Unmanned aerial vehicle intelligent operation and maintenance system based on Internet of Things

Abstract

The invention relates to the field of unmanned aerial vehicle operation and maintenance, in particular to an intelligent unmanned aerial vehicle operation and maintenance system based on the Internet of Things. The data acquisition module is used for acquiring environment characteristics of the unmanned aerial vehicle in a predetermined space range; the environment analysis module is used for calculating an environment interference characterization coefficient for the unmanned aerial vehicle based on the environment characteristics so as to analyze a chaos tendency category of environment interference; according to the invention, the preset obstacle avoidance action is executed on the obstacle in advance when the distance between the obstacle and the predetermined range, so that whether the unmanned aerial vehicle can stably and safely complete the avoidance operation or not is judged according to the current environment condition and the performance state of the unmanned aerial vehicle, and then the stability of the unmanned aerial vehicle is improved. And the obstacle avoidance action of the unmanned aerial vehicle is corrected or the acquisition precision of the sensor is adjusted, so that the stability and safety of the unmanned aerial vehicle during obstacle avoidance action execution are improved on the premise of ensuring the data reliability.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle operation and maintenance, and in particular to an intelligent unmanned aerial vehicle operation and maintenance system based on the Internet of Things. Background Art

[0002] When flying in various complex environments, drones may encounter obstacles such as buildings, high-voltage lines and mountains. In order to ensure the safe and stable operation of drones and avoid collisions with obstacles and crashes, drones are often equipped with high-precision Internet of Things devices to perceive the environment and obstacle characteristics, analyze relevant data, and perform obstacle avoidance actions. In actual situations, diversified application scenarios have put forward higher requirements for drone obstacle avoidance operation and maintenance technology, and also provided a broad space for its development.

[0003] Chinese patent publication number: CN115617077A, discloses an automatic obstacle avoidance drone and an automatic obstacle avoidance method, wherein the drone includes a drone body, and the drone body is provided with a radar, a camera body, a distance sensor, and a controller; the radar, the camera body, and the distance sensor monitor environmental data, and upload the environmental data to the controller; the controller is used to receive environmental data, and determine whether there is an obstacle in front of the drone according to the environmental data, and when there is an obstacle, determine the distance between the drone and the obstacle, and control the drone to avoid the obstacle when the distance is less than a preset threshold. Through the above technical solution, the drone disclosed by the present invention can automatically identify obstacles according to multiple environmental data, and autonomously avoid obstacles according to the distance between the obstacles, thereby avoiding the drone from colliding with obstacles due to negligence of staff.

[0004] However, there are still the following problems in the prior art:

[0005] When a drone avoids obstacles encountered during flight, in addition to considering the distance between the obstacle and the drone, it is also necessary to consider the impact of environmental factors on the effectiveness of the obstacle avoidance operation performed by the drone. For example, if the interference of environmental factors is too strong, the drone's own state may be unable to stably perform obstacle avoidance operations. At the same time, the data collection accuracy of the sensors deployed on the drone may also affect the drone's correct judgment of obstacles and the normal execution of obstacle avoidance operations. The drone may fail to avoid obstacles in time, reducing the stability and safety of the drone when performing obstacle avoidance actions. Summary of the invention

[0006] To this end, the present invention provides an intelligent operation and maintenance system for unmanned aerial vehicles based on the Internet of Things, so as to overcome the problem in the prior art that when a unmanned aerial vehicle avoids obstacles encountered during flight, in addition to considering the distance to the obstacles, it is also necessary to consider the influence of environmental factors on the effect of the obstacle avoidance operation performed by the unmanned aerial vehicle. For example, if the interference of environmental factors is too strong, it may cause the state of the unmanned aerial vehicle itself to be unable to stably perform the obstacle avoidance operation. At the same time, the data collection accuracy of the sensors deployed on the unmanned aerial vehicle may also affect the unmanned aerial vehicle's correct judgment of obstacles and the normal execution of the obstacle avoidance operation. The unmanned aerial vehicle may fail to avoid obstacles in time, thereby reducing the stability and safety of the unmanned aerial vehicle when performing obstacle avoidance actions.

