An intelligent power management method for a drone

Abstract

The application relates to the technical field of unmanned planes, in particular to an intelligent power management method for unmanned planes, which comprises the following steps: periodically acquiring flight parameters of the unmanned planes; determining flight change parameters of each unmanned plane based on the flight parameters; determining whether the operation parameters of each unmanned plane are qualified based on the flight change parameters and historical change fluctuation parameters of corresponding lines; and when determining that the operation parameters of the unmanned plane are abnormal, adjusting the power management parameters of the unmanned plane based on environmental influence parameters and power fluctuation parameters, including adjusting the endurance distance of the unmanned plane to a corresponding value or adjusting the flight speed of the unmanned plane to a corresponding value. According to the application, the power management parameters are dynamically adjusted according to the flight state of the unmanned plane, the reasonable distribution of the power is improved, and the operation efficiency of the unmanned plane is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, and particularly relates to an intelligent power management method for unmanned aerial vehicles. BACKGROUND

[0002] An unmanned aerial vehicle is an aerial vehicle without a pilot on board, which has a power device and a navigation module, and flies within a certain range by radio remote control equipment or computer pre-programmed autonomous control. In the early 20th century, unmanned aerial vehicles first appeared in the United States and were used in the military. After nearly a hundred years of development, unmanned aerial vehicle technology has become more and more mature and has been widely used in various fields, especially in the field of agriculture, where unmanned aerial vehicles have appeared more and more frequently. Agricultural unmanned aerial vehicles are divided into electric, oil and hybrid power unmanned aerial vehicles according to power. With the development of science and technology and the increase of labor costs, unmanned aerial vehicles are increasingly used in agricultural fields such as pesticide spraying, irrigation operations, forest fire fighting, power line searching and water quality monitoring.

[0003] China is a large agricultural country, and developing safe, energy-saving and efficient precision agriculture is an important symbol of agricultural modernization and a goal of China's agricultural development. Unmanned aerial vehicles have very important application value in the development of precision agriculture, and therefore have become an urgent need in the development of modern agriculture in China. Agricultural unmanned aerial vehicles have higher research significance and application prospects in the fields of pesticide spraying and liquid fertilizer.

[0004] Chinese patent application publication No. CN114895709A discloses an unmanned aerial vehicle control system and method for precise pesticide spraying in orchard operations, which comprises a management control module, an unmanned aerial vehicle control module, a wireless controller, a GPS positioning module, a wireless communication module, a power supply module, a fruit tree archive module and a pesticide spraying system. The management control module is connected with the unmanned aerial vehicle control module, the unmanned aerial vehicle control module is connected with the unmanned aerial vehicle, and the GPS positioning module, the wireless communication module, the power supply module, the fruit tree archive module and the pesticide spraying system are respectively connected with the management control module. The quantitative information of the pesticide to be sprayed is transmitted to the management control module, and the management control module transmits information to the unmanned aerial vehicle control module. It can be seen that the above technical solution has the following problems: the power management parameters are not dynamically adjusted according to the flight state of the unmanned aerial vehicle, which affects the reasonable allocation of power and further affects the work efficiency of the unmanned aerial vehicle. SUMMARY

[0005] Therefore, the present application provides an intelligent power management method for unmanned aerial vehicles to overcome the problem in the prior art that the power management parameters are not dynamically adjusted according to the flight state of the unmanned aerial vehicle, which affects the reasonable allocation of power and further affects the work efficiency of the unmanned aerial vehicle.

[0006] To achieve the above object, the application provides an intelligent power management method for unmanned aerial vehicles, comprising:

[0007] Periodically acquiring flight parameters of each unmanned aerial vehicle;

[0008] Periodically dividing the abnormal tendency of the unmanned aerial vehicle based on the abnormal crystallization tendency value;

[0009] When it is determined that the unmanned aerial vehicle is a strong abnormal tendency unmanned aerial vehicle, it is determined whether the operation parameter of the unmanned aerial vehicle is qualified based on the flight change parameter, wherein,

[0010] When it is determined that the operation parameter of the unmanned aerial vehicle is abnormal, the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter and the power fluctuation parameter, including adjusting the endurance distance of the unmanned aerial vehicle to the corresponding value, or adjusting the flight speed of the unmanned aerial vehicle to the corresponding value based on the historical change fluctuation parameter;

[0011] When the adjustment of the flight speed of the unmanned aerial vehicle based on the historical change fluctuation parameter is completed, the flight speed of the unmanned aerial vehicle is corrected based on the fan curvature characteristic value of the unmanned aerial vehicle;

[0012] When the adjustment of the endurance distance of the unmanned aerial vehicle is completed, it is determined whether to correct the endurance distance of the unmanned aerial vehicle based on the operation influence value;

[0013] When it is determined that the unmanned aerial vehicle is a weak abnormal tendency unmanned aerial vehicle, or when it is determined that the operation parameter of the unmanned aerial vehicle is qualified, the unmanned aerial vehicle is controlled to continuously use the current operation parameter to operate.

[0014] Further, the abnormal tendency of the unmanned aerial vehicle is divided based on the abnormal crystallization tendency value, comprising:

[0015] The running temperature of the unmanned aerial vehicle and the running humidity of the unmanned aerial vehicle are acquired by the temperature sensor and the humidity sensor arranged on the unmanned aerial vehicle respectively, so as to determine the abnormal crystallization tendency coefficient;

[0016] The abnormal crystallization tendency value is determined based on the abnormal crystallization tendency coefficient of each unmanned aerial vehicle;

[0017] If the abnormal crystallization tendency value is less than or equal to the preset abnormal crystallization tendency value, each unmanned aerial vehicle is divided into a weak abnormal tendency unmanned aerial vehicle, and each unmanned aerial vehicle is controlled to continuously use the current operation parameter to operate;

[0018] If the abnormal crystallization tendency value is greater than the preset abnormal crystallization tendency value, each unmanned aerial vehicle is divided into a strong abnormal tendency unmanned aerial vehicle, and it is determined whether the operation parameter of each unmanned aerial vehicle is qualified based on the flight change parameter and the historical change fluctuation parameter of the corresponding line.

[0019] Further, it is determined whether the operation parameter of the single unmanned aerial vehicle is qualified based on the flight change parameter of the single unmanned aerial vehicle, comprising:

[0020] identify acceleration abnormal change nodes and angular velocity abnormal change nodes based on the obtained data detected by the gyroscope and the speed meter of the unmanned aerial vehicle within the preset monitoring time length;

[0021] determine the flight change parameter based on the number of acceleration abnormal change nodes and angular velocity abnormal change nodes;

[0022] When the flight change parameter is greater than the preset flight change parameter, determine whether the operation parameter of the single unmanned aerial vehicle is qualified in combination with the historical change fluctuation parameter.

[0023] Further, based on the spraying route of the single unmanned aerial vehicle within the current preset monitoring time length, and each historical flight change parameter of each unmanned aerial vehicle on the spraying route corresponding to the spraying route, the historical change fluctuation parameter is determined.

