impact, the final impact ratio of the two evaluation results on the comprehensive risk value is also different. Therefore, it is necessary to determine the proportion of the impact of the two in order to obtain a more accurate value in the subsequent comprehensive risk value calculation.

[0028] Furthermore, step S400 includes the following steps:

[0029] Step S401: randomly select an inspection record to obtain the first characteristic risk value F1 and the second characteristic risk value F2 of the inspection record; obtain the influence ratio of each characteristic factor in the inspection record on the two evaluation results according to the formula:

[0030]

[0031] Where z1 and z2 are both positive integers, and z1∈(1,n), z2∈(1,n), P z1 is the influence ratio of the z1th characteristic factor on the evaluation result of the wind turbine operation status, P z2 is the influence ratio of the z2th characteristic factor on the evaluation result of the fan wear condition, and n is the number of characteristic factors contained in the inspection record; the comprehensive risk value F of the fan presented in the inspection record is calculated;

[0032] Step S402: Whenever the fan undergoes maintenance, an abnormal mark is placed on the latest inspection record generated before the maintenance; the comprehensive risk value of all inspection records containing abnormal marks is obtained, and the minimum comprehensive risk value is selected as the risk threshold F for determining whether the fan has an abnormality. min ;

[0033] Step S403: The meteorological information at the location of the wind turbine is divided according to the type of environmental data, the value change of each type of environmental data is obtained, and the environmental data whose value change amplitude exceeds the set safety amplitude range is extracted to generate a number of real-time characteristic factors;

[0034] Step S404: compare the several real-time characteristic factors with the characteristic factors in each inspection record to determine the characteristic factors corresponding to each real-time characteristic factor in each inspection record; obtain the influence proportions P and Q of any real-time characteristic factor on the two evaluation results in each inspection record, and calculate the average value to obtain the average influence proportion P ave and Q ave ; Get the first characteristic risk value (F1) of the latest inspection record generated ’ and the second characteristic risk value (F2) ’ , the comprehensive risk value F of the current wind turbine ’ If F ’ >F min , a temporary inspection alarm is generated to remind the staff that a drone needs to be dispatched to inspect the wind turbine.

[0035] In order to better implement the above method, a UAV inspection system for offshore wind turbines is also proposed. The inspection system includes a wind turbine data acquisition module, a monitoring data analysis module, an influencing factor analysis module and a real-time inspection judgment module;

[0036] The wind turbine data acquisition module is used to monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine. During each inspection, the wind turbine is photographed by a drone and the monitoring data monitored by the monitoring equipment is received. An inspection data management system is built to store the monitoring data and photos collected by the drone during each inspection and generate a corresponding inspection record.

[0037] A monitoring data analysis module is used to perform risk assessment on the operating status of the fan based on the monitoring data stored in any inspection record; obtain the operating status presented in the photos of the inspection record, and perform wear assessment on the fan based on the differences between the adjacent inspection records;

[0038] The influencing factor analysis module is used to select an inspection record at random, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operation status and wear of the fan; compare the evaluation results of the inspection record with those of adjacent inspection records to obtain the influence of each characteristic factor on the two evaluation results;

[0039] The real-time inspection judgment module is used to set the performance impact ratio for the two evaluation results of any inspection record based on the influence ratio of each characteristic factor, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether there is an abnormality in the wind turbine; obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

[0040] Further, the wind turbine data acquisition module includes a monitoring data acquisition unit and an inspection record generation unit;

[0041] The monitoring data acquisition unit is used to monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine. During each inspection, the wind turbine is photographed by a drone and the monitoring data monitored by the monitoring equipment is received. The inspection record generation unit is used to build an inspection data management system to store the monitoring data and photos collected by the drone during each inspection and generate a corresponding inspection record.

