Unmanned aerial vehicle power intelligence analysis method, system and readable storage medium

Abstract

The application discloses an unmanned aerial vehicle electric quantity intelligent analysis method and system and a readable storage medium. The method comprises the following steps: obtaining a flight task of a current unmanned aerial vehicle; judging whether the remaining electric quantity of the current unmanned aerial vehicle meets the demand based on the flight task; if not, outputting an alarm reminder and not executing the current flight task; if yes, executing the current flight task, obtaining environment data and electric quantity consumption parameters; obtaining target electric quantity required by a remaining route in the flight task based on the environment data by using big data analysis; comparing the actual remaining electric quantity of the unmanned aerial vehicle with the target electric quantity; and controlling the unmanned aerial vehicle to perform an action based on the comparison result. The application can identify the electric quantity of the unmanned aerial vehicle, control the unmanned aerial vehicle to land according to actual flight data, and avoid the problem of unmanned aerial vehicle out of control and crashing due to insufficient electric quantity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle operation, and more particularly, to an unmanned aerial vehicle power intelligent analysis method, system and readable storage medium. BACKGROUND

[0002] At present, with the continuous development of unmanned and intelligentization and automation, unmanned aerial vehicles have also developed unprecedentedly, and can be applied in aerial photography, agriculture, plant protection, surveying and mapping, news reporting, power inspection and other fields, which can improve the use efficiency and reduce the safety accidents of the past human operation on the basis of expanding the use of unmanned aerial vehicles.

[0003] However, since the operation of unmanned aerial vehicles needs strong power system support, different application environments correspond to different power consumption, and since the current unmanned aerial vehicles are still manually controlled for operation, power shortage may lead to crashes, or automatic cruise unmanned aerial vehicles may also cause problems such as automatic recognition errors due to sudden power drop or abnormal power. SUMMARY

[0004] The purpose of the present application is to provide an unmanned aerial vehicle power intelligent analysis method, system and readable storage medium, which can identify based on the power of the unmanned aerial vehicle, and control the unmanned aerial vehicle to land according to the actual flight data, so as to avoid the problem of unmanned aerial vehicle power shortage out of control and crash.

[0005] The present application provides an unmanned aerial vehicle power intelligent analysis method in the first aspect, comprising the following steps:

[0006] Obtain the flight task of the current unmanned aerial vehicle, judge whether the remaining power of the current unmanned aerial vehicle meets the demand based on the flight task, wherein,

[0007] If not, output an alarm reminder and do not execute the current flight task;

[0008] If yes, execute the current flight task, and obtain flight data during the flight of the unmanned aerial vehicle based on the flight task, wherein the flight data at least includes environmental data and power consumption parameters;

[0009] Based on the environmental data, the target power required for the remaining route in the flight task is obtained by big data analysis, the actual remaining power of the current unmanned aerial vehicle is compared with the target power, and the unmanned aerial vehicle is controlled to act based on the comparison result.

[0010] In the scheme, the flight task of the current unmanned aerial vehicle is obtained, and whether the remaining power of the current unmanned aerial vehicle meets the demand is judged based on the flight task, specifically including:

[0011] The flight task corresponding to the current unmanned aerial vehicle is obtained, and the flight time, flight route and required power for flight are identified based on the flight task;

[0012] The required power for flight and the remaining power of the unmanned aerial vehicle are compared to determine whether the remaining power of the current unmanned aerial vehicle meets the demand, wherein,

[0013] If the remaining power of the unmanned aerial vehicle is greater than the required power for flight, it means that it meets the demand, otherwise it does not meet the demand.

[0014] In the scheme, the flight data is obtained during the flight of the unmanned aerial vehicle based on the flight task, specifically including:

[0015] When the unmanned aerial vehicle flies based on the flight task, the environment data is obtained based on the sensor group arranged on the unmanned aerial vehicle;

[0016] When the unmanned aerial vehicle flies based on the flight task, the power consumption parameter is obtained based on the operation self-checking data of the unmanned aerial vehicle.

[0017] In the scheme, the target power required for the remaining route in the flight task is obtained based on the environment data using big data analysis, specifically including:

[0018] The temperature parameter, humidity parameter, air parameter and wind resistance parameter in the environment where the current unmanned aerial vehicle is located are identified based on the environment data;

[0019] The first target power corresponding to the remaining route is obtained by analyzing the historical data in big data analysis based on the temperature parameter and the humidity parameter;

[0020] The second target power corresponding to the remaining route is obtained by analyzing the neural network model in big data analysis based on the air parameter and the wind resistance parameter;

[0021] The target power required for the remaining route in the flight task is obtained based on the first target power and the second target power.

