Meteorological data-combined intelligent turbine power generation control method for low-altitude aircraft

Abstract

The invention relates to the technical field of aircraft energy management, in particular to a low-altitude aircraft intelligent turbine power generation control method combined with meteorological data, which comprises the following steps: S1, acquiring data of wind speed, wind direction, temperature, humidity and air pressure around an aircraft in real time; s2, predicting meteorological conditions in a short time in the future; s3, judging whether a condition for starting turbine power generation is met or not according to an analysis result in the S2; s4, selecting a corresponding power generation mode according to the current flight task and the power demand; s5, the power generation power of the turbine is dynamically adjusted, so that the power generation output is matched with the power demand of the aircraft; and S6, adjusting a power generation strategy according to the latest meteorological data so as to cope with the change of meteorological conditions. The power generation power is dynamically adjusted through the real-time meteorological data analysis and prediction technology, the energy utilization efficiency and cruising ability of the aircraft are improved, the power generation strategy is optimized in advance through intelligent prediction, and efficient and stable operation under the complex meteorological condition is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft energy management, and in particular to a low-altitude aircraft intelligent turbine power generation control method combined with meteorological data. Background Art

[0002] With the widespread application of low-altitude aircraft in the fields of cruising, logistics transportation, environmental monitoring, etc., the requirements for their endurance and energy management are increasing. Traditional aircraft mostly rely on batteries or fuel to drive, and these forms of energy have limited endurance in complex meteorological environments and low energy efficiency. In recent years, turbine power generation technology has gradually been applied to low-altitude aircraft to supplement the power needs of aircraft through wind power generation. However, how to dynamically adjust the power generation strategy based on real-time meteorological data and flight mission requirements so that aircraft can efficiently utilize wind energy in different environments has become a key challenge in existing technologies.

[0003] In the prior art, turbine power generation systems mostly rely on fixed meteorological conditions and preset power generation modes, and are unable to flexibly respond to complex and changeable meteorological conditions, resulting in problems such as low power generation efficiency and energy waste. In addition, the existing system lacks a real-time feedback mechanism and is unable to dynamically adjust the power generation according to the real-time changing meteorological conditions. It is easy to have a mismatch in power generation when the wind speed is too high or insufficient, affecting the endurance and operating efficiency of the aircraft. Therefore, there is an urgent need for an intelligent control method that can combine real-time meteorological data and dynamically adjust the power generation to solve the above problems. Summary of the invention

[0004] Based on the above objectives, the present invention provides a low-altitude aircraft intelligent turbine power generation control method combined with meteorological data.

[0005] The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data includes the following steps:

[0006] S1: Deploy a meteorological sensor array on the aircraft to obtain real-time data on wind speed, wind direction, temperature, humidity and air pressure around the aircraft, and transmit the acquired data to the ground control center through wireless communication technology;

[0007] S2: The ground control center analyzes the received data and combines historical meteorological data with the preset meteorological model to predict the meteorological conditions in the short term in the future;

[0008] S3: Based on the analysis results of S2, determine whether the conditions for starting turbine power generation are met;

[0009] S4: If it is determined in S3 that the start-up conditions are met, the aircraft will select a corresponding power generation mode according to the current flight mission and power demand; the power generation mode includes a full power generation mode, a partial power generation mode and a standby mode;

[0010] S5: During the power generation process, the turbine power generation is dynamically adjusted according to the real-time power of the aircraft, the flight speed and the changes in meteorological conditions, so that the power generation output matches the power demand of the aircraft;

[0011] S6: The aircraft continuously acquires meteorological data and feeds real-time data back to the ground control center. The control center will adjust the power generation strategy based on the latest meteorological data to cope with changes in meteorological conditions.

[0012] Optionally, the S1 specifically includes:

[0013] S11: The meteorological sensor array includes a wind speed sensor, a wind direction sensor, a temperature and humidity sensor, and an air pressure sensor, and all sensors are arranged on the external structural surface of the aircraft; wherein the wind speed sensor is installed at the front nose of the aircraft; the wind direction sensor is installed on both sides of the wings of the aircraft; the temperature and humidity sensor is installed in a protective cover at the bottom of the aircraft; and the air pressure sensor is installed at a position above the tail of the aircraft;

[0014] S12: The wind speed sensor is used to measure the air flow speed around the aircraft in real time, the wind direction sensor is used to detect the wind direction changes in front and side of the aircraft in real time, the temperature and humidity sensor is used to measure the ambient temperature and humidity in real time, and the air pressure sensor is used to detect the air pressure value at the height of the aircraft; each sensor is connected to the central data processing unit of the aircraft through a data interface;

[0015] S13: The data acquired by all sensors will be preliminarily processed by the data processing unit to eliminate high-frequency noise and instantaneous abnormal data caused by aircraft vibration or airflow fluctuations, ensuring the smoothness and accuracy of meteorological data; the processed data will be transmitted to the ground control center through the wireless communication module, which adopts the wireless LAN technology based on the IEEE 802.11 protocol to ensure the transmission rate and stability of data in complex environments.