[0007] To achieve the above object, the present invention provides an intelligent operation and maintenance system for unmanned aerial vehicles based on the Internet of Things, which includes:

[0008] A data storage module, used to store the reaction characteristics of the drone performing the obstacle avoidance action, wherein the reaction characteristics include the reaction time of the drone performing the obstacle avoidance action and the time required for the drone to stabilize after completing the obstacle avoidance operation;

[0009] A data acquisition module is used to collect environmental characteristics of the drone within a predetermined space, wherein the environmental characteristics include the number of obstacles, the distance between the drone and the obstacles, and the airflow intensity;

[0010] An environmental analysis module, connected to the data acquisition module, for acquiring the environmental features acquired by the data acquisition module, so as to calculate the environmental interference characterization coefficient for the UAV based on the environmental features, so as to analyze the chaotic tendency category of the environmental interference;

[0011] A stability analysis module, which is connected to the data acquisition module, the data storage module and the environment analysis module respectively, and is used to operate and maintain the stability of the drone based on the analysis results of the environment analysis module, including:

[0012] Determine whether to enter the verification time based on the distance between the obstacle and the drone to perform obstacle avoidance verification, including controlling the drone to perform a preset obstacle avoidance action in advance, obtaining the reaction characteristics of the drone performing the obstacle avoidance operation, and analyzing the obstacle avoidance reaction characterization parameters of the drone to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard;

[0013] Or, based on the environmental interference characterization coefficient for the drone, a gain and a sampling rate of a sensor for obstacle avoidance in the drone are adjusted.

[0014] Furthermore, the environment analysis module is used to calculate the environmental interference characterization coefficient for the UAV based on the environmental characteristics, including:

[0015] The ratio of the number of obstacles to the number threshold and the sum of the ratios of the mean distances between the UAV and each obstacle to the mean threshold are calculated and the corresponding weight coefficients are assigned as the first environmental interference feature;

[0016] to calculate the ratio of the airflow intensity to the airflow intensity threshold and assign a corresponding weight coefficient as a second environmental interference feature;

[0017] Used to determine the sum of the first environmental interference feature and the second environmental interference feature as the environmental interference characterization coefficient for the UAV.

[0018] Furthermore, the environmental analysis module is used to analyze the chaotic tendency category of environmental interference, including:

[0019] If the environmental interference characterization coefficient is greater than or equal to a preset environmental interference characterization coefficient threshold, the environmental interference is determined to be a high chaos tendency category;

[0020] If the environmental interference characterization coefficient is less than a preset environmental interference characterization coefficient threshold, the environmental interference is determined to be a low chaos tendency category.

[0021] Furthermore, the stability analysis module is used to operate and maintain the stability of the drone based on the analysis result of the environment analysis module, including:

[0022] If the environmental interference is of the high chaos tendency category, it is determined whether to enter the verification time based on the distance between the obstacle and the drone to perform obstacle avoidance verification, including controlling the drone to perform a preset obstacle avoidance action in advance, obtaining the reaction characteristics of the drone performing the obstacle avoidance operation, and analyzing the obstacle avoidance reaction characterization parameters of the drone to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard;

[0023] If the environmental interference is of a low chaos tendency category, the gain and sampling rate of the sensor for obstacle avoidance in the drone are adjusted based on the environmental interference characterization coefficient for the drone.

[0024] Furthermore, the stability analysis module is used to determine whether to enter the verification time based on the distance between the obstacle and the drone, including:

[0025] If the distance between the obstacle and the drone is less than or equal to the distance threshold, the timing of entering the verification is determined.

[0026] Furthermore, the stability analysis module is used to control the drone to pre-execute a preset obstacle avoidance action, including:

[0027] Turn at a predetermined radius, roll the fuselage, and raise the nose at a predetermined attitude.

[0028] Furthermore, the stability analysis module is used to analyze the obstacle avoidance reaction characterization parameters of the drone, including:

[0029] The ratio of the reaction time of the drone to perform the obstacle avoidance action to the reaction time threshold is used as the first obstacle avoidance reaction feature;

[0030] The ratio of the time required for the drone to stabilize after completing the obstacle avoidance operation to the required time threshold is used as the second obstacle avoidance reaction feature;

[0031] The sum of the first obstacle avoidance reaction characteristic and the second obstacle avoidance reaction characteristic is determined as an obstacle avoidance reaction characterization parameter.