[0022] In the scheme, the actual remaining power of the current unmanned aerial vehicle is compared with the target power, and the unmanned aerial vehicle is controlled to act based on the comparison result, specifically including:

[0023] obtaining a comparison result of comparing an actual residual electric quantity of the current unmanned aerial vehicle with the target electric quantity, wherein,

[0024] if the comparison result shows that the actual residual electric quantity is less than the target electric quantity, outputting an alarm prompt and terminating the current flight task, and controlling the current unmanned aerial vehicle to land;

[0025] if the comparison result shows that the residual electric quantity is greater than or equal to the target electric quantity, judging whether to control the current unmanned aerial vehicle to land based on the electric quantity consumption parameter, wherein, when the electric quantity consumption parameter exceeds a preset consumption rate, the unmanned aerial vehicle is controlled to land, otherwise, the current flight task is continued.

[0026] In the scheme, the target landing point is screened based on the geographical position of the current unmanned aerial vehicle to control the unmanned aerial vehicle to go to the target landing point for landing.

[0027] The second aspect of the present application also provides an unmanned aerial vehicle electric quantity intelligent analysis system, comprising a memory and a processor, the memory comprising an unmanned aerial vehicle electric quantity intelligent analysis method program, the unmanned aerial vehicle electric quantity intelligent analysis method program being executed by the processor to realize the following steps:

[0028] obtaining a flight task of the current unmanned aerial vehicle, judging whether the residual electric quantity of the current unmanned aerial vehicle meets the demand based on the flight task, wherein,

[0029] if not, outputting an alarm prompt and not executing the current flight task;

[0030] if yes, executing the current flight task, and obtaining flight data in the process of the unmanned aerial vehicle flying based on the flight task, wherein, the flight data at least comprises environmental data and an electric quantity consumption parameter;

[0031] obtaining a target electric quantity required by a residual route in the flight task based on the environmental data by using big data analysis, comparing the actual residual electric quantity of the current unmanned aerial vehicle with the target electric quantity, and controlling the unmanned aerial vehicle to act based on the comparison result.

[0032] In the scheme, the flight task of the current unmanned aerial vehicle is obtained, and whether the residual electric quantity of the current unmanned aerial vehicle meets the demand is judged based on the flight task, which specifically comprises:

[0033] obtaining a flight task corresponding to the current unmanned aerial vehicle, and identifying a flight time, a flight route and a required electric quantity based on the flight task;

[0034] comparing the required electric quantity for the flight with the residual electric quantity of the unmanned aerial vehicle to determine whether the current residual electric quantity of the unmanned aerial vehicle meets the requirement, wherein

[0035] If the residual electric quantity of the unmanned aerial vehicle is greater than the required electric quantity for the flight, it indicates that the requirement is met, otherwise the requirement is not met.

[0036] In this scheme, the flight data is obtained during the flight of the unmanned aerial vehicle based on the flight task, specifically including:

[0037] The environment data is obtained based on the sensor group arranged on the unmanned aerial vehicle during the flight of the unmanned aerial vehicle based on the flight task;

[0038] The electric quantity consumption parameter is obtained based on the operation self-checking data of the unmanned aerial vehicle during the flight of the unmanned aerial vehicle based on the flight task.

[0039] In this scheme, the target electric quantity required for the remaining route in the flight task is obtained based on the environment data using big data analysis, specifically including:

[0040] The temperature parameter, humidity parameter, air parameter and wind resistance parameter in the environment where the current unmanned aerial vehicle is located are identified based on the environment data;

[0041] The first target electric quantity in the remaining route is obtained by analyzing the historical data in big data analysis based on the temperature parameter and the humidity parameter;

[0042] The second target electric quantity in the remaining route is obtained by analyzing the neural network model in big data analysis based on the air parameter and the wind resistance parameter;

[0043] The target electric quantity required for the remaining route in the flight task is obtained based on the first target electric quantity and the second target electric quantity.

[0044] In this scheme, the actual residual electric quantity of the current unmanned aerial vehicle is compared with the target electric quantity, and the unmanned aerial vehicle is controlled to act based on the comparison result, specifically including:

[0045] The comparison result of comparing the actual residual electric quantity of the current unmanned aerial vehicle with the target electric quantity is obtained, wherein,

[0046] If the comparison result shows that the actual residual electric quantity is less than the target electric quantity, an alarm is output, the current flight task is terminated, and the current unmanned aerial vehicle is controlled to land;

[0047] If the comparison result shows that the remaining power is greater than or equal to the target power, it is determined whether to control the current unmanned aerial vehicle to land based on the power consumption parameter, wherein when the power consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is controlled to land, otherwise the current flight task is continued.

[0048] In this scheme, the target landing point is screened based on the geographic position of the current unmanned aerial vehicle to control the unmanned aerial vehicle to go to the target landing point for landing.

[0049] The third aspect of the present application provides a computer readable storage medium, which comprises a kind of unmanned aerial vehicle power intelligent analysis method program of machine, the steps of the unmanned aerial vehicle power intelligent analysis method program are realized when the processor is executed, as described in any one of the above, a kind of unmanned aerial vehicle power intelligent analysis method.