[0016] Optionally, the S2 specifically includes:

[0017] S21: After receiving the transmitted meteorological data, the ground control center processes the data through a data cleaning algorithm to remove noise data and outliers to ensure the accuracy of the data;

[0018] S22: Compare and analyze the cleaned real-time meteorological data with historical meteorological data, where the historical meteorological data includes wind speed, wind direction, temperature, humidity and air pressure data in the same region, season and meteorological conditions in the past; the specific comparison process uses a dynamic time warping algorithm to capture the time series similarity between the real-time data and the historical data;

[0019] S23: The ground control center calls the preset meteorological model based on the compared and analyzed data, and combines the Bayesian network algorithm pre-trained with the regional meteorological characteristics. By inputting the analysis results of the current real-time data and historical data, it directly predicts the meteorological change trend in the short term in the future, especially the changes in wind speed and wind direction;

[0020] S24: Combined with the model prediction results, a trend chart of future meteorological conditions is generated for determining subsequent turbine power generation start-up conditions.

[0021] Optionally, the S22 specifically includes:

[0022] S221: Representing the real-time meteorological data and the historical meteorological data as two time series respectively, and assigning a value to the data at each time point;

[0023] S222: Calculate the difference between each time point in the real-time meteorological data sequence and each time point in the historical meteorological data sequence, and generate a matrix representing the difference between the two sequences, which is used to determine the matching degree between the data;

[0024] S223: Based on the difference matrix, a cumulative difference value matrix is ​​constructed, and by comparing the cumulative values ​​of different paths, the minimum difference value from the first data point to each data point is found, thereby determining the optimal matching path between the two sequences;

[0025] S224: determining the best matching point between the real-time meteorological data and the historical meteorological data at each time point according to the result of the accumulated difference value, and constructing a path including all matching points;

[0026] S225: Calculate the total difference value between the two time series according to the optimal matching path, where the total difference value is used to represent the overall similarity between the two series.

[0027] Optionally, the S23 specifically includes:

[0028] S231: After receiving the comparison and analysis results of the real-time data and the historical data, the ground control center first inputs the data into a preset Bayesian network model, wherein the Bayesian network model is pre-trained based on regional meteorological characteristics, and the nodes in the model represent meteorological variables, including wind speed, wind direction, temperature and air pressure, and the edges represent conditional probability relationships between the variables;

[0029] S232: Calculate the joint probability distribution of each meteorological variable under the current meteorological conditions according to the conditional probability formula of the Bayesian network;

[0030] S233: Using the current real-time meteorological data as observational evidence, update the posterior probability distribution of the unobserved meteorological variables in the Bayesian network;

[0031] S234: Based on the updated posterior probability distribution, predict the changing trend of wind speed and wind direction in the short future time. Specifically, first extract the probability values ​​of wind speed and wind direction in the posterior probability distribution to obtain their probability distribution under various future values; then determine the changing range of wind speed and wind direction based on the probability distribution, and generate the prediction results of wind speed and wind direction in the short future time.

[0032] Optionally, the S3 specifically includes:

[0033] S31: Extract the current wind speed based on the analysis results of step S2 and wind direction , and compare the wind speed with the preset start wind speed threshold For comparison, if the current wind speed , then the wind speed meets the starting conditions;

[0034] S32: Under the premise that the wind speed conditions are met, analyze the wind speed and wind direction change trends in the short future period predicted by the Bayesian network. If the predicted future wind speed still meets the start wind speed threshold , and predict the angle between the wind direction and the aircraft's forward direction The maximum allowable deviation angle If the weather conditions are within , it is considered that the future meteorological conditions are suitable for starting power generation;

[0035] S33: Based on the current meteorological conditions and the predicted short-term meteorological changes, if the current and future wind speeds and wind directions meet the conditions for starting power generation, the ground control center determines that the conditions for starting turbine power generation are met and issues a start command.

[0036] Optionally, the S4 specifically includes:

[0037] S41: After determining that the conditions for starting turbine power generation are met, first evaluate the current flight mission type of the aircraft, including whether the aircraft is in a cruise mode, a low-speed flight mode, or a hovering mode, and determine the required level of power generation in combination with the power demand of the aircraft;

[0038] S42: When the aircraft is in a mission state with high power demand, select the full power generation mode;

[0039] S43: If the flight mission is in a medium power demand state, select the partial power generation mode;

[0040] S44: When the aircraft is in a state of low power demand or is about to complete a mission, select standby mode.

