Fan power generation intelligent inspection system based on multi-scene optimization

Abstract

The invention belongs to the field of intelligent inspection of fan power generation, relates to a data analysis technology, is used for solving the problem that an existing fan power generation inspection system cannot ensure the inspection effect and efficiency by adopting a mode of combining multiple technical means, and particularly relates to an intelligent inspection system of fan power generation based on multi-scene optimization. Comprising a fan scale analysis module, an inspection mode analysis module, an inspection mode evaluation module and an inspection optimization module, and the fan scale analysis module, the inspection mode analysis module, the inspection mode evaluation module and the inspection optimization module are in communication connection in sequence. The fan scale analysis module is used for analyzing the scale grade of an inspection object, different inspection modes are adopted for different scale grades, the inspection mode evaluation module is used for evaluating the effect after inspection is completed, and the inspection optimization module is used for optimizing the inspection efficiency; different inspection modes are adopted according to the scale grade of the inspection object, so that the inspection effect is ensured to meet the requirement, and the inspection efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent inspection of wind turbine power generation and relates to data analysis technology, and specifically is an intelligent inspection system for wind turbine power generation based on multi-scenario optimization. Background Art

[0002] In the context of today's energy transformation, wind turbine power generation, as a renewable energy production method, is playing an increasingly important role. Compared with other new energy sources, wind energy has the outstanding advantages of being safe, clean, locally sourced and inexhaustible. In recent years, my country's wind power generation has increased year by year.

[0003] Existing wind turbine power generation inspection systems often use a single technical means to inspect wind turbines. Due to the differences in wind turbine scales in different wind turbine power generation scenarios, it is impossible to combine multiple technical means to ensure the inspection effect, nor is it possible to optimize efficiency while ensuring the effect.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a wind turbine power generation intelligent inspection system based on multi-scenario optimization, which is used to solve the problem that the existing wind turbine power generation inspection system cannot adopt a combination of multiple technical means to ensure the inspection effect and efficiency due to the differences in wind turbine scales in different wind turbine power generation scenarios; The technical problem to be solved by the present invention is: how to provide a wind turbine power generation intelligent inspection system based on multi-scenario optimization that can adopt a combination of multiple technical means to ensure the inspection effect and efficiency.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A wind turbine power generation intelligent inspection system based on multi-scenario optimization, comprising a wind turbine scale analysis module, an inspection mode analysis module, an inspection mode evaluation module and an inspection optimization module; the wind turbine scale analysis module, the inspection mode analysis module, the inspection mode evaluation module and the inspection optimization module are sequentially connected in communication; The wind turbine scale analysis module is used to analyze the scale level of the wind farm: mark the wind farm as an inspection object, obtain the wind turbine blade length YC, the number of wind turbines SL and the site area MJ of the inspection object, and perform numerical calculation to obtain the scale coefficient GM of the inspection object, and mark the inspection object as a first-level scale object, a second-level scale object and a third-level scale object according to the scale coefficient GM; The inspection mode analysis module is used to analyze the inspection mode according to the scale level of the inspection object: generate an inspection cycle of non-fixed duration, perform periodic inspection on the inspection object, and the implementation methods of the inspection include measures A, measures B, measures C and measures D; the inspection mode includes effect mode, mixed mode and economic mode; the first-level scale object adopts the effect mode, the second-level scale object adopts the mixed mode, and the third-level scale object adopts the economic mode; The inspection mode evaluation module is used to evaluate the effect after the inspection is completed: after all wind turbines in the inspection object have completed the inspection, the single-machine inspection time SC, the inspection problem discovery rate FX and the power generation failure rate BH are obtained, and the effect coefficient XG of the inspection object is obtained by numerical calculation. According to the effect coefficient XG, it is determined whether the inspection mode adopted by the inspection object meets the requirements, and the inspection mode that does not meet the requirements is upgraded; The inspection optimization module is used to optimize the inspection of the inspection objects whose inspection modes meet the requirements: determine whether the efficiency of the inspection mode meets the requirements through the single-machine inspection duration SC, and perform efficiency optimization analysis on the inspection modes that do not meet the requirements.

[0007] Furthermore, the process of obtaining the fan blade length YC includes: marking the distance from the tip of the fan blade to the center position of the blade root as the single blade length, and marking the average value of the single blade lengths of all fans in the inspection object as the fan blade length YC; the process of obtaining the site area MJ includes: determining the coordinates of each fan on the map through satellite signals, and drawing points on the map according to the coordinates, connecting any two of the coordinate points to form a number of convex polygons, then the convex polygon is the largest convex polygon when there is no coordinate point outside, and the actual area corresponding to the area of ​​the largest convex polygon on the map is marked as the site area MJ.