[0050] The unmanned aerial vehicle power intelligent analysis method, system and readable storage medium disclosed by the present application can identify based on the power of the unmanned aerial vehicle, and control the unmanned aerial vehicle to land according to the actual flight data, to avoid the problem of unmanned aerial vehicle power shortage out of control crash. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The flow chart of the unmanned aerial vehicle power intelligent analysis method of the present application is shown.

[0052] Figure 2 The block diagram of the unmanned aerial vehicle power intelligent analysis system of the present application is shown. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0054] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can also be implemented in other ways different from those described herein, therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below.

[0055] Figure 1 The flow chart of the unmanned aerial vehicle power intelligent analysis method of the present application is shown.

[0056] As Figure 1 shown, the present application discloses a kind of unmanned aerial vehicle power intelligent analysis method, comprising the following steps:

[0057] S102, acquire a flight task of a current unmanned aerial vehicle, and determine whether a remaining power of the current unmanned aerial vehicle meets a requirement based on the flight task;

[0058] S104, if the requirement is not met, output an alarm prompt and do not execute the current flight task;

[0059] S106, if the requirement is met, execute the current flight task, and acquire flight data during flight of the unmanned aerial vehicle based on the flight task, wherein the flight data at least includes environmental data and a power consumption parameter;

[0060] S108, acquire a target power required for a remaining route in the flight task based on the environmental data by using big data analysis, compare the actual remaining power of the current unmanned aerial vehicle with the target power, and control the unmanned aerial vehicle to perform an action based on a comparison result.

[0061] It should be noted that, in the embodiment, before the unmanned aerial vehicle executes an automatic flight task, it is determined whether the current power of the unmanned aerial vehicle can support the unmanned aerial vehicle to fly a complete route based on the flight task. If the remaining power of the current unmanned aerial vehicle meets the requirement, the current flight task can be executed. If the requirement is not met, an alarm prompt that the requirement is not met is output, and the current flight task is not executed. Further, during execution of the flight task by the unmanned aerial vehicle, flight data is acquired to perform real-time analysis, so that the unmanned aerial vehicle is controlled to perform an action (for example, emergency landing) to reduce the problem of out-of-control unmanned aerial vehicle. Specifically, the target power required for the remaining route is identified based on the environmental data by using big data analysis. When it is identified that the actual remaining power is less than the target power, the unmanned aerial vehicle is controlled to land, so that the problem that the current unmanned aerial vehicle cannot fly due to insufficient power and then crashes is avoided.

[0062] According to the embodiment of the application, the flight task of the current unmanned aerial vehicle is acquired, and it is determined whether the remaining power of the current unmanned aerial vehicle meets the requirement based on the flight task. Specifically, the method comprises the following steps.

[0063] A flight task corresponding to the current unmanned aerial vehicle is acquired, and flight time, a flight route, and a required power for flight are identified based on the flight task;

[0064] The required power for flight and the remaining power of the unmanned aerial vehicle are compared to determine whether the remaining power of the current unmanned aerial vehicle meets the requirement, wherein,

[0065] If the remaining power of the unmanned aerial vehicle is greater than the power required for the flight, it indicates that the requirement is met, otherwise it is not met.

[0066] It should be noted that in the present embodiment, since the automatic flight task not only includes flight time, flight route, flight endpoint, flight target and the like, but also includes power required for flight, before the unmanned aerial vehicle performs the flight task, the power required for flight and the remaining power of the unmanned aerial vehicle can be compared to determine whether the current unmanned aerial vehicle can meet the requirement (i.e. whether it can fly the entire journey according to the flight task), wherein if the remaining power of the unmanned aerial vehicle is greater than the power required for the flight, it indicates that the requirement is met, otherwise it is not met.

[0067] According to the embodiment of the present application, the flight data is obtained during the flight of the unmanned aerial vehicle based on the flight task, specifically comprising:

[0068] When the unmanned aerial vehicle flies based on the flight task, the environment data is obtained based on the sensor group arranged on the unmanned aerial vehicle;

[0069] When the unmanned aerial vehicle flies based on the flight task, the power consumption parameter is obtained based on the operation self-checking data of the unmanned aerial vehicle.

[0070] It should be noted that in the present embodiment, the disclosed flight data includes the environment data and the power consumption parameter, wherein the environment data is obtained based on the sensor group arranged on the unmanned aerial vehicle, for example, the environment temperature is obtained by a temperature sensor, the environment air quality is obtained by an air quality sensor, and the like, and when the unmanned aerial vehicle flies based on the flight task, the power consumption parameter is obtained based on the operation self-checking data of the unmanned aerial vehicle, wherein since the unmanned aerial vehicle monitors its own operation data for self-checking when it operates, the power consumption parameter can be obtained based on the operation self-checking data.