[0041] Optionally, the S5 specifically includes

[0042] S51: Monitor the aircraft's power status in real time and calculate the difference between the remaining power and the mission power requirement ;like , it means that the remaining power is insufficient and the system needs to increase the power generation; if , then the power is sufficient and the system reduces the power generation;

[0043] S52: According to the real-time change of flight speed, the impact of flight speed on power demand is evaluated. When the flight speed increases, the load power increases, and the power generation system increases the power output accordingly; when the speed decreases, the load power decreases, and the power generation power also needs to be reduced accordingly;

[0044] S53: Combined with meteorological conditions, including real-time wind speed and real-time wind direction When the wind speed is high, the power output of the turbine generator increases. When the wind speed is low or the wind direction is unfavorable, the power generation needs to be reduced.

[0045] S54: Comprehensive power status , Flight speed and weather conditions , , a feedback control algorithm is used to dynamically adjust the turbine power generation so that the power generation output matches the power demand of the aircraft.

[0046] Optionally, the calculation formula of the turbine power generation is: ,in, is the power generated by the turbine, Basic power requirements for current missions; is the difference between the remaining power and the power requirement of the mission; , Represents the corresponding control adjustment coefficient, which is used to control the impact of power compensation and environmental conditions; , is the influencing factor corresponding to the flight speed and wind speed; is the real-time flight speed of the aircraft; is the wind speed; is the angle between the wind direction and the aircraft's forward direction; The effect of wind direction on power generation efficiency.

[0047] Optionally, the S6 specifically includes:

[0048] S61: The aircraft continuously collects real-time meteorological data of the surrounding environment through the deployed meteorological sensors, including wind speed, wind direction, temperature, humidity and air pressure, and sends these data to the ground control center through the wireless communication system;

[0049] S62: After receiving the real-time transmitted meteorological data, the ground control center will clean and pre-process it to remove noise and abnormal data; the processed data will be analyzed in combination with historical meteorological data and preset meteorological models to predict the changing trend of meteorological conditions in the short term;

[0050] S63: The ground control center adjusts the current power generation strategy; when it is predicted that the wind speed will increase and the wind direction is favorable to the aircraft's forward direction, the control center will issue a command to increase the power generation; when it is predicted that the wind speed will decrease or the wind direction will be unfavorable, the system will reduce the power generation;

[0051] S64: The ground control center feeds back the adjusted power generation strategy to the aircraft through wireless communication technology. The aircraft automatically adjusts the output power of the turbine generator according to the received instructions to cope with the changes in the current meteorological conditions.

[0052] Beneficial effects of the present invention:

[0053] The present invention, by combining real-time meteorological data analysis and prediction technology, can dynamically adjust the power output of turbine generators according to changes in meteorological conditions such as wind speed and wind direction. By real-time collection and feedback of meteorological data, the ground control center can timely optimize the power generation strategy to ensure that the power generation efficiency is improved when wind conditions are favorable, and the power output is reduced when the wind speed is insufficient or the wind direction is unfavorable, thereby effectively improving the energy utilization efficiency and endurance of the aircraft.

[0054] The present invention adopts intelligent prediction technology based on historical meteorological data and preset models. By predicting the short-term meteorological trend, the power generation strategy is adjusted in advance, thereby avoiding the impact of sudden changes in meteorological conditions on the power generation system. This technical means improves the response speed and flexibility of the system, ensures that the aircraft can operate stably and efficiently in complex meteorological environments, and significantly improves the energy waste and insufficient endurance problems existing in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or 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 in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic diagram of an intelligent turbine power generation control method according to an embodiment of the present invention;

[0057] Figure 2 The figure is a schematic diagram of a flow chart of selecting a power generation mode according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0059] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0060] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0061] like Figure 1-Figure 2 As shown, the low-altitude aircraft intelligent turbine power generation control method combined with meteorological data includes the following steps:

[0062] S1: Deploy a meteorological sensor array on the aircraft to obtain real-time data on wind speed, wind direction, temperature, humidity and air pressure around the aircraft, and transmit the acquired data to the ground control center through wireless communication technology;

[0063] S2: The ground control center analyzes the received data and combines historical meteorological data with the preset meteorological model to predict the meteorological conditions in the short term in the future;

[0064] S3: Based on the analysis results of S2, determine whether the conditions for starting turbine power generation are met;

[0065] S4: If the start-up conditions are met in S3, the aircraft will select the corresponding power generation mode according to the current flight mission and power demand; the power generation mode includes full power generation mode, partial power generation mode and standby mode;

[0066] S5: During the power generation process, the turbine power generation is dynamically adjusted according to the real-time power of the aircraft, the flight speed and the changes in meteorological conditions, so that the power generation output matches the power demand of the aircraft;

[0067] S6: The aircraft continuously acquires meteorological data and feeds real-time data back to the ground control center. The control center will adjust the power generation strategy based on the latest meteorological data to cope with changes in meteorological conditions.

