Artificial Intelligence Monitoring of Wind-Solar Hybrid Power Generation System Based on Multidimensional Sensing Data

Abstract

The present invention discloses an artificial intelligence monitoring integrated wind-solar power generation system based on multi-dimensional sensing data, which integrates multi-dimensional sensing data of irradiance sensors, wind direction and wind speed sensors, reads data such as the instantaneous power of wind power generation and solar power generation in real time through a management center, performs intelligent calibration on the instantaneous wind power generation power and efficiency, and the instantaneous solar power generation power and efficiency within a specified sampling time, so as to obtain an intelligent calibration reference value range after the system leaves the factory initially. Finally, the target value obtained within the sampling time period after the system actually operates is compared and analyzed with the calibration reference value range, and the current equipment operation state is intelligently characterized according to the analysis result, and aging assessment and maintenance suggestions are given.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring wind-solar integrated power generation systems, and in particular to artificial intelligence monitoring of wind-solar integrated power generation systems based on multi-dimensional sensor data. Background Art

[0002] In today's global energy landscape, with the increasing depletion of traditional fossil energy and the increasing awareness of environmental protection, the development and utilization of new energy has become the focus of attention of countries around the world. As the most promising renewable energy sources, wind and solar energy account for an increasing proportion of the energy structure. However, wind and solar power generation systems are greatly affected by natural conditions (such as irradiance, wind speed, wind direction, etc.), and their output power has significant intermittency and uncertainty, which poses a challenge to the stable operation of the power grid and the effective management of energy.

[0003] Traditional wind and solar power generation monitoring systems often rely on single sensor data, such as monitoring solar power generation efficiency only through irradiance sensors, or monitoring wind power generation status only through wind direction and wind speed sensors. This monitoring method is difficult to fully and accurately reflect the actual operating status of the system. In addition, these systems usually lack intelligent calibration and fault prediction capabilities, resulting in delayed equipment maintenance, reduced operating efficiency, and even possible safety accidents.

[0004] With the rapid development of artificial intelligence technology, its advantages in data processing, pattern recognition, and predictive analysis have provided new possibilities for the intelligent monitoring of wind and solar power generation systems. By integrating multi-dimensional sensor data, such as irradiance, wind direction, wind speed, etc., combined with a remote monitoring platform, real-time monitoring and intelligent analysis of key indicators such as instantaneous power of wind power generation and solar power generation can be achieved. This can not only improve the accuracy and comprehensiveness of monitoring, but also realize intelligent evaluation of the operating status of equipment through mining and learning of historical data, timely discover potential faults, and provide a scientific basis for equipment repair and maintenance.

[0005] Against the backdrop of increasing uncertainty in the current geopolitical situation, strategic competition in the global new energy industry is becoming increasingly fierce. Improving the operating efficiency of wind and solar power generation systems, reducing maintenance costs, and extending equipment life are of great significance to enhancing the competitiveness of the country's new energy industry. Therefore, the development of an artificial intelligence monitoring wind and solar integrated power generation system based on multi-dimensional sensor data has become a technical problem that needs to be solved urgently in the field of new energy. Summary of the invention

[0006] In view of the above situation, the object of the present invention is to provide an artificial intelligence monitoring integrated wind-solar power generation system based on multi-dimensional sensing data, obtain target values within the sampling time period after the actual operation of the system, compare and analyze them with the calibration reference value range, and intelligently characterize the current equipment operation status according to the analysis results, and give aging evaluation and maintenance suggestions.

[0007] The technical solution it adopts is to include a wind power generation intelligent monitoring module, a solar power generation intelligent monitoring module, and a management center. The working process of the wind power generation intelligent monitoring module includes wind energy intelligent calibration and wind energy intelligent monitoring. The working process of wind energy intelligent calibration is as follows;

[0008] S101, Start to initiate wind energy intelligent calibration;

[0009] S102, Within one day or one week after the equipment is installed and operated, remotely obtain real-time data of wind speed changes through a wind speed and direction sensor, and draw a real-time change curve v(t) of the wind speed within this time period;

[0010] S103, Through the management center, obtain the curve graph P(t) of the instantaneous power of wind power generation changing with time and the real-time change curve v(t) of the wind speed within this time period;

[0011] S104, According to the real-time change curve v(t) of the wind speed and the real-time change curve P(t) of the power, draw the real-time change curve P(v) of the wind power generation power with respect to the wind speed within the wind speed interval within the corresponding time period;

