Hydrogen energy endurance supply regulation and control management system and method suitable for hydrogen energy unmanned aerial vehicle

Abstract

The invention discloses a hydrogen energy endurance supply regulation and control management system and method suitable for a hydrogen energy unmanned aerial vehicle, and belongs to the technical field of energy supply regulation and control. The method comprises the following steps: constructing a theoretical endurance mileage model, and calculating the theoretical endurance mileage which can be reached by the unmanned aerial vehicle in an ideal state after one hydrogen storage bottle is used up; collecting flight data of the unmanned aerial vehicle, inputting the flight data into a pre-trained artificial intelligence algorithm model, obtaining the endurance loss amount of the unmanned aerial vehicle in the current flight state, and further obtaining a first endurance mileage; a flight attitude combination used by the unmanned aerial vehicle in the flight process is obtained, and the predicted endurance mileage is calculated; based on the predicted endurance mileage of the unmanned aerial vehicle needing hydrogenation and the hydrogen capacity and the hydrogen concentration of the current remaining hydrogen storage cylinders, the unmanned aerial vehicle matched with the hydrogen concentration of the remaining hydrogen storage cylinders is screened out, and the hydrogen concentration of the hydrogen storage cylinders is preferentially distributed to the matched unmanned aerial vehicle; in the hydrogenation process, the required hydrogen energy capacity and the corresponding hydrogen concentration are calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of energy supply regulation, and specifically to a hydrogen energy endurance supply regulation management system and method applicable to hydrogen energy drones. Background Art

[0002] With the continuous progress of technology, drones are increasingly widely used in various fields. However, the endurance of drones has always been a key factor restricting their wider application and greater effectiveness. Traditional lithium battery-powered drones have relatively low energy density and limited endurance mileage, making it difficult to meet the requirements of some long-distance and long-time operations. As a clean and efficient energy source, hydrogen energy has the advantage of high energy density and has gradually become an important research direction for improving the endurance of drones.

[0003] When estimating the endurance mileage of drones in the prior art, simple parameters such as battery capacity and motor power are often only considered, and complex flight states and the characteristics of hydrogen energy are not fully combined for comprehensive calculation. During the flight of a drone, the flight speed, flight route, and flight attitude are constantly changing. Different flight attitudes will affect energy consumption. For each distance the drone flies, the concentration of the fuel cell on the drone will constantly change. It is difficult for the prior art to monitor and analyze the impact of these changes on energy consumption in real time. When refueling multiple drones, the prior art lacks consideration of the adaptability between the fuel cells of the drones and the hydrogen concentration in the hydrogen storage bottles, and usually adopts simple average distribution or first-come, first-served methods, which may cause some drones to have their fuel cells unable to work in the best state due to hydrogen concentration mismatch. Summary of the Invention

[0004] The purpose of the present invention is to provide a hydrogen energy endurance supply regulation management system and method applicable to hydrogen energy drones to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides a hydrogen energy endurance supply regulation management method applicable to hydrogen energy drones, including the following steps:

[0007] Collect drone parameters and hydrogen storage bottle parameters, construct a theoretical endurance mileage model, and calculate the theoretical endurance mileage that the drone can reach after using up one hydrogen storage bottle under ideal conditions;

[0008] Collect the flight data of the drone, including flight speed, flight route, and flight attitude, input it into a pre-trained artificial intelligence algorithm model to obtain the endurance loss amount of the drone in the current flight state; deduct the endurance loss amount from the theoretical endurance mileage to obtain the first endurance mileage;

[0009] Based on the flight data of the drone, obtain the flight attitude combinations used by the drone during flight; when the drone does not use a flight attitude combination, take the first endurance mileage as the predicted endurance mileage; when the drone uses a flight attitude combination, calculate the saved mileage by analyzing the flight attitude changes and the corresponding energy savings; accumulate the saved mileage to the first endurance mileage as the predicted endurance mileage.

[0010] When the remaining hydrogen amount of the drone drops to a preset hydrogen refueling threshold, send a hydrogen refueling demand signal; based on the predicted endurance mileage of the drones that need hydrogen refueling, as well as the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottles, screen out the drones that are compatible with the hydrogen concentration of the remaining hydrogen storage bottles, and preferentially allocate the hydrogen concentration of the hydrogen storage bottles to the compatible drones; during the hydrogen refueling process, calculate the hydrogen energy amount required for each drone to refuel and the currently allocated hydrogen energy amount to obtain the required hydrogen energy capacity and the corresponding hydrogen concentration.

[0011] Combined with the first aspect, in the first implementation manner of the first aspect of this application, the collection of drone parameters and hydrogen storage bottle parameters, the construction of a theoretical endurance mileage model, and the calculation of the theoretical endurance mileage that the drone can reach after using up one hydrogen storage bottle under ideal conditions include:

[0012] The drone parameters include aerodynamic parameters, power system parameters, and flight performance parameters; the hydrogen storage bottle parameters include the volume of the hydrogen storage bottle and the hydrogen concentration.

[0013] According to the volume V and hydrogen concentration c of the hydrogen storage bottle, combined with the density of hydrogen , calculate the mass of hydrogen in the hydrogen storage bottle ; use the heat of combustion of hydrogen to obtain the total chemical energy of hydrogen ; obtain the power generation efficiency of the fuel cell and the drive efficiency of the motor from the power system parameters, and convert the total chemical energy of hydrogen into mechanical energy available for the drone ;

[0014] Obtain the cruising speed of the drone from the flight performance parameters, and obtain the drag coefficient and wing area of the drone from the aerodynamic parameters; according to the principles of aerodynamics, the drag D received by the drone during flight is expressed as , where is the air density, is the cruising speed of the drone, is the drag coefficient of the drone, and S is the wing area of the drone; the power required by the drone is ;

[0015] According to the law of conservation of energy, energy is equal to power multiplied by time, and the formula is , calculate the theoretical endurance of the drone , combined with the cruising speed of the drone, the theoretical range is obtained .

[0016] In combination with the first aspect, in a second implementation of the first aspect of the present application, the flight data of the drone, including the flight speed, flight route, and flight attitude, is collected and input into a pre-trained artificial intelligence algorithm model to obtain the endurance loss of the drone in the current flight state, including:

[0017] The artificial intelligence algorithm model selects a multi-layer perceptron model, divides the flight data of the drone into a training set, a validation set and a test set, and uses the training set data to train the model. During the training process, the model parameters are adjusted to minimize the error between the model's prediction results and the actual endurance loss, and the model parameters are updated using a gradient descent algorithm; the model is verified using a validation set and the model's hyperparameters are adjusted; the trained model is evaluated using the test set data, the model's prediction error is calculated, and the model is optimized.