[0008] Furthermore, the process of grading the scale level of the inspection object specifically includes: performing numerical calculations on the fan blade length YC, the number of fans SL and the site area MJ to obtain the scale coefficient GM of the inspection object; grading the scale level of the inspection object by comparing the scale coefficient GM of the inspection object with the preset high scale value GMmax and low scale value GMmin: if the scale coefficient GM is greater than or equal to the high scale value GMmax, the inspection object is marked as a first-level scale object; if the scale coefficient GM is less than the high scale value GMmax and greater than or equal to the low scale value GMmin, the inspection object is marked as a second-level scale object; if the scale coefficient GM is less than the low scale value GMmin, the inspection object is marked as a third-level scale object.

[0009] Furthermore, the implementation methods of the inspection specifically include: Measure A is to use temperature sensors and vibration sensors to collect the operating status of the internal equipment of the wind turbine in real time; Measure B is to use drones equipped with high-definition cameras and infrared thermal imagers to conduct aerial inspections of the wind turbine at different heights and angles; Measure C is to use intelligent robots carrying sensors to inspect the equipment structure and line connections inside the wind turbine along a predetermined route; Measure D is for inspection personnel to carry detection equipment to inspect the blades, tower and internal equipment of the wind turbine in turn.

[0010] Furthermore, the implementation methods adopted by different inspection modes specifically include: when the effect mode is used to inspect the first-level scale objects, the combination of measures adopted is measure A + measure B + measure C; when the mixed mode is used to inspect the second-level scale objects, the combination of measures adopted is measure A + measure B; when the economic mode is used to inspect the third-level scale objects, the combination of measures adopted is measure A + measure D.

[0011] Furthermore, the specific process of evaluating the effectiveness of the inspection mode includes: the single-machine inspection time SC is the ratio of the inspection time after all wind turbines have completed the inspection to the number of wind turbines SL; the process of obtaining the inspection problem detection rate FX includes: marking the total number of problems found in the inspection history records as the number of problems WT, adding 1 to the number of inspections XJ after each inspection, and numerically calculating the number of problems WT, the number of inspections XJ, and the number of wind turbines SL to obtain the inspection problem detection rate; the process of obtaining the power generation failure rate BH includes: marking the total number of wind turbines whose power generation does not meet the theoretical requirements in the inspection history records as the power generation failure number FW, and numerically calculating the power generation failure number FW, the total number of inspections XJ, and the number of wind turbines SL to obtain the power generation failure rate BH; comparing the inspection problem detection rate FX with the power generation failure rate BH H performs numerical calculation to obtain the inspection effect coefficient XG of the inspection unit adopting the inspection mode; compares the inspection effect coefficient XG with the preset inspection effect threshold XGmax: if the inspection effect coefficient XG is greater than or equal to the inspection effect threshold XGmax, then it is judged that the inspection mode adopted by the inspection object meets the requirements, and the single-machine inspection time SC of the inspection object is sent to the inspection optimization module; if the inspection effect coefficient XG is less than the inspection effect threshold XGmax, then it is judged that the inspection mode adopted by the inspection object does not meet the requirements, and the inspection mode needs to be upgraded: if the inspection object adopts the economic mode, it is upgraded to the mixed mode; if the inspection object adopts the mixed mode, it is upgraded to the effect mode; if the inspection object adopts the effect mode, the inspection mode is upgraded through upgrading measures.

[0012] Furthermore, the specific process of evaluating the efficiency of the inspection mode includes: comparing the single-machine inspection time SC with the preset single-machine inspection time threshold SCmax: if the single-machine inspection time SC is less than the single-machine inspection time threshold SCmax, then it is judged that the efficiency of the inspection object adopting the inspection mode meets the requirements and no processing is required; if the single-machine inspection time SC is greater than the single-machine inspection time threshold SCmax, then it is judged that the efficiency of the inspection object adopting the inspection mode does not meet the requirements, and efficiency optimization analysis is performed.