[0032] Furthermore, the stability analysis module is used to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard, including:

[0033] If the obstacle avoidance reaction characterization parameter is less than the obstacle avoidance reaction characterization parameter threshold, it is determined that the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard.

[0034] Furthermore, the stable analysis module is used to adjust the gain and sampling rate of the sensor used for obstacle avoidance in the drone, including:

[0035] Increasing the gain, wherein the amount of gain increase is positively correlated with the environmental interference characterization coefficient;

[0036] The sampling rate is increased, and the amount of increase in the sampling rate is positively correlated with the environmental interference characterization coefficient.

[0037] Furthermore, the stability analysis module is also used to issue a warning signal, including:

[0038] If the obstacle avoidance maneuver performed by the drone does not meet the timely obstacle avoidance criteria, a warning signal is issued.

[0039] Compared with the prior art, the present invention provides a data storage module for storing reaction characteristics of the drone when performing obstacle avoidance actions; a data acquisition module for acquiring environmental characteristics of the drone within a predetermined spatial range; an environmental analysis module for calculating environmental interference characterization coefficients for the drone based on environmental characteristics to analyze the chaotic tendency category of environmental interference; and a stability analysis module for adaptively operating and maintaining the stability of the drone. The present invention performs a preset obstacle avoidance action on an obstacle in advance when the obstacle is within a predetermined range, so as to determine whether the drone can stably and safely complete the avoidance operation according to the current environmental conditions and the performance status of the drone itself, and then, correct the obstacle avoidance action of the drone or adjust the acquisition accuracy of the sensor, so as to improve the stability and safety of the drone when performing obstacle avoidance actions while ensuring data reliability.

[0040] In particular, the present invention calculates the environmental interference characterization coefficient for the UAV through environmental characteristics. In actual situations, environmental factors will affect the normal flight of the UAV. For example, strong airflow may cause abnormal shaking of the UAV fuselage, making it impossible to fly normally and smoothly, and may even deviate from the planned flight path. At the same time, the relevant influencing factors of obstacles observed by sensors will also affect whether the UAV can perform obstacle avoidance operations in a timely manner and the effect of performing obstacle avoidance operations. For example, if there are too many obstacles within a predetermined spatial range, or the distance between the UAV and the obstacles is too close, it may affect the UAV's correct judgment of obstacles, the timeliness of performing obstacle avoidance operations, and the normal execution of obstacle avoidance operations. Therefore, the present application calculates the environmental interference characterization coefficient for the UAV by obtaining the number of obstacles within a predetermined spatial range and the distance between the UAV and the obstacles in combination with the airflow intensity, so as to characterize the degree of interference of environmental factors on the stable and safe flight of the UAV, provide data support for the subsequent analysis of the chaotic tendency category of environmental interference, and then adaptively operate and maintain the stability of the UAV.

[0041] In particular, in the case where the environmental interference is of a high chaos tendency category, the present invention determines the verification timing according to the distance between the drone and the obstacle because the environmental factors have too strong an impact on the drone's flight. On the premise of ensuring that the drone and the obstacle are at a safe distance, the drone is controlled in advance to perform a preset obstacle avoidance action, including adjusting the drone's posture and smoothly performing a steering action to change the drone's flight direction. At the same time, the drone's flight speed is controlled to ensure the stability and safety of the drone's execution of the preset obstacle avoidance action. The effect of the drone's execution of the preset obstacle avoidance action is observed in advance before the drone reaches the obstacle. Whether the current performance status of the human-machine itself can complete the obstacle avoidance operation to avoid the situation where the drone fails to avoid the obstacle in time. Therefore, the present application calculates the obstacle avoidance reaction characterization parameters through the reaction characteristics of the drone when performing the preset obstacle avoidance action to characterize the execution effect of the drone, and then determines whether the execution of the drone meets the timely obstacle avoidance standard. Since the preset obstacle avoidance action pre-executed by the drone and the obstacle for which the subsequent obstacle avoidance operation actually needs to be performed are the same target, the preset obstacle avoidance action for the same target obstacle is corrected in time to ensure that the drone can stably and safely complete the actual obstacle avoidance operation.