[0071] According to the embodiment of the present application, the target power required for the remaining route in the flight task is obtained based on the environment data using big data analysis, specifically comprising:

[0072] The temperature parameter, humidity parameter, air parameter and wind resistance parameter in the environment where the current unmanned aerial vehicle is located are identified based on the environment data;

[0073] The first target power corresponding to the remaining route is obtained by analyzing the historical data in big data analysis based on the temperature parameter and the humidity parameter;

[0074] analyze the remaining route corresponding second target electric quantity based on the air parameter and the wind resistance parameter by using a neural network model in big data analysis;

[0075] obtain the target electric quantity required by the remaining route in the flight task based on the first target electric quantity and the second target electric quantity.

[0076] It should be noted that in the present embodiment, when using big data analysis, historical data can be used for big data analysis, or a neural network model can be used for training analysis. Based on the temperature parameter and the humidity parameter, historical data in big data analysis is analyzed to obtain the first target electric quantity corresponding to the remaining route. Specifically, the first target electric quantity existing under the current temperature and humidity is obtained by comparing the corresponding consumed electric quantity under different temperature and humidity environments by using historical data. Based on the air parameter and the wind resistance parameter, a neural network model in big data analysis is used to analyze and obtain the second target electric quantity corresponding to the remaining route. Specifically, since the air quality (such as the sand dust index) in the air parameter and the wind resistance index have a wide range of variation, historical data is not suitable for comparison. Instead, a trained neural network model is obtained based on historical data by using a neural network model, and the current air parameter and wind resistance parameter are input into the trained neural network model to analyze and obtain the second target electric quantity. Thus, the target electric quantity required by the remaining route in the flight task is obtained based on the first target electric quantity and the second target electric quantity.

[0077] According to the embodiment of the present application, the actual remaining electric quantity of the unmanned aerial vehicle is compared with the target electric quantity, and the unmanned aerial vehicle is controlled to act based on the comparison result, specifically including:

[0078] obtaining a comparison result of comparing the actual remaining electric quantity of the unmanned aerial vehicle with the target electric quantity, wherein,

[0079] If the comparison result shows that the actual remaining electric quantity is less than the target electric quantity, an alarm is output, and the current flight task is terminated, and the current unmanned aerial vehicle is controlled to land;

[0080] If the comparison result shows that the actual remaining electric quantity is greater than or equal to the target electric quantity, it is judged whether to control the current unmanned aerial vehicle to land based on the electric quantity consumption parameter. When the electric quantity consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is controlled to land, otherwise the current flight task is continued.

[0081] It should be noted that in the embodiment, when the actual remaining power is compared with the target power, if the comparison result shows that the actual remaining power is less than the target power, an alarm is output and the current flight task is terminated, and the current unmanned aerial vehicle is controlled to land; if the comparison result shows that the actual remaining power is greater than or equal to the target power, whether the current unmanned aerial vehicle is controlled to land is determined based on the power consumption parameter, wherein when the power consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is controlled to land, otherwise the current flight task is continued. Specifically, even if the current remaining power is greater than the target power, but the power consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is also controlled to land.

[0082] According to the embodiment of the present application, the target landing point is screened based on the geographic position of the current unmanned aerial vehicle to control the unmanned aerial vehicle to go to the target landing point for landing.

[0083] It should be noted that in the embodiment, when the unmanned aerial vehicle is controlled to land, the target landing point needs to be screened based on the geographic position of the current unmanned aerial vehicle, so as to control the unmanned aerial vehicle to go to the target landing point for landing. Specifically, since the unmanned aerial vehicle needs to be controlled to land, the current remaining power also needs to be considered, and the target landing point is screened based on the actual remaining power and the geographic position for landing.

[0084] It is worth mentioning that the unmanned aerial vehicle is controlled to land, specifically including:

[0085] The basic power consumption of the landing path between the geographic position and the target landing point is obtained;

[0086] The consumption power required by the unmanned aerial vehicle during the landing path process is obtained;

[0087] The total power consumption of the basic power consumption and the consumption power is calculated, and the target landing point is screened based on the total power consumption and the actual remaining power.

[0088] It should be noted that in the embodiment, since the basic power consumption of the distance between the current position and the landing point needs to be considered during landing, and the consumption power of the unmanned aerial vehicle caused by the environmental data in the current landing path also needs to be considered, the actual remaining power needs to be greater than the total power consumption of the basic power consumption and the consumption power, so as to ensure the safe landing of the unmanned aerial vehicle. Therefore, when the target landing point is screened, the condition that the actual remaining power needs to be greater than the total power consumption of the basic power consumption and the consumption power needs to be met.

[0089] It is worth mentioning that the target landing point is screened based on the total power consumption and the actual residual power, and specifically includes:

[0090] A landing point group is obtained based on the screened result;

[0091] The landing point with the minimum total power consumption is obtained as the target landing point based on the landing point group; or

[0092] The target landing point is obtained based on the total power consumption and the position relationship of the landing point group.