[0013] Furthermore, the specific process of efficiency optimization analysis includes: marking the time of movement between each wind turbine during drone inspection or manual inspection as movement time YD; numerically calculating the average value and standard deviation of all movement time YD to obtain the maximum allowable time ZD; among the inspection objects, if the movement time YD from a certain wind turbine to other wind turbines is greater than the maximum allowable time ZD, then the wind turbine is marked as an optimized wind turbine, and an independent drone or inspection personnel is configured to inspect the optimized wind turbine.

[0014] The present invention has the following beneficial effects: The scale coefficient is obtained by calculating and analyzing the blade length, number of wind turbines and site area of ​​the wind farm, and the scale level of the wind farm is graded according to the scale coefficient to ensure that the scale level of the wind farm under different power generation scenarios can be judged; By adopting a variety of inspection technical means, different inspection modes are generated, and corresponding inspection modes are adopted for inspection objects of different scales and levels, ensuring that inspection objects from large to small can adopt inspection modes that are both effective and efficient; By calculating and analyzing the inspection problem discovery rate and power generation failure rate after the inspection is completed, the effect coefficient of the inspection object adopting the corresponding inspection mode is obtained, so as to evaluate the inspection effect according to the effect coefficient and upgrade the inspection mode for the inspection objects that do not meet the effect requirements; 4. By analyzing the inspection time of a single machine after the inspection is completed, the inspection objects whose inspection efficiency does not meet the requirements are judged, and the optimized fans are found through efficiency optimization analysis to optimize the efficiency, ensuring that the inspection efficiency is improved while the inspection effect meets the requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 The figure is a system block diagram of the present invention as a whole. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0018] like Figure 1 As shown, an intelligent inspection system for wind turbine power generation based on multi-scenario optimization includes a wind turbine scale analysis module, an inspection mode analysis module, an inspection mode evaluation module and an inspection optimization module; the wind turbine scale analysis module, the inspection mode analysis module, the inspection mode evaluation module and the inspection optimization module are communicatively connected in sequence.

[0019] The wind turbine scale analysis module is used to analyze the scale level of the wind farm: mark the wind farm as an inspection object, obtain the wind turbine blade length YC, the number of wind turbines SL and the site area MJ of the inspection object; wherein, the process of obtaining the wind turbine blade length YC includes: marking the distance from the tip of the wind turbine blade to the center of the blade root as the single blade length, and marking the average value of the single blade length of all wind turbines in the inspection object as the wind turbine blade length YC; the process of obtaining the site area MJ includes: determining the coordinates of each wind turbine on the map through satellite signals, and drawing points on the map according to the coordinates, connecting any two of the coordinate points to form a number of convex polygons, then the convex polygon is the largest convex polygon when there is no coordinate point outside, and the actual area corresponding to the area of ​​the largest convex polygon on the map is marked as the site area MJ; the scale coefficient of the inspection object is obtained by the formula GM=k1*SL+k2*MJ+k3*YC. GM; k1, k2, k3 are all proportional coefficients, and k1>k2>k3>1; compare the scale coefficient GM of the inspection object with the preset high scale value GMmax and low scale value GMmin, and classify the scale level of the inspection object: if the scale coefficient GM is greater than or equal to the high scale value GMmax, the inspection object is marked as a first-level scale object; if the scale coefficient GM is less than the high scale value GMmax and greater than or equal to the low scale value GMmin, the inspection object is marked as a second-level scale object; if the scale coefficient GM is less than the low scale value GMmin, the inspection object is marked as a third-level scale object; the scale coefficient is obtained by calculating and analyzing the blade length, number of wind turbines and site area of ​​the wind farm, so that the scale level of the wind farm is graded according to the scale coefficient, so as to ensure that the scale level of the wind farm under different power generation scenarios can be judged.

[0020] The inspection mode analysis module is used to analyze the inspection mode according to the scale level of the inspection object: generate an inspection cycle of non-fixed duration, and conduct periodic inspections on the inspection objects. The implementation methods of the inspection include: measures A, B, C and D; measure A is to use temperature sensors and vibration sensors to collect the operating status of the internal equipment of the wind turbine in real time; measure B is to use drones equipped with high-definition cameras and infrared thermal imagers to conduct aerial inspections of the wind turbine at different heights and angles; measure C is to use intelligent robots carrying sensors to inspect the internal equipment structure and line connections of the wind turbine according to a predetermined route; measure D is for the inspection personnel to carry detection equipment to inspect the blades, tower and internal equipment of the wind turbine in turn; According to the different scale levels of the inspection objects, the corresponding inspection modes are adopted: the inspection mode for the first-level scale objects is the effect mode, the inspection mode for the second-level scale objects is the mixed mode, and the inspection mode for the third-level scale objects is the economic mode; when the effect mode is used to inspect the first-level scale objects, the combination of measures adopted is measures A + measures B + measures C; when the mixed mode is used to inspect the second-level scale objects, the combination of measures adopted is measures A + measures B; when the economic mode is used to inspect the third-level scale objects, the combination of measures adopted is measures A + measures D; by adopting a variety of inspection technical means, different inspection modes are generated, and corresponding inspection modes are adopted for inspection objects of different scale levels, ensuring that inspection objects from large to small can adopt inspection modes that are both effective and efficient.