[0042] In particular, when the environmental interference is of the low chaos tendency category, the present invention adjusts the accuracy coefficient of the sensor used for obstacle avoidance in the drone to ensure that the drone can correctly judge the obstacle when it is near an obstacle and smoothly perform the obstacle avoidance operation to ensure the effect of the obstacle avoidance operation. Furthermore, the present invention improves the stability and safety of the drone when performing obstacle avoidance actions while ensuring data reliability and high data accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a functional module diagram of a drone intelligent operation and maintenance system based on the Internet of Things according to an embodiment of the invention;

[0044] Figure 2 A logical decision diagram for analyzing the chaotic tendency category of environmental interference for an embodiment of the invention;

[0045] Figure 3 A logic decision diagram for determining whether to enter the verification timing in an embodiment of the invention;

[0046] Figure 4 The present invention is a logical decision diagram for determining whether the obstacle avoidance operation performed by the drone meets the obstacle avoidance standard. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0049] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] See also Figures 1 to 4 As shown, Figure 1 : is a functional module diagram of the intelligent operation and maintenance system of a drone based on the Internet of Things according to an embodiment of the present invention, Figure 2 A logical decision diagram for analyzing the chaotic tendency category of environmental interference in an embodiment of the present invention, Figure 3 The following is a logic decision diagram for determining whether to enter the verification timing according to an embodiment of the present invention. Figure 4 The following is a logic determination diagram for determining whether the obstacle avoidance operation performed by the drone meets the obstacle avoidance standard according to an embodiment of the present invention. The drone intelligent operation and maintenance system based on the Internet of Things according to an embodiment of the present invention includes:

[0051] A data storage module, used to store the reaction characteristics of the drone performing the obstacle avoidance action, wherein the reaction characteristics include the reaction time of the drone performing the obstacle avoidance action and the time required for the drone to stabilize after completing the obstacle avoidance operation;

[0052] A data acquisition module is used to collect environmental characteristics of the drone within a predetermined space, wherein the environmental characteristics include the number of obstacles, the distance between the drone and the obstacles, and the airflow intensity;

[0053] An environmental analysis module, connected to the data acquisition module, for acquiring the environmental features acquired by the data acquisition module, so as to calculate the environmental interference characterization coefficient for the UAV based on the environmental features, so as to analyze the chaotic tendency category of the environmental interference;

[0054] A stability analysis module, which is connected to the data acquisition module, the data storage module and the environment analysis module respectively, and is used to operate and maintain the stability of the drone based on the analysis results of the environment analysis module, including:

[0055] Determine whether to enter the verification time based on the distance between the obstacle and the drone to perform obstacle avoidance verification, including controlling the drone to perform a preset obstacle avoidance action in advance, obtaining the reaction characteristics of the drone performing the obstacle avoidance operation, and analyzing the obstacle avoidance reaction characterization parameters of the drone to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard;

[0056] Or, based on the environmental interference characterization coefficient for the drone, a gain and a sampling rate of a sensor for obstacle avoidance in the drone are adjusted.

[0057] It can be understood that the reaction time of the UAV in performing obstacle avoidance actions refers to the time from the UAV flight controller outputting the control command to the UAV starting to perform the obstacle avoidance action. At the same time, in order to avoid obstacles, the UAV will perform a variety of obstacle avoidance actions, such as adjusting the flight attitude and changing the flight direction. Therefore, the time required for the fuselage to stabilize after completing the obstacle avoidance operation refers to the time from the completion of the UAV obstacle avoidance action to the stabilization of the fuselage and the start of steady flight. This will not be repeated.

[0058] Specifically, there is no limitation on the equipment for collecting environmental characteristics of the drone within the predetermined spatial range. The drone can be equipped with a laser radar to detect the number of obstacles within the predetermined spatial range and the distance between the drone and the obstacles. The drone can be equipped with an anemometer to detect the airflow intensity within the predetermined spatial range. Of course, other forms can also be used, which will not be repeated here.

[0059] It can be understood that the predetermined spatial range refers to the spatial range in which the UAV maintains a safe flying distance from other obstacles. Technical personnel in this field can determine it based on the flight speed and maneuverability limitations of the UAV, and it can be set within the interval [30m, 50m]. This will not be repeated here.