[0012] S105, According to the real-time change curve P(v) of the wind power generation power with respect to the wind speed within the wind speed interval , use the wind power generation power reference formula P = η × ρ × S × v 3 , where ρ is the air density, S is the blade swept area, v is the real-time wind speed, and calculate the curve of the power generation efficiency changing with the wind speed within this time period;

[0013] S106, Calculate the average power generation power in this time period and the average wind speed ), T represents the time length of this time period, and use the above wind power generation power reference formula to calculate the average power generation efficiency within this time period, that is , and at the same time calculate the standard deviation of the wind power generation efficiency fluctuation within this time period;

[0014] Or directly according to the curve of power generation efficiency varying with wind speed , count the wind speed range within the average power generation efficiency and the standard deviation of the fluctuation of wind power generation efficiency ;

[0015] S107: When it is detected that the wind speed range exceeds the original statistical time period, the statistical time period will be intelligently extended, and the real-time change curve P(v) with wind speed within the newly learned and statistically new time period will be re-learned and statistically analyzed , the curve of power generation efficiency varying with wind speed and the updated average power generation efficiency ;

[0016] S108, by monitoring the curve of real-time power generation efficiency varying with wind speed and the average power generation efficiency , define the reference value range as follows

[0017] , ; among them is the average power generation efficiency within the latest statistical period is the new standard deviation of the fluctuation of wind power generation efficiency. Define this range as the working efficiency reference range after the actual operation start calibration of the system in a certain place, that is, the normally measured power generation efficiency will be within this range;

[0018] According to the wind energy power generation efficiency reference range, combined with the measured real-time wind speed change curve v(t) and the wind energy power generation power reference formula, the calibration reference range of the real-time wind energy power generation power is given as follows

[0019] ;

[0020] Among them and are respectively the average wind energy power generation power and the standard deviation of power fluctuation within the statistical wind speed range.

[0021] Furthermore, the intelligent wind energy monitoring work process is as follows;

[0022] S109, start the intelligent wind energy monitoring module;

[0023] S110, first determine an arbitrary sampling time period, that is, after the equipment is installed, started, and actually operating, in normal wind speed weather, select an arbitrary short sampling time period;

[0024] S111, repeat the above process of S101~106;

[0025] ​S112. Calculate and extract the real-time wind power generation during this sampling period curve and power generation efficiency curve as well as the average wind power generation efficiency ;

[0026] S113. During this sampling period and within the corresponding interval , evaluate the curve of the actual operating wind power generation varying with wind speed , and the deviation between it and the real-time change curve P(v) of wind speed within the wind speed interval at the start of the intelligent calibration phase. Evaluate the actual power generation efficiency curve , average power generation efficiency and the calibration reference interval for comparison, and evaluate the rationality of the change in wind power generation efficiency according to the deviation range;

[0027] S114. According to the evaluation and prediction results, that is, the degree of deviation between the actual value and the start calibration reference value, give the user reference opinions on the aging and life assessment of the motor. According to the deviation degree, give suggestions on whether it is operating normally overall and the degree of aging.

[0028] Furthermore, the working process of the solar power generation intelligent monitoring module includes solar intelligent calibration and solar intelligent monitoring. The working process of solar intelligent calibration is as follows;

[0029] S201. When the power generation system is initially installed at the factory, start solar intelligent calibration;

[0030] S202. During a normal day of sunlight, obtain the curve of the instantaneous power of solar power generation varying with time during this period through the management center , and calculate the total power generation of solar energy during this period ;

[0031] S203. Record the real-time sunlight intensity through an irradiance meter and draw the real-time change curve of the solar radiation power during this period ;

[0032] S204. According to the real-time change curve of the solar radiation power and the curve of the instantaneous power of solar power generation varying with time , determine the sunlight intensity interval of solar energy, and draw the curve of the solar cell power varying with sunlight intensity ( I );

[0033] S205. According to the curve of the solar cell power varying with sunlight intensity ( I), using the formula , where ζ is the efficiency of the solar cell, to calculate and evaluate the relationship curve of the power generation efficiency of the solar cell changing with the sunlight intensity and the curve of the change in the power generation efficiency of the solar cell ;

[0034] S206, according to the curve of the change in the power generation efficiency of the solar cell and the curve of the change in the power of the solar cell with the sunlight intensity ( I ), to evaluate the length of the effective sunlight period within a day ;