[0024] Further, in combination with the historical change fluctuation parameter of the single unmanned aerial vehicle, whether the operation parameter of the single unmanned aerial vehicle is qualified is determined, including:

[0025] When the historical change fluctuation parameter is less than or equal to the second preset historical change fluctuation parameter and greater than the first preset historical change fluctuation parameter, the single unmanned aerial vehicle is marked as a turbulent unmanned aerial vehicle, and the flight speed of the single unmanned aerial vehicle is adjusted to a corresponding value based on the historical change fluctuation parameter.

[0026] Further, when the historical change fluctuation parameter is greater than the second preset historical change fluctuation parameter, it is determined that the operation parameter of the single unmanned aerial vehicle is qualified, and the single unmanned aerial vehicle is marked as a stable unmanned aerial vehicle.

[0027] Further, based on the wind speed of the single spraying route at a plurality of time nodes, an environmental influence parameter is determined.

[0028] Further, when the historical change fluctuation parameter is less than or equal to the first preset historical change fluctuation parameter, it is determined that the operation parameter of the single unmanned aerial vehicle is abnormal, and the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter, including:

[0029] When the environmental influence parameter is less than or equal to the first preset environmental influence parameter, the single unmanned aerial vehicle is marked as a turbulent unmanned aerial vehicle, and the flight speed of the single unmanned aerial vehicle is adjusted to a corresponding value based on the historical change fluctuation parameter.

[0030] Further, when the environmental influence parameter is greater than the second preset environmental influence parameter, the endurance distance of the single unmanned aerial vehicle is adjusted to a corresponding value based on the environmental influence parameter.

[0031] Further, when the environmental influence parameter is less than or equal to the second preset environmental influence parameter and greater than the first preset environmental influence parameter, the power management parameter of the unmanned aerial vehicle is adjusted based on the power fluctuation parameter, including:

[0032] acquire residual power of the single unmanned aerial vehicle at a plurality of historical time nodes;

[0033] calculate absolute values of differences between the acquired residual power at adjacent time nodes, to obtain a plurality of power consumption rates;

[0034] solve a difference between the single power consumption rate and a mean value of the plurality of power consumption rates, and calculate a ratio of the difference to the mean value of the plurality of power consumption rates, to obtain a power fluctuation parameter corresponding to the adjacent time nodes;

[0035] when the power fluctuation parameter is greater than a preset power parameter, adjust a cruising distance of the single unmanned aerial vehicle to a corresponding value based on an environmental influence parameter;

[0036] when the adjustment of the cruising distance of the unmanned aerial vehicle is completed, determine whether to correct the cruising distance of the unmanned aerial vehicle based on a running influence value, including:

[0037] determine a ratio of a difference between the reference flight duration of the unmanned aerial vehicle at the standard temperature and the reference flight duration of the unmanned aerial vehicle at the preset critical temperature to the reference flight duration of the unmanned aerial vehicle at the standard temperature as the running influence value;

[0038] if the running influence value is less than or equal to a preset running influence value, control the unmanned aerial vehicle to continue to run using the current running parameter;

[0039] if the running influence value is greater than the preset running influence value, correct the cruising distance of the unmanned aerial vehicle based on the running influence value;

[0040] when each power fluctuation parameter is less than or equal to the preset power parameter, mark the single unmanned aerial vehicle as a turbulent unmanned aerial vehicle, and adjust a flight speed of the single unmanned aerial vehicle to a corresponding value based on a historical change fluctuation parameter;

[0041] when the adjustment of the flight speed of the unmanned aerial vehicle based on the historical change fluctuation parameter is completed, correct the flight speed of the unmanned aerial vehicle based on a fan curvature representation value of the unmanned aerial vehicle, including:

[0042] acquire a three-dimensional model of the fan, extract a cross-sectional curve of the fan, discretize the cross-sectional curve into a plurality of discrete points, and determine a maximum value of curvatures of the calculated discrete points as the fan curvature representation value;

[0043] the flight speed of the single unmanned aerial vehicle is positively correlated with the fan curvature representation value.

[0044] Compared with the prior art, the beneficial effects of the present application are that whether the operation parameter of the single unmanned aerial vehicle is qualified is determined based on the flight change parameter of the single unmanned aerial vehicle, the flight change parameter represents the stability of the flight state of the unmanned aerial vehicle, when the flight change parameter is greater than the preset flight change parameter, the flight change parameter is larger, and further combined with the historical data judgment, the historical change fluctuation parameter represents the dispersion degree of the flight change parameter of the unmanned aerial vehicle on a single line. When the historical change fluctuation parameter is less than or equal to the first preset historical change fluctuation parameter, the dispersion degree of the flight change parameter of the unmanned aerial vehicle is small, and the stability of the unmanned aerial vehicle on the current line is abnormal in a small range. In this case, the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter. When the historical change fluctuation parameter is less than or equal to the second preset historical change fluctuation parameter and greater than the first preset historical change fluctuation parameter, the dispersion degree of the flight change parameter of the unmanned aerial vehicle is moderate, the unmanned aerial vehicle is marked as a turbulent unmanned aerial vehicle, and the flight speed is reduced to reduce the power consumption due to the high-frequency change of the operation parameter. When the historical change fluctuation parameter is greater than the second preset historical change fluctuation parameter, the unmanned aerial vehicle operation appears small range jolt due to sudden situation, and in this case, it is determined that the operation parameter of the single unmanned aerial vehicle is qualified. By combining the historical change fluctuation parameter, the flight stability of the unmanned aerial vehicle on the current spraying line is further evaluated, and the unmanned aerial vehicle is classified and processed according to the size of the historical fluctuation parameter. The historical flight data of the unmanned aerial vehicle is comprehensively considered, the accuracy of the judgment of the operation state of the unmanned aerial vehicle is improved. Different processing measures are taken for different types of unmanned aerial vehicles, the flight management of the unmanned aerial vehicle is optimized, and the operation efficiency and safety of the unmanned aerial vehicle are improved. The operation state of the unmanned aerial vehicle is evaluated based on the flight change parameter and the historical change fluctuation parameter, the unmanned aerial vehicle with problems is screened out, and the efficiency of the operation state monitoring of the unmanned aerial vehicle is improved.