[0093] It should be noted that in this embodiment, when actually landing, it can be immediately and quickly landed, at which time it is required to land quickly, and accordingly, the minimum total power consumption is required, at which time the landing point with the minimum total power consumption is obtained as the target landing point based on the landing point group. For unmanned aerial vehicles that do not need to land immediately and quickly, the unmanned aerial vehicle can be landed in combination with the position relationship under the condition of meeting the power demand, specifically, the landing point with the closest position relationship is screened as the target landing point or the landing point on the flight route is screened as the target landing point, wherein when landing, recording work can also be performed on the flight route, thereby improving the work efficiency of the unmanned aerial vehicle.

[0094] It is worth mentioning that the method further includes:

[0095] Outputting a task level based on the power consumption parameter, wherein,

[0096] If the maximum value of the power consumption parameter exceeds the I-type level parameter value, the task level of the current unmanned aerial vehicle is defined as II type, otherwise it is defined as I type.

[0097] It should be noted that in the present embodiment, due to the difference in task level, the above embodiment illustrates that the remaining power of the unmanned aerial vehicle can execute the flight task if it meets the power required by the task, but as the use frequency of the power storage material increases, the application efficiency of the power will also decrease, that is, two unmanned aerial vehicles with full power can execute the same flight task, but the power consumption parameters are different, and the corresponding supported flight time is also different, that is, only relying on the power state to identify whether it meets the requirement of the whole flight, both unmanned aerial vehicles can meet the requirement, but in actual application, the power consumption parameters are different, and there may be a certain unmanned aerial vehicle that cannot support the whole flight requirement, therefore, the task level can be defined based on the power consumption parameter, different task levels have different requirements for the power consumption parameter, accordingly, if the maximum value of the power consumption parameter exceeds the I-type level parameter value, the task level of the current unmanned aerial vehicle is defined as II-type, otherwise, it is defined as I-type, thereby introducing the power consumption parameter into the flight task judgment, enriching the judgment means, and improving the application efficiency of the unmanned aerial vehicle.

[0098] Figure 2 A block diagram of an unmanned aerial vehicle power intelligent analysis system is shown.

[0099] As Figure 2 shown, the present application discloses an unmanned aerial vehicle power intelligent analysis system, comprising a memory and a processor, the memory comprises an unmanned aerial vehicle power intelligent analysis method program, the unmanned aerial vehicle power intelligent analysis method program is executed by the processor to realize the following steps:

[0100] obtaining the flight task of the current unmanned aerial vehicle, judging whether the remaining power of the current unmanned aerial vehicle meets the requirement based on the flight task, wherein,

[0101] if not, output an alarm reminder and do not execute the current flight task;

[0102] if yes, execute the current flight task, and obtain flight data during the flight of the unmanned aerial vehicle based on the flight task, wherein the flight data at least comprises environmental data and power consumption parameter;

[0103] based on the environmental data, using big data analysis to obtain the target power required by the remaining route in the flight task, comparing the actual remaining power of the current unmanned aerial vehicle with the target power, and controlling the unmanned aerial vehicle to act based on the comparison result.

[0104] It should be noted that in the embodiment, before the unmanned aerial vehicle performs the automatic flight task, whether the current power of the unmanned aerial vehicle can support the unmanned aerial vehicle to fly the whole flight task is judged based on the flight task, if the remaining power of the current unmanned aerial vehicle meets the demand, the current flight task can be performed, if the demand is not met, an alarm reminding of not meeting the demand is output, and the current flight task is not performed, further, during the flight task of the unmanned aerial vehicle, flight data is acquired to analyze in real time to control the unmanned aerial vehicle to act (such as emergency landing) to reduce the problem of unmanned aerial vehicle out of control, specifically, the target power required for the remaining route is identified based on the environmental data by using big data analysis, when it is identified that the actual remaining power is less than the target power, the unmanned aerial vehicle is controlled to land, thereby avoiding the problem of the current unmanned aerial vehicle running out of power and falling due to being unable to fly.

[0105] According to the embodiment of the application, the flight task of the current unmanned aerial vehicle is acquired, and whether the remaining power of the current unmanned aerial vehicle meets the demand is judged based on the flight task, specifically comprising:

[0106] The flight task corresponding to the current unmanned aerial vehicle is acquired, and the flight time, flight route and flight required power are identified based on the flight task;

[0107] The flight required power and the remaining power of the unmanned aerial vehicle are compared to judge whether the remaining power of the current unmanned aerial vehicle meets the demand, wherein,

[0108] If the remaining power of the unmanned aerial vehicle is greater than the flight required power, it means that it meets the demand, otherwise it does not meet the demand.

[0109] It should be noted that in the embodiment, since the automatic flight task not only includes the flight time, flight route, flight end point, flight target and the like, but also includes the flight required power, before the unmanned aerial vehicle performs the flight task, the flight required power and the remaining power of the unmanned aerial vehicle can be compared to judge whether the current unmanned aerial vehicle can meet the demand (i.e. whether it can fly the whole flight task according to the flight task), if the remaining power of the unmanned aerial vehicle is greater than the flight required power, it means that it meets the demand, otherwise it does not meet the demand.