[0060] Specifically, there is no limitation on the specific structures of the data storage module, the environmental analysis module and the stable analysis module, and they themselves or each unit therein can be composed of logic components or a combination of logic components, and the logic components include a field programmable processor, a computer or a microprocessor in a computer.

[0061] Specifically, the environment analysis module is used to calculate the environmental interference characterization coefficient for the UAV based on the environmental characteristics, including:

[0062] The ratio of the number of obstacles to the number threshold and the sum of the ratios of the mean distances between the UAV and each obstacle to the mean threshold are calculated and the corresponding weight coefficients are assigned as the first environmental interference feature;

[0063] to calculate the ratio of the airflow intensity to the airflow intensity threshold and assign a corresponding weight coefficient as the second environmental interference feature, where the weight coefficient is assigned by multiplying the value by the weight coefficient;

[0064] Used to determine the sum of the first environmental interference feature and the second environmental interference feature as the environmental interference characterization coefficient for the UAV.

[0065] In this embodiment, when performing weighted summation, the weight corresponding to the first environmental interference feature is set to 0.45, and the weight corresponding to the second environmental interference feature is set to 0.55;

[0066] The number threshold of obstacles, the mean distance threshold between the UAV and each obstacle, and the airflow intensity threshold are pre-determined. The number data of obstacles detected by the UAV at different times during flight, the mean distance between obstacles, and the airflow intensity are obtained several times, and the number mean and the airflow intensity mean are solved. The number threshold of obstacles is set to be between 1.15 and 1.23 times the number mean, the mean distance threshold between the UAV and each obstacle is set to be between 1.12 and 1.24 times the average of the mean distance between the UAV and each obstacle, and the airflow intensity threshold is set to be between 1.25 and 1.35 times the mean airflow intensity.

[0067] The mean distance between obstacles is the mean distance between each obstacle and its nearest obstacle.

[0068] The present invention calculates the environmental interference characterization coefficient for the UAV through environmental characteristics. In actual situations, environmental factors will affect the normal flight of the UAV. For example, strong airflow may cause abnormal shaking of the UAV fuselage, making it impossible to fly normally and smoothly, and may even deviate from the planned flight path. At the same time, the relevant influencing factors of obstacles observed by sensors will also affect whether the UAV can perform obstacle avoidance operations in a timely manner and the effect of performing obstacle avoidance operations. For example, if there are too many obstacles within a predetermined spatial range, or the distance between the UAV and the obstacles is too close, it may affect the UAV's correct judgment of obstacles, the timeliness of performing obstacle avoidance operations, and the normal execution of obstacle avoidance operations. Therefore, the present application calculates the environmental interference characterization coefficient for the UAV by obtaining the number of obstacles within a predetermined spatial range and the distance between the UAV and the obstacles in combination with the airflow intensity, so as to characterize the degree of interference of environmental factors on the stable and safe flight of the UAV, provide data support for the subsequent analysis of the chaotic tendency category of environmental interference, and then adaptively operate and maintain the stability of the UAV.

[0069] Specifically, the environmental analysis module is used to analyze the chaotic tendency category of environmental interference, including:

[0070] If the environmental interference characterization coefficient is greater than or equal to a preset environmental interference characterization coefficient threshold, the environmental interference is determined to be a high chaos tendency category;

[0071] If the environmental interference characterization coefficient is less than a preset environmental interference characterization coefficient threshold, the environmental interference is determined to be a low chaos tendency category.

[0072] The environmental interference characterization coefficient H0 is selected in the interval [1.55,1.67].

[0073] Specifically, the stability analysis module is used to operate and maintain the stability of the drone based on the analysis results of the environment analysis module, including:

[0074] If the environmental interference is of the high chaos tendency category, it is determined whether to enter the verification time based on the distance between the obstacle and the drone to perform obstacle avoidance verification, including controlling the drone to perform a preset obstacle avoidance action in advance, obtaining the reaction characteristics of the drone performing the obstacle avoidance operation, and analyzing the obstacle avoidance reaction characterization parameters of the drone to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard;

[0075] If the environmental interference is of a low chaos tendency category, the gain and sampling rate of the sensor for obstacle avoidance in the drone are adjusted based on the environmental interference characterization coefficient for the drone.