[0045] Further, the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter. The environmental influence parameter represents the change of the wind speed over time. The change of the wind speed will affect the flight of the unmanned aerial vehicle. The greater the change, the more obvious the influence. When the environmental influence parameter is in different ranges, the influence of the change of the wind speed on the flight of the unmanned aerial vehicle is different. When it is determined that the operation parameter of the unmanned aerial vehicle is abnormal, the change of the wind speed is analyzed considering the environmental factor. When the environmental influence parameter is less than or equal to a first preset environmental influence parameter, the external environment is stable at this time. The unmanned aerial vehicle is abnormally bumpy due to its own reasons. In this case, the flight speed of the unmanned aerial vehicle is reduced to ensure the stable operation of the unmanned aerial vehicle. When the environmental influence parameter is greater than a second preset environmental influence parameter, the external environment fluctuates abnormally at this time. The endurance distance of the unmanned aerial vehicle is adjusted to avoid the abnormal power consumption of the unmanned aerial vehicle due to the fluctuation of the external environment, so that the unmanned aerial vehicle cannot return to the charging room in time. When the environmental influence parameter is less than or equal to the second preset environmental influence parameter and greater than the first preset environmental influence parameter, the power management parameter of the unmanned aerial vehicle is adjusted in combination with the power fluctuation parameter. The influence of the environmental factor on the flight of the unmanned aerial vehicle is fully considered. The unmanned aerial vehicle is processed in combination with the environmental influence parameter. The flight stability and safety of the unmanned aerial vehicle in a complex environment are improved.

[0046] Further, the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter. The environmental influence parameter represents the change of the wind speed over time. The change of the wind speed will affect the flight of the unmanned aerial vehicle. The greater the change, the more obvious the influence. When the environmental influence parameter is in different ranges, the influence of the change of the wind speed on the flight of the unmanned aerial vehicle is different. When it is determined that the operation parameter of the unmanned aerial vehicle is abnormal, the change of the wind speed is analyzed considering the environmental factor. When the environmental influence parameter is less than or equal to a first preset environmental influence parameter, the external environment is stable at this time. The unmanned aerial vehicle is abnormally bumpy due to its own reasons. In this case, the flight speed of the unmanned aerial vehicle is reduced to ensure the stable operation of the unmanned aerial vehicle. When the environmental influence parameter is greater than a second preset environmental influence parameter, the external environment fluctuates abnormally at this time. The endurance distance of the unmanned aerial vehicle is adjusted to avoid the abnormal power consumption of the unmanned aerial vehicle due to the fluctuation of the external environment, so that the unmanned aerial vehicle cannot return to the charging room in time. When the environmental influence parameter is less than or equal to the second preset environmental influence parameter and greater than the first preset environmental influence parameter, the power management parameter of the unmanned aerial vehicle is adjusted in combination with the power fluctuation parameter. The influence of the environmental factor on the flight of the unmanned aerial vehicle is fully considered. The unmanned aerial vehicle is processed in combination with the environmental influence parameter. The flight stability and safety of the unmanned aerial vehicle in a complex environment are improved.

[0047] Further, the endurance distance of the unmanned aerial vehicle is adjusted according to the size of the environmental influence parameter. When the environmental influence parameter is large, the flight of the unmanned aerial vehicle is greatly affected. The endurance distance is shortened to ensure the flight safety. The endurance distance of the unmanned aerial vehicle is dynamically adjusted according to the environmental influence parameter. The operation efficiency of the unmanned aerial vehicle is optimized under the premise of ensuring the flight safety. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A step flow chart for the intelligent power management method for the unmanned aerial vehicle according to an embodiment of the present application;

[0049] Figure 2 A logic determination chart for determining whether the operation parameter of the single unmanned aerial vehicle is qualified based on the flight change parameter of the single unmanned aerial vehicle according to an embodiment of the present application;

[0050] Figure 3 A logic determination chart for determining whether the operation parameter of the single unmanned aerial vehicle is qualified in combination with the historical change fluctuation parameter of the single unmanned aerial vehicle according to an embodiment of the present application;

[0051] Figure 4 A logic determination chart for adjusting the power management parameter of the unmanned aerial vehicle based on the environmental influence parameter according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objects and advantages of the present application clearer, the present application will be further described in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0053] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and not to limit the protection scope of the present application.

[0054] It should be noted that, in the description of the present application, the terms of "upper", "lower", "left", "right", "inner", "outer" and the like indicating the direction or positional relationship are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and not to indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0055] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms of "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0056] Please refer to Figure 1 Fig. 1 shows a step flow chart for the intelligent power management method for the unmanned aerial vehicle according to an embodiment of the present application, and the intelligent power management method for the unmanned aerial vehicle according to the present application comprises:

[0057] S1, periodically acquiring flight parameters of each unmanned aerial vehicle, including operating temperature, operating humidity, actual flight speed, pesticide spraying amount, flight height and flight angle;

[0058] S2, periodically dividing the abnormal tendency of the unmanned aerial vehicle based on the abnormal crystallization tendency value;

[0059] S3, when determining that each unmanned aerial vehicle is a strong abnormal tendency unmanned aerial vehicle, determining whether the operating parameters of each unmanned aerial vehicle are qualified based on the flight change parameter, including:

[0060] determining that the operating parameters of the unmanned aerial vehicle are abnormal, adjusting the power management parameters of the unmanned aerial vehicle based on the environmental influence parameters and the power fluctuation parameters, including adjusting the endurance distance of the unmanned aerial vehicle to a corresponding value, or adjusting the flight speed of the unmanned aerial vehicle to a corresponding value based on the historical change fluctuation parameter;

[0061] after completing the adjustment of the flight speed of the unmanned aerial vehicle based on the historical change fluctuation parameter, correcting the flight speed of the unmanned aerial vehicle based on the fan curvature characteristic value of the unmanned aerial vehicle;

[0062] after completing the adjustment of the endurance distance of the unmanned aerial vehicle, determining whether to correct the endurance distance of the unmanned aerial vehicle based on the operating influence value;

[0063] or, determining that the operating parameters of the unmanned aerial vehicle are qualified, and controlling the unmanned aerial vehicle to continue to use the current operating parameters.

[0064] Specifically, when the endurance distance of the unmanned aerial vehicle is less than or equal to a preset critical time length, the unmanned aerial vehicle returns to the unmanned aerial vehicle hangar for automatic battery replacement and pesticide charging.

[0065] Specifically, the amount of pesticide charged for the unmanned aerial vehicle is adjusted to a corresponding value according to the actual operating time of the unmanned aerial vehicle.

[0066] Specifically, the unmanned aerial vehicle determines whether to return to the unmanned aerial vehicle hangar for charging and pesticide charging based on the endurance distance.

[0067] Specifically, the unmanned aerial vehicle hangar includes a weather monitoring module arranged outside the unmanned aerial vehicle hangar to acquire and display the temperature, humidity and wind speed outside the hangar in real time.

[0068] Specifically, the unmanned aerial vehicle hangar is provided with a constant temperature adjustment module for maintaining the temperature inside the hangar, and the constant temperature is maintained within the interval [10℃, 30℃].

[0069] The process of determining the abnormal crystallization tendency value includes:

[0070] Based on the temperature and humidity monitored by the temperature sensor and the humidity sensor arranged on the unmanned aerial vehicle, the high temperature duration when the operating temperature of the unmanned aerial vehicle is greater than the preset critical temperature and the over-wet duration when the operating humidity of the unmanned aerial vehicle is greater than the preset critical humidity within the preset crystallization detection time are determined.