[0035] The specific method is that when it is monitored that the efficiency = 0 or the curve of the instantaneous power of solar power generation changing with time = 0, the corresponding time period is an invalid time period, and the remaining time periods are counted as effective time periods ;

[0036] Or, monitor the real-time change curve of the solar radiation power when = 0, and define the critical value as the sunlight intensity threshold for operation in this place , and the sunlight time period higher than the threshold is the effective sunlight time, and the length of the time period is recorded as ; ;

[0037] At the same time, within the effective sunlight time period determined according to the previous evaluation , calculate and evaluate the average power generation power of the solar cell and the average sunlight intensity , and use the relationship curve of the power generation efficiency of the solar cell changing with the sunlight intensity within the effective time period or the curve of the change in the power generation efficiency of the solar cell to calculate the average power generation efficiency within this time period ;

[0038] S207, when it is monitored that the maximum sunlight intensity exceeds the previously calibrated range, the statistical time period will be intelligently extended and the above S201 - S206 processes will be repeated to re - learn and statistically analyze the curve of the change in the power of the solar cell with the sunlight intensity within the new time period , the relationship curve of the power generation efficiency of the solar cell changing with the sunlight intensity and the average power generation efficiency ;

[0039] S208, by monitoring the relationship curve of the power generation efficiency of the solar cell changing with the sunlight intensity and the average power generation efficiency , the reference value range is defined as follows,

[0040] , ;

[0041] Among them, is the average power generation efficiency, is the standard deviation of the power generation efficiency of the solar cell varying with the sunlight intensity. Therefore, this interval is defined as the start-up calibration efficiency reference interval after the system operates in a certain place actually, that is, the measured power generation efficiency will be within this interval;

[0042] According to the solar power generation efficiency reference interval, combined with the real-time change curve of the measured solar radiation power , the corresponding real-time calibration reference interval of the solar cell power is given as follows:

[0043] , ;

[0044] Among them, and are respectively the average power of solar power generation and the standard deviation of power fluctuation within the statistical sunlight intensity interval.

[0045] Furthermore, the working process of solar intelligent monitoring is as follows;

[0046] S209, after the equipment installation start-up calibration operation, start the solar cell intelligent monitoring;

[0047] S210, regularly start the intelligent monitoring process, that is, within a planned sampling time period, start the solar cell intelligent monitoring;

[0048] S211, restart the above S202~S206 processes;

[0049] S212, calculate and obtain the actual operating solar power generation power curve , the power generation efficiency curve and the average power generation efficiency ;

[0050] S213, within this sampling time period, evaluate the deviation of the solar power generation power curve , the power generation efficiency curve and the average power generation efficiency from the simulated reference interval values in the start-up calibration stage , , and evaluate the deviation of the actual operating power generation efficiency curve from the average power generation efficiency ​​Compare with the calibration reference interval value, evaluate its deviation range, and evaluate the rationality of the change in power generation efficiency;

[0051] S214. According to the evaluation and prediction results, that is, the deviation degree between the actual value and the start calibration reference value, give reference opinions on the aging and life evaluation of the solar cell and the maintenance of surface contamination.

[0052] Due to the adoption of the above technical solutions, the present invention has the following advantages compared with the prior art;

[0053] The management center reads the instantaneous power and other data of wind power generation and solar power generation in real time, performs intelligent calibration on the instantaneous wind power generation power and efficiency, and the instantaneous solar power generation power and efficiency within the scheduled sampling time, thereby obtaining the intelligent calibration reference value interval after the system is initially factory-produced. Finally, the target value obtained within the sampling time period after the system actually operates is compared and analyzed with the calibration reference value interval, and the current equipment operation state is intelligently characterized according to the analysis results, and aging evaluation and maintenance suggestions are given. Description of the Drawings

[0054] Figure 1 It is a flowchart of the wind power generation intelligent monitoring module of the artificial intelligence monitoring wind-solar integrated power generation system based on multi-dimensional sensing data of the present invention.

[0055] Figure 2 It is a flowchart of the solar power generation intelligent monitoring module of the artificial intelligence monitoring wind-solar integrated power generation system based on multi-dimensional sensing data of the present invention. Detailed Embodiments

[0056] Regarding the foregoing and other technical contents, features and effects of the present invention, they will be clearly presented in the following detailed description of the embodiments in conjunction with the reference drawings. The structural contents mentioned in the following embodiments are all referenced to the drawings of the specification. Figures 1 to 2 In the following detailed description of the embodiments, the foregoing and other technical contents, features and effects of the present invention will be clearly presented. The structural contents mentioned in the following embodiments are all referenced to the drawings of the specification.