[0076] Specifically, the stability analysis module is used to determine whether to enter the verification time based on the distance between the obstacle and the drone, including:

[0077] If the distance between the obstacle and the UAV is less than or equal to the distance threshold, determining the time to enter the verification;

[0078] If the distance between the obstacle and the drone is greater than the distance threshold, it is determined that the verification time has not been reached.

[0079] Specifically, the distance threshold may be set according to a predetermined spatial range, and may be set between 1.5 times and 2 times the predetermined spatial range.

[0080] Specifically, the stable analysis module is used to control the drone to perform preset obstacle avoidance actions in advance, including:

[0081] Turn at a predetermined radius, roll the fuselage, and raise the nose at a predetermined attitude.

[0082] It is understandable that the preset obstacle avoidance actions can be set by those skilled in the art, with the purpose of verifying the effectiveness of the obstacle avoidance actions performed by the drone under environmental interference. For example, common obstacle avoidance actions that can be set include turning with a predetermined radius, flipping the fuselage, raising the nose in a predetermined posture, etc., which will not be repeated here.

[0083] The predetermined radius is related to the size of the UAV and can be set by those skilled in the art;

[0084] The predetermined posture can be reflected by different pitch angles, which can be set by those skilled in the art, and will not be described in detail here.

[0085] Specifically, the stability analysis module is used to analyze the obstacle avoidance reaction characterization parameters of the drone, including:

[0086] The ratio of the reaction time of the drone to perform the obstacle avoidance action to the reaction time threshold is used as the first obstacle avoidance reaction feature;

[0087] The ratio of the time required for the drone to stabilize after completing the obstacle avoidance operation to the required time threshold is used as the second obstacle avoidance reaction feature;

[0088] The sum of the first obstacle avoidance reaction characteristic and the second obstacle avoidance reaction characteristic is determined as an obstacle avoidance reaction characterization parameter.

[0089] The reaction time threshold for the drone to perform obstacle avoidance actions and the time threshold required for the drone to stabilize its fuselage after completing the obstacle avoidance operation are obtained in advance, and relevant data of several times when the drone encounters obstacles and completes flight missions during flight are obtained. The reaction time data for the drone to perform obstacle avoidance actions and the time required for the drone to stabilize its fuselage after completing the obstacle avoidance operation are called, and the average reaction time for the drone to perform obstacle avoidance actions and the average time required for the drone to stabilize its fuselage after completing the obstacle avoidance operation are solved. The reaction time threshold for the drone to perform obstacle avoidance actions is set to between 1.03 and 1.11 times the average reaction time for the drone to perform obstacle avoidance actions, and the time threshold required for the drone to stabilize its fuselage after completing the obstacle avoidance operation is set to between 1.09 and 1.21 times the average time required for the drone to stabilize its fuselage after completing the obstacle avoidance operation.

[0090] Specifically, the stability analysis module is used to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard, including:

[0091] If the obstacle avoidance reaction characterization parameter is less than the obstacle avoidance reaction characterization parameter threshold, it is determined that the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard;

[0092] If the obstacle avoidance reaction characterization parameter is greater than or equal to the obstacle avoidance reaction characterization parameter threshold, it is determined that the obstacle avoidance operation performed by the drone does not meet the timely obstacle avoidance standard.

[0093] The obstacle avoidance response characterization parameters are selected in the interval [1.64,1.78].

[0094] In the case where the environmental interference is of a high chaos tendency category, the present invention determines the verification time according to the distance between the drone and the obstacle, and controls the drone to perform a preset obstacle avoidance action in advance on the premise that the drone and the obstacle are at a safe distance, including adjusting the attitude of the drone and smoothly performing a turning action to change the flight direction of the drone. At the same time, the flight speed of the drone is controlled to ensure the stability and safety of the drone in performing the preset obstacle avoidance action. Through the effect of the drone performing the preset obstacle avoidance action, the current performance state of the drone is observed in advance before reaching the obstacle to see whether the obstacle avoidance operation can be completed, so as to avoid the situation where the drone is not able to avoid the obstacle in time. Therefore, the present application calculates the obstacle avoidance reaction characterization parameter through the reaction characteristics of the drone when performing the preset obstacle avoidance action to characterize the execution effect of the drone, and then determines whether the execution of the drone meets the timely obstacle avoidance standard. Since the preset obstacle avoidance action performed by the drone in advance and the obstacle for which the subsequent obstacle avoidance operation actually needs to be performed are the same target, the preset obstacle avoidance action for the same target obstacle is promptly corrected to ensure that the drone can stably and safely complete the actual obstacle avoidance operation.