[0071] determining a ratio of the average of the over-wet time length and the high-temperature time length to the preset crystallization detection time length as an abnormal crystallization tendency coefficient of the single unmanned aerial vehicle;

[0072] solving a mean value of the abnormal crystallization tendency coefficients of the unmanned aerial vehicles to obtain an abnormal crystallization tendency value.

[0073] dividing the abnormal tendency of the unmanned aerial vehicles based on the abnormal crystallization tendency value, including:

[0074] if the abnormal crystallization tendency value is less than or equal to a preset abnormal crystallization tendency value, the unmanned aerial vehicles are divided into weak abnormal tendency unmanned aerial vehicles, and the unmanned aerial vehicles are controlled to continue to use the current operation parameters to operate;

[0075] if the abnormal crystallization tendency value is greater than the preset abnormal crystallization tendency value, the unmanned aerial vehicles are divided into strong abnormal tendency unmanned aerial vehicles, and whether the operation parameters of the unmanned aerial vehicles are qualified is determined based on the flight change parameters and the historical change fluctuation parameters of the corresponding line;

[0076] Specifically, the preset critical temperature is selected in the interval [38℃, 45℃], and the preset critical humidity is selected in the interval [80%RH, 87%RH]. The preset critical temperature and the preset critical humidity can be selected by the person skilled in the art, the high-temperature aging test can be performed on each component of the unmanned aerial vehicle, and the performance degradation and failure occurrence of the component at different temperatures are recorded to determine the critical temperature. The unmanned aerial vehicle can be tested in different humidity environments, and the humidity data of the occurrence of crystallization and electronic component failure are counted to determine the preset critical humidity. It can be understood that the division of whether the crystallization failure is easy to occur can be realized. In the embodiment, preferably, the preset critical temperature is 40℃, and the preset critical humidity is 80%RH.

[0077] Specifically, the preset abnormal crystallization tendency value is selected in the interval [0.21, 0.32]. The person skilled in the art can select and determine the preset abnormal crystallization tendency value by themselves. Based on a large amount of statistical experiments and actual flight data, a plurality of flight tests in different temperature and humidity environments are performed, the ratio of the average of the over-wet time length and the high-temperature time length to the preset crystallization detection time length when the crystallization failure occurs is recorded, and the preset abnormal crystallization tendency value is determined. In the embodiment, preferably, the preset abnormal crystallization tendency value is 0.2.

[0078] Specifically, the abnormal tendency of the unmanned aerial vehicle is divided based on the abnormal crystallization tendency value, the high-temperature duration represents the time that the unmanned aerial vehicle is in a temperature higher than a preset critical temperature within a preset crystallization detection duration, and reflects the influence time of a high-temperature environment on the unmanned aerial vehicle. The over-humid duration represents the time that the unmanned aerial vehicle is in a humidity higher than a preset critical humidity within the preset crystallization detection duration, and reflects the influence time of an over-humid environment on the unmanned aerial vehicle. The abnormal crystallization tendency value reflects the relative degree of the unmanned aerial vehicle in a crystallization-prone environment within the preset crystallization detection duration. When the abnormal crystallization tendency value is greater than a preset abnormal crystallization tendency value, the unmanned aerial vehicle is in a high-temperature and over-humid environment for a longer time within the preset crystallization detection duration, and components such as electronic elements and batteries are more susceptible to the influence of water vapor and high temperature. The chip of the unmanned aerial vehicle is composed of a large number of microstructures such as transistors, and is affected by external high temperature and humidity. The transistors will malfunction, causing errors in the logical operation of the chip and making it impossible to accurately control the flight attitude and speed of the unmanned aerial vehicle. In the case where the abnormal crystallization tendency value is greater than the preset abnormal crystallization tendency value, the running parameters are periodically detected to determine whether they are qualified, so as to monitor the running of the unmanned aerial vehicle in detail. By calculating the abnormal crystallization tendency value, it is possible to discover in advance that the unmanned aerial vehicle is in a crystallization-prone environment, and take timely monitoring measures to improve the reliability and safety of the unmanned aerial vehicle.

[0079] Specifically, the determination process of the flight change parameter is,

[0080] obtaining data detected by a gyroscope and a speed meter of the unmanned aerial vehicle within a preset monitoring duration;

[0081] identifying acceleration abnormal change nodes and angular velocity abnormal change nodes;

[0082] determining the flight change parameter as a ratio of a sum of the number of acceleration abnormal change nodes and the number of angular velocity abnormal change nodes to the preset monitoring duration.

[0083] Specifically, the case where a change amount of the acceleration value within a preset instantaneous duration is greater than a preset acceleration change amount is determined as an acceleration abnormal change node.

[0084] The case where a change amount of the angular velocity within the preset instantaneous duration is greater than a preset angular velocity change amount is determined as an angular velocity abnormal change node.

[0085] Specifically, in the embodiment, the preset instantaneous duration is 1s, the preset acceleration change amount is 0.3g, and the preset angular velocity change amount is 5° / s, where g is the gravitational acceleration.

[0086] It should be noted that the preset instantaneous duration, the preset acceleration change amount, and the preset angular velocity change amount in the embodiment are all obtained based on a large number of experiments and actual flight data statistical analysis. It can be understood that a person skilled in the art can determine them according to actual conditions.

[0087] Referring to Figure 2 Fig. 1 is a logic decision diagram for determining whether the operation parameters of a single UAV are qualified based on the flight change variable of the single UAV according to an embodiment of the present application. In this embodiment, the process of determining whether the operation parameters of a single UAV are qualified based on the flight change variable of the single UAV includes:

[0088] If the flight change variable is less than or equal to the preset flight change variable, it is determined that the operation parameters of the single UAV are qualified.

[0089] If the flight change variable is greater than the preset flight change variable, it is determined whether the operation parameters of the single UAV are qualified in combination with the historical change fluctuation variable.

[0090] Specifically, the preset flight change variable is selected within the interval [7, 11] and its unit is times / h. The preset flight change variable is determined by collecting and analyzing a large number of UAV flight experiments and actual flight data. The operation state, operation effect and power consumption data of the UAV under different flight change variables are obtained, and the preset flight change variable is determined based on the statistical flight change variable of the stable operation of the UAV. In this embodiment, preferably, the preset flight change variable is 9.

[0091] Specifically, it should be noted that the selection of data in this embodiment is based on a large number of experimental and actual flight data statistical analysis. It can be understood that those skilled in the art can determine the selection according to actual conditions.

[0092] Referring to Figure 3 Fig. 2 is a logic decision diagram for determining whether the operation parameters of a single UAV are qualified in combination with the historical change fluctuation variable of the single UAV according to an embodiment of the present application. In this embodiment, the process of determining whether the operation parameters of a single UAV are qualified in combination with the historical change fluctuation variable of the single UAV includes:

[0093] Obtaining the spraying route of the single UAV within the current preset monitoring time length, solving the variance of each historical flight change variable of each UAV on the spraying route corresponding to the spraying route, and obtaining the historical change fluctuation variable;

[0094] If the historical change fluctuation variable is less than or equal to the first preset historical change fluctuation variable, it is determined that the operation parameters of the single UAV are abnormal, and the power management parameters of the UAV are adjusted based on the environmental influence parameters;

[0095] If the historical change fluctuation variable is less than or equal to the second preset historical change fluctuation variable and greater than the first preset historical change fluctuation variable, the single UAV is marked as a turbulent UAV, and the flight speed of the single UAV is adjusted to a corresponding value based on the historical change fluctuation variable.