[0068] S1 specifically includes:

[0069] S11: The meteorological sensor array includes a wind speed sensor, a wind direction sensor, a temperature and humidity sensor, and an air pressure sensor. All sensors are arranged on the external structural surface of the aircraft to ensure that the acquired data accurately reflects the meteorological environment around the aircraft. Among them, the wind speed sensor is installed at the front nose of the aircraft to monitor the speed of the air flow in front in real time. The wind direction sensor is installed on both sides of the aircraft wing to measure the wind direction changes from different directions to ensure that the acquired wind direction data has comprehensive spatial coverage. The temperature and humidity sensor is installed in a protective cover at the bottom of the aircraft to avoid being affected by direct sunlight and precipitation. The air pressure sensor is installed above the tail of the aircraft to accurately measure the air pressure changes at the current altitude of the aircraft to avoid the influence of airflow disturbance on the measurement accuracy.

[0070] S12: The wind speed sensor is used to measure the air flow speed around the aircraft in real time, the wind direction sensor is used to detect the wind direction changes in front and on the side of the aircraft in real time, the temperature and humidity sensor is used to measure the ambient temperature and humidity in real time, and the air pressure sensor is used to detect the air pressure value at the altitude of the aircraft; each sensor is connected to the central data processing unit of the aircraft through a data interface to ensure the accuracy and continuity of data collection;

[0071] S13: The data acquired by all sensors will be preliminarily processed by the data processing unit, and the Kalman filter algorithm will be used to filter the data to eliminate high-frequency noise and instantaneous abnormal data caused by aircraft vibration or airflow fluctuations, so as to ensure the smoothness and accuracy of the meteorological data; the processed data will be transmitted to the ground control center through the wireless communication module. The wireless communication module adopts the wireless LAN technology based on the IEEE 802.11 protocol to ensure the transmission rate and stability of the data in complex environments, and has strong anti-interference ability; the above steps ensure that the aircraft can obtain accurate meteorological data of the surrounding environment in real time and quickly transmit it to the ground control center by reasonably arranging the meteorological sensor array on the surface of the aircraft and combining it with efficient wireless communication technology, which helps to improve the power generation efficiency and meteorological adaptability of the aircraft.

[0072] S2 specifically includes:

[0073] S21: After receiving the transmitted meteorological data, the ground control center processes the data through a data cleaning algorithm to remove noise data and outliers to ensure the accuracy of the data;

[0074] S22: Compare and analyze the cleaned real-time meteorological data with historical meteorological data, which includes wind speed, wind direction, temperature, humidity and air pressure data in the same region, season and meteorological conditions in the past. The dynamic time warping (DTW) algorithm is used in the specific comparison process to capture the time series similarity between the real-time data and the historical data.

[0075] S23: The ground control center calls the preset meteorological model based on the compared and analyzed data, and combines the Bayesian network algorithm pre-trained with the regional meteorological characteristics. By inputting the analysis results of the current real-time data and historical data, it directly predicts the meteorological change trend in the short term in the future, especially the changes in wind speed and wind direction;

[0076] S24: Combined with the model prediction results, a trend chart of future meteorological conditions is generated for determining subsequent turbine power generation start-up conditions.

[0077] S22 specifically includes:

[0078] S221: Representing the real-time meteorological data and the historical meteorological data as two time series respectively, and assigning a value to the data at each time point;

[0079] S222: Calculate the difference between each time point in the real-time meteorological data sequence and each time point in the historical meteorological data sequence, and generate a matrix representing the difference between the two sequences, which is used to determine the matching degree between the data;

[0080] S223: Based on the difference matrix, a cumulative difference value matrix is ​​constructed, and by comparing the cumulative values ​​of different paths, the minimum difference value from the first data point to each data point is found, thereby determining the optimal matching path between the two sequences;

[0081] S224: determining the best matching point between the real-time meteorological data and the historical meteorological data at each time point according to the result of the accumulated difference value, and constructing a path including all matching points;

[0082] S225: Calculate the total difference value between the two time series according to the optimal matching path, where the total difference value is used to represent the overall similarity between the two series.