[0042] Further, the monitoring data analysis module includes a risk assessment unit and a wear assessment unit;

[0043] The risk assessment unit is used to perform risk assessment on the operating status of the fan based on the monitoring data stored in any inspection record; the wear assessment unit is used to obtain the operating conditions presented in the photos of the inspection record and perform wear assessment on the fan based on the differences between adjacent inspection records.

[0044] Further, the influencing factor analysis module includes a characteristic factor extraction unit and an influence degree calculation unit;

[0045] The characteristic factor extraction unit is used to arbitrarily select an inspection record, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operating status and wear of the fan; the influence degree calculation unit is used to compare the evaluation results of the inspection record with those of adjacent inspection records to obtain the proportion of the influence of each characteristic factor on the two evaluation results.

[0046] Furthermore, the real-time inspection judgment module includes a risk threshold confirmation unit and a temporary inspection generation unit;

[0047] The risk threshold confirmation unit is used to set the performance impact ratio for the two evaluation results of any inspection record based on the proportion of the influence of each characteristic factor, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether there is an abnormality in the wind turbine; the temporary inspection generation unit is used to obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention analyzes the complex situation of the offshore environment to help the staff to timely judge the abnormal situation of the fan in the change of the environment where the fan is located, and to timely investigate the abnormal situation to avoid affecting the normal operation of the fan;

[0050] 2. The present invention conducts a dual assessment of the operating performance and appearance structure of the fan itself, conducts a detailed analysis of possible abnormal conditions of the fan, and combines the impact of environmental factors on the operating performance and structure to accurately assess the actual risk value of the fan, helping staff to promptly troubleshoot abnormalities;

[0051] 3. The present invention predicts the real-time situation of the wind turbine based on meteorological information, and conducts temporary inspections when there is a risk in the predicted results. This can effectively improve the inspection efficiency and help to detect abnormal situations in a timely manner, thereby avoiding special circumstances that cause the wind turbine to become abnormal but cannot be repaired in time, thus affecting the overall operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the steps of a drone inspection system applied to offshore wind turbines;

[0053] Figure 2 The figure is a schematic diagram of the structure of a UAV inspection system used for offshore wind turbines. DETAILED DESCRIPTION

[0054] 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 only 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.

[0055] Example: Figure 1 to Figure 2 As shown, the present invention provides a drone inspection method applied to offshore wind turbines, the inspection method comprising the following steps:

[0056] Step S100: Monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine, take photos of the wind turbine by drone during each inspection, and receive monitoring data monitored by the monitoring equipment; build an inspection data management system to store the monitoring data and photos collected by the drone during each inspection, and generate a corresponding inspection record;

[0057] Wherein, step S100 includes the following steps:

[0058] Step S101: When the drone inspects the wind turbine, the drone receives the monitoring data stored in all monitoring devices, wherein the monitoring data includes environmental data and wind turbine performance data. When the drone completes the reception, each monitoring device restarts the monitoring and overwrites the historical monitoring data.

[0059] Step S102: pre-set the inspection route and several shooting points of the drone. When the drone flies to any shooting point, it takes several photos of various parts of the wind turbine. When the drone returns, the received monitoring data and several photos are stored in the inspection data management system, and an inspection record is generated.

[0060] Step S200: Based on the monitoring data stored in any inspection record, a risk assessment is performed on the operating state of the fan; the operating state presented in the photo of the inspection record is obtained, and the wear assessment of the fan is performed based on the difference between the inspection record and the adjacent inspection record;

[0061] Wherein, step S200 includes the following steps:

[0062] Step S201: Obtain the fan performance data stored in any inspection record, and divide the fan performance data into different types according to the data type; preset corresponding evaluation rules for each type of performance data to obtain the risk value presented by the fan in any type of performance data; accumulate the risk values ​​presented by each type of performance data to obtain the first characteristic risk value F1 of the fan;