[0018] In combination with the first aspect, in a third implementation of the first aspect of the present application, the step of obtaining a flight attitude combination used by the drone during flight based on the flight data of the drone includes:

[0019] Based on the flight data of the UAV, the attitude angle of the UAV, including the pitch angle, roll angle and yaw angle, is calculated through the complementary filtering algorithm; different attitude combinations are defined according to the flight mission of the UAV; the clustering algorithm is used to analyze the attitude angle of the UAV, identify different attitude combinations, and divide similar attitude data points into different clusters, each cluster representing a attitude combination.

[0020] In combination with the first aspect, in a fourth implementation of the first aspect of the present application, the use of a clustering algorithm to analyze the attitude angles of the drone to identify different attitude combinations includes:

[0021] The attitude angles of the drone are integrated into a data set. The clustering algorithm selects the K-means clustering algorithm, determines the K value by the elbow method, calculates the sum of square errors of clustering under different K values, specifically the sum of squares of the distances from each data point to the center of the cluster to which it belongs, and randomly selects K points in the data set as the initial cluster centers;

[0022] For each attitude angle data point in the dataset, calculate its distances to the K clustering centers using the Euclidean distance; assign each data point to the cluster where the nearest clustering center is located; recalculate the mean of the data points in each cluster and use it as the new clustering center; repeat the above steps and iterate continuously until the clustering centers no longer change. At this time, the attitude angle data is divided into K different clusters.

[0023] Combined with the first aspect, in the fifth implementation manner of the first aspect of this application, when the drone uses a flight attitude combination, by analyzing the flight attitude changes and the corresponding energy savings, calculate the saved mileage, including:

[0024] Based on the flight mechanics principle and the power system parameters of the drone, consider the effects of flight attitude, flight speed, flight altitude, and air density on energy consumption, and establish an energy consumption model; assume that the drone flies in a conventional flight attitude without using the flight attitude combination, and according to the energy consumption model, combined with the power system parameters, calculate the normal energy consumption under the same flight conditions; collect the actual energy consumption of the drone when using the flight attitude combination; by comparing the normal energy consumption and the actual energy consumption, calculate the energy savings; establish a relationship model between energy and flight mileage, and substitute the energy savings into the relationship model to calculate the saved mileage.

[0025] Combined with the first aspect, in the sixth implementation manner of the first aspect of this application, based on the predicted endurance mileage of the hydrogen - refueling - required drone, as well as the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottles, screen out the drones that are compatible with the hydrogen concentration of the remaining hydrogen storage bottles, and preferentially allocate the hydrogen concentration of the hydrogen storage bottles to the compatible drones, including:

[0026] Based on the working characteristics of the fuel cells carried by each drone, determine its adaptation range for hydrogen concentration and formulate adaptation criteria; when the hydrogen concentration of the hydrogen storage bottle is within the adaptation range of a certain drone for hydrogen concentration, determine that the drone is compatible with the hydrogen concentration of the hydrogen storage bottle;

[0027] Screen the compatible drones and perform hydrogen concentration allocation.

[0028] Combined with the first aspect, in the seventh implementation manner of the first aspect of this application, the screening of compatible drones and the hydrogen concentration allocation include:

[0029] Taking each hydrogen storage bottle as an object, traverse all the drones that need hydrogen refueling. For each hydrogen storage bottle, check one by one whether each drone meets its adaptation range for hydrogen concentration; record the drones that meet the adaptation criteria to form a list of compatible drones; each list of compatible drones corresponds to a hydrogen storage bottle, and the list contains the numbers, models, and predicted endurance mileage of the compatible drones;

[0030] In each list of drones to be refueled with hydrogen, sort according to the predicted endurance of the drones, and give priority to the drones with longer predicted endurance. According to the determined allocation order, allocate hydrogen concentration from the hydrogen storage cylinder to the applicable drones, and determine the amount of hydrogen to be allocated each time according to the design of the fuel cell of the drone and the control accuracy of the hydrogen refueling equipment. Continuously perform the allocation operation until the hydrogen capacity of the hydrogen storage cylinder cannot meet the hydrogen refueling requirements of the next applicable drone, or all applicable drones of the hydrogen storage cylinder have completed hydrogen refueling. Repeat the screening and allocation steps for the next hydrogen storage cylinder until all drones that need to be refueled with hydrogen have been processed or all the hydrogen in all hydrogen storage cylinders has been allocated.

[0031] Combined with the first aspect, in the eighth implementation manner of the first aspect of the present application, during the hydrogen refueling process, by calculating the amount of hydrogen energy required for each drone to be refueled with hydrogen and the amount of hydrogen energy that has been allocated currently, obtaining the required hydrogen energy capacity and the corresponding hydrogen concentration includes:

[0032] Calculate in real time the total amount of electrical energy required for the drone during the planned flight period, convert the amount of hydrogen energy using the power generation efficiency, and calculate the amount of hydrogen energy required for the drone to be refueled with hydrogen. Each time the drone is refueled with hydrogen, the metering device of the hydrogen refueling equipment monitors in real time the amount of hydrogen charged, converts it into an energy unit, and records the number of the hydrogen storage cylinder and the hydrogen concentration used for this hydrogen refueling. As the hydrogen refueling process progresses, when a certain drone is refueled multiple times, continuously accumulate the amount of hydrogen energy for each hydrogen refueling.

[0033] At any moment during the hydrogen refueling process, for all drones that need to be refueled with hydrogen, accumulate the amount of hydrogen energy required for each drone to obtain the total amount of hydrogen energy required. Obtain in real time the hydrogen concentration of the remaining hydrogen storage cylinders currently. When it is found that the existing hydrogen storage cylinders cannot meet the needs of all drones that have not been refueled with hydrogen, the system issues an alarm to prompt the operator that a new hydrogen source needs to be allocated.

[0034] In the second aspect, the present invention provides a hydrogen energy endurance supply regulation and management system applicable to hydrogen energy drones, including:

[0035] Theoretical endurance calculation module: including: a theoretical endurance model construction unit and a theoretical endurance calculation unit; wherein, the theoretical endurance model construction unit collects drone parameters and hydrogen storage cylinder parameters, constructs a theoretical endurance model, and the theoretical endurance calculation unit calculates the theoretical endurance that the drone can reach under ideal conditions after using up one hydrogen storage cylinder.