[0083] The calculation steps for the alignment analysis using the dynamic time warping algorithm are as follows:

[0084] First, the real-time meteorological data and historical meteorological data are represented as two time series, denoted as and ,in, Indicates real-time weather data in The data value at a time point, Represents the data value of historical meteorological data at the time point, and are the lengths of the two time series respectively;

[0085] Then, calculate the real-time meteorological data series and historical meteorological data series The Euclidean distance between them defines the distance matrix ,in Indicates the sequence The data value at each time point is Sequence number The distance between the data values ​​at each time point is calculated as: ,in, Real-time weather data series In the The data value at a time point, For historical meteorological data series In the The data value at a time point, represents the squared difference between two time points;

[0086] Next, based on the distance matrix , construct the cumulative distance matrix ,in Indicates from and The first data point to and The minimum cumulative distance of data points is calculated as follows: ,in, and Respectively represent the cumulative distance value of the cumulative distance matrix at the previous position;

[0087] Then, according to the cumulative distance matrix The results of the experiment are used to find the optimal matching path between real-time meteorological data and historical meteorological data. ,in Representing time series and In the and The matching relationship at each time point;

[0088] Finally, through the optimal matching path and cumulative distance , we get the similarity measure between real-time meteorological data and historical meteorological data, where is the minimum cumulative distance between two sequences, indicating the overall similarity between them.

[0089] S23 specifically includes:

[0090] S231: After receiving the comparison and analysis results of the real-time data and the historical data, the ground control center first inputs the data into a preset Bayesian network model. The Bayesian network model is pre-trained based on regional meteorological characteristics. The nodes in the model represent meteorological variables, including wind speed, wind direction, temperature and air pressure, and the edges represent the conditional probability relationship between the variables.

[0091] S232: According to the conditional probability formula of the Bayesian network, the joint probability distribution of each meteorological variable under the current meteorological conditions is calculated. The calculation formula of the joint probability distribution is: ,in, represents the joint probability of all meteorological variables; Indicates meteorological variables; Represents meteorological variables The parent node set (that directly affects meteorological variables); Indicates meteorological variables under the parent node condition The conditional probability of

[0092] S233: Using the current real-time meteorological data as observation evidence, update the posterior probability distribution of the unobserved meteorological variables in the Bayesian network. The update formula is: ,in, Indicates that evidence is observed Under the condition of The posterior probability of Indicates the meteorological variable that needs to be predicted (such as future wind speed and wind direction ); Represents the current real-time meteorological data (the observed meteorological variable values); Indicated in meteorological variables Evidence was observed under the conditions The conditional probability of Represents meteorological variables The prior probability of (calculated based on historical data); Observational evidence The marginal probability of

[0093] S234: Based on the updated posterior probability distribution, predict the wind speed and wind direction change trend in the short term in the future. Specifically, first extract the wind speed in the posterior probability distribution and wind direction The probability value of the wind speed and wind direction is obtained to obtain its probability distribution under various future values; then, according to the probability distribution, the change range of wind speed and wind direction is determined, and the wind speed and wind direction prediction results in the short future time are generated; by calculating the joint probability distribution of the Bayesian network and using the current real-time meteorological data to update the posterior probability of the meteorological variables, the wind speed and wind direction change trend in the short future time can be accurately predicted; this method makes full use of the conditional dependency between meteorological variables, improves the accuracy of meteorological forecasts, provides reliable data support for the startup and dynamic adjustment of turbine power generation, and enhances the adaptability and energy utilization efficiency of aircraft under complex meteorological conditions.

[0094] S3 specifically includes:

[0095] S31: Extract the current wind speed based on the analysis results of step S2 and wind direction , and compare the wind speed with the preset start wind speed threshold For comparison, if the current wind speed , then the wind speed meets the starting conditions;

[0096] S32: Under the premise that the wind speed conditions are met, analyze the wind speed and wind direction change trends in the short future period (such as within 10 minutes) predicted by the Bayesian network. If the predicted future wind speed still meets the start wind speed threshold , and predict the angle between the wind direction and the aircraft's forward direction The maximum allowable deviation angle If the weather conditions are within , it is considered that the future meteorological conditions are suitable for starting power generation;

[0097] S33: Combining the current meteorological conditions with the predicted short-term meteorological changes, if the current and future wind speeds and wind directions meet the conditions for starting power generation, the ground control center determines that the conditions for starting turbine power generation are met and issues a start command; if the forecast results show that the future wind speed or wind direction is unstable, the start decision is delayed; by combining the current meteorological data with the forecast of meteorological conditions in the short term in the future, it is possible to more accurately determine whether the conditions for starting turbine power generation are met. This method not only avoids false starts caused by short-term fluctuations in meteorological conditions, but also improves the system's response flexibility and energy utilization efficiency, ensuring power generation operations under optimal meteorological conditions.

[0098] S4 specifically includes:

[0099] S41: After determining that the conditions for starting turbine power generation are met, first evaluate the current flight mission type of the aircraft, including whether the aircraft is in a cruise mode, a low-speed flight mode, or a hovering mode, and determine the required level of power generation in combination with the power demand of the aircraft;

[0100] S42: When the aircraft is in a mission state with high power demand, select the full power generation mode;

[0101] S43: If the flight mission is in a medium power demand state, select the partial power generation mode;

[0102] S44: When the aircraft is in a state of low power demand or is about to complete a mission, select standby mode; by changing in real time according to the flight mission and power demand, the aircraft is able to dynamically switch between full power, partial power and standby modes to ensure that the generated power matches the actual demand to maximize energy efficiency.