[0063] Step S202: extracting a number of photos stored in the inspection record, and determining the shooting point of each photo according to a preset shooting point; sorting all the inspection records according to the order in which the records are generated, obtaining a number of photos of the previous inspection record of the inspection record, and similarly determining the shooting point of each photo; respectively identifying the photos obtained at the same shooting point in the two inspection records, extracting the structural features presented in the photos, performing structural feature comparison on the two photos at the same shooting point, respectively identifying a number of wear areas in the two photos, and respectively obtaining the total area of ​​the wear areas presented in the two photos;

[0064] Step S203: respectively obtain the record generation time of the two inspection records to obtain the time interval Δt between the two inspection records; according to the formula:

[0065]

[0066] Wherein, A1 is the total area of ​​the wear area presented in the photo of the previous inspection record, A2 is the total area of ​​the wear area presented in the photo of the inspection record, k is the area conversion ratio of the photo taken by the drone, and the area conversion ratio is the ratio of the actual area to the area presented in the photo is k; the wear coefficient W presented by the fan in the inspection record is calculated;

[0067] Example 1: Suppose that two inspection records are taken at three locations, and the difference in the area of ​​the worn areas shown in the photos is 5 cm 2 , 6cm 2 , 4cm 2 ; and the wear area shown in each photo of the previous inspection record is 10cm 2 , 10cm 2 , 10cm 2 , the area conversion ratio of the drone is set to 2 and the time interval between two inspection records is 1 month, so the wear coefficients are calculated to be 5 / (10×1)×2=1cm 2 / month, 6 / (10×1)×2=1.2cm 2 / month, 4 / (10×1)×2=0.8cm 2 / month; the average wear coefficient is 1cm 2 / moon;

[0068] Step S204: Identify the wear area of ​​each photo stored in the inspection record to obtain the wear area and several wear coefficients of the fan, and calculate the average value of the several wear coefficients to obtain the comprehensive wear coefficient Wav of the inspection record. e ; According to the formula:

[0069] F2=A×k×Waa ve

[0070] Wherein, A is the wear area of ​​the fan, which is obtained by accumulating the total area of ​​the wear areas presented in each photo; and the second characteristic risk value F2 of the fan is calculated.

[0071] Step S300: randomly select an inspection record, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operation status and wear of the fan; compare the difference in evaluation results between the inspection record and the adjacent inspection records to obtain the influence of each characteristic factor on the two evaluation results;

[0072] Wherein, step S300 includes the following steps:

[0073] Step S301: Set the monitoring data about the environment in the inspection record as environmental data, sort all the inspection records according to the order in which the records are generated, and obtain the environmental data in any two adjacent inspection records respectively; divide the environmental data into several categories according to the type of environmental data, compare the same type of environmental data in the two inspection records, preset corresponding comparison rules for each type of environmental data, and obtain the difference between the two inspection records in any type of environmental data;

[0074] Step S302: Preset different safety ranges for various types of environmental data. If the difference of one type of environmental data exceeds the safety range, set the one type of environmental data as a characteristic factor.

[0075] Step S303: Set the first characteristic risk ratio of two adjacent inspection records to ΔF1 and the second characteristic risk ratio to ΔF2; obtain the number of characteristic factors between the two adjacent inspection records as m, according to the formula:

[0076]

[0077] Among them, C i is the difference amplitude of the i-th characteristic factor between two adjacent inspection records; the influence degree of the i-th characteristic factor on the operation status of the fan is calculated as α i And the influence degree on the wear of the fan β i , the influence of the i-th characteristic factor on the two evaluation results is P i =|α i | / (|α i |+|β i |) and Q i =|β i | / (|α i |+|β i |);

[0078] Example 2: The first characteristic risk ratio of two adjacent inspection records is 3.6, the second characteristic risk difference is 2.4, and there are three characteristic factors, the difference ranges are 50%, 40%, and 60%, respectively. Taking the first characteristic factor as an example, we get 3.6 / 3=1+0.5×α i , α i =0.4; 2.4 / 3 = 1 + 0.5 × β i , β i =-0.4; the influence proportions are 50% and 50% respectively.