[0110] According to the embodiment of the application, the flight data is acquired during the flight of the unmanned aerial vehicle based on the flight task, specifically comprising:

[0111] When the unmanned aerial vehicle flies based on the flight task, the environmental data is acquired based on the sensor group arranged on the unmanned aerial vehicle.

[0112] The power consumption parameter is obtained based on operation self-checking data of the unmanned aerial vehicle when the unmanned aerial vehicle flies based on the flight task.

[0113] It should be noted that in the embodiment, the flight data disclosed includes the environment data and the power consumption parameter, wherein the environment data is obtained based on a sensor group arranged on the unmanned aerial vehicle, for example, the environment temperature is obtained by a temperature sensor, the environment air quality is obtained by an air quality sensor, and the like, and the power consumption parameter is obtained based on operation self-checking data of the unmanned aerial vehicle when the unmanned aerial vehicle flies based on the flight task, wherein since the unmanned aerial vehicle monitors its own operation data for self-checking in real time when the unmanned aerial vehicle operates, the power consumption parameter can be obtained based on the operation self-checking data.

[0114] According to the embodiment of the present application, the target power required for the remaining route in the flight task is obtained based on the environment data by using big data analysis, specifically comprising:

[0115] The temperature parameter, the humidity parameter, the air parameter and the wind resistance parameter in the environment where the current unmanned aerial vehicle is located are identified based on the environment data;

[0116] The first target power corresponding to the remaining route is obtained by using historical data in big data analysis based on the temperature parameter and the humidity parameter;

[0117] The second target power corresponding to the remaining route is obtained by using a neural network model in big data analysis based on the air parameter and the wind resistance parameter;

[0118] The target power required for the remaining route in the flight task is obtained based on the first target power and the second target power.

[0119] It should be noted that in the present embodiment, when using big data analysis, historical data can be used for big data analysis, or a neural network model can be used for training analysis. Based on the temperature parameter and the humidity parameter, historical data in big data analysis is analyzed to obtain the corresponding first target electric quantity in the remaining route. Specifically, historical data is used to compare the electric quantity consumed under different temperature and humidity environments to obtain the first target electric quantity existing under the current temperature and humidity. Based on the air parameter and the wind resistance parameter, a neural network model in big data analysis is used to analyze and obtain the corresponding second target electric quantity in the remaining route. Specifically, since the air quality (such as the sand dust index) and the wind resistance index in the air parameter have a wide range of changes, historical data is not suitable for comparison. Instead, a neural network model is used to train based on historical data to obtain a corresponding trained neural network model, and the current air parameter and wind resistance parameter are input into the trained neural network model to analyze and obtain the second target electric quantity. Thus, the target electric quantity required in the remaining route of the flight task is obtained based on the first target electric quantity and the second target electric quantity.

[0120] According to the embodiment of the present application, the actual remaining electric quantity of the unmanned aerial vehicle is compared with the target electric quantity, and the unmanned aerial vehicle is controlled to act based on the comparison result, specifically including:

[0121] The comparison result of comparing the actual remaining electric quantity of the unmanned aerial vehicle with the target electric quantity is obtained, wherein,

[0122] If the comparison result shows that the actual remaining electric quantity is less than the target electric quantity, an alarm is output, and the current flight task is terminated, and the current unmanned aerial vehicle is controlled to land;

[0123] If the comparison result shows that the actual remaining electric quantity is greater than or equal to the target electric quantity, it is judged whether to control the current unmanned aerial vehicle to land based on the electric quantity consumption parameter. When the electric quantity consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is controlled to land, otherwise the current flight task is continued.

[0124] It should be noted that in the embodiment, when the actual remaining power is compared with the target power, if the comparison result shows that the actual remaining power is less than the target power, an alarm is output and the current flight task is terminated, and the current unmanned aerial vehicle is controlled to land; if the comparison result shows that the actual remaining power is greater than or equal to the target power, whether the current unmanned aerial vehicle is controlled to land is determined based on the power consumption parameter, wherein when the power consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is controlled to land, otherwise the current flight task is continued. Specifically, even if the current remaining power is greater than the target power, but the power consumption parameter exceeds the preset consumption rate, the unmanned aerial vehicle is also controlled to land.

[0125] According to the embodiment of the present application, the target landing point is screened based on the geographic position of the current unmanned aerial vehicle to control the unmanned aerial vehicle to go to the target landing point for landing.

[0126] It should be noted that in the embodiment, when the unmanned aerial vehicle is controlled to land, the target landing point needs to be screened based on the geographic position of the current unmanned aerial vehicle, so as to control the unmanned aerial vehicle to go to the target landing point for landing. Specifically, since the unmanned aerial vehicle needs to be controlled to land, the current remaining power also needs to be considered, and the target landing point is screened based on the actual remaining power and the geographic position.

[0127] It is worth mentioning that the unmanned aerial vehicle is controlled to land, specifically including:

[0128] The basic power consumption of the landing path between the geographic position and the target landing point is obtained;

[0129] The consumption power required by the unmanned aerial vehicle during the landing path process is obtained;

[0130] The total power consumption of the basic power consumption and the consumption power is calculated, and the target landing point is screened based on the total power consumption and the actual remaining power.

