High-precision encoder control system based on artificial intelligence

Abstract

The application discloses a high-precision encoder control system based on artificial intelligence, which comprises an information interconnection module, a blade posture prediction module and a high-precision control module. The information interconnection module is used for interconnecting information data between a plurality of groups of wind generator groups through a high-speed communication network. The blade posture prediction module is used for analyzing and predicting the optimal posture angle of the blades of the wind generator groups in real time. The high-precision control module is used for controlling the high-precision control of the blade posture of the target wind generator group according to the analysis result of the blade posture prediction module. The information interconnection module and the blade posture prediction module are network-connected. The blade posture prediction module and the high-precision control module are network-connected. The application has the characteristics of high control precision and strong practicability.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically to a high-precision encoder control system based on artificial intelligence. Background Technology

[0002] In the field of wind power generation, precise control of blade attitude is a key factor in ensuring efficient operation of the unit and maximizing energy conversion efficiency. Traditional blade attitude control systems rely on simple sensors and fixed algorithms, which are difficult to cope with complex and ever-changing wind conditions, resulting in limited control accuracy and consequently affecting power generation efficiency and unit stability.

[0003] With the advancement of technology, especially the rapid development of artificial intelligence, revolutionary changes have been brought about in the blade attitude control of wind turbine generators. By combining artificial intelligence algorithms with high-precision sensors, more intelligent and precise control strategies can be achieved. Among them, the 23-bit absolute encoder, as a high-precision position sensor, has shown great potential in wind turbine generator blade attitude control due to its high resolution, high reliability, and strong anti-interference capabilities. Summary of the Invention

[0004] The purpose of this invention is to provide a high-precision encoder control system based on artificial intelligence to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a high-precision encoder control system based on artificial intelligence, comprising an information interconnection module, a blade attitude prediction module, and a high-precision control module. The information interconnection module is used to interconnect information data between several groups of wind turbine generators via a high-speed communication network. The blade attitude prediction module is used to analyze and predict the optimal attitude angle of the wind turbine generator group blades in real time. The high-precision control module is used to control the attitude of the target wind turbine generator group blades with high precision based on the analysis results of the blade attitude prediction module. The information interconnection module is network-connected to the blade attitude prediction module, and the blade attitude prediction module is network-connected to the high-precision control module.

[0006] According to the above technical solution, the information interconnection module includes a cluster equipment information acquisition module, a cluster location acquisition module, and a regional terrain capture module. The cluster equipment information acquisition module is used to collect the operating data of each wind turbine cluster in real time. The cluster location acquisition module is used to locate and acquire the location of each wind turbine cluster connected to the information interconnection. The regional terrain capture module is used to capture the overall terrain and landform of the area where each wind turbine cluster is located.

[0007] According to the above technical solution, the blade attitude prediction module includes an influence condition analysis module and a blade angle adjustment module. The influence condition analysis module is used to analyze the influence of terrain on airflow among various wind turbine groups and preset mutual influence parameters. The blade angle adjustment module is used to analyze the blade attitude adjustment data corresponding to each wind turbine group when the airflow direction changes.

[0008] According to the above technical solution, the blade angle adjustment module further includes an adjustment angle calculation submodule and an adjustment timing calculation submodule. The adjustment angle calculation submodule is used to analyze and calculate the target angle value for the optimal adjustment of the blades of the target wind turbine, and the adjustment timing calculation submodule is used to analyze and calculate the optimal start time for controlling the blade adjustment of the target wind turbine.

[0009] According to the above technical solution, the high-precision control module includes a trigger timing control module and a blade angle control module. The trigger timing control module is used to control the timing when the drive mechanism of the target wind turbine group adjusts the blade attitude. The blade angle control module is used to control and adjust the blade angle to achieve the precise command target angle.

[0010] According to the above technical solution, the cluster equipment information acquisition module further includes a wind speed acquisition submodule, a wind direction acquisition submodule, and a blade angle monitoring submodule. The wind speed acquisition submodule is used to monitor and acquire the environmental airflow wind speed data received by the wind turbine cluster. The wind direction acquisition submodule is used to monitor and acquire the environmental airflow wind direction data received by the wind turbine cluster. The blade angle monitoring submodule is used to monitor and acquire the rotation angle data of the blades.