[0103] The specific steps for selecting the corresponding power generation mode are as follows:

[0104] First, after determining in step S3 that the conditions for starting turbine power generation are met, the current power demand of the aircraft is calculated. , the formula is: ,in, It indicates the basic power required by the basic systems of the aircraft (such as communication, navigation, and control systems). The power required for the current mission (such as additional demand for propulsion systems or special equipment), To provide safe redundant power for emergency situations;

[0105] Then, based on the calculated power demand , assess the level of need , the demand level is determined based on a preset power demand threshold range; specifically,

[0106] like When If it is high, it means the aircraft is in a high-load mission state;

[0107] like When Medium, indicating that the aircraft is in a medium load mission state;

[0108] like , then the demand level If it is low, it means the aircraft is in low load or standby mode;

[0109] Finally, according to the level of demand Determine the power generation mode:

[0110] when When it is high, the full power generation mode is selected. At this time, the turbine generator will output at maximum power to meet the power needs of all aircraft systems;

[0111] when When the demand is medium, select the partial power generation mode, and the power generation capacity is appropriately reduced according to the demand to avoid over-generation and energy waste;

[0112] when When it is low, select standby mode, the turbine power generation system will enter energy-saving state, only maintaining the minimum power supply required for basic functions.

[0113] S5 specifically includes

[0114] S51: Monitor the aircraft's power status in real time and calculate the difference between the remaining power and the mission power requirement ; The calculation formula is: ,in, Indicates the power required for the current task, Indicates the current remaining battery power; if , it means that the remaining power is insufficient and the system needs to increase the power generation; if , then the power is sufficient and the system reduces the power generation;

[0115] S52: Evaluate the impact of flight speed on power demand based on real-time changes in flight speed; Flight speed The relationship between the load power and the power demand is determined by the load power calculation formula. When the flight speed increases, the load power increases, and the power generation system increases the power output accordingly; when the speed decreases, the load power decreases, and the power generation power also needs to be reduced accordingly.

[0116] S53: Combined with meteorological conditions, including real-time wind speed and real-time wind direction The change of wind speed can be used to evaluate the power generation efficiency of turbines. When the wind speed is high, the power output of turbine power generation increases. When the wind speed is low or the wind direction is unfavorable, the power generation needs to be reduced. The power generation efficiency of turbines Determined based on wind speed and direction, the power output is adjusted based on these conditions;

[0117] S54: Comprehensive power status , Flight speed and weather conditions , , a feedback control algorithm is used to dynamically adjust the turbine power generation so that the power generation output matches the power demand of the aircraft.

[0118] The calculation formula of turbine power generation is: ,in, is the power generated by the turbine, Basic power requirements for current missions; is the difference between the remaining power and the power requirement of the mission; , Represents the corresponding control adjustment coefficient, which is used to control the impact of power compensation and environmental conditions; , is the influencing factor corresponding to the flight speed and wind speed; is the real-time flight speed of the aircraft; is the wind speed; is the angle between the wind direction and the aircraft's forward direction; The above steps combine the real-time changes of power status, flight speed and meteorological conditions, and use feedback control algorithms. The system can dynamically adjust the power output of the turbine generator to ensure that the generated power is accurately matched to the actual power demand of the aircraft, thereby improving energy utilization efficiency and adapting to changes in power demand under different flight conditions.

[0119] S6 specifically includes:

[0120] S61: The aircraft continuously collects real-time meteorological data of the surrounding environment through the deployed meteorological sensors, including wind speed, wind direction, temperature, humidity and air pressure, and sends these data to the ground control center through the wireless communication system;

[0121] S62: After receiving the real-time transmitted meteorological data, the ground control center will clean and pre-process it to remove noise and abnormal data to ensure the validity of the data; the processed data will be analyzed in combination with historical meteorological data and preset meteorological models to predict the changing trend of meteorological conditions in the short term;

[0122] S63: The ground control center adjusts the current power generation strategy; when it is predicted that the wind speed will increase and the wind direction is favorable to the aircraft's forward direction, the control center will issue a command to increase the power generation; when it is predicted that the wind speed will decrease or the wind direction will be unfavorable, the system will reduce the power generation to ensure that the power generation system can still operate stably under the condition of low wind energy utilization;

[0123] S64: The ground control center feeds back the adjusted power generation strategy to the aircraft through wireless communication technology. The aircraft automatically adjusts the output power of the turbine generator according to the received instructions to cope with the changes in the current meteorological conditions. The above steps continuously monitor and feedback meteorological data. The ground control center can dynamically adjust the power generation strategy based on real-time data to ensure that the aircraft maintains efficient and stable power generation output under various meteorological conditions. This real-time adjustment mechanism enhances the system's responsiveness, optimizes energy utilization efficiency, and effectively extends the aircraft's flight time.