[0036] First endurance calculation module: It includes: a flight data acquisition unit, an artificial intelligence algorithm processing unit, and a first endurance calculation unit; among them, the flight data acquisition unit collects the flight data of the drone, including flight speed, flight route, and flight attitude, and the artificial intelligence algorithm processing unit inputs it into a pre-trained artificial intelligence algorithm model to obtain the endurance loss of the drone in the current flight state; the first endurance calculation unit deducts the endurance loss from the theoretical endurance to obtain the first endurance;

[0037] Predicted endurance calculation module: It includes: a flight attitude combination recognition unit, an energy saving analysis unit, and a predicted endurance calculation unit; among them, the flight attitude combination recognition unit obtains the flight attitude combinations used by the drone during flight based on the flight data of the drone; the energy saving analysis unit takes the first endurance as the predicted endurance when the drone does not use the flight attitude combination; when the drone uses the flight attitude combination, it calculates the saved mileage through the analysis of flight attitude changes and corresponding energy savings; the predicted endurance calculation unit adds the saved mileage to the first endurance as the predicted endurance;

[0038] Hydrogen refueling demand and distribution module: It includes: a hydrogen refueling demand monitoring unit, a hydrogen concentration adaptation screening and distribution unit, and a hydrogen refueling energy calculation unit; among them, the hydrogen refueling demand monitoring unit sends a hydrogen refueling demand signal when the remaining hydrogen volume of the drone drops to a preset hydrogen refueling threshold; the hydrogen concentration adaptation screening and distribution unit screens out the drones that are compatible with the hydrogen concentration of the remaining hydrogen storage bottles based on the predicted endurance of the drones that need to be refueled and the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottles, and preferentially distributes the hydrogen concentration of the hydrogen storage bottles to the compatible drones; during the hydrogen refueling process, the hydrogen refueling energy calculation unit calculates the hydrogen energy required for each drone to be refueled and the currently allocated hydrogen energy to obtain the required hydrogen energy capacity and the corresponding hydrogen concentration.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. By collecting the parameters of the drone and the hydrogen storage bottles, combining the law of conservation of energy and the principles of flight mechanics to construct a theoretical endurance model, using artificial intelligence algorithms to analyze flight data, accurately calculating the endurance loss under different flight states, and comprehensively considering the energy savings brought by flight attitude combinations, the present invention can obtain a highly accurate predicted endurance.

[0041] 2. By using artificial intelligence algorithms to analyze the impact of the flight speed, flight route, and flight attitude data of the drone on endurance loss in real time, the present invention can timely adjust the hydrogen energy supply strategy according to the real-time changes in the flight state, ensuring that the drone can operate efficiently in various complex flight scenarios.

[0042] 3. Based on the predicted endurance of the drone, the hydrogen capacity and concentration of the hydrogen storage bottle, the drones compatible with the hydrogen concentration of the hydrogen storage bottle are screened out, and the hydrogen concentration is preferentially allocated, which can ensure that the fuel cells of each drone work under the optimal hydrogen concentration conditions, improve the energy utilization efficiency, and extend the endurance of the drone. Description of the Drawings

[0043] Figure 1 is a schematic diagram of the steps of the hydrogen energy endurance supply regulation and management method applicable to hydrogen energy drones of the present invention;

[0044] Figure 2 is a system structure diagram of the hydrogen energy endurance supply regulation and management system applicable to hydrogen energy drones of the present invention. Detailed Embodiments

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0046] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution,

[0047] As Figure 1 shown in the schematic diagram of the steps of the hydrogen energy endurance supply regulation and management method applicable to hydrogen energy drones of the present invention, the present invention provides a hydrogen energy endurance supply regulation and management method applicable to hydrogen energy drones, including the following steps:

[0048] Step S100: Collect drone parameters and hydrogen storage bottle parameters, construct a theoretical endurance model, and calculate the theoretical endurance that the drone can reach after using up one hydrogen storage bottle under ideal conditions;

[0049] Specifically, the drone parameters include aerodynamic parameters, power system parameters, and flight performance parameters; the hydrogen storage bottle parameters include the volume and hydrogen concentration of the hydrogen storage bottle;

[0050] According to the volume V and hydrogen concentration c of the hydrogen storage bottle, combined with the density of hydrogen , calculate the mass of hydrogen in the hydrogen storage bottle ; use the heat of combustion of hydrogen to obtain the total chemical energy of hydrogen ; obtain the power generation efficiency of the fuel cell and the drive efficiency of the motor from the power system parameters, and convert the total chemical energy of hydrogen into mechanical energy available for the drone ;

[0051] Obtain the cruise speed of the UAV from the flight performance parameters, and obtain the drag coefficient and wing area of the UAV from the aerodynamic parameters; According to the aerodynamic principle, the drag D received by the UAV during flight is expressed as , where is the air density, is the cruise speed of the UAV, is the drag coefficient of the UAV, and S is the wing area of the UAV; The power required by the UAV is ;

[0052] According to the law of conservation of energy, energy is equal to power multiplied by time, and the formula is , calculate the theoretical endurance time of the UAV , combined with the cruise speed of the UAV, to obtain the theoretical endurance mileage .

[0053] In a specific embodiment, the drag coefficient of the UAV = 0.25, the wing area S = 1.5 square meters, the power generation efficiency of the fuel cell = 0.6, the drive efficiency of the motor = 0.85. The cruise speed of the UAV = 20 m / s. The volume V of the hydrogen storage cylinder is 5 liters, and the hydrogen concentration c = 99%. The density of hydrogen is 0.0899 kg / m³, and the calorific value of hydrogen combustion is 142000 kJ / kg. The air density is 1.225 kg / m³.

[0054] Convert the volume V of the hydrogen storage cylinder = 5 liters to cubic meters, 5 liters = 0.005 cubic meters. Since the hydrogen concentration c = 99%, the actual volume of hydrogen contained is cubic meters; According to the density formula, calculate the mass of hydrogen kg.

[0055] Calculate the total chemical energy using the calorific value of hydrogen combustion, kJ. The mechanical energy available to the UAV kJ.

[0056] According to the aerodynamic principle, the drag received by the UAV during flight N. The power P required by the UAV = 91.875 * 20 = 1837.5 W.