[0011] According to the above technical solution, the operation method of the system includes:

[0012] Step S1: Establish an efficient integrated information management platform by connecting the server information channels of multiple wind turbine groups connected to the system through the information interconnection module, and carry out data exchange and information sharing within the platform;

[0013] Step S2: Collect the real-time monitored ambient airflow wind speed value v, wind direction angle value j for each wind turbine group, and collect the precise blade angle value for each wind turbine group. ;

[0014] Step S3: Further obtain the location of each wind turbine group, select all wind turbine group locations in the efficient information integrated management platform, and then use Internet information to capture the terrain and landform of the selected area.

[0015] Step S4: Taking the area where each wind turbine cluster is located as the prediction target, and combining the captured regional topographic features, perform fitting analysis on the range of wind direction angle values ​​affected by the regional topography, from each wind turbine cluster area to the prediction target area. ;

[0016] Step S5: When a wind turbine cluster within the selected area detects a wind direction angle value... And satisfy When this happens, the blade attitude prediction angle adjustment command is initiated, in which... This represents the optimal wind direction angle value corresponding to the current blade angle of one of the wind turbine generator groups. The minimum wind speed angle difference required for blade attitude prediction control is the system preset value; among all wind turbine groups connected to the system, the wind turbine groups that meet the blade attitude angle adjustment conditions are taken as prediction targets for blade angle analysis and calculation.

[0017] Step S6: Obtain the blade angle analysis and calculation results, and start the adjustment program at the optimal time according to the calculation results. During the adjustment, the measured blade angle information is converted into an electrical signal output by a 23-bit absolute encoder. After the signal is transmitted to the system, the precise position of the blade is known in real time by decoding the signal.

[0018] According to the above technical solution, step S5 further includes:

[0019] Step S51: Set the wind direction angle value The range of affected wind direction angles corresponding to each wind turbine group Match one by one and filter out The corresponding wind turbine groups were marked, and each marked wind turbine group was used as a prediction target for blade angle analysis and calculation. ;

[0020] Step S52: Establish a large database, compare and organize historical wind energy capture data, and obtain the conversion parameter values ​​between the predicted wind direction angle value j and the precise blade angle value u of the wind turbine group. ;in ;

[0021] Step S53: Using the formula Calculate and obtain the target angle value for blade adjustment of the wind turbine generator group;

[0022] Step S54: The timing calculation submodule calculates the blade adjustment timing formula for the current wind turbine cluster, including:

[0023] ...Formula (1)

[0024] ...Formula (2)

[0025] ...Formula (3)

[0026] ...Formula (4)

[0027] In the above formula The target angle value for adjusting the blades of the current wind turbine generator group. The precise blade angle value of the current wind turbine cluster, The angle difference that currently requires blade adjustment. The current blade adjustment time value, Adjust the speed value for the blades, The time value for the airflow to travel from the first sensing area to the current target area. The distance from the initial sensing area to the current predicted target area is represented by v, where v is the current ambient airflow speed. The angle between the straight line from the initial sensing area to the current predicted target area and the straight line of airflow direction. This is the opportune time to adjust the blades of the current wind turbine generator group.

[0028] According to the above technical solution, step S6 further includes:

[0029] Step S61: When At that time, the trigger timing control module controls the drive mechanism of the wind turbine generator group in the current predicted target area, starting from the moment the wind direction change is sensed in the first sensing area. The program will begin executing after a few seconds, causing the wind turbine blades to start rotating, adjusting the blade angle, and then proceeding until the adjusted angle reaches its set value. The adjustment process will be completed in time;