[0131] It should be noted that in the embodiment, since the basic power consumption of the distance between the current position and the landing point needs to be considered during landing, and the consumption power of the unmanned aerial vehicle caused by the environmental data in the current landing path also needs to be considered, the actual remaining power needs to be greater than the total power consumption of the basic power consumption and the consumption power, so as to ensure the safe landing of the unmanned aerial vehicle. Therefore, when the target landing point is screened, the condition that the actual remaining power needs to be greater than the total power consumption of the basic power consumption and the consumption power needs to be met.

[0132] It is worth mentioning that the target landing point is screened based on the total power consumption and the actual residual power, and specifically includes:

[0133] A landing point group is obtained based on the screened result;

[0134] The landing point with the minimum total power consumption is obtained as the target landing point based on the landing point group; or

[0135] The target landing point is obtained based on the total power consumption and the position relationship of the landing point group.

[0136] It should be noted that in the present embodiment, when actually landing, the landing can be performed immediately and quickly, in which case the landing is required to be performed quickly, and accordingly the minimum total power consumption is required, and the landing point with the minimum total power consumption is obtained as the target landing point based on the landing point group. For the unmanned aerial vehicle that does not need to land immediately and quickly, the landing can be performed in combination with the position relationship under the condition of meeting the power demand, and specifically, the landing point with the closest position relationship is screened as the target landing point or the landing point on the flight route is screened as the target landing point. In addition, the landing can also be performed on the flight route to improve the operation efficiency of the unmanned aerial vehicle.

[0137] It is worth mentioning that the method further includes:

[0138] The task level is output based on the power consumption parameter, wherein

[0139] If the maximum value of the power consumption parameter exceeds the I-type level parameter value, the task level of the current unmanned aerial vehicle is defined as II type, otherwise it is defined as I type.

[0140] It should be noted that in the present embodiment, due to the difference in task level, the above embodiment illustrates that the unmanned aerial vehicle can perform the flight task if the remaining power meets the power required by the task, but as the use frequency of the power storage material increases, the application efficiency of the power will also decrease, that is, two unmanned aerial vehicles with full power perform the same flight task, the power consumption parameters are different, and the corresponding supported flight time is also different, that is, only relying on the power state to identify whether it meets the requirement of the whole flight, both unmanned aerial vehicles can meet the requirement, but in actual application, the power consumption parameters are different, and there may be a certain unmanned aerial vehicle that cannot support the whole flight requirement, therefore, the task level can be defined based on the power consumption parameter, different task levels have different requirements for the power consumption parameter, accordingly, if the maximum value of the power consumption parameter exceeds the I-type level parameter value, the task level of the current unmanned aerial vehicle is defined as II-type, otherwise, it is defined as I-type, thereby introducing the power consumption parameter into the flight task judgment, enriching the judgment means, and improving the application efficiency of the unmanned aerial vehicle.

[0141] The third aspect of the present application provides a computer readable storage medium, the computer readable storage medium comprises an unmanned aerial vehicle power intelligent analysis method program, when the unmanned aerial vehicle power intelligent analysis method program is executed by a processor, the steps of the unmanned aerial vehicle power intelligent analysis method according to any one of the above are realized.

[0142] The unmanned aerial vehicle power intelligent analysis method, system and readable storage medium disclosed by the present application can identify based on the power of the unmanned aerial vehicle, and control the unmanned aerial vehicle to land according to the actual flight data, so as to avoid the problem of unmanned aerial vehicle out of control and crash due to insufficient power.

[0143] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed components can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components above can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; and part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0145] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0146] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction-related hardware, and the aforementioned program can be stored in a computer-readable storage medium, and when the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic discs or optical discs, and various storage media that can store program codes.

[0147] Alternatively, the integrated units of the present application, if implemented in the form of software functional modules and sold or used as independent products, can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of software products, and the computer software products are stored in a storage medium, including a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes mobile storage devices, ROMs, RAMs, magnetic discs or optical discs, and various storage media that can store program codes.

Claims

1. A method for intelligent analysis of the battery power of an unmanned aerial vehicle, characterized in that, Includes the following steps: The current flight mission of the unmanned aerial vehicle (UAV) is obtained, and based on the flight mission, it is determined whether the remaining battery power of the UAV meets the requirements. If the conditions are not met, an alarm will be issued and the current flight mission will not be executed. If the conditions are met, the current flight mission will be executed, and during the flight of the unmanned aerial vehicle based on the flight mission, flight data will be acquired, wherein the flight data includes at least environmental data and power consumption parameters; Based on the environmental data, big data analytics is used to obtain the target power required for the remaining route of the flight mission. Specifically, this includes: identifying temperature, humidity, air quality, and wind resistance parameters in the environment where the unmanned aerial vehicle (UAV) is currently located; analyzing historical data from big data analytics based on the temperature and humidity parameters to obtain a first target power for the remaining route; analyzing the air quality and wind resistance parameters using a neural network model from big data analytics to obtain a second target power for the remaining route; and obtaining the target power required for the remaining route of the flight mission based on the first and second target power. The drone is controlled to perform actions based on a comparison between its current remaining battery power and the target battery power.