[0021] The inspection mode evaluation module is used to evaluate the effect after the inspection is completed: after all wind turbines in the inspection object have completed the inspection, the single-machine inspection time SC, the inspection problem discovery rate FX and the power generation failure rate BH are obtained; among which, the single-machine inspection time SC is the ratio of the inspection time after all wind turbines have completed the inspection to the number of wind turbines SL; the process of obtaining the inspection problem discovery rate FX includes: marking the total number of problems found in the inspection history as the number of problems WT, adding 1 to the inspection number XJ after each inspection, and converting the problem into a problem number WT by the formula FX=WT / (XJ*SL). The number of problems WT, the number of inspections XJ and the number of wind turbines SL are numerically calculated to obtain the inspection problem discovery rate; the process of obtaining the power generation failure rate BH includes: marking the total number of wind turbines whose power generation does not meet the theoretical requirements in the inspection history records as the power generation failure number FW, and using the formula BH=FW / (XJ*SL) to numerically calculate the power generation failure number FW, the total number of inspections XJ and the number of wind turbines SL to obtain the power generation failure rate BH; the inspection effect coefficient XG of the inspection unit adopting the inspection mode is obtained by the formula XG=α1*FX-α2*BH; among which α1, α 2 are proportional coefficients, and α1>α2>1; compare the inspection effect coefficient XG with the preset inspection effect threshold XGmax: if the inspection effect coefficient XG is greater than or equal to the inspection effect threshold XGmax, then it is judged that the inspection mode adopted by the inspection object meets the requirements, and the single-machine inspection time SC of the inspection object is sent to the inspection optimization module; if the inspection effect coefficient XG is less than the inspection effect threshold XGmax, then it is judged that the inspection mode adopted by the inspection object does not meet the requirements and the inspection mode needs to be upgraded: if the inspection object adopts the economic mode, then upgrade It is a mixed mode; if the inspection object adopts the mixed mode, it is upgraded to the effect mode; if the inspection object adopts the effect mode, the inspection mode is upgraded through upgrading measures; the upgrading measures include increasing the number of drones, intelligent robots, and sensors, increasing the inspection frequency, and increasing manual inspections; by calculating and analyzing the inspection problem discovery rate and the power generation unqualified rate after the inspection is completed, the effect coefficient of the inspection object adopting the corresponding inspection mode is obtained, so as to evaluate the inspection effect according to the effect coefficient, and upgrade the inspection mode for the inspection objects that do not meet the effect requirements.

[0022] The inspection optimization module is used to optimize the inspection efficiency of the inspection objects whose inspection modes meet the requirements: compare the single-machine inspection time SC with the preset single-machine inspection time threshold SCmax: if the single-machine inspection time SC is less than the single-machine inspection time threshold SCmax, it is judged that the efficiency of the inspection object adopting this inspection mode meets the requirements and no processing is required; if the single-machine inspection time SC is greater than the single-machine inspection time threshold SCmax, it is judged that the efficiency of the inspection object adopting this inspection mode does not meet the requirements, and an efficiency optimization analysis is performed: the time spent moving between each wind turbine during the drone inspection or manual inspection is marked as the moving time YD; all moving times YD are The moving average time YP and the moving time standard deviation YB are calculated by taking the average value and the standard deviation of the moving time, and the maximum allowable time ZD is obtained by the formula ZD=YD+2*YB; among the inspection objects, if the moving time YD from a certain fan to other fans is greater than the maximum allowable time ZD, the fan will be marked as the optimized fan, and an independent drone or inspection personnel will be configured to inspect the optimized fan; by analyzing the inspection time of a single machine after the inspection is completed, the inspection objects whose inspection efficiency does not meet the requirements are judged, and the optimized fans are found through efficiency optimization analysis for efficiency optimization, ensuring that the inspection efficiency is improved while the inspection effect meets the requirements.