[0124] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0125] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A low-altitude aircraft intelligent turbine power generation control method in combination with meteorological data, characterized in that: The following steps are involved: S1: Deploy a meteorological sensor array on the aircraft to obtain real-time data on wind speed, wind direction, temperature, humidity and air pressure around the aircraft, and transmit the acquired data to the ground control center through wireless communication technology; S2: The ground control center analyzes the received data and combines historical meteorological data with the preset meteorological model to predict the meteorological conditions in the short term in the future; S3: Based on the analysis results of S2, determine whether the conditions for starting turbine power generation are met; S4: If it is determined in S3 that the start-up conditions are met, the aircraft will select a corresponding power generation mode according to the current flight mission and power demand; the power generation mode includes a full power generation mode, a partial power generation mode and a standby mode; S5: During the power generation process, the turbine power generation is dynamically adjusted according to the real-time power of the aircraft, the flight speed and the changes in meteorological conditions, so that the power generation output matches the power demand of the aircraft; S6: The aircraft continuously acquires meteorological data and feeds real-time data back to the ground control center. The control center will adjust the power generation strategy based on the latest meteorological data to cope with changes in meteorological conditions.

2. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 1 is characterized in that: The S1 specifically includes: S11: The meteorological sensor array includes a wind speed sensor, a wind direction sensor, a temperature and humidity sensor, and an air pressure sensor, and all sensors are arranged on the external structural surface of the aircraft; wherein the wind speed sensor is installed at the front nose of the aircraft; the wind direction sensor is installed on both sides of the wings of the aircraft; the temperature and humidity sensor is installed in a protective cover at the bottom of the aircraft; and the air pressure sensor is installed at a position above the tail of the aircraft; S12: The wind speed sensor is used to measure the air flow speed around the aircraft in real time, the wind direction sensor is used to detect the wind direction changes in front and side of the aircraft in real time, the temperature and humidity sensor is used to measure the ambient temperature and humidity in real time, and the air pressure sensor is used to detect the air pressure value at the height of the aircraft; each sensor is connected to the central data processing unit of the aircraft through a data interface; S13: The data acquired by all sensors will be preliminarily processed by the data processing unit to eliminate high-frequency noise and instantaneous abnormal data caused by aircraft vibration or airflow fluctuations, ensuring the smoothness and accuracy of meteorological data; the processed data will be transmitted to the ground control center through the wireless communication module, which adopts the wireless LAN technology based on the IEEE 802.11 protocol to ensure the transmission rate and stability of data in complex environments.

3. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 1 is characterized in that: The S2 specifically includes: S21: After receiving the transmitted meteorological data, the ground control center processes the data through a data cleaning algorithm to remove noise data and outliers to ensure the accuracy of the data; S22: Compare and analyze the cleaned real-time meteorological data with historical meteorological data, where the historical meteorological data includes wind speed, wind direction, temperature, humidity and air pressure data in the same region, season and meteorological conditions in the past; the specific comparison process uses a dynamic time warping algorithm to capture the time series similarity between the real-time data and the historical data; S23: The ground control center calls the preset meteorological model based on the compared and analyzed data, and combines the Bayesian network algorithm pre-trained with the regional meteorological characteristics. By inputting the analysis results of the current real-time data and historical data, it directly predicts the meteorological change trend in the short term in the future, especially the changes in wind speed and wind direction; S24: Combined with the model prediction results, a trend chart of future meteorological conditions is generated for determining subsequent turbine power generation start-up conditions.

4. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 3 is characterized in that: The S22 specifically includes: S221: Representing the real-time meteorological data and the historical meteorological data as two time series respectively, and assigning a value to the data at each time point; S222: Calculate the difference between each time point in the real-time meteorological data sequence and each time point in the historical meteorological data sequence, and generate a matrix representing the difference between the two sequences, which is used to determine the matching degree between the data; S223: Based on the difference matrix, a cumulative difference value matrix is ​​constructed, and by comparing the cumulative values ​​of different paths, the minimum difference value from the first data point to each data point is found, thereby determining the optimal matching path between the two sequences; S224: determining the best matching point between the real-time meteorological data and the historical meteorological data at each time point according to the result of the accumulated difference value, and constructing a path including all matching points; S225: Calculate the total difference value between the two time series according to the optimal matching path, where the total difference value is used to represent the overall similarity between the two series.

5. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 4 is characterized in that: The S23 specifically includes: S231: After receiving the comparison and analysis results of the real-time data and the historical data, the ground control center first inputs the data into a preset Bayesian network model, wherein the Bayesian network model is pre-trained based on regional meteorological characteristics, and the nodes in the model represent meteorological variables, including wind speed, wind direction, temperature and air pressure, and the edges represent conditional probability relationships between the variables; S232: Calculate the joint probability distribution of each meteorological variable under the current meteorological conditions according to the conditional probability formula of the Bayesian network; S233: Using the current real-time meteorological data as observational evidence, update the posterior probability distribution of the unobserved meteorological variables in the Bayesian network; S234: Based on the updated posterior probability distribution, predict the changing trend of wind speed and wind direction in the short future time. Specifically, first extract the probability values ​​of wind speed and wind direction in the posterior probability distribution to obtain their probability distribution under various future values; then determine the changing range of wind speed and wind direction based on the probability distribution, and generate the prediction results of wind speed and wind direction in the short future time.

6. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 1 is characterized in that: The S3 specifically includes: S31: Extract the current wind speed based on the analysis results of step S2 and wind direction , and compare the wind speed with the preset start wind speed threshold For comparison, if the current wind speed , then the wind speed meets the starting conditions; S32: Under the premise that the wind speed conditions are met, analyze the wind speed and wind direction change trends in the short future period predicted by the Bayesian network. If the predicted future wind speed still meets the start wind speed threshold , and predict the angle between the wind direction and the aircraft's forward direction The maximum allowable deviation angle If the weather conditions are within , it is considered that the future meteorological conditions are suitable for starting power generation; S33: Based on the current meteorological conditions and the predicted short-term meteorological changes, if the current and future wind speeds and wind directions meet the conditions for starting power generation, the ground control center determines that the conditions for starting turbine power generation are met and issues a start command.

7. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 1 is characterized in that: The S4 specifically includes: S41: After determining that the conditions for starting turbine power generation are met, first evaluate the current flight mission type of the aircraft, including whether the aircraft is in a cruise mode, a low-speed flight mode, or a hovering mode, and determine the required level of power generation in combination with the power demand of the aircraft; S42: When the aircraft is in a mission state with high power demand, select the full power generation mode; S43: If the flight mission is in a medium power demand state, select the partial power generation mode; S44: When the aircraft is in a state of low power demand or is about to complete a mission, select standby mode.

8. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 1 is characterized in that: The S5 specifically includes S51: Monitor the aircraft's power status in real time and calculate the difference between the remaining power and the mission power requirement ;like , it means that the remaining power is insufficient and the system needs to increase the power generation; if , then the power is sufficient and the system reduces the power generation; S52: According to the real-time change of flight speed, the impact of flight speed on power demand is evaluated. When the flight speed increases, the load power increases, and the power generation system increases the power output accordingly; when the speed decreases, the load power decreases, and the power generation power also needs to be reduced accordingly; S53: Combined with meteorological conditions, including real-time wind speed and real-time wind direction When the wind speed is high, the power output of the turbine generator increases. When the wind speed is low or the wind direction is unfavorable, the power generation needs to be reduced. S54: Comprehensive power status , Flight speed and weather conditions , , a feedback control algorithm is used to dynamically adjust the turbine power generation so that the power generation output matches the power demand of the aircraft.

9. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 8 is characterized in that: The calculation formula of the turbine power generation is: ,in, is the power generated by the turbine, Basic power requirements for current missions; is the difference between the remaining power and the power requirement of the mission; , Represents the corresponding control adjustment coefficient, which is used to control the impact of power compensation and environmental conditions; , is the influencing factor corresponding to the flight speed and wind speed; is the real-time flight speed of the aircraft; is the wind speed; is the angle between the wind direction and the aircraft's forward direction; The effect of wind direction on power generation efficiency.

10. The low-altitude aircraft intelligent turbine power generation control method combined with meteorological data according to claim 1 is characterized in that: The S6 specifically includes: S61: The aircraft continuously collects real-time meteorological data of the surrounding environment through the deployed meteorological sensors, including wind speed, wind direction, temperature, humidity and air pressure, and sends these data to the ground control center through the wireless communication system; S62: After receiving the real-time transmitted meteorological data, the ground control center will clean and pre-process it to remove noise and abnormal data; the processed data will be analyzed in combination with historical meteorological data and preset meteorological models to predict the changing trend of meteorological conditions in the short term; S63: The ground control center adjusts the current power generation strategy; when it is predicted that the wind speed will increase and the wind direction is favorable to the aircraft's forward direction, the control center will issue a command to increase the power generation; when it is predicted that the wind speed will decrease or the wind direction will be unfavorable, the system will reduce the power generation; S64: The ground control center feeds back the adjusted power generation strategy to the aircraft through wireless communication technology. The aircraft automatically adjusts the output power of the turbine generator according to the received instructions to cope with the changes in the current meteorological conditions.

Images