[0057] An artificial intelligence monitoring wind-solar integrated power generation system based on multi-dimensional sensing data includes a wind power generation intelligent monitoring module, a solar power generation intelligent monitoring module, and a management center. The working process of the wind power generation intelligent monitoring module includes wind energy intelligent calibration and wind energy intelligent monitoring. The working process of wind energy intelligent calibration is as follows;

[0058] S101. Start the wind energy intelligent calibration;

[0059] S102. Within one day or one week after the equipment is installed and operated, remotely obtain the real-time data of the wind speed change through the wind speed and direction sensor, and draw the real-time change curve v(t) of the wind speed within this time period;

[0060] S103. Obtain the curve graph P(t) of the instantaneous power of wind power generation varying with time and the real-time wind speed change curve v(t) within this time period through the management center;

[0061] S104. According to the real-time wind speed change curve v(t) and the real-time power change curve P(t), plot the real-time change curve P(v) of the wind power generation power with respect to the wind speed within the wind speed range statistically corresponding to the time period ;

[0062] S105. According to the real-time change curve P(v) of the wind power generation power with respect to the wind speed within the wind speed range , use the wind power generation power reference formula P = η × ρ × S × v 3 , where ρ is the air density, S is the blade swept area, v is the real-time wind speed, and calculate and evaluate the curve of the power generation efficiency varying with the wind speed within this time period;

[0063] S106. Calculate the average power generation power within this time period and the average wind speed ), T represents the time length of this time period, and use the above wind power generation power reference formula to calculate the average power generation efficiency within this time period, that is , and at the same time calculate the standard deviation of the fluctuation of the wind power generation efficiency within this time period;

[0064] Or directly according to the curve of the power generation efficiency varying with the wind speed, statistically calculate the average power generation efficiency within the wind speed range and the standard deviation of the fluctuation of the wind power generation efficiency;

[0065] S107: When it is detected that the wind speed range exceeds the original statistical time period, intelligently extend the statistical time period and re-learn and statistically calculate the real-time change curve P(v) of the wind power generation power with respect to the wind speed within the wind speed range within the new time period, the curve of the power generation efficiency varying with the wind speed, and the updated average power generation efficiency ;

[0066] S108. By monitoring the curve of the real-time power generation efficiency varying with the wind speed and the average power generation efficiency , define the reference value range as follows,

[0067] ​ , ; Among them, is the average power generation efficiency within the latest statistical interval, is the standard deviation of the fluctuation of the new wind power generation efficiency. Define this interval as the working efficiency reference interval after the system is calibrated during actual operation at a certain location. That is, the normally measured power generation efficiency will be within this interval;

[0068] According to the wind power generation efficiency reference interval, combined with the measured real-time wind speed change curve v(t) and the wind power generation power reference formula, the calibration reference interval of the real-time wind power generation power is given accordingly as follows,

[0069] ;

[0070] Among them, and are respectively the average wind power generation power and the standard deviation of power fluctuation within the statistical wind speed interval.

[0071] Furthermore, the intelligent wind energy monitoring work process is as follows;

[0072] S109, Start the intelligent wind energy monitoring module;

[0073] S110, First determine an arbitrary sampling time period, that is, after the equipment is installed, started, and actually operating, select an arbitrary short sampling time period under normal wind speed weather;

[0074] S111, Repeat the above process of S101~106;

[0075] S112, Calculate and extract the real-time wind power generation power curve, power generation efficiency curve and the average wind power generation efficiency within this sampling time period;

[0076] S113, Evaluate the deviation between the curve of the actual operating wind power generation power changing with the wind speed within this sampling time period and the corresponding interval and the real-time change curve P(v) of the wind speed within the wind speed interval during the start-up intelligent calibration stage. Evaluate the deviation between the actual power generation efficiency curve , the average power generation efficiency and the calibration reference interval and evaluate the rationality of the change in wind power generation efficiency according to its deviation range;

[0077] S114. Based on the evaluation and prediction results, that is, the deviation degree between the actual value and the starting calibration reference value, give user reference opinions on the aging and life evaluation of the motor, and give suggestions on whether it is operating normally overall and the aging degree according to the deviation degree.