[0096] If the historical change fluctuation parameter is greater than the second preset historical change fluctuation parameter, it is determined that the operation parameter of the single unmanned aerial vehicle is qualified, and the single unmanned aerial vehicle is marked as a stable unmanned aerial vehicle.

[0097] Specifically, the first preset historical change fluctuation parameter B1 is selected in the interval [0.09J0, 0.17J0], and the second preset historical change fluctuation parameter B2 is selected in the interval [0.25J0, 0.5J0], J0 is the average value of each historical flight change parameter of each unmanned aerial vehicle corresponding to the single spraying route on the spraying route, and in the embodiment, preferably, the first preset historical change fluctuation parameter B1 is 0.09J0, and the second preset historical change fluctuation parameter B2 is 0.25J0.

[0098] Specifically, whether the operation parameter of the single unmanned aerial vehicle is qualified is determined based on the flight change parameter of the single unmanned aerial vehicle, the flight change parameter characterizes the stability of the flight state of the unmanned aerial vehicle, when the flight change parameter is greater than the preset flight change parameter, the flight change parameter is larger, and further combined with the historical data judgment, the historical change fluctuation parameter characterizes the dispersion degree of the flight change parameter of the unmanned aerial vehicle on the single route. When the historical change fluctuation parameter is less than or equal to the first preset historical change fluctuation parameter, the dispersion degree of the flight change parameter of the unmanned aerial vehicle is small, and the stability of the unmanned aerial vehicle on the current route is abnormal in a small range. In this case, the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter. When the historical change fluctuation parameter is less than or equal to the second preset historical change fluctuation parameter and greater than the first preset historical change fluctuation parameter, the dispersion degree of the flight change parameter of the unmanned aerial vehicle is moderate, the unmanned aerial vehicle is marked as a dynamic unmanned aerial vehicle, and the flight speed is reduced to reduce the power consumption due to the high-frequency change of the operation parameter. When the historical change fluctuation parameter is greater than the second preset historical change fluctuation parameter, the unmanned aerial vehicle runs in a small range of vibration due to sudden conditions. In this case, it is determined that the operation parameter of the single unmanned aerial vehicle is qualified. By combining the historical change fluctuation parameter, the flight stability of the unmanned aerial vehicle on the current spraying route is further evaluated, and the unmanned aerial vehicle is classified according to the size of the historical fluctuation parameter. The historical flight data of the unmanned aerial vehicle is comprehensively considered, and the accuracy of the judgment of the operation state of the unmanned aerial vehicle is improved. Different processing measures are taken for different types of unmanned aerial vehicles, the flight management of the unmanned aerial vehicle is optimized, and the operation efficiency and safety of the unmanned aerial vehicle are improved. The operation state of the unmanned aerial vehicle is evaluated based on the flight change parameter and the historical change fluctuation parameter, and the unmanned aerial vehicle with problems is screened out, and the efficiency of the operation state monitoring of the unmanned aerial vehicle is improved.

[0099] Specifically, the flight speed of the single unmanned aerial vehicle is adjusted to a corresponding value based on the historical change fluctuation parameter, wherein,

[0100] The reduction range of the flight speed of the single unmanned aerial vehicle is positively correlated with the historical change fluctuation parameter.

[0101] In this embodiment, optionally,

[0102] The historical change fluctuation parameter is compared with a first preset historical comparison threshold and a second preset historical comparison threshold;

[0103] If the historical change fluctuation parameter is less than or equal to the first preset historical comparison threshold, the flight speed of the single unmanned aerial vehicle is adjusted to 0.92 times the initial flight speed;

[0104] If the historical change fluctuation parameter is less than or equal to the second preset historical comparison threshold and greater than the first preset historical comparison threshold, the flight speed of the single unmanned aerial vehicle is adjusted to 0.82 times the initial flight speed;

[0105] If the historical change fluctuation parameter is greater than the second preset historical comparison threshold, the flight speed of the single unmanned aerial vehicle is adjusted to 0.75 times the initial flight speed;

[0106] The first preset historical comparison threshold is 1.2B1, and the second preset historical comparison threshold is 1.4B1.

[0107] After adjusting the flight speed of the unmanned aerial vehicle based on the historical change fluctuation parameter, the flight speed of the unmanned aerial vehicle is corrected based on the fan curvature characteristic value, comprising:

[0108] A three-dimensional model of the fan is obtained, a cross-sectional curve of the fan is extracted, the cross-sectional curve is discretized into a plurality of discrete points, and the maximum value of the curvatures of the calculated discrete points is determined as the fan curvature characteristic value;

[0109] The reduction amplitude of the flight speed of the single unmanned aerial vehicle is positively correlated with the fan curvature characteristic value.

[0110] In this embodiment, optionally,

[0111] The fan curvature characteristic value is compared with a first preset curvature threshold and a second preset curvature threshold;

[0112] If the fan curvature characteristic value is less than or equal to the first preset curvature threshold, the flight speed of the single unmanned aerial vehicle is adjusted to 0.95 times the current flight speed;

[0113] If the fan curvature characteristic value is less than or equal to the second preset curvature threshold and greater than the first preset curvature threshold, the flight speed of the single unmanned aerial vehicle is adjusted to 0.9 times the current flight speed;

[0114] If the fan curvature characteristic value is greater than the second preset curvature threshold, the flight speed of the single unmanned aerial vehicle is adjusted to 0.8 times the current flight speed;

[0115] The first preset curvature threshold is 5.3m −1 , and the second preset curvature threshold is 7.2m−1 .

[0116] The greater the blade curvature, the more curved the blades, the more complex the airflow on the blade surface, and the easier it is to generate turbulence, which in turn leads to a decrease in lift and an increase in air resistance. A greater reduction in flight speed is necessary to minimize the impact of air resistance on flight and improve flight stability.

[0117] The greater the historical variation fluctuation parameter, the more unstable the drone's flight state. By adjusting the reduction of the flight speed of a single drone in a positive correlation with the historical variation fluctuation parameter, the drone has more time to cope with various changes during flight, thus improving the stability of the drone's flight.

[0118] Specifically, when the number of turbulent drones reaches a preset observation quantity, the flight speed of each stable drone is increased to the corresponding value. In this embodiment, optionally, the flight speed of each stable drone can be increased by 1.1 times its current flight speed.

[0119] Specifically, when the number of turbulent drones reaches the preset observation quantity, there are unfavorable factors in the current flight environment. In order to improve the overall operational efficiency, the flight speed of stable drones is appropriately adjusted to improve the overall operational efficiency.