[0095] Specifically, the stable analysis module is used to adjust the gain and sampling rate of the sensor used for obstacle avoidance in the drone, including:

[0096] Increasing the gain, wherein the amount of gain increase is positively correlated with the environmental interference characterization coefficient;

[0097] The sampling rate is increased, and the amount of increase in the sampling rate is positively correlated with the environmental interference characterization coefficient.

[0098] In this embodiment, optionally,

[0099] The environmental interference characterization coefficient is compared with the first environmental interference characterization coefficient comparison threshold and the second environmental interference characterization coefficient comparison threshold,

[0100] When the environmental interference characterization coefficient is greater than or equal to the second environmental interference characterization coefficient comparison threshold, the gain increase amount is determined to be the first gain increase amount, and the first gain increase amount is set to be 0.48 times the reference gain;

[0101] When the environmental interference characterization coefficient is greater than the first environmental interference characterization coefficient comparison threshold and less than the second environmental interference characterization coefficient comparison threshold, the gain increase amount is determined to be the second gain increase amount, and the second gain increase amount is set to be 0.36 times the reference gain;

[0102] When the environmental interference characterization coefficient is less than or equal to the first environmental interference characterization coefficient comparison threshold, the gain increase amount is determined to be a third gain increase amount, and the third gain increase amount is set to be 0.27 times the reference gain;

[0103] The first environmental interference characterization coefficient comparison threshold is 1.2 times the environmental interference characterization coefficient threshold, and the second environmental interference characterization coefficient comparison threshold is 1.4 times the environmental interference characterization coefficient threshold.

[0104] It is understandable that the determination of the baseline gain can be based on the default gain setting of the drone system.

[0105] There is no limitation on the method of increasing the gain. The gain of the sensor can be increased through the flight control system to improve the sensor's response sensitivity to obstacles, which will not be elaborated here.

[0106] The environmental interference characterization coefficient is compared with the first environmental interference characterization coefficient comparison threshold and the second environmental interference characterization coefficient comparison threshold,

[0107] When the environmental interference characterization coefficient is greater than or equal to the second environmental interference characterization coefficient comparison threshold, the sampling rate increase amount is determined to be the first sampling rate increase amount, and the first sampling rate increase amount is set to be 0.57 times of b0 of the initial sampling rate;

[0108] When the environmental interference characterization coefficient is greater than the first environmental interference characterization coefficient comparison threshold and less than the second environmental interference characterization coefficient comparison threshold, the sampling rate increase amount is determined to be the second sampling rate increase amount, and the second sampling rate increase amount is set to be 0.45 times the initial sampling rate;

[0109] When the environmental interference characterization coefficient is less than or equal to the first environmental interference characterization coefficient comparison threshold, the sampling rate increase amount is determined to be a third sampling rate increase amount, and the third sampling rate increase amount is set to be 0.34 times the initial sampling rate;

[0110] The first environmental interference characterization coefficient comparison threshold is 1.2 times the environmental interference characterization coefficient threshold, and the second environmental interference characterization coefficient comparison threshold is 1.4 times the environmental interference characterization coefficient threshold.

[0111] There is no limitation on the method of increasing the sampling rate of the sensor used for obstacle avoidance in the drone. The sampling frequency can be increased by the flight control system to quickly respond to obstacles, which will not be described in detail.

[0112] Specifically, the stability analysis module is also used to issue an early warning signal, including:

[0113] If the obstacle avoidance maneuver performed by the drone does not meet the timely obstacle avoidance criteria, a warning signal is issued.

[0114] Specifically, in some possible implementations, the warning signal can be sent to a remote user terminal for confirmation to confirm whether to stop the drone from continuing to fly, which will not be elaborated here.