[0078] Further, the working process of the solar power generation intelligent monitoring module includes solar intelligent calibration and solar intelligent monitoring. The working process of solar intelligent calibration is as follows:

[0079] S201. When the power generation system is initially installed at the factory, start solar intelligent calibration.

[0080] S202. On a day with normal sunlight, obtain the curve graph of the instantaneous power of solar power generation changing with time during this period through the management center , and calculate the total power generation of solar energy during this period ;

[0081] S203. Record the real-time sunlight intensity through an irradiance meter and draw the real-time change curve of the solar radiation power during this period ;

[0082] S204. According to the real-time change curve of the solar radiation power and the curve graph of the instantaneous power of solar power generation changing with time , determine the sunlight intensity interval of solar energy , and draw the curve of the solar cell power changing with the sunlight intensity ( I );

[0083] S205. According to the curve of the solar cell power changing with the sunlight intensity ( I ), use the formula , where ζ is the efficiency of the solar cell, to calculate and evaluate the relationship curve of the power generation efficiency of the solar cell changing with the sunlight intensity and the curve of the change in the power generation efficiency of the solar cell ;

[0084] S206. According to the curve of the change in the power generation efficiency of the solar cell and the curve of the solar cell power changing with the sunlight intensity ( I ), evaluate the length of the effective sunlight time period within a day ;

[0085] Specifically, when it is monitored that the efficiency = 0 or the curve graph of the instantaneous power of solar power generation changing with time The time period corresponding to =0 is an invalid time period, and the remaining time periods are counted as valid time periods ;

[0086] Or, monitor The real-time change curve of the solar radiation power when =0 The critical value is defined as the sunshine intensity threshold for operation in this area , and only the sunshine time period above the threshold is valid sunshine time, and the time period length is recorded as ;

[0087] At the same time, within the valid sunshine time period determined according to the previous evaluation , calculate the evaluated average power generation power of the solar cell And the average sunshine intensity , and use the relationship curve of the power generation efficiency of the solar cell changing with the sunshine intensity within the valid time period Or the change curve of the power generation efficiency of the solar cell , calculate the average power generation efficiency within this time period ;

[0088] S207, when it is monitored that the maximum sunshine intensity exceeds the previously calibrated range, the statistical time period will be intelligently extended and the above S201 - S206 process will be repeated to re - learn and statistically analyze the change curve of the solar cell power changing with the sunshine intensity within the new time period The relationship curve of the power generation efficiency of the solar cell changing with the sunshine intensity And the average power generation efficiency ;

[0089] S208, by monitoring the relationship curve of the power generation efficiency of the solar cell changing with the sunshine intensity And the average power generation efficiency , define the reference value interval as follows

[0090] , ;

[0091] Among them, Is the average power generation efficiency, Is the standard deviation of the power generation efficiency of the solar cell changing with the sunshine intensity. Therefore, this interval is defined as the start - up calibration efficiency reference interval after the system operates in a certain area, that is, the normally measured power generation efficiency will be within this interval;

[0092] According to the solar power generation efficiency reference interval, combined with the real - time change curve of the measured solar radiation power , the following real - time calibration reference interval of the solar cell power is given accordingly: ​

[0093] , ;

[0094] Among them, and are respectively the standard deviation of the average power generation and power fluctuation of solar power generation within the statistical sunshine intensity interval.

[0095] Furthermore, the working process of solar energy intelligent monitoring is as follows;

[0096] S209, after the equipment installation, startup and calibration are completed and running, start the intelligent monitoring of the solar cell;

[0097] S210, regularly start the intelligent monitoring process, that is, within a scheduled sampling time period, start the intelligent monitoring of the solar cell;

[0098] S211, restart the above S202 - S206 process;

[0099] S212, calculate the actually operating solar power generation power curve , the power generation efficiency curve and the average power generation efficiency ;

[0100] S213, within this sampling time period, evaluate the deviation of the solar power generation power curve , the power generation efficiency curve and the average power generation efficiency from the simulated reference interval values in the startup calibration stage , , evaluate the deviation of the actually operating power generation efficiency curve from the average power generation efficiency and compare it with the calibration reference interval value, evaluate its deviation range, and evaluate the rationality of the change in power generation efficiency;

[0101] S214, according to the evaluation and prediction results, that is, the deviation degree between the actual value and the startup calibration reference value, give reference opinions on the aging and life assessment of the solar cell and the maintenance of surface contamination. For example, according to the deviation degree, give whether it is operating normally overall or the aging degree (such as 5%, 10%, etc.), the expected service life, and even the possibility of abnormal operation or damage due to external factors, and give suggestions on the subsequent equipment maintenance.