[0120] Please see Figure 4 As shown, this is a logic decision diagram for adjusting the power management parameters of a drone based on environmental impact parameters according to an embodiment of the present invention. In this embodiment, the process of adjusting the power management parameters of the drone based on environmental impact parameters includes:

[0121] Based on the wind speed data of a single spraying line at several time points, wind speed time-domain curves are plotted, and the derivatives of the wind speed time-domain curves are solved to obtain environmental impact parameters.

[0122] If the environmental impact parameter is less than or equal to the first preset environmental impact parameter, then the individual drone will be marked as a turbulent drone, and the flight speed of the individual drone will be adjusted to the corresponding value based on the historical change fluctuation parameter.

[0123] If the environmental impact parameter is less than or equal to the second preset environmental impact parameter and greater than the first preset environmental impact parameter, then the power management parameters for the drone are adjusted based on the power fluctuation parameter.

[0124] If the environmental impact parameter is greater than the second preset environmental impact parameter, the flight range of a single drone will be adjusted to the corresponding value based on the environmental impact parameter.

[0125] Specifically, the first preset environmental impact parameter H1 is in the range [0.05m / s]. 2 0.1m / s2 ] is selected, the second preset environmental influence parameter H2 is selected within the interval [0.15 m / s 2 , 0.2 m / s 2 ], and the skilled in the art can determine the preset environmental influence parameters by themselves. The wind speed data of a large number of spraying lines at several time nodes can be collected, the wind speed time domain curve is drawn and the derivative is solved to obtain several environmental influence parameters. The environmental influence parameters are analyzed to determine the preset environmental influence parameters. It can be understood that the influence degree of different wind speeds can be divided. In the embodiment, preferably, the first preset environmental influence parameter H1 is 0.08 m / s 2 , and the second preset environmental influence parameter H2 is 0.19 m / s 2 .

[0126] Specifically, the power management parameter of the unmanned aerial vehicle is adjusted based on the environmental influence parameter. The environmental influence parameter represents the change of the wind speed with time. The change of the wind speed will affect the flight of the unmanned aerial vehicle. The greater the change, the more obvious the influence. When the environmental influence parameter is in different ranges, the influence degree of the change of the wind speed on the flight of the unmanned aerial vehicle is different. When it is determined that the running parameter of the unmanned aerial vehicle is abnormal, the change of the wind speed is analyzed considering the environmental factors. When the environmental influence parameter is less than or equal to the first preset environmental influence parameter, the external environment is stable at this time. The unmanned aerial vehicle is abnormally bumpy due to the unmanned aerial vehicle itself. In this case, the flight speed of the unmanned aerial vehicle is reduced to ensure the stable operation of the unmanned aerial vehicle. When the environmental influence parameter is greater than the second preset environmental influence parameter, the external environment has abnormal fluctuations at this time. The endurance distance of the unmanned aerial vehicle is adjusted to avoid abnormal power consumption of the unmanned aerial vehicle due to the fluctuation of the external environment, so that the unmanned aerial vehicle cannot return to the hangar for charging in time. When the environmental influence parameter is less than or equal to the second preset environmental influence parameter and greater than the first preset environmental influence parameter, the power management parameter of the unmanned aerial vehicle is adjusted in combination with the power fluctuation parameter. The influence of the environmental factors on the flight of the unmanned aerial vehicle is fully considered. The unmanned aerial vehicle is processed according to the environmental influence parameter. The flight stability and safety of the unmanned aerial vehicle in complex environment are improved.

[0127] Specifically, the power management parameter of the unmanned aerial vehicle is adjusted based on the power fluctuation parameter, including:

[0128] obtaining the residual power of the single unmanned aerial vehicle at several historical time nodes;

[0129] calculating the absolute value of the difference between the residual power of each adjacent time node, obtaining several power consumption rates;

[0130] solving the difference between the single power consumption rate and the average of the power consumption rates, and calculating the ratio of the difference to the average of the power consumption rates, to obtain the power fluctuation parameter corresponding to the adjacent time nodes;

[0131] when the power fluctuation parameter is greater than the preset power parameter, adjusting the endurance distance of the single unmanned aerial vehicle to a corresponding value based on an environmental influence parameter;

[0132] when each power fluctuation parameter is less than or equal to the preset power parameter, marking the single unmanned aerial vehicle as a turbulent unmanned aerial vehicle, and adjusting the flight speed of the single unmanned aerial vehicle to a corresponding value based on a historical change fluctuation parameter.

[0133] Specifically, the preset power parameter is selected in the interval [1.3, 1.4], and a person skilled in the art can determine the preset power parameter by himself. The residual power of each unmanned aerial vehicle at several historical time nodes can be collected, the power fluctuation parameters of each unmanned aerial vehicle can be calculated, and the endurance and running stability of the unmanned aerial vehicle under different power fluctuation parameters can be analyzed. The preset power parameter is determined through a large amount of data statistical analysis. In this embodiment, preferably, the preset power parameter is 1.3.

[0134] Specifically, the current endurance distance of the single unmanned aerial vehicle is adjusted to a corresponding value based on the environmental influence parameter, wherein,

[0135] The reduction range of the endurance time is positively correlated with the environmental influence parameter.

[0136] In this embodiment, optionally,

[0137] The environmental influence parameter is compared with a first preset environmental comparison threshold and a second preset environmental comparison threshold;

[0138] If the environmental influence parameter is less than or equal to the first preset environmental comparison threshold, the endurance distance of the single unmanned aerial vehicle is adjusted to 0.91 times the current endurance distance;

[0139] If the environmental influence parameter is less than or equal to the second preset environmental comparison threshold and greater than the first preset environmental comparison threshold, the endurance distance of the single unmanned aerial vehicle is adjusted to 0.81 times the current endurance distance;

[0140] If the environmental influence parameter is greater than the second preset environmental comparison threshold, the endurance distance of the single unmanned aerial vehicle is adjusted to 0.71 times the current endurance distance;

[0141] The first preset environmental comparison threshold is 1.2H2, and the second preset environmental comparison threshold is 1.3H2.

[0142] When the adjustment of the endurance distance of the unmanned aerial vehicle is completed, it is determined whether to correct the endurance distance of the unmanned aerial vehicle based on a running influence value, comprising:

[0143] The ratio of the difference between the reference flight time of the unmanned aerial vehicle at the standard temperature and the reference flight time of the unmanned aerial vehicle at the preset critical temperature to the reference flight time of the unmanned aerial vehicle at the standard temperature is determined as the running influence value;

[0144] If the running influence value is less than or equal to the preset running influence value, the unmanned aerial vehicle is controlled to continue running using the current running parameter;

[0145] If the running influence value is greater than the preset running influence value, the endurance distance of the unmanned aerial vehicle is corrected based on the running influence value.