[0079] Step S400: Based on the influence ratio of each characteristic factor, the performance influence ratio is set for the two evaluation results of any inspection record, the comprehensive risk value of any inspection record is obtained, and the risk threshold for judging whether the wind turbine is abnormal is confirmed; the meteorological information of the location of the wind turbine is obtained in real time, the change of the operating state of the wind turbine is predicted, and it is determined whether to generate a temporary inspection alarm to send a reminder to the staff;

[0080] Wherein, step S400 includes the following steps:

[0081] Step S401: randomly select an inspection record to obtain the first characteristic risk value F1 and the second characteristic risk value F2 of the inspection record; obtain the influence ratio of each characteristic factor in the inspection record on the two evaluation results according to the formula:

[0082]

[0083] Where z1 and z2 are both positive integers, and z1∈(1,n), z2∈(1,n), P z1 is the influence ratio of the z1th characteristic factor on the evaluation result of the wind turbine operation status, P z2 is the influence ratio of the z2th characteristic factor on the evaluation result of the fan wear condition, and n is the number of characteristic factors contained in the inspection record; the comprehensive risk value F of the fan presented in the inspection record is calculated;

[0084] Step S402: Whenever the fan undergoes maintenance, an abnormal mark is placed on the latest inspection record generated before the maintenance; the comprehensive risk value of all inspection records containing abnormal marks is obtained, and the minimum comprehensive risk value is selected as the risk threshold F for determining whether the fan has an abnormality. min ;

[0085] Step S403: The meteorological information at the location of the wind turbine is divided according to the type of environmental data, the value change of each type of environmental data is obtained, and the environmental data whose value change amplitude exceeds the set safety amplitude range is extracted to generate a number of real-time characteristic factors;

[0086] Step S404: compare the several real-time characteristic factors with the characteristic factors in each inspection record to determine the characteristic factors corresponding to each real-time characteristic factor in each inspection record; obtain the influence proportions P and Q of any real-time characteristic factor on the two evaluation results in each inspection record, and calculate the average value to obtain the average influence proportion P ave and Q ave ; Get the first characteristic risk value (F1) of the latest inspection record generated ’ and the second characteristic risk value (F2) ’ , the comprehensive risk value F of the current wind turbine ’ If F ’ >F min , a temporary inspection alarm is generated to remind the staff that a drone needs to be dispatched to inspect the wind turbine.

[0087] A drone inspection system for offshore wind turbines, the inspection system comprising a wind turbine data acquisition module, a monitoring data analysis module, an influencing factor analysis module and a real-time inspection judgment module;

[0088] The wind turbine data acquisition module is used to monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine. During each inspection, the wind turbine is photographed by a drone and the monitoring data monitored by the monitoring equipment is received. An inspection data management system is built to store the monitoring data and photos collected by the drone during each inspection and generate a corresponding inspection record.

[0089] A monitoring data analysis module is used to perform risk assessment on the operating status of the fan based on the monitoring data stored in any inspection record; obtain the operating status presented in the photos of the inspection record, and perform wear assessment on the fan based on the differences between the adjacent inspection records;

[0090] The influencing factor analysis module is used to select an inspection record at random, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operation status and wear of the fan; compare the evaluation results of the inspection record with those of adjacent inspection records to obtain the influence of each characteristic factor on the two evaluation results;

[0091] The real-time inspection judgment module is used to set the performance impact ratio for the two evaluation results of any inspection record based on the influence ratio of each characteristic factor, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether there is an abnormality in the wind turbine; obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

[0092] Among them, the fan data acquisition module includes a monitoring data acquisition unit and an inspection record generation unit;

[0093] The monitoring data acquisition unit is used to monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine. During each inspection, the wind turbine is photographed by a drone and the monitoring data monitored by the monitoring equipment is received. The inspection record generation unit is used to build an inspection data management system to store the monitoring data and photos collected by the drone during each inspection and generate a corresponding inspection record.