[0057] According to the law of conservation of energy, t = 32280 / 1837.5 ≈ 17.57 seconds. Combining with the cruising speed of the drone, the theoretical endurance mileage L = 20 * 17.57 = 351.4 meters.

[0058] Step S200: Collect the flight data of the drone, including flight speed, flight route, and flight attitude, and input them into a pre-trained artificial intelligence algorithm model to obtain the endurance loss amount of the drone in the current flight state; deduct the endurance loss amount from the theoretical endurance mileage to obtain the first endurance mileage.

[0059] Specifically, the artificial intelligence algorithm model selects a multi-layer perceptron model. The flight data of the drone is divided into a training set, a validation set, and a test set. The model is trained using the training set data. During the training process, by adjusting the parameters of the model, the error between the prediction result of the model and the actual endurance loss amount is minimized, and the gradient descent algorithm is used to update the model parameters; the validation set is used to validate the model and adjust the hyperparameters of the model; the test set data is used to evaluate the trained model, calculate the prediction error of the model, and optimize the model.

[0060] In a specific embodiment, the collected flight speed, flight route (latitude and longitude coordinates are converted into displacement vectors relative to the starting point), and flight attitude data are integrated into a feature matrix, and each row represents the flight state data at a time point. At the same time, the corresponding actual endurance loss amount is sorted into a label vector. The data is divided into a training set, a validation set, and a test set according to the ratio of 70%, 15%, and 15%. That is, from 6000 data points, 4200 are selected as the training set, 900 as the validation set, and 900 as the test set. Ensure that the data in each set can represent different flight stages and states.

[0061] Construct a multi-layer perceptron model with two hidden layers. The number of nodes in the input layer is determined according to the number of features. Since there are 6 features in total, including flight speed, flight route (represented by 2 displacement vectors), and flight attitude (3 angles), the input layer has 6 nodes. The first hidden layer is set with 10 nodes, the second hidden layer is set with 8 nodes, and the output layer is 1 node for predicting the endurance loss amount. ReLU is selected as the activation function for the hidden layer, and no activation function is used for the output layer because the endurance loss amount is a continuous value.

[0062] The model is trained using the training set data, and the training process is set to 100 epochs. In each epoch, the predicted results of the model are calculated through forward propagation. Then, the mean squared error (MSE) is used as the loss function to calculate the error between the predicted results and the actual endurance loss. The gradient descent algorithm is used to update the model parameters, and the learning rate is set to 0.001. When updating the parameters, the parameter values are adjusted in the direction that reduces the loss function according to the gradient of the loss function with respect to the parameters. During the training process, the model is validated using the validation set data every 5 epochs. The validation set data is input into the model, and the loss value of the model on the validation set is calculated (also using MSE). According to the change in the loss value on the validation set, the hyperparameters of the model are adjusted. For example, when it is found that the loss value on the validation set no longer decreases or even shows an upward trend in several consecutive epochs, try to adjust hyperparameters such as the number of hidden layer nodes and the learning rate. After adjusting the learning rate to 0.0005, it is found that the loss value on the validation set has decreased, indicating that the adjustment is effective. The mean squared error of the model on the test set is 25, and the mean absolute error is 3 kJ. Increase the number of hidden layers. After adding one hidden layer and retraining, it is found that the mean squared error of the model on the test set is reduced to 20, and the mean absolute error is reduced to 2.5 kJ, indicating that the performance of the model has been improved.

[0063] The real-time collected flight data is input into the optimized model to obtain the predicted endurance loss. In the current flight state, the predicted endurance loss by the model is 100 kJ. Given that the energy corresponding to the previously calculated theoretical endurance mileage is 32.28 kJ, the predicted endurance loss is deducted from the energy corresponding to the theoretical endurance mileage. Due to the certain conversion relationship between energy and mileage (known from the previously calculated parameters such as power and speed), the first endurance mileage is calculated through conversion. The conversion relationship is that every 1 kJ of energy corresponds to 10 meters of mileage, so the first endurance mileage = (32.28 - 10) * 10 = 222.8 meters.

[0064] Step S300: Based on the flight data of the drone, obtain the flight attitude combinations used by the drone during flight; when the drone does not use flight attitude combinations, take the first endurance mileage as the predicted endurance mileage; when the drone uses flight attitude combinations, calculate the saved mileage through the analysis of flight attitude changes and the corresponding energy savings; add the saved mileage to the first endurance mileage as the predicted endurance mileage;

[0065] Specifically, based on the flight data of the drone, the attitude angles of the drone, including pitch angle, roll angle, and yaw angle, are calculated through a complementary filtering algorithm; different attitude combinations are defined according to the flight mission of the drone; a clustering algorithm is used to analyze the attitude angles of the drone to identify different attitude combinations, and similar attitude data points are divided into different clusters, and each cluster represents an attitude combination.

[0066] Further, integrate the attitude angles of the UAV into a dataset. The clustering algorithm selects the K-means clustering algorithm, determines the value of K through the elbow method, and calculates the sum of squared errors of clustering under different values of K, specifically the sum of the squares of the distances from each data point to the center of its affiliated cluster. Randomly select K points in the dataset as the initial clustering centers;

[0067] For each attitude angle data point in the dataset, calculate its distance from the K clustering centers using the Euclidean distance; assign each data point to the cluster where the nearest clustering center is located; recalculate the mean of the data points in each cluster and use it as the new clustering center; repeat the above steps and iterate continuously until the clustering centers no longer change. At this time, the attitude angle data is divided into K different clusters.

[0068] Further, based on the principles of flight mechanics and the power system parameters of the UAV, consider the effects of flight attitude, flight speed, flight altitude, and air density on energy consumption, and establish an energy consumption model; assume that the UAV flies in a conventional flight attitude without using a flight attitude combination. According to the energy consumption model and combined with the power system parameters, calculate the normal energy consumption under the same flight conditions; collect the actual energy consumption of the UAV when using the flight attitude combination; calculate the energy savings by comparing the normal energy consumption and the actual energy consumption; establish a relationship model between energy and flight mileage, and substitute the energy savings into the relationship model to calculate the saved mileage.

[0069] In a specific embodiment, according to the logistics distribution flight mission performed by the UAV this time, define the following attitude combinations:

[0070] Horizontal cruise attitude combination: The pitch angle is between -5° and 5°, the roll angle is between -3° and 3°, and the yaw angle change rate is small (not exceeding 5° per minute).