[0030] Step S62: When When the wind direction changes in the target area are detected, the timing control module controls the drive mechanism of the wind turbine group in the current prediction target area. Starting from the moment the wind direction changes in the first sensing area, the program is immediately executed to make the wind turbine blades start to rotate and adjust the blade angle. When the airflow with the changing wind direction reaches the wind turbine group in the current prediction target area, the program is adjusted to the optimal target angle value under the measured wind direction angle value based on the real-time data collected by the current group of equipment, and the adjustment program is completed.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention, by setting up an information interconnection module, a blade attitude prediction module and a high-precision control module, can analyze and predict the optimal attitude of the blades of the wind turbine generator when the airflow and wind direction change, as well as the optimal time to adjust the blades. It can also use a 23-bit absolute encoder to capture minute changes in the blades, so that the control system can achieve high-precision control of the blade attitude based on the data provided by the encoder, thereby optimizing the wind energy capture efficiency. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figure 1 This invention provides a technical solution: a high-precision encoder control system based on artificial intelligence, comprising an information interconnection module, a blade attitude prediction module, and a high-precision control module. The information interconnection module is used to interconnect information data between several groups of wind turbine generators via a high-speed communication network. The blade attitude prediction module is used to analyze and predict the optimal attitude angle of the wind turbine generator blades in real time. The high-precision control module is used to control the attitude of the target wind turbine generator blades with high precision based on the analysis results of the blade attitude prediction module. The information interconnection module is network-connected with the blade attitude prediction module, and the blade attitude prediction module is network-connected with the high-precision control module. By setting up the information interconnection module, the blade attitude prediction module, and the high-precision control module, the optimal attitude of the wind turbine generator blades and the optimal timing for adjusting the blades can be analyzed and predicted when the airflow and wind direction change. Furthermore, a 23-bit absolute encoder can capture minute changes in the blades, enabling the control system to achieve high-precision control of the blade attitude based on the data provided by the encoder, thereby optimizing wind energy capture efficiency.

[0036] The information interconnection module includes a cluster equipment information acquisition module, a cluster location acquisition module, and a regional terrain capture module. The cluster equipment information acquisition module is used to collect the operating data of each wind turbine cluster in real time. The cluster location acquisition module is used to locate and acquire the location of each wind turbine cluster connected to the information interconnection. The regional terrain capture module is used to capture the overall terrain and landform of the area where each wind turbine cluster is located.

[0037] The blade attitude prediction module includes an influence condition analysis module and a blade angle adjustment module. The influence condition analysis module is used to analyze the influence of terrain on airflow among various wind turbine groups and preset the mutual influence parameters. The blade angle adjustment module is used to analyze the blade attitude adjustment data of each wind turbine group when the airflow and wind direction change.

[0038] The blade angle adjustment module further includes an adjustment angle calculation submodule and an adjustment timing calculation submodule. The adjustment angle calculation submodule is used to analyze and calculate the target angle value for the optimal adjustment of the blades of the target wind turbine, and the adjustment timing calculation submodule is used to analyze and calculate the optimal start time for controlling the blade adjustment of the target wind turbine.

[0039] The high-precision control module includes a trigger timing control module and a blade angle control module. The trigger timing control module is used to control the timing when the drive mechanism of the target wind turbine group adjusts the blade attitude. The blade angle control module is used to control the adjustment of the blade angle to achieve the precise command target angle. During this process, a 23-bit absolute encoder captures minute changes in the blade to achieve high-precision control of the blade attitude, thereby optimizing the wind energy capture efficiency.

[0040] The equipment information acquisition module for the wind turbine cluster further includes a wind speed acquisition submodule, a wind direction acquisition submodule, and a blade angle monitoring submodule. The wind speed acquisition submodule is used to monitor and acquire the environmental airflow wind speed data received by the wind turbine cluster, the wind direction acquisition submodule is used to monitor and acquire the environmental airflow wind direction data received by the wind turbine cluster, and the blade angle monitoring submodule is used to monitor and acquire the rotation angle data of the blades.

[0041] The system's operation methods include:

[0042] Step S1: Establish an efficient information management platform by connecting the server information channels of multiple wind turbine groups connected to the system through the information interconnection module, and carry out data communication and information sharing within the platform; one wind turbine group consists of several wind turbine groups distributed in the same area, while multiple wind turbine groups are distributed in different areas.

[0043] Step S2: Collect the real-time monitored ambient airflow wind speed value v, wind direction angle value j for each wind turbine group, and collect the precise blade angle value for each wind turbine group. ;

[0044] Step S3: Further obtain the location of each wind turbine group, select all wind turbine group locations in the efficient information integrated management platform, and then use Internet information to capture the terrain and landform of the selected area.