[0102] The above is a further detailed description of the present invention in combination with specific embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to this; for those skilled in the art of the present invention and related technical fields, any expansion, operation methods, and data replacement made on the premise of the technical solution idea of the present invention should fall within the protection scope of the present invention.​

Claims

1. An artificial intelligence monitoring integrated wind and solar power generation system based on multi-dimensional sensing data, characterized in that, It includes a wind power generation intelligent monitoring module, a solar power generation intelligent monitoring module, and a management center. The working process of the wind power generation intelligent monitoring module includes wind energy intelligent calibration and wind energy intelligent monitoring. The working process of wind energy intelligent calibration is as follows; S101. Start to initiate wind energy intelligent calibration; S102. Within one day or one week after the equipment is installed and running, remotely obtain the real-time data of wind speed changes through a wind speed and direction sensor, and draw the real-time change curve v(t) of the wind speed within this time period; S103. Through the management center, obtain the curve graph P(t) of the instantaneous power of wind power generation changing with time and the real-time change curve v(t) of the wind speed within this time period; S104. According to the real-time wind speed change curve v(t) and the real-time power change curve P(t), plot the real-time change curve P(v) of the wind power generation power with respect to the wind speed within the wind speed range statistically corresponding to its corresponding time period ; S105, according to the real-time variation curve P(v) of wind speed within the wind speed range , using the reference formula for wind power generation P = η × ρ × S × v 3 , where ρ is the air density, S is the blade swept area, v is the real-time wind speed, calculate and evaluate the curve of the power generation efficiency varying with wind speed during this time period; S106, calculate the average power generation during this time period ), and the average wind speed ), where T represents the time length of this time period, and using the above reference formula for wind power generation, calculate the average power generation efficiency during this time period, that is , and at the same time calculate the standard deviation of the fluctuation of the wind power generation efficiency during this time period ; Or directly according to the curve of power generation efficiency varying with wind speed , count the average power generation efficiency within the wind speed range and the standard deviation of the fluctuation of wind power generation efficiency ; S107: When it is detected that the wind speed exceeds the range within the original statistical time period, the statistical time period will be intelligently extended to re-learn and statistically calculate the wind speed range within the new time period the real-time change curve P(v) of the wind speed and the curve of the power generation efficiency changing with the wind speed within as well as the updated average power generation efficiency ; S108, by monitoring the curve of the real-time power generation efficiency varying with the wind speed and the average power generation efficiency , the reference value range is defined as follows , ; Among them, is the average power generation efficiency within the latest statistical period, is the standard deviation of the fluctuation of the new wind power generation efficiency. This interval is defined as the reference interval for the working efficiency after the actual operation start-up calibration of the system in a certain place. That is, the normally measured power generation efficiency will be within this interval;​ According to the reference interval of wind power generation efficiency, combined with the measured real-time wind speed change curve v(t) and the wind power generation power reference formula, the calibration reference interval of the real-time wind power generation power is given as follows, ； Among them, and are the average power of wind power generation and the standard deviation of power fluctuation within the statistical wind speed range, respectively.

2. The artificial intelligence monitoring integrated wind-solar power generation system based on multi-dimensional sensing data according to claim 1, characterized in that The working process of wind energy intelligent monitoring is as follows; S109. Start the wind energy intelligent monitoring module; S110. First, determine an arbitrary sampling time period, that is, after the equipment is installed, started, and actually running, in normal wind speed weather, select an arbitrary short sampling time period; S111. Repeat the processes of S101 - 106 above; S112. Calculate and extract the real-time wind power generation power during the sampling period curve and power generation efficiency curve as well as the average wind power generation efficiency ; S113, within this sampling time period and corresponding interval , evaluate the curve of the actual operating wind power generation power varying with wind speed , and the deviation from the real-time change curve P(v) of wind speed within the wind speed interval when starting the intelligent calibration stage. Evaluate the actual power generation efficiency curve , average power generation efficiency and calibration reference interval for comparison, and evaluate the rationality of the change in wind power generation efficiency according to its deviation range; S114. According to the evaluation and prediction results, that is, the deviation degree between the actual value and the start calibration reference value, give the user reference opinions on the aging and life evaluation of the motor, and give suggestions on whether it is operating normally overall and the aging degree according to the deviation degree.