[0023] An intelligent inspection system for wind turbine power generation based on multi-scenario optimization marks a wind farm as an inspection object when working, obtains the wind turbine blade length YC, the number of wind turbines SL and the site area MJ of the inspection object, and performs numerical calculation to obtain the scale coefficient, and classifies the scale level of the inspection object according to the scale coefficient; generates multiple inspection modes by combining multiple inspection technical means, and adopts corresponding inspection modes according to different scale levels; after the inspection is completed, the inspection problem discovery rate FX and the power generation unqualified rate BH are numerically calculated to obtain the effect coefficient, and the inspection mode that does not meet the requirements is judged and upgraded according to the effect coefficient; finally, the inspection efficiency of the inspection mode whose inspection effect meets the requirements is judged according to the inspection time of a single machine, and the optimized wind turbine is found through efficiency optimization analysis to optimize the efficiency.

[0024] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0025] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; such as: formula GM = k1*SL + k2*MJ + k3*YC; technicians in this field collect multiple groups of sample data and set corresponding scale coefficients for each group of sample data; substitute the set scale coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of k1, k2 and k3 as 3.39, 2.01 and 1.64 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding scale coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the scale coefficient is proportional to the value of the number of wind turbines.

[0026] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0027] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A wind turbine power generation intelligent inspection system based on multi-scenario optimization, characterized in that: It includes a wind turbine scale analysis module, an inspection mode analysis module, an inspection mode evaluation module and an inspection optimization module; the wind turbine scale analysis module, the inspection mode analysis module, the inspection mode evaluation module and the inspection optimization module are sequentially connected in communication; The wind turbine scale analysis module is used to analyze the scale level of the wind farm: mark the wind farm as an inspection object, obtain the wind turbine blade length YC, the number of wind turbines SL and the site area MJ of the inspection object, and perform numerical calculation to obtain the scale coefficient GM of the inspection object, and mark the inspection object as a first-level scale object, a second-level scale object and a third-level scale object according to the scale coefficient GM; The inspection mode analysis module is used to analyze the inspection mode according to the scale level of the inspection object: generate an inspection cycle of non-fixed duration, perform periodic inspection on the inspection object, and the implementation methods of the inspection include measures A, measures B, measures C and measures D; the inspection mode includes effect mode, mixed mode and economic mode; the first-level scale object adopts the effect mode, the second-level scale object adopts the mixed mode, and the third-level scale object adopts the economic mode; The inspection mode evaluation module is used to evaluate the effect after the inspection is completed: after all wind turbines in the inspection object have completed the inspection, the single-machine inspection time SC, the inspection problem discovery rate FX and the power generation failure rate BH are obtained, and the effect coefficient XG of the inspection object is obtained by numerical calculation. According to the effect coefficient XG, it is determined whether the inspection mode adopted by the inspection object meets the requirements, and the inspection mode that does not meet the requirements is upgraded; The inspection optimization module is used to optimize the inspection efficiency of the inspection objects whose inspection modes meet the requirements: determine whether the efficiency of the inspection mode meets the requirements through the single-machine inspection duration SC, and perform efficiency optimization analysis on the inspection modes that do not meet the requirements.

2. According to claim 1, a wind turbine power generation intelligent inspection system based on multi-scenario optimization is characterized in that: The process of obtaining the fan blade length YC includes: marking the distance from the tip of the fan blade to the center of the blade root as the single blade length, and marking the average value of the single blade lengths of all fans in the inspection object as the fan blade length YC; the process of obtaining the site area MJ includes: determining the coordinates of each fan on the map through satellite signals, and drawing points on the map according to the coordinates, connecting any two of the coordinate points to form a number of convex polygons, then the convex polygon is the largest convex polygon when there is no coordinate point outside, and the actual area corresponding to the area of ​​the largest convex polygon on the map is marked as the site area MJ.

3. According to claim 2, a wind turbine power generation intelligent inspection system based on multi-scenario optimization is characterized in that: The process of grading the scale level of the inspection object specifically includes: performing numerical calculations on the fan blade length YC, the number of fans SL and the site area MJ to obtain the scale coefficient GM of the inspection object; comparing the scale coefficient GM of the inspection object with the preset high scale value GMmax and low scale value GMmin, and grading the scale level of the inspection object: if the scale coefficient GM is greater than or equal to the high scale value GMmax, the inspection object is marked as a first-level scale object; if the scale coefficient GM is less than the high scale value GMmax and greater than or equal to the low scale value GMmin, the inspection object is marked as a second-level scale object; if the scale coefficient GM is less than the low scale value GMmin, the inspection object is marked as a third-level scale object.