[0045] Step S4: Taking the area where each wind turbine cluster is located as the prediction target, and combining the captured regional topographic features, perform fitting analysis on the range of wind direction angle values ​​affected by the regional topography, from each wind turbine cluster area to the prediction target area. The affected wind direction angle range This indicates that the wind turbine group first senses the change in wind direction and then compares it with the predicted target wind turbine group, at the wind direction angle value. If the two areas are affected by the terrain, the airflow in the area where the wind direction change is first sensed is difficult to effectively reach the predicted target wind turbine group area; for example, there are high mountains between the first sense area and the predicted target area, and the airflow direction is perpendicular to the mountains and blows towards the predicted target wind turbine group area, and the two areas are far apart, the airflow after being blocked by the mountains cannot affect the airflow direction change of the predicted target area; therefore, by repeating step S4, all wind turbine groups can be preset in the system respectively.

[0046] Step S5: When a wind turbine cluster within the selected area detects a wind direction angle value... And satisfy When this happens, the blade attitude prediction angle adjustment command is initiated, in which... This represents the optimal wind direction angle value corresponding to the current blade angle of one of the wind turbine generator groups. The minimum wind speed angle difference required for blade attitude prediction control is a system preset value. Among all wind turbine generator groups connected to the system, those meeting the blade attitude angle adjustment conditions are individually used as prediction targets for blade angle analysis and calculation. When the wind direction change angle is less than the preset value... At this time, because the adjustment angle is relatively small, the adjustment cycle is correspondingly short, and the small angle deviation has a low impact on the wind energy capture efficiency of other wind turbine groups in the area. Furthermore, performing a prediction program with a small wind direction angle deviation will inevitably cause a surge in system computing power, increase hardware costs, and result in the economic benefits of wind energy efficiency not outweighing the additional costs. Therefore, through this step, only when the wind direction change angle is greater than the preset value... Only then is the blade attitude prediction program activated to optimize wind energy capture efficiency and enhance system reliability.

[0047] Step S6: Obtain the blade angle analysis and calculation results, and start the adjustment program at the optimal time according to the calculation results. During the adjustment, the measured blade angle information is converted into an electrical signal output by a 23-bit absolute encoder. After the signal is transmitted to the system, the precise position of the blade is known in real time by decoding the signal.

[0048] Step S5 further includes:

[0049] Step S51: Set the wind direction angle value The range of affected wind direction angles corresponding to each wind turbine group Match one by one and filter out The corresponding wind turbine groups were marked, and each marked wind turbine group was used as a prediction target for blade angle analysis and calculation. This step allows for further screening of wind turbine groups that are affected by topographical factors and have mutual influences under specific wind direction angles. It directly filters out wind turbine groups that have an influence on the predicted target area under the current wind direction angle, thereby effectively reducing unnecessary subsequent blade attitude analysis calculations, reducing the system's maximum computing load, optimizing the program algorithm, and further increasing the practicality of the invention.

[0050] Step S52: Establish a large database, compare and organize historical wind energy capture data, and obtain the conversion parameter values ​​between the predicted wind direction angle value j and the precise blade angle value u of the wind turbine group. ;in ;

[0051] Step S53: Using the formula Calculate and obtain the target angle value for blade adjustment of the wind turbine generator group;

[0052] Step S54: The timing calculation submodule calculates the blade adjustment timing formula for the current wind turbine cluster, including:

[0053] ...Formula (1)

[0054] ...Formula (2)

[0055] ...Formula (3)

[0056] ...Formula (4)

[0057] In the above formula The target angle value for adjusting the blades of the current wind turbine generator group. The precise blade angle value of the current wind turbine cluster, The angle difference that currently requires blade adjustment. The current blade adjustment time value, Adjust the speed value for the blades, The time value for the airflow to travel from the first sensing area to the current target area. The distance from the initial sensing area to the current predicted target area is represented by v, where v is the current ambient airflow speed. The angle between the straight line from the initial sensing area to the current predicted target area and the straight line of airflow direction. This is the opportune time to adjust the blades of the current wind turbine generator group;