2. The intelligent power analysis method for unmanned aerial vehicles according to claim 1, characterized in that, The process of acquiring the current flight mission of the unmanned aerial vehicle (UAV) and determining whether the remaining battery power of the UAV meets the requirements based on the flight mission specifically includes: Obtain the flight mission corresponding to the current unmanned aerial vehicle, and identify the flight time, flight route, and power consumption required for the flight based on the flight mission; The required power for flight is compared with the remaining power of the unmanned aerial vehicle (UAV) to determine whether the current remaining power of the UAV meets the requirements. If the remaining battery power of the unmanned aerial vehicle is greater than the battery power required for flight, then the condition is met; otherwise, the condition is not met.

3. The intelligent power analysis method for unmanned aerial vehicles according to claim 1, characterized in that, The acquisition of flight data during the flight of the unmanned aerial vehicle based on the flight mission specifically includes: When the unmanned aerial vehicle is flying based on the flight mission, the environmental data is acquired based on the sensor group installed on the unmanned aerial vehicle; When the unmanned aerial vehicle is flying based on the flight mission, the power consumption parameters are obtained based on the unmanned aerial vehicle's operation self-test data.

4. The intelligent power analysis method for unmanned aerial vehicles according to claim 1, characterized in that, The process of comparing the current remaining battery power of the unmanned aerial vehicle (UAV) with the target battery power, and controlling the UAV to perform actions based on the comparison result, specifically includes: Obtain the comparison result between the current actual remaining battery power of the unmanned aerial vehicle and the target battery power, wherein, If the comparison result shows that the actual remaining battery power is less than the target battery power, an alarm will be output, the current flight mission will be terminated, and the current unmanned aerial vehicle will be controlled to land. If the comparison result shows that the remaining power is greater than or equal to the target power, then a decision is made based on the power consumption parameter to control the current unmanned aerial vehicle to land. If the power consumption parameter exceeds a preset consumption rate, then the unmanned aerial vehicle is controlled to land; otherwise, the current flight mission continues.

5. The intelligent power analysis method for unmanned aerial vehicles according to claim 4, characterized in that, Select a target landing point based on the current geographical location of the unmanned aerial vehicle (UAV) and control the UAV to proceed to the target landing point for landing.

6. A smart power analysis system for unmanned aerial vehicles, characterized in that, The system includes a memory and a processor. The memory contains a program for intelligent analysis of the battery power of unmanned aerial vehicles (UAVs). When the processor executes the program, the UAV battery power intelligent analysis method performs the following steps: The current flight mission of the unmanned aerial vehicle (UAV) is obtained, and based on the flight mission, it is determined whether the remaining battery power of the UAV meets the requirements. If the conditions are not met, an alarm will be issued and the current flight mission will not be executed. If the conditions are met, the current flight mission will be executed, and during the flight of the unmanned aerial vehicle based on the flight mission, flight data will be acquired, wherein the flight data includes at least environmental data and power consumption parameters; Based on the environmental data, big data analytics is used to obtain the target power required for the remaining route of the flight mission. Specifically, this includes: identifying temperature, humidity, air quality, and wind resistance parameters in the environment where the unmanned aerial vehicle (UAV) is currently located; analyzing historical data from big data analytics based on the temperature and humidity parameters to obtain a first target power for the remaining route; analyzing the air quality and wind resistance parameters using a neural network model from big data analytics to obtain a second target power for the remaining route; and obtaining the target power required for the remaining route of the flight mission based on the first and second target power. The drone is controlled to perform actions based on a comparison between its current remaining battery power and the target battery power.

7. The intelligent power analysis system for unmanned aerial vehicles according to claim 6, characterized in that, The process of comparing the current remaining battery power of the unmanned aerial vehicle (UAV) with the target battery power, and controlling the UAV to perform actions based on the comparison result, specifically includes: Obtain the comparison result between the current actual remaining battery power of the unmanned aerial vehicle and the target battery power, wherein, If the comparison result shows that the actual remaining battery power is less than the target battery power, an alarm will be output, the current flight mission will be terminated, and the current unmanned aerial vehicle will be controlled to land. If the comparison result shows that the remaining power is greater than or equal to the target power, then a decision is made based on the power consumption parameter to control the current unmanned aerial vehicle to land. If the power consumption parameter exceeds a preset consumption rate, then the unmanned aerial vehicle is controlled to land; otherwise, the current flight mission continues.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for intelligent analysis of the battery power of an unmanned aerial vehicle (UAV). When the program is executed by a processor, it implements the steps of an intelligent analysis method for the battery power of an UAV as described in any one of claims 1 to 5.

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