[0146] The reference flight duration of the unmanned aerial vehicle at the standard temperature is the flight duration of the actually tested unmanned aerial vehicle at the standard temperature when the battery is fully charged;

[0147] The reference flight duration of the unmanned aerial vehicle at the preset critical temperature is the flight duration of the actually tested unmanned aerial vehicle at the preset critical temperature when the battery is fully charged.

[0148] The preset running influence value is selected in the interval [0.09, 0.14], and a person skilled in the art can select and determine the preset running influence value. The preset running influence value can be determined based on a large number of experiments and actual flight data statistical analysis. The unmanned aerial vehicle is tested multiple times under different temperature environments, the flight duration at the standard temperature and the preset critical temperature is recorded, the running influence value is calculated, and the endurance of the unmanned aerial vehicle under different running influence values is determined to quantify the influence of temperature change on the endurance distance of the unmanned aerial vehicle and the influence on the flight stability. In the embodiment, preferably, the preset running influence value is 0.1.

[0149] The running influence value represents the influence of temperature change on the flight duration of the unmanned aerial vehicle, and reflects the influence degree of temperature on the endurance distance. When the running influence value is greater than the preset running influence value, the microscopic performance of the battery and the electronic components is greatly affected by the temperature change, and the flight is more easily affected by the environmental change. In this case, the endurance distance is corrected to ensure the running safety of the unmanned aerial vehicle.

[0150] The endurance distance of the unmanned aerial vehicle is corrected based on the running influence value, wherein

[0151] The reduction range of the endurance distance of the unmanned aerial vehicle is positively correlated with the preset running influence value.

[0152] In the embodiment, optionally,

[0153] The preset running influence value is compared with the first preset running influence comparison value and the second preset influence comparison value;

[0154] If the preset running influence value is less than or equal to the first preset running influence comparison value, the endurance distance of the single unmanned aerial vehicle is adjusted to 0.97 times of the current endurance distance;

[0155] If the preset running influence value is less than or equal to the second preset running influence comparison value and greater than the first preset running influence comparison value, the endurance distance of the single unmanned aerial vehicle is adjusted to 0.92 times of the current endurance distance;

[0156] If the preset operation influence value is greater than the second preset operation influence comparison value, the endurance distance of the single unmanned aerial vehicle is adjusted to 0.86 times of the current endurance distance.

[0157] The first preset operation influence comparison value is 1.23X0, and the second preset influence comparison value is 1.44X0, X0 being a preset operation influence value.

[0158] When the environmental influence parameter is greater, the influence of the environment on the unmanned aerial vehicle flight is greater, and the wind speed change is severe, which can increase the resistance of the unmanned aerial vehicle flight, resulting in accelerated power consumption. The reduction range of the endurance distance of the single unmanned aerial vehicle is positively correlated with the environmental influence parameter, so that the endurance capability of the unmanned aerial vehicle is matched with the actual environment. The unmanned aerial vehicle is prevented from falling midway or failing to complete a work task due to insufficient endurance capability in a harsh environment, power is reasonably distributed, and the power utilization efficiency is improved.

[0159] Specifically, the power management parameter of the unmanned aerial vehicle is adjusted based on the power fluctuation parameter. The power fluctuation parameter represents the fluctuation of power consumption of the unmanned aerial vehicle. When the power fluctuation parameter is greater than a preset power parameter, the power consumption of the unmanned aerial vehicle is unstable, which can affect the endurance capability and flight safety of the unmanned aerial vehicle. At this time, the endurance distance of the single unmanned aerial vehicle is adjusted to a corresponding value to ensure that the unmanned aerial vehicle can complete the flight return task. When each power fluctuation parameter is less than or equal to the preset power parameter, the power consumption of the unmanned aerial vehicle is stable, and the battery of the unmanned aerial vehicle operates stably. However, due to the abnormal bumping of the single unmanned aerial vehicle, the single unmanned aerial vehicle is marked as a turbulent unmanned aerial vehicle, and the flight speed of the single unmanned aerial vehicle is adjusted. In a light wind environment, the motor of the unmanned aerial vehicle only needs to make a small power adjustment to change the attitude when the unmanned aerial vehicle changes the heading at a low speed. Compared with the large adjustment at a high speed, the power consumption is obviously reduced. By reducing the flight speed, the additional power consumption caused by frequent acceleration and deceleration is reduced, and the stable operation of the unmanned aerial vehicle is ensured. The unmanned aerial vehicle is comprehensively managed in combination with environmental factors and power consumption, abnormal power consumption of the unmanned aerial vehicle is found in time, and corresponding measures are taken, so that the endurance capability and flight safety of the unmanned aerial vehicle are ensured.

[0160] Specifically, according to the size of the environmental influence parameter, the endurance distance of the unmanned aerial vehicle is adjusted. When the environmental influence parameter is large, the influence on the unmanned aerial vehicle flight is large, and the endurance distance is shortened to ensure flight safety. The endurance distance of the unmanned aerial vehicle is dynamically adjusted according to the environmental influence parameter, the work efficiency of the unmanned aerial vehicle is optimized under the premise of ensuring flight safety.