3. The artificial intelligence monitoring integrated wind-solar power generation system based on multi-dimensional sensing data according to claim 1 or 2, characterized in that The working process of the solar power generation intelligent monitoring module includes solar energy intelligent calibration and solar energy intelligent monitoring. The working process of solar energy intelligent calibration is as follows; S201. When the power generation system is initially installed at the factory, start solar energy intelligent calibration; S202. On a normal sunny day, obtain the curve graph showing the variation of the instantaneous power of solar power generation with time during this period through the management center , and calculate the total power generation of solar energy during this period ; S203, Record the real-time sunlight intensity with an irradiance meter and plot the real-time change curve of the solar radiation power during this time period ; S204, according to the real-time change curve of solar radiation power and the curve graph of the instantaneous power of solar power generation changing with time , determine the sunshine intensity interval of solar energy , and draw the curve of the power of the solar cell changing with the sunshine intensity ( I ); S205, according to the variation curve of the power of the solar cell with the sunlight intensity ( I ), use the formula , where ζ is the efficiency of the solar cell, to calculate and evaluate the relationship curve of the power generation efficiency of the solar cell varying with the sunlight intensity and the variation curve of the power generation efficiency of the solar cell ; S206, according to the solar cell power generation efficiency change curve and the solar cell power change curve with the sunlight intensity ( I ) to evaluate the length of the effective sunshine time period within a day ; The specific method is that when it is monitored that the efficiency = 0 or the curve of the instantaneous power of solar power generation changing with time = 0, the corresponding time period is an invalid time period, and the remaining time periods are counted as valid time periods ; Alternatively, monitor the real-time change curve of the solar radiation power when = 0 The critical value is defined as the sunshine intensity threshold for operation at this location , and only the sunshine time period above the threshold is the effective sunshine time, and the length of the time period is denoted as ; Meanwhile, within the effective sunshine time period determined according to the previous evaluation calculate the evaluated average solar cell power generation and the average sunshine intensity and use the relationship curve of the solar cell power generation efficiency varying with the sunshine intensity within the effective time period or the curve of the solar cell power generation efficiency variation to calculate the average power generation efficiency within this time period ; S207. When it is monitored that the maximum sunshine intensity exceeds the previously calibrated range, the statistical time period will be intelligently extended, and the above S201 - S206 process will be repeated to relearn and statistically analyze the power change curve of the solar cell varying with the sunshine intensity within the new time period. The relationship curve of the power generation efficiency of the solar cell varying with the sunshine intensity and the average power generation efficiency ; S208, by monitoring the relationship curve of the power generation efficiency of the solar cell varying with the sunlight intensity and the average power generation efficiency , the reference value range is defined as follows [ , ]； Among them, is the average power generation efficiency, is the standard deviation of the power generation efficiency of the solar cell varying with the sunlight intensity. Therefore, this interval is defined as the start-up calibration efficiency reference interval after the system actually operates at a certain location, that is, the normally measured power generation efficiency will be within this interval; According to the reference interval of solar power generation efficiency and in combination with the real-time change curve of the measured solar radiation power , the following calibration reference interval of the real-time solar cell power is correspondingly given: [ , ]； Among them, and are respectively the average power of solar power generation and the standard deviation of power fluctuation within the statistical sunshine intensity range.

4. The artificial intelligence monitoring integrated wind-solar power generation system based on multi-dimensional sensing data according to claim 3, characterized in that, The working process of solar energy intelligent monitoring is as follows; S209. After the equipment is installed, started, and calibrated for operation, start the intelligent monitoring of solar cells; S210. Regularly start the intelligent monitoring process, that is, within a scheduled sampling time period, start the intelligent monitoring of solar cells; S211. Restart the processes of S202 - S206 above; S212, calculate and obtain the actual operating solar power generation curve , power generation efficiency curve and average power generation efficiency ; S213. During this sampling period, evaluate the solar power generation curve , the power generation efficiency curve and the average power generation efficiency for their deviations from the simulated reference interval values during the start-up calibration phase , . Evaluate the actual operating power generation efficiency curve by comparing it with the average power generation efficiency and the calibration reference interval values, evaluate its deviation range, and evaluate the rationality of the change in power generation efficiency; S214. According to the evaluation and prediction results, that is, the deviation degree between the actual value and the start calibration reference value, give the reference opinions on the aging and life evaluation of solar cells and the maintenance of surface contamination.

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