[0094] Among them, the monitoring data analysis module includes a risk assessment unit and a wear assessment unit;

[0095] The risk assessment unit is used to perform risk assessment on the operating status of the fan based on the monitoring data stored in any inspection record; the wear assessment unit is used to obtain the operating conditions presented in the photos of the inspection record and perform wear assessment on the fan based on the differences between adjacent inspection records.

[0096] Among them, the influencing factor analysis module includes a characteristic factor extraction unit and an influence degree calculation unit;

[0097] The characteristic factor extraction unit is used to arbitrarily select an inspection record, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operating status and wear of the fan; the influence degree calculation unit is used to compare the evaluation results of the inspection record with those of adjacent inspection records to obtain the proportion of the influence of each characteristic factor on the two evaluation results.

[0098] Among them, the real-time inspection judgment module includes a risk threshold confirmation unit and a temporary inspection generation unit;

[0099] The risk threshold confirmation unit is used to set the performance impact ratio for the two evaluation results of any inspection record based on the proportion of the influence of each characteristic factor, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether there is an abnormality in the wind turbine; the temporary inspection generation unit is used to obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

[0100] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A drone inspection method for offshore wind turbines, characterized in that: The inspection method comprises the following steps: Step S100: Monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine, take photos of the wind turbine by drone during each inspection, and receive monitoring data monitored by the monitoring equipment; build an inspection data management system to store the monitoring data and photos collected by the drone during each inspection, and generate a corresponding inspection record; Step S200: Based on the monitoring data stored in any inspection record, a risk assessment is performed on the operating state of the fan; the operating state presented in the photo of the inspection record is obtained, and the wear assessment of the fan is performed based on the difference between the inspection record and the adjacent inspection record; Step S300: randomly select an inspection record, analyze the monitoring data about the environment stored in the inspection record, and extract the characteristic factors that affect the operation status and wear of the fan; compare the difference in evaluation results between the inspection record and the adjacent inspection records to obtain the influence of each characteristic factor on the two evaluation results; Step S400: Based on the influence ratio of each characteristic factor, set the performance impact ratio for the two evaluation results of any inspection record, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether there is an abnormality in the wind turbine; obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

2. The method for inspecting an offshore wind turbine using a drone according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: When the drone inspects the wind turbine, the drone receives the monitoring data stored in all monitoring devices, wherein the monitoring data includes environmental data and wind turbine performance data. When the drone completes the reception, each monitoring device restarts the monitoring and overwrites the historical monitoring data. Step S102: pre-set the inspection route and several shooting points of the drone. When the drone flies to any shooting point, it takes several photos of various parts of the wind turbine. When the drone returns, the received monitoring data and several photos are stored in the inspection data management system, and an inspection record is generated.