[0071] Turning attitude combination: The yaw angle change rate is large (exceeding 10° per minute). At the same time, the pitch angle and roll angle are adjusted accordingly according to the turning direction. For example, when turning left, the roll angle is positive, and when turning right, the roll angle is negative, and the pitch angle is adjusted between -10° and 10° to maintain flight balance.

[0072] Climbing attitude combination: The pitch angle is greater than 10°, the roll angle is between -5° and 5°, and the yaw angle remains relatively stable.

[0073] Integrate the attitude angle data at 3000 time points obtained into a dataset, and each data point contains the pitch angle, roll angle, and yaw angle at the corresponding moment.

[0074] The elbow method is used to determine the value of K. Tests are carried out in the range of K = 2 to K = 10. For each value of K, the K-means clustering algorithm is executed. For example, when K = 3, 3 points are randomly selected from the dataset as the initial cluster centers C1, C2, and C3. Calculate the sum of squared errors (SSE) of the clustering for different values of K, that is, the sum of the squares of the distances from each data point to the center of its belonging cluster. Assign each data point to the cluster where the nearest cluster center is located. Then recalculate the mean of the data points in each cluster and use it as the new cluster center. Repeat this process until the cluster centers no longer change. Record the SSE value at this time. By plotting the relationship curve between the value of K and the SSE value, it is found that when K = 3, an obvious elbow point appears on the curve, so it is determined that K = 3 is the appropriate number of clusters.

[0075] Re-cluster with the determined K = 3. Randomly select 3 initial cluster centers again and perform iterations according to the above steps of distance calculation, data point assignment, and cluster center update. After multiple iterations, the attitude angle dataset is divided into 3 different clusters. Analyze each cluster, and according to the attitude angle range and change characteristics of the data points in the cluster, match them with the predefined attitude combinations. It is found that the attitude angle range of the data points in Cluster1 is consistent with the definition of the horizontal cruise attitude combination, Cluster2 is consistent with the turning attitude combination, and Cluster3 is consistent with the climbing attitude combination.

[0076] Based on the principles of flight mechanics, considering the influence of flight attitude, flight speed, flight altitude, and air density on energy consumption, an energy consumption model is established. Let the air density be , the flight speed be v, the wing area be S, the drag coefficient be , the lift coefficient be , the gravitational acceleration be g, and the mass of the UAV be m.

[0077] Flight drag , lift . In the horizontal cruise attitude, and are at relatively stable design values; in the turning attitude, to maintain centripetal force, and will be adjusted according to the turning radius and speed. In terms of the power system parameters, the known fuel cell power generation efficiency and the motor drive efficiency are known. The power required for the UAV to fly . Based on these parameters, an energy consumption model E = P * t is established, where t is the flight time. In this embodiment, the calculated normal energy consumption is 1,058,820 joules.

[0078] During this flight segment, the actual power output varies with time. Through integral calculation, the actual energy consumption is 900,000 joules. The energy savings is 1,058,820 - 900,000 = 158,820 joules. A relationship model between energy and flight mileage is established. Substituting the energy savings into the relationship model, the saved mileage is calculated as 158,820 / 1000 = 158.82 meters.

[0079] The previously calculated first endurance mileage is known to be 222.8 meters. Adding the saved mileage to the first endurance mileage, the predicted endurance mileage is 222.8 + 158.82 = 381.62 meters.

[0080] Step S400: When the remaining hydrogen amount of the drone drops to a preset hydrogen refueling threshold, send a hydrogen refueling demand signal; based on the predicted endurance mileage of the drones that need hydrogen refueling, as well as the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottles, screen out the drones that are compatible with the hydrogen concentration of the remaining hydrogen storage bottles, and preferentially allocate the hydrogen concentration of the hydrogen storage bottles to the compatible drones; during the hydrogen refueling process, by calculating the hydrogen energy amount required for each drone to be refueled and the currently allocated hydrogen energy amount, obtain the required hydrogen energy capacity and the corresponding hydrogen concentration.

[0081] Specifically, based on the working characteristics of the fuel cells carried by each drone, determine its adaptation range for hydrogen concentration and formulate an adaptation standard; when the hydrogen concentration of the hydrogen storage bottle is within the adaptation range of a certain drone for hydrogen concentration, determine that the drone is compatible with the hydrogen concentration of the hydrogen storage bottle;

[0082] Screen out the compatible drones and perform hydrogen concentration allocation.

[0083] Furthermore, taking each hydrogen storage bottle as an object, traverse all the drones that need hydrogen refueling. For each hydrogen storage bottle, check one by one whether each drone meets its adaptation range for hydrogen concentration; record the drones that meet the adaptation standard to form a list of compatible drones; each list of compatible drones corresponds to a hydrogen storage bottle, and the list contains the numbers, models, and predicted endurance mileage of the compatible drones;

[0084] In each list of compatible drones, sort according to the predicted endurance mileage of the drones, giving priority to the drones with longer predicted endurance mileage; in the determined allocation order, allocate the hydrogen concentration from the hydrogen storage bottle to the compatible drones, and determine the amount of hydrogen allocated each time according to the design of the drone fuel cell and the control accuracy of the hydrogen refueling equipment; continue the allocation operation until the hydrogen capacity of the hydrogen storage bottle cannot meet the hydrogen refueling demand of the next compatible drone, or all the compatible drones of the hydrogen storage bottle have completed hydrogen refueling; repeat the screening and allocation steps for the next hydrogen storage bottle until all the drones that need hydrogen refueling have been processed or all the hydrogen in all the hydrogen storage bottles has been allocated.

[0085] Further, the total amount of electric energy required by the UAV during the planned flight is calculated in real time, and the amount of hydrogen energy is converted using the power generation efficiency to calculate the amount of hydrogen energy required for hydrogen refueling of the UAV; each time the UAV is refueled with hydrogen, the metering device of the hydrogen refueling equipment monitors the amount of hydrogen filled in real time, converts it into an energy unit, and records the serial number of the hydrogen storage bottle and the hydrogen concentration used for this hydrogen refueling; as the hydrogen refueling process progresses, when a certain UAV is refueled multiple times, the hydrogen energy of each hydrogen refueling is continuously accumulated.

[0086] At any moment during the hydrogen refueling process, for all UAVs that need to be refueled with hydrogen, the required hydrogen energy of each UAV is accumulated to obtain the total required hydrogen energy; the hydrogen concentration of the current remaining hydrogen storage bottles is obtained in real time. When it is found that the existing hydrogen storage bottles cannot meet the needs of all non-hydrogen-refueled UAVs, the system issues an alarm to prompt the operator to deploy new hydrogen sources.