[0058] The angle difference and adjustment required for blade adjustment were calculated using formulas (1) and (2) respectively. The required equipment working time value; then, using formula (3), firstly utilize The effective wind speed distribution from the initial sensing area to the current prediction target area was calculated. Then, the time it takes for the airflow to travel from the initial sensing area to the current prediction target area was accurately calculated and predicted by combining the distance between the two. Finally, the blade adjustment timing of the current wind turbine group is accurately calculated using formula (4). This allows for effective analysis and prediction of the blade adjustment timing of the current wind turbine group. After the airflow with a change in wind direction is detected by the first sensing area, the area composed of several wind turbine groups can be controlled and managed in a comprehensive manner. This ensures that no matter how far away the wind turbine group is, as long as it is not affected by the terrain, it can be adjusted at the appropriate time. The airflow with a change in wind direction will arrive at the corresponding area at the moment the adjustment is completed, maximizing the wind energy capture efficiency of the wind turbine group. With the 23-bit absolute encoder as a high-precision position sensor, the existing blade attitude control system can effectively avoid the serious adjustment lag, poor adjustment accuracy, and program chaos that occur when dealing with complex and ever-changing wind conditions. This greatly improves the power generation efficiency and unit stability of wind power generation.

[0059] Step S6 further includes:

[0060] Step S61: When At that time, the trigger timing control module controls the drive mechanism of the wind turbine generator group in the current predicted target area, starting from the moment the wind direction change is sensed in the first sensing area. The program will begin executing after a few seconds, causing the wind turbine blades to start rotating, adjusting the blade angle, and then proceeding until the adjusted angle reaches its set value. The adjustment process will be completed in time;

[0061] Step S62: When When the wind direction changes in the target area are detected, the trigger timing control module controls the drive mechanism of the wind turbine cluster. Starting immediately after the wind direction change is detected in the first area, the program is executed to rotate the wind turbine blades and adjust their angles. When the changing wind reaches the wind turbine cluster in the target area, the blades are adjusted to the optimal target angle based on real-time data collected from the cluster's equipment, completing the adjustment process. If the predicted airflow arrival time is longer than the blade adjustment time, the blades will not be adjusted immediately upon sensing a change in wind direction in the area. This avoids the blades still capturing wind energy from before the change in direction, thus failing to maximize energy capture efficiency. If the predicted airflow arrival time is shorter than the blade adjustment time, the blades are adjusted immediately, aiming to maximize the angle adjustment as soon as the changing airflow arrives at the wind turbine cluster, thereby increasing energy recovery efficiency. After the changing airflow arrives, the program does not continue to execute according to the predicted angle but adjusts according to the actual monitoring data, making the adjusted angle more accurate than the predicted value.

[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0063] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-precision encoder control system based on artificial intelligence, characterized in that: The system includes an information interconnection module, a blade attitude prediction module, and a high-precision control module. The information interconnection module is used to interconnect information data between several groups of wind turbine generators through a high-speed communication network. The blade attitude prediction module is used to analyze and predict the optimal attitude angle of the wind turbine generator group blades in real time. The high-precision control module is used to control the attitude of the target wind turbine generator group blades with high precision based on the analysis results of the blade attitude prediction module. The information interconnection module is network-connected with the blade attitude prediction module, and the blade attitude prediction module is network-connected with the high-precision control module. The system's operation methods include: Step S1: Establish an efficient integrated information management platform by connecting the server information channels of multiple wind turbine groups connected to the system through the information interconnection module, and carry out data exchange and information sharing within the platform; Step S2: Collect the real-time monitored ambient airflow wind speed value v, wind direction angle value j for each wind turbine group, and collect the precise blade angle value for each wind turbine group. ; Step S3: Further obtain the location of each wind turbine group, select all wind turbine group locations in the efficient information integrated management platform, and then use Internet information to capture the terrain and landform of the selected area. Step S4: Taking the area where each wind turbine cluster is located as the prediction target, and combining the captured regional topographic features, perform fitting analysis on the range of wind direction angle values ​​affected by the regional topography, from each wind turbine cluster area to the prediction target area. ; Step S5: When a wind turbine cluster within the selected area detects a wind direction angle value... And satisfy When this happens, the blade attitude prediction angle adjustment command is initiated, in which... This represents the optimal wind direction angle value corresponding to the current blade angle of one of the wind turbine generator groups. The minimum wind speed angle difference required for blade attitude prediction control is the system preset value; among all wind turbine groups connected to the system, the wind turbine groups that meet the blade attitude angle adjustment conditions are taken as prediction targets for blade angle analysis and calculation. Step S6: Obtain the blade angle analysis and calculation results, and start the adjustment program at the optimal time according to the calculation results. During the adjustment, the measured blade angle information is converted into an electrical signal output by a 23-bit absolute encoder. After the signal is transmitted to the system, the precise position of the blade is known in real time by decoding the signal. Step S5 further includes: Step S51: Set the wind direction angle value The range of affected wind direction angles corresponding to each wind turbine group Match one by one and filter out The corresponding wind turbine groups were marked, and each marked wind turbine group was used as a prediction target for blade angle analysis and calculation. ; Step S52: Establish a large database, compare and organize historical wind energy capture data, and obtain the conversion parameter values ​​between the predicted wind direction angle value j and the precise blade angle value u of the wind turbine group. ;in ; Step S53: Using the formula Calculate and obtain the target angle value for blade adjustment of the wind turbine generator group; Step S54: The timing calculation submodule calculates the blade adjustment timing formula for the current wind turbine cluster, including: ...Official (1) ...Official (2) ...Official (3) ...Official (4) In the above formula The target angle value for adjusting the blades of the current wind turbine generator group. The precise blade angle value of the current wind turbine cluster, The angle difference that currently requires blade adjustment. The current blade adjustment time value, Adjust the speed value for the blades, The time value for the airflow to travel from the first sensing area to the current target area. The distance from the initial sensing area to the current predicted target area is represented by v, where v is the current ambient airflow speed. The angle between the straight line from the initial sensing area to the current predicted target area and the straight line of airflow direction. This is the opportune time to adjust the blades of the current wind turbine generator group.

2. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: The information interconnection module includes a cluster equipment information acquisition module, a cluster location acquisition module, and a regional terrain capture module. The cluster equipment information acquisition module is used to collect the operating data of each wind turbine cluster in real time. The cluster location acquisition module is used to locate and acquire the location of each wind turbine cluster connected to the information interconnection. The regional terrain capture module is used to capture the overall terrain and landform of the area where each wind turbine cluster is located.

3. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: The blade attitude prediction module includes an influence condition analysis module and a blade angle adjustment module. The influence condition analysis module is used to analyze the influence of terrain on airflow among various wind turbine groups and preset mutual influence parameters. The blade angle adjustment module is used to analyze the blade attitude adjustment data of each wind turbine group when the airflow direction changes.

4. The high-precision encoder control system based on artificial intelligence according to claim 3, characterized in that: The blade angle adjustment module further includes an adjustment angle calculation submodule and an adjustment timing calculation submodule. The adjustment angle calculation submodule is used to analyze and calculate the target angle value for the optimal adjustment of the blades of the target wind turbine, and the adjustment timing calculation submodule is used to analyze and calculate the optimal start time for controlling the blade adjustment of the target wind turbine.

5. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: The high-precision control module includes a trigger timing control module and a blade angle control module. The trigger timing control module is used to control the timing when the drive mechanism of the target wind turbine group adjusts the blade attitude. The blade angle control module is used to control and adjust the blade angle to achieve the precise command target angle.

6. The high-precision encoder control system based on artificial intelligence according to claim 2, characterized in that: The equipment information acquisition module for the wind turbine cluster further includes a wind speed acquisition submodule, a wind direction acquisition submodule, and a blade angle monitoring submodule. The wind speed acquisition submodule is used to monitor and acquire the wind speed data of the ambient airflow received by the wind turbine cluster. The wind direction acquisition submodule is used to monitor and acquire the wind direction data of the ambient airflow received by the wind turbine cluster. The blade angle monitoring submodule is used to monitor and acquire the rotation angle data of the blades.

7. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: Step S6 further includes: Step S61: When At that time, the trigger timing control module controls the drive mechanism of the wind turbine generator group in the current predicted target area, starting from the moment the wind direction change is sensed in the first sensing area. The program will begin executing after a few seconds, causing the wind turbine blades to start rotating, adjusting the blade angle, and then proceeding until the adjusted angle reaches its set value. The adjustment process will be completed in time; Step S62: When When the wind direction changes in the target area are detected, the timing control module controls the drive mechanism of the wind turbine group in the current prediction target area. Starting from the moment the wind direction changes in the first sensing area, the program is immediately executed to make the wind turbine blades start to rotate and adjust the blade angle. When the airflow with the changing wind direction reaches the wind turbine group in the current prediction target area, the program is adjusted to the optimal target angle value under the measured wind direction angle value based on the real-time data collected by the current group of equipment, and the adjustment program is completed.

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