3. The method for inspection of offshore wind turbines by using a drone according to claim 2 is characterized in that: The step S200 includes the following steps: Step S201: Obtain the fan performance data stored in any inspection record, and divide the fan performance data into different types according to the data type; preset corresponding evaluation rules for each type of performance data to obtain the risk value presented by the fan in any type of performance data; accumulate the risk values ​​presented by each type of performance data to obtain the first characteristic risk value F1 of the fan; Step S202: extracting a number of photos stored in the inspection record, and determining the shooting point of each photo according to a preset shooting point; sorting all the inspection records according to the order in which the records are generated, obtaining a number of photos of the previous inspection record of the inspection record, and similarly determining the shooting point of each photo; respectively identifying the photos obtained at the same shooting point in the two inspection records, extracting the structural features presented in the photos, performing structural feature comparison on the two photos at the same shooting point, respectively identifying a number of wear areas in the two photos, and respectively obtaining the total area of ​​the wear areas presented in the two photos; Step S203: respectively obtain the record generation time of the two inspection records to obtain the time interval Δt between the two inspection records; according to the formula: Wherein, A1 is the total area of ​​the wear area presented in the photo of the previous inspection record, A2 is the total area of ​​the wear area presented in the photo of the inspection record, k is the area conversion ratio of the photo taken by the drone, and the area conversion ratio is the ratio of the actual area to the area presented in the photo is k; the wear coefficient W presented by the fan in the inspection record is calculated; Step S204: Identify the wear area of ​​each photo stored in the inspection record to obtain the wear area and several wear coefficients of the fan, and calculate the average value of the several wear coefficients to obtain the comprehensive wear coefficient W of the inspection record. ave ; According to the formula: <h2 style=";text-align:left;direction:ltr">F2 = A×k×W<h2 style=";text-align:left;direction:ltr"> ave <h2 style=";text-align:left;direction:ltr"> ; Wherein, A is the wear area of ​​the fan, which is obtained by accumulating the total area of ​​the wear area presented in each photo; and the second characteristic risk value F2 of the fan is calculated.

4. The method for inspecting an offshore wind turbine using a drone according to claim 3 is characterized in that: The step S300 includes the following steps: Step S301: Set the monitoring data about the environment in the inspection record as environmental data, sort all the inspection records according to the order in which the records are generated, and obtain the environmental data in any two adjacent inspection records respectively; divide the environmental data into several categories according to the type of environmental data, compare the same type of environmental data in the two inspection records, preset corresponding comparison rules for each type of environmental data, and obtain the difference between the two inspection records in any type of environmental data; Step S302: Preset different safety ranges for various types of environmental data. If the difference of one type of environmental data exceeds the safety range, set the one type of environmental data as a characteristic factor. Step S303: Set the first characteristic risk ratio of two adjacent inspection records to ΔF1 and the second characteristic risk ratio to ΔF2; obtain the number of characteristic factors between the two adjacent inspection records as m, according to the formula: Among them, C i is the difference amplitude of the i-th characteristic factor between two adjacent inspection records; the influence degree of the i-th characteristic factor on the operation status of the fan is calculated as α i And the influence degree on the wear of the fan β i , the influence of the i-th characteristic factor on the two evaluation results is P i =|α i | / (|α i |+|β i |) and Q i =|β i | / (|α i |+|β i |).

5. The method for inspecting an offshore wind turbine using a drone according to claim 4 is characterized in that: The step S400 includes the following steps: Step S401: randomly select an inspection record to obtain the first characteristic risk value F1 and the second characteristic risk value F2 of the inspection record; obtain the influence ratio of each characteristic factor in the inspection record on the two evaluation results according to the formula: Where z1 and z2 are both positive integers, and z1∈(1,n), z2∈(1,n), P z1 is the influence ratio of the z1th characteristic factor on the evaluation result of the wind turbine operation status, P z2 is the influence ratio of the z2th characteristic factor on the evaluation result of the fan wear condition, and n is the number of characteristic factors contained in the inspection record; the comprehensive risk value F of the fan presented in the inspection record is calculated; Step S402: Whenever the fan undergoes maintenance, an abnormal mark is placed on the latest inspection record generated before the maintenance; the comprehensive risk value of all inspection records containing abnormal marks is obtained, and the minimum comprehensive risk value is selected as the risk threshold F for determining whether the fan has an abnormality. min ; Step S403: The meteorological information at the location of the wind turbine is divided according to the type of environmental data, the value change of each type of environmental data is obtained, and the environmental data whose value change amplitude exceeds the set safety amplitude range is extracted to generate a number of real-time characteristic factors; Step S404: compare the several real-time characteristic factors with the characteristic factors in each inspection record to determine the characteristic factors corresponding to each real-time characteristic factor in each inspection record; obtain the influence proportions P and Q of any real-time characteristic factor on the two evaluation results in each inspection record, and calculate the average value to obtain the average influence proportion P ave and Q ave ; Get the first characteristic risk value (F1) of the latest inspection record generated ’ and the second characteristic risk value (F2) ’ , the comprehensive risk value F of the current wind turbine ’ If F ’ >F min , a temporary inspection alarm is generated to remind the staff that a drone needs to be dispatched to inspect the wind turbine.