[0161] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

[0162] The above only describes the preferred embodiments of the present application and is not intended to limit the present application; the present application can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent power management of unmanned aerial vehicles (UAVs), characterized in that, The method comprises the following steps: Periodically acquiring flight parameters of each unmanned aerial vehicle; Periodically dividing the abnormal tendency of the unmanned aerial vehicle based on the abnormal crystallization tendency value, the process of determining the abnormal crystallization tendency value comprising: determining the high-temperature duration that the operating temperature of the unmanned aerial vehicle is greater than the preset critical temperature and the over-humidity duration that the operating humidity of the unmanned aerial vehicle is greater than the preset critical humidity based on the temperature and humidity monitored by the temperature sensor and the humidity sensor arranged on the unmanned aerial vehicle within the preset crystallization detection duration; Determining the abnormal crystallization tendency coefficient of the single unmanned aerial vehicle by taking the ratio of the average of the over-humidity duration and the high-temperature duration to the preset crystallization detection duration as the abnormal crystallization tendency coefficient of the single unmanned aerial vehicle; When the abnormal crystallization tendency value is greater than the preset abnormal crystallization tendency value, determining that each unmanned aerial vehicle is a strong abnormal tendency unmanned aerial vehicle, and determining whether the operating parameters of the unmanned aerial vehicle are qualified based on the flight change parameter and the historical change fluctuation parameter of the corresponding line, wherein, The determination process of the flight change parameter comprises: acquiring the data detected by the gyroscope and the speed meter of the unmanned aerial vehicle within the preset monitoring duration; identifying acceleration abnormal change nodes and angular velocity abnormal change nodes; determining the flight change parameter by taking the ratio of the sum of the number of calculated acceleration abnormal change nodes and the number of angular velocity abnormal change nodes to the preset monitoring duration; determining that the acceleration abnormal change node occurs when the change amount of the acceleration value within the preset instantaneous duration is greater than the preset acceleration change amount; and determining that the angular velocity abnormal change node occurs when the change amount of the angular velocity within the preset instantaneous duration is greater than the preset angular velocity change amount; The operating parameters of the unmanned aerial vehicle include the power management parameters of the unmanned aerial vehicle; When the flight change parameter is greater than the preset flight change parameter and the historical change fluctuation parameter is less than or equal to the first preset historical change fluctuation parameter, determining that the operating parameters of the single unmanned aerial vehicle are abnormal, and adjusting the power management parameters of the unmanned aerial vehicle based on the environmental influence parameter and the power fluctuation parameter; The process of adjusting the power management parameters of the unmanned aerial vehicle based on the environmental influence parameter comprises: Drawing a wind speed time domain curve based on the acquired wind speed of the single spraying line at a plurality of time nodes, and obtaining the environmental influence parameter by solving the derivative of the wind speed time domain curve; If the environmental influence parameter is less than or equal to the first preset environmental influence parameter, marking the single unmanned aerial vehicle as a turbulent unmanned aerial vehicle, and adjusting the flight speed of the single unmanned aerial vehicle to a corresponding value based on the historical change fluctuation parameter; If the environmental influence parameter is less than or equal to the second preset environmental influence parameter and greater than the first preset environmental influence parameter, adjusting the power management parameters of the unmanned aerial vehicle based on the power fluctuation parameter; If the environmental influence parameter is greater than the second preset environmental influence parameter, adjusting the endurance distance of the single unmanned aerial vehicle to a corresponding value based on the environmental influence parameter; The process of adjusting the power management parameters of the unmanned aerial vehicle based on the power fluctuation parameter comprises: Acquiring the residual power of the single unmanned aerial vehicle at a plurality of historical time nodes, calculating the absolute value of the difference between the residual power of each adjacent time node, obtaining a plurality of power consumption rates, solving the difference between the single power consumption rate and the average of each power consumption rate, and calculating the ratio of the difference to the average of each power consumption rate to obtain the power fluctuation parameter of the corresponding adjacent time node; when the power fluctuation parameter is greater than the preset power parameter, adjusting the endurance distance of the single unmanned aerial vehicle to a corresponding value based on the environmental influence parameter; when each power fluctuation parameter is less than or equal to the preset power parameter, marking the single unmanned aerial vehicle as a dynamic unmanned aerial vehicle, and adjusting the flight speed of the single unmanned aerial vehicle to a corresponding value based on the historical change fluctuation parameter; when the adjustment of the flight speed of the unmanned aerial vehicle based on the historical change fluctuation parameter is completed, correcting the flight speed of the unmanned aerial vehicle based on the fan curvature characteristic value of the unmanned aerial vehicle, obtaining a spraying route of the single unmanned aerial vehicle within a preset monitoring time length, solving the variance of each historical flight change parameter of each unmanned aerial vehicle on the spraying route, and obtaining the historical change fluctuation parameter; when the adjustment of the endurance distance of the unmanned aerial vehicle is completed, determining whether to correct the endurance distance of the unmanned aerial vehicle based on the operation influence value, wherein the operation influence value is determined as the ratio of the difference between the reference flight time of the unmanned aerial vehicle at a standard temperature and the reference flight time of the unmanned aerial vehicle at a preset critical temperature to the reference flight time of the unmanned aerial vehicle at the standard temperature, the reference flight time of the unmanned aerial vehicle at the standard temperature is the flight time of the actual experimental unmanned aerial vehicle at the standard temperature when the battery is fully charged, and the reference flight time of the unmanned aerial vehicle at the preset critical temperature is the flight time of the actual experimental unmanned aerial vehicle at the preset critical temperature when the battery is fully charged; determining that the unmanned aerial vehicle is a weak abnormal tendency unmanned aerial vehicle, or determining that the operation parameters of the unmanned aerial vehicle are qualified, and controlling the unmanned aerial vehicle to continuously use the current operation parameters. 2.The intelligent electric quantity management method for the UAV according to claim 1, characterized in that, dividing the abnormal tendency of the unmanned aerial vehicle based on the abnormal crystallization tendency value, including: if the abnormal crystallization tendency value is less than or equal to a preset abnormal crystallization tendency value, dividing each unmanned aerial vehicle into a weak abnormal tendency unmanned aerial vehicle, and controlling each unmanned aerial vehicle to continuously use the current operation parameters. 3.The intelligent power management method for the UAV of claim 2, wherein, determining whether the operation parameters of the single unmanned aerial vehicle are qualified based on the flight change parameter of the single unmanned aerial vehicle, including: when the flight change parameter is greater than a preset flight change parameter, determining whether the operation parameters of the single unmanned aerial vehicle are qualified in combination with the historical change fluctuation parameter. 4.The intelligent power management method for the UAV of claim 3, wherein, determining whether the operation parameters of the single unmanned aerial vehicle are qualified in combination with the historical change fluctuation parameter of the single unmanned aerial vehicle, including: when the historical change fluctuation parameter is less than or equal to a second preset historical change fluctuation parameter and greater than a first preset historical change fluctuation parameter, marking the single unmanned aerial vehicle as a dynamic unmanned aerial vehicle, and adjusting the flight speed of the single unmanned aerial vehicle to a corresponding value based on the historical change fluctuation parameter, wherein the reduction range of the flight speed of the single unmanned aerial vehicle is positively correlated with the historical change fluctuation parameter. when the historical change fluctuation parameter is greater than the second preset historical change fluctuation parameter, determining that the operation parameters of the single unmanned aerial vehicle are qualified, and marking the single unmanned aerial vehicle as a stable unmanned aerial vehicle. 5.The intelligent power management method for the UAV of claim 4, wherein, adjusting the endurance distance of the single unmanned aerial vehicle to a corresponding value based on the environmental influence parameter, 6.The intelligent power management method for the UAV of claim 5, wherein, the reduction range of the endurance distance is positively correlated with the environmental influence parameter.

7. The intelligent power management method for unmanned aerial vehicles according to claim 6, characterized in that: when the adjustment of the endurance distance of the unmanned aerial vehicle is completed, determining whether to correct the endurance distance of the unmanned aerial vehicle based on the operation influence value, including: ​ If the operation influence value is less than or equal to the preset operation influence value, the unmanned aerial vehicle is controlled to continue operating using the current operation parameter; If the operation influence value is greater than the preset operation influence value, the endurance distance of the unmanned aerial vehicle is corrected based on the operation influence value, wherein, The reduction range of the endurance distance of the unmanned aerial vehicle is positively correlated with the operation influence value; When the adjustment of the flight speed of the unmanned aerial vehicle based on the historical change fluctuation parameter is completed, the flight speed of the unmanned aerial vehicle is corrected based on the fan curvature characteristic value, comprising: A three-dimensional model of the fan is obtained, a cross-sectional curve of the fan is extracted, the cross-sectional curve is discretized into a plurality of discrete points, and the maximum value of the curvatures of the calculated discrete points is determined as the fan curvature characteristic value; The reduction range of the flight speed of the single unmanned aerial vehicle is positively correlated with the fan curvature characteristic value.

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