[0087] In a specific embodiment, there are 5 UAVs that need to be refueled with hydrogen, numbered U1, U2, U3, U4, and U5 respectively, and their models are ModelA, ModelB, ModelA, ModelC, and ModelB respectively. The fuel cell parameters carried by each UAV are different. After preliminary testing and analysis, the following adaptation ranges of hydrogen concentration for them are determined:

[0088] U1 (ModelA): The adaptation range of hydrogen concentration is 95% - 99%.

[0089] U2 (ModelB): The adaptation range of hydrogen concentration is 90% - 96%.

[0090] U3 (ModelA): The adaptation range of hydrogen concentration is 95% - 99%.

[0091] U4 (ModelC): The adaptation range of hydrogen concentration is 93% - 97%.

[0092] U5 (ModelB): The adaptation range of hydrogen concentration is 90% - 96%.

[0093] It is known that their predicted endurance ranges are 300 km, 250 km, 320 km, 280 km, and 260 km respectively.

[0094] There are 3 remaining hydrogen storage bottles in front, numbered H1, H2, and H3, and their hydrogen capacities and concentration information are as follows:

[0095] H1: The hydrogen capacity is 500 liters, and the hydrogen concentration is 96%.

[0096] H2: The hydrogen capacity is 400 liters, and the hydrogen concentration is 93%.

[0097] H3: The hydrogen capacity is 350 liters, and the hydrogen concentration is 98%.

[0098] Each drone is equipped with a hydrogen quantity monitoring sensor to monitor the remaining hydrogen quantity in real time. The preset hydrogen refueling threshold is 20% of the total capacity for the remaining hydrogen quantity. When the remaining hydrogen quantity of U1 drops to this threshold, a hydrogen refueling demand signal is first triggered, and then drones such as U2 and U3 also reach the threshold one after another and send signals.

[0099] Taking the H1 hydrogen storage cylinder (hydrogen concentration 96%) as an example, traverse 5 drones that need hydrogen refueling:

[0100] U1 (ModelA), the adaptation range is 95% - 99%, 96% is within its adaptation range, record the list of adapted drones from U1 to H1.

[0101] U2 (ModelB), the adaptation range is 90% - 96%, 96% is within its adaptation range, record the list of adapted drones from U2 to H1.

[0102] U3 (ModelA), the adaptation range is 95% - 99%, 96% is within its adaptation range, record the list of adapted drones from U3 to H1.

[0103] U4 (ModelC), the adaptation range is 93% - 97%, 96% is within its adaptation range, record the list of adapted drones from U4 to H1.

[0104] U5 (ModelB), the adaptation range is 90% - 96%, 96% is within its adaptation range, record the list of adapted drones from U5 to H1.

[0105] Sort the list of adapted drones for H1 in descending order according to the predicted endurance: U3 (320 km), U1 (300 km), U4 (280 km), U5 (260 km), U2 (250 km). In the same way, screen the adapted drones and sort the lists for the H2 and H3 hydrogen storage cylinders. H2 (hydrogen concentration 93%): The adapted drones are U2, U4, U5, and after sorting, it is U4 (280 km), U5 (260 km), U2 (250 km). H3 (hydrogen concentration 98%): The adapted drones are U1, U3, and after sorting, it is U3 (320 km), U1 (300 km).

[0106] In the order of sorting, hydrogen is first added to U3. According to the design of the U3 fuel cell and the control accuracy of the hydrogen addition equipment, the amount of hydrogen allocated each time is determined. After calculation, 150 liters of hydrogen are required to add hydrogen to U3. The hydrogen addition equipment starts to work, and the metering device monitors the amount of hydrogen filled in real time. After the hydrogen addition is completed, the number H1 of the hydrogen storage bottle used for this hydrogen addition and the hydrogen concentration of 96% are recorded. At this time, the remaining hydrogen capacity of H1 is 500 - 150 = 350 liters. Then, hydrogen is added to U1, which requires 120 liters of hydrogen. After the hydrogen addition is completed, the remaining hydrogen capacity of H1 is 350 - 120 = 230 liters. Hydrogen is continued to be added to U4, which requires 100 liters of hydrogen. At this time, the remaining hydrogen capacity of H1 is 230 - 100 = 130 liters. When adding hydrogen to U5, U5 requires 120 liters of hydrogen, but the remaining hydrogen capacity of H1 is insufficient, so the distribution operation of H1 is stopped.

[0107] Next, the distribution of H2 and H3 is carried out.

[0108] The hydrogen concentration and capacity information of the current remaining hydrogen storage bottles are obtained in real time. When it is found that the existing hydrogen storage bottles cannot meet the needs of all the unmanned aerial vehicles that have not been hydrogenated, the system issues an alarm. After calculation, according to the current distribution situation, the hydrogen energy of all the remaining hydrogen storage bottles is not enough to meet the remaining hydrogenation requirements of U2, and the system immediately issues an alarm to prompt the operator that a new hydrogen source needs to be allocated.

[0109] In the second aspect, the present invention provides a hydrogen energy endurance supply regulation and management system applicable to hydrogen energy unmanned aerial vehicles, including:

[0110] Theoretical endurance mileage calculation module: including: a theoretical endurance mileage model construction unit and a theoretical endurance mileage calculation unit; wherein, the theoretical endurance mileage model construction unit collects the parameters of the unmanned aerial vehicle and the hydrogen storage bottle, constructs a theoretical endurance mileage model, and the theoretical endurance mileage calculation unit calculates the theoretical endurance mileage that the unmanned aerial vehicle can reach after using up a hydrogen storage bottle under ideal conditions;

[0111] The first endurance mileage calculation module: including: a flight data collection unit, an artificial intelligence algorithm processing unit, and a first endurance mileage calculation unit; wherein, the flight data collection unit collects the flight data of the unmanned aerial vehicle, including flight speed, flight route, and flight attitude, and the artificial intelligence algorithm processing unit inputs it into a pre-trained artificial intelligence algorithm model to obtain the endurance loss amount of the unmanned aerial vehicle in the current flight state; the first endurance mileage calculation unit deducts the endurance loss amount from the theoretical endurance mileage to obtain the first endurance mileage;