6. A drone inspection system for offshore wind turbines, used to execute a drone inspection method for offshore wind turbines according to any one of claims 1 to 5, characterized in that: The inspection system includes a fan data acquisition module, a monitoring data analysis module, an influencing factor analysis module and a real-time inspection judgment module; The wind turbine data acquisition module is used to monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine, photograph the wind turbine through the drone during each inspection, and receive the monitoring data monitored by the monitoring equipment; construct an inspection data management system to store the monitoring data and photos collected by the drone during each inspection, and generate a corresponding inspection record; The monitoring data analysis module is used to perform risk assessment on the operating status of the wind turbine based on the monitoring data stored in any inspection record; Obtaining the operating conditions presented in the photos of the inspection records, and performing wear assessment on the fan based on the differences between the photos and the adjacent inspection records; The influencing factor analysis module is used to select an inspection record at random, analyze the monitoring data about the environment stored in the inspection record, and extract characteristic factors that affect the operation status and wear of the fan; Compare the difference in evaluation results between the inspection record and the adjacent inspection records to obtain the influence ratio of each characteristic factor on the two evaluation results; The real-time inspection judgment module is used to set the performance impact ratio for the two evaluation results of any inspection record based on the influence ratio of each characteristic factor, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether the wind turbine has an abnormality; obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating status of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

7. The UAV inspection system for offshore wind turbines according to claim 6 is characterized in that: The wind turbine data acquisition module includes a monitoring data acquisition unit and an inspection record generation unit; The monitoring data acquisition unit is used to monitor the operating performance and environment of the wind turbine by installing monitoring equipment on the wind turbine. During each inspection, the wind turbine is photographed by a drone and the monitoring data monitored by the monitoring equipment is received. The inspection record generation unit is used to build an inspection data management system to store the monitoring data and photos collected by the drone during each inspection and generate a corresponding inspection record.

8. The UAV inspection system for offshore wind turbines according to claim 6 is characterized by: The monitoring data analysis module includes a risk assessment unit and a wear assessment unit; The risk assessment unit is used to perform risk assessment on the operating status of the wind turbine based on the monitoring data stored in any inspection record; The wear assessment unit is used to obtain the operating conditions presented in the photos of the inspection records, and perform wear assessment on the fan based on the differences between the wind turbine and adjacent inspection records.

9. The unmanned aerial vehicle inspection system for offshore wind turbines according to claim 6 is characterized in that: The influencing factor analysis module includes a characteristic factor extraction unit and an influence degree calculation unit; The characteristic factor extraction unit is used to arbitrarily select an inspection record, analyze the monitoring data about the environment stored in the inspection record, and extract characteristic factors that affect the operating state and wear of the fan; The influence degree calculation unit is used to compare the difference in evaluation results between the inspection record and adjacent inspection records to obtain the influence degree ratio of each characteristic factor on the two evaluation results.

10. The unmanned aerial vehicle inspection system for offshore wind turbines according to claim 6 is characterized in that: The real-time inspection judgment module includes a risk threshold confirmation unit and a temporary inspection generation unit; The risk threshold confirmation unit is used to set the performance impact ratio for the two evaluation results of any inspection record based on the impact ratio of each characteristic factor, obtain the comprehensive risk value of any inspection record, and confirm the risk threshold for judging whether the wind turbine is abnormal; The temporary inspection generation unit is used to obtain the meteorological information of the location of the wind turbine in real time, predict the change of the operating state of the wind turbine, and determine whether to generate a temporary inspection alarm to send a reminder to the staff.

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