4. The wind turbine power generation intelligent inspection system based on multi-scenario optimization according to claim 3 is characterized in that: The specific implementation methods of the inspection include: Measure A is to use temperature sensors and vibration sensors to collect the operating status of the internal equipment of the wind turbine in real time; Measure B is to use drones equipped with high-definition cameras and infrared thermal imagers to conduct aerial inspections of the wind turbine at different heights and angles; Measure C is to use intelligent robots carrying sensors to inspect the equipment structure and line connections inside the wind turbine along a predetermined route; Measure D is for inspection personnel to carry detection equipment to inspect the blades, tower and internal equipment of the wind turbine in turn.

5. The wind turbine power generation intelligent inspection system based on multi-scenario optimization according to claim 4 is characterized in that: The implementation methods adopted by different inspection modes specifically include: when the effect mode is used to inspect the first-level scale objects, the combination of measures adopted is measure A + measure B + measure C; when the mixed mode is used to inspect the second-level scale objects, the combination of measures adopted is measure A + measure B; when the economic mode is used to inspect the third-level scale objects, the combination of measures adopted is measure A + measure D.

6. The wind turbine power generation intelligent inspection system based on multi-scenario optimization according to claim 5 is characterized in that: The specific process of evaluating the effectiveness of the inspection mode includes: the single-machine inspection time SC is the ratio of the inspection time after all wind turbines have completed the inspection to the number of wind turbines SL; the process of obtaining the inspection problem detection rate FX includes: marking the total number of problems found in the inspection history records as the number of problems WT, adding 1 to the number of inspections XJ after each inspection, and numerically calculating the number of problems WT, the number of inspections XJ and the number of wind turbines SL to obtain the inspection problem detection rate; the process of obtaining the power generation failure rate BH includes: marking the total number of wind turbines whose power generation does not meet the theoretical requirements in the inspection history records as the power generation failure number FW, and numerically calculating the power generation failure number FW, the total number of inspections XJ and the number of wind turbines SL to obtain the power generation failure rate BH; the inspection problem detection rate FX and the power generation failure rate BH are compared. Perform numerical calculation to obtain the inspection effect coefficient XG of the inspection mode adopted by the inspection unit; compare the inspection effect coefficient XG with the preset inspection effect threshold XGmax: if the inspection effect coefficient XG is greater than or equal to the inspection effect threshold XGmax, then it is judged that the inspection mode adopted by the inspection object meets the requirements, and the single-machine inspection duration SC of the inspection object is sent to the inspection optimization module; if the inspection effect coefficient XG is less than the inspection effect threshold XGmax, then it is judged that the inspection mode adopted by the inspection object does not meet the requirements, and the inspection mode needs to be upgraded: if the inspection object adopts the economic mode, it is upgraded to the mixed mode; if the inspection object adopts the mixed mode, it is upgraded to the effect mode; if the inspection object adopts the effect mode, the inspection mode is upgraded through upgrade measures.

7. The wind turbine power generation intelligent inspection system based on multi-scenario optimization according to claim 6 is characterized in that: The specific process of evaluating the efficiency of the inspection mode includes: comparing the single-machine inspection time SC with the preset single-machine inspection time threshold SCmax: if the single-machine inspection time SC is less than the single-machine inspection time threshold SCmax, it is judged that the efficiency of the inspection object adopting this inspection mode meets the requirements and no processing is required; if the single-machine inspection time SC is greater than the single-machine inspection time threshold SCmax, it is judged that the efficiency of the inspection object adopting this inspection mode does not meet the requirements, and efficiency optimization analysis is performed.

8. The wind turbine power generation intelligent inspection system based on multi-scenario optimization according to claim 7 is characterized in that: The specific process of efficiency optimization analysis includes: marking the time of movement between each wind turbine during drone inspection or manual inspection as movement time YD; numerically calculating the average value and standard deviation of all movement time YD to obtain the maximum allowable time ZD; among the inspection objects, if the movement time YD from a certain wind turbine to other wind turbines is greater than the maximum allowable time ZD, then the wind turbine is marked as the optimized wind turbine, and an independent drone or inspection personnel is configured to inspect the optimized wind turbine.

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