[0112] Predicted Endurance Calculation Module: It includes: a flight attitude combination recognition unit, an energy saving analysis unit, and a predicted endurance calculation unit; among them, the flight attitude combination recognition unit obtains the flight attitude combinations used by the UAV during flight based on the flight data of the UAV; when the UAV does not use a flight attitude combination, the energy saving analysis unit takes the first endurance as the predicted endurance; when the UAV uses a flight attitude combination, it calculates the saved mileage through the analysis of the flight attitude change and the corresponding energy saving; the predicted endurance calculation unit adds the saved mileage to the first endurance as the predicted endurance;

[0113] Hydrogen Refueling Demand and Allocation Module: It includes: a hydrogen refueling demand monitoring unit, a hydrogen concentration adaptation screening and allocation unit, and a hydrogen refueling energy calculation unit; among them, when the remaining hydrogen amount of the UAV drops to a preset hydrogen refueling threshold, the hydrogen refueling demand monitoring unit issues a hydrogen refueling demand signal; based on the predicted endurance of the UAV that needs to be refueled, as well as the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage cylinders, the hydrogen concentration adaptation screening and allocation unit screens out the UAVs that are adapted to the hydrogen concentration of the remaining hydrogen storage cylinders, and preferentially allocates the hydrogen concentration of the hydrogen storage cylinders to the adapted UAVs; during the hydrogen refueling process, the hydrogen refueling energy calculation unit obtains the required hydrogen energy capacity and the corresponding hydrogen concentration by calculating the hydrogen energy required for each UAV to be refueled and the currently allocated hydrogen energy.

[0114] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed claims.

Claims

1. A hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs, characterized in that: The following steps are involved: Collect the parameters of the drone and the hydrogen storage bottle, build a theoretical cruising range model, and calculate the theoretical cruising range that the drone can achieve after using up a bottle of hydrogen storage bottle under ideal conditions; Collect the flight data of the drone, including flight speed, flight route and flight attitude, and input it into the pre-trained artificial intelligence algorithm model to obtain the endurance loss of the drone in the current flight state; deduct the endurance loss from the theoretical endurance mileage to obtain the first endurance mileage; Based on the flight data of the UAV, a flight attitude combination used by the UAV during the flight is obtained; when the UAV does not use the flight attitude combination, the first cruising range is used as the predicted cruising range; when the UAV uses the flight attitude combination, the saved mileage is calculated by analyzing the flight attitude change and the corresponding energy saving; the saved mileage is added to the first cruising range as the predicted cruising range; When the remaining hydrogen amount of the UAV drops to the preset hydrogen refueling threshold, a hydrogen refueling demand signal is issued; based on the predicted cruising range of the UAV that needs hydrogen refueling and the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottle, the UAVs that are compatible with the hydrogen concentration of the remaining hydrogen storage bottle are screened out, and the hydrogen concentration of the hydrogen storage bottle is preferentially allocated to the compatible UAVs; during the hydrogen refueling process, the required hydrogen energy capacity and the corresponding hydrogen concentration are obtained by calculating the amount of hydrogen energy required for hydrogen refueling of each UAV and the currently allocated hydrogen energy.

2. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 1 is characterized in that: The method of collecting the parameters of the drone and the hydrogen storage bottle, constructing a theoretical cruising range model, and calculating the theoretical cruising range that the drone can achieve after using up a bottle of hydrogen storage bottle under ideal conditions includes: The UAV parameters include aerodynamic parameters, power system parameters and flight performance parameters; the hydrogen storage bottle parameters include the volume and hydrogen concentration of the hydrogen storage bottle; According to the volume V of the hydrogen storage bottle and the hydrogen concentration c, combined with the density of hydrogen , calculate the mass of hydrogen in the hydrogen storage bottle ; Using the heat of combustion of hydrogen , and the total chemical energy of hydrogen is ; Obtain the power generation efficiency of fuel cells from power system parameters and the motor drive efficiency , converting the total chemical energy of hydrogen into mechanical energy that can be used by the drone ; The cruising speed of the UAV is obtained from the flight performance parameters, and the drag coefficient and wing area of ​​the UAV are obtained from the aerodynamic parameters. According to the principles of aerodynamics, the drag D of the UAV during flight is expressed as ,in, is the air density, is the cruising speed of the drone, is the drag coefficient of the drone, S is the wing area of ​​the drone; the power required by the drone is ; According to the law of conservation of energy, energy equals power multiplied by time, and the formula is: , calculate the theoretical endurance of the drone , combined with the cruising speed of the drone, the theoretical range is obtained .

3. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 1 is characterized in that: The collected flight data of the drone, including flight speed, flight route and flight attitude, is input into a pre-trained artificial intelligence algorithm model to obtain the endurance loss of the drone in the current flight state, including: The artificial intelligence algorithm model selects a multi-layer perceptron model, divides the flight data of the drone into a training set, a validation set and a test set, and uses the training set data to train the model. During the training process, the model parameters are adjusted to minimize the error between the model's prediction results and the actual endurance loss, and the model parameters are updated using a gradient descent algorithm; the model is verified using a validation set and the model's hyperparameters are adjusted; the trained model is evaluated using the test set data, the model's prediction error is calculated, and the model is optimized.

4. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 1 is characterized in that: The method of obtaining the flight attitude combination used by the drone during the flight based on the flight data of the drone includes: Based on the flight data of the UAV, the attitude angle of the UAV, including the pitch angle, roll angle and yaw angle, is calculated through the complementary filtering algorithm; different attitude combinations are defined according to the flight mission of the UAV; the clustering algorithm is used to analyze the attitude angle of the UAV, identify different attitude combinations, and divide similar attitude data points into different clusters, each cluster representing a attitude combination.

5. According to the hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs as claimed in claim 4, the use of a clustering algorithm to analyze the attitude angles of the UAV and identify different attitude combinations includes: The attitude angles of the drone are integrated into a data set. The clustering algorithm selects the K-means clustering algorithm, determines the K value by the elbow method, calculates the sum of square errors of clustering under different K values, specifically the sum of squares of the distances from each data point to the center of the cluster to which it belongs, and randomly selects K points in the data set as the initial cluster centers; For each posture angle data point in the data set, use the Euclidean distance to calculate its distance from the K cluster centers; assign each data point to the cluster where the cluster center closest to it is located; recalculate the mean of the data points in each cluster and use it as the new cluster center; repeat the above steps and iterate continuously until the cluster center no longer changes, at which point the posture angle data is divided into K different clusters.

6. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 1 is characterized in that: When the drone uses a flight attitude combination, the mileage saved is calculated by analyzing the flight attitude changes and the corresponding energy savings, including: Based on the principles of flight mechanics and the power system parameters of the UAV, an energy consumption model is established by considering the effects of flight attitude, flight speed, flight altitude and air density on energy consumption. It is assumed that the UAV flies in a conventional flight attitude without using a flight attitude combination. According to the energy consumption model and combined with the power system parameters, the normal energy consumption under the same flight conditions is calculated. The actual energy consumption of the UAV when using the flight attitude combination is collected. The energy savings are calculated by comparing the normal energy consumption with the actual energy consumption. A relationship model between energy and flight mileage is established, and the energy savings are substituted into the relationship model to calculate the saved mileage.

7. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 1 is characterized in that: The method of selecting drones that match the hydrogen concentration of the remaining hydrogen storage bottles based on the predicted cruising range of the drones that need hydrogen refueling and the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottles, and preferentially allocating the hydrogen concentration of the hydrogen storage bottles to the matching drones, includes: Based on the working characteristics of the fuel cell carried by each drone, determine its adaptation range for hydrogen concentration and formulate adaptation standards; when the hydrogen concentration of the remaining hydrogen storage bottle is within the adaptation range of a drone for hydrogen concentration, determine that the drone is compatible with the hydrogen concentration of the hydrogen storage bottle; Screen suitable drones and distribute hydrogen concentration.

8. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 7 is characterized in that: The screening and adapting of drones and the distribution of hydrogen concentration include: Take each hydrogen storage bottle as an object, traverse all drones that need hydrogen refueling, and for each hydrogen storage bottle, check whether each drone meets its adaptation range for hydrogen concentration; record the drones that meet the adaptation standards to form a list of adapted drones; each list of adapted drones corresponds to a hydrogen storage bottle, and the list contains the number, model and predicted range of the adapted drone; In each list of compatible drones, the drones are sorted according to their predicted range, with priority given to drones with longer predicted ranges; hydrogen concentration is allocated to the compatible drones from the hydrogen storage bottles in the determined allocation order, and the amount of hydrogen allocated each time is determined based on the design of the drone fuel cell and the control accuracy of the hydrogen refueling equipment; the allocation operation is continued until the hydrogen capacity of the hydrogen storage bottle cannot meet the hydrogen refueling needs of the next compatible drone, or all compatible drones of the hydrogen storage bottle have completed hydrogen refueling; the screening and allocation steps are repeated for the next hydrogen storage bottle until all drones that need hydrogen refueling have been processed or the hydrogen in all hydrogen storage bottles has been allocated.

9. The hydrogen energy endurance supply control management method applicable to hydrogen-powered UAVs according to claim 1 is characterized in that: In the hydrogen refueling process, the required hydrogen energy capacity and the corresponding hydrogen concentration are obtained by calculating the amount of hydrogen energy required for each drone and the currently allocated amount of hydrogen energy, including: The total amount of electric energy required by the drone during the planned flight is calculated in real time, and the amount of hydrogen energy is converted using the power generation efficiency to calculate the amount of hydrogen energy required for hydrogenation of the drone. Each time the drone is hydrogenated, the metering device of the hydrogenation equipment monitors the amount of hydrogen charged in real time, converts it into energy units, and records the number of the hydrogen storage bottle used for this hydrogenation and the hydrogen concentration. As the hydrogenation process progresses, when a drone is hydrogenated multiple times, the amount of hydrogen energy for each hydrogenation is continuously accumulated. At any time during the hydrogen refueling process, for all drones that need hydrogen refueling, the amount of hydrogen energy required for each drone is accumulated to obtain the total amount of hydrogen energy required; the hydrogen concentration of the current remaining hydrogen storage bottles is obtained in real time. When it is found that the existing hydrogen storage bottles cannot meet the needs of all un-hydrogenated drones, the system will issue an alarm to prompt the operator to allocate a new hydrogen source.

10. A hydrogen energy endurance supply control and management system applicable to hydrogen-powered UAVs, using the hydrogen energy endurance supply control and management method applicable to hydrogen-powered UAVs as claimed in any one of claims 1 to 9, characterized in that: include: Theoretical cruising range calculation module: including: theoretical cruising range model construction unit and theoretical cruising range calculation unit; wherein the theoretical cruising range model construction unit collects the parameters of the UAV and the parameters of the hydrogen storage bottle, constructs the theoretical cruising range model, and the theoretical cruising range calculation unit calculates the theoretical cruising range that the UAV can achieve after using up a bottle of hydrogen storage bottle under ideal conditions; The first cruising range calculation module includes: a flight data collection unit, an artificial intelligence algorithm processing unit and a first cruising range calculation unit; wherein the flight data collection unit collects the flight data of the UAV, including the flight speed, flight route and flight attitude, and the artificial intelligence algorithm processing unit inputs the data into a pre-trained artificial intelligence algorithm model to obtain the cruising range loss of the UAV in the current flight state; the first cruising range calculation unit deducts the cruising range loss from the theoretical cruising range to obtain the first cruising range; The predicted cruising range calculation module includes: a flight attitude combination recognition unit, an energy saving analysis unit and a predicted cruising range calculation unit; wherein the flight attitude combination recognition unit obtains the flight attitude combination used by the UAV during the flight based on the flight data of the UAV; the energy saving analysis unit uses the first cruising range as the predicted cruising range when the UAV does not use the flight attitude combination; when the UAV uses the flight attitude combination, the saved mileage is calculated by analyzing the flight attitude change and the corresponding energy saving; the predicted cruising range calculation unit adds the saved mileage to the first cruising range as the predicted cruising range; Hydrogenation demand and allocation module: including: hydrogenation demand monitoring unit, hydrogen concentration adaptation screening and allocation unit and hydrogenation energy calculation unit; wherein, the hydrogenation demand monitoring unit sends a hydrogenation demand signal when the remaining hydrogen amount of the UAV is reduced to a preset hydrogenation threshold; the hydrogen concentration adaptation screening and allocation unit screens out the UAVs that are adapted to the hydrogen concentration of the remaining hydrogen storage bottles based on the predicted cruising range of the UAVs that need hydrogenation and the hydrogen capacity and hydrogen concentration of the current remaining hydrogen storage bottles, and preferentially allocates the hydrogen concentration of the hydrogen storage bottles to the adapted UAVs; during the hydrogenation process, the hydrogenation energy calculation unit obtains the required hydrogen energy capacity and the corresponding hydrogen concentration by calculating the amount of hydrogen energy required for hydrogenation of each UAV and the currently allocated amount of hydrogen energy.

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