[0115] When the environmental interference is of the low chaos tendency category, the present invention adjusts the accuracy coefficient of the sensor used for obstacle avoidance in the drone to ensure that the drone can correctly judge the obstacle when it is near an obstacle and smoothly perform the obstacle avoidance operation to ensure the effect of the obstacle avoidance operation. Furthermore, the present invention improves the stability and safety of the drone when performing obstacle avoidance actions while ensuring data reliability and high data accuracy.

[0116] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent operation and maintenance system for unmanned aerial vehicles based on the Internet of Things, characterized in that: include: A data storage module, used to store the reaction characteristics of the drone performing the obstacle avoidance action, wherein the reaction characteristics include the reaction time of the drone performing the obstacle avoidance action and the time required for the drone to stabilize after completing the obstacle avoidance operation; A data acquisition module is used to collect environmental characteristics of the drone within a predetermined space, wherein the environmental characteristics include the number of obstacles, the distance between the drone and the obstacles, and the airflow intensity; An environmental analysis module, connected to the data acquisition module, for acquiring the environmental features acquired by the data acquisition module, so as to calculate the environmental interference characterization coefficient for the UAV based on the environmental features, so as to analyze the chaotic tendency category of the environmental interference; A stability analysis module, which is connected to the data acquisition module, the data storage module and the environment analysis module respectively, and is used to operate and maintain the stability of the drone based on the analysis results of the environment analysis module, including: Determine whether to enter the verification time based on the distance between the obstacle and the drone to perform obstacle avoidance verification, including controlling the drone to perform a preset obstacle avoidance action in advance, obtaining the reaction characteristics of the drone performing the obstacle avoidance operation, and analyzing the obstacle avoidance reaction characterization parameters of the drone to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard; Or, based on the environmental interference characterization coefficient for the drone, a gain and a sampling rate of a sensor for obstacle avoidance in the drone are adjusted.

2. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The environmental analysis module is used to calculate the environmental interference characterization coefficient for the UAV based on the environmental characteristics, include, The ratio of the number of obstacles to the number threshold and the sum of the ratios of the mean distances between the UAV and each obstacle to the mean threshold are calculated and the corresponding weight coefficients are assigned as the first environmental interference feature; to calculate the ratio of the airflow intensity to the airflow intensity threshold and assign a corresponding weight coefficient as a second environmental interference feature; Used to determine the sum of the first environmental interference feature and the second environmental interference feature as the environmental interference characterization coefficient for the UAV.

3. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The environmental analysis module is used to analyze the chaotic tendency category of environmental interference, including: If the environmental interference characterization coefficient is greater than or equal to a preset environmental interference characterization coefficient threshold, the environmental interference is determined to be a high chaos tendency category; If the environmental interference characterization coefficient is less than a preset environmental interference characterization coefficient threshold, the environmental interference is determined to be a low chaos tendency category.

4. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The stability analysis module is used to operate and maintain the stability of the UAV based on the analysis result of the environment analysis module, including: If the environmental interference is of the high chaos tendency category, it is determined whether to enter the verification time based on the distance between the obstacle and the drone to perform obstacle avoidance verification, including controlling the drone to perform a preset obstacle avoidance action in advance, obtaining the reaction characteristics of the drone performing the obstacle avoidance operation, and analyzing the obstacle avoidance reaction characterization parameters of the drone to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard; If the environmental interference is of a low chaos tendency category, the gain and sampling rate of the sensor for obstacle avoidance in the drone are adjusted based on the environmental interference characterization coefficient for the drone.

5. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The stability analysis module is used to determine whether to enter the verification time based on the distance between the obstacle and the drone. include, If the distance between the obstacle and the drone is less than or equal to the distance threshold, the timing of entering the verification is determined.

6. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The stable analysis module is used to control the drone to perform preset obstacle avoidance actions in advance. include, Turn at a predetermined radius, roll the fuselage, and raise the nose at a predetermined attitude.

7. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The stable analysis module is used to analyze the obstacle avoidance reaction characterization parameters of the UAV, including: The ratio of the reaction time of the drone to perform the obstacle avoidance action to the reaction time threshold is used as the first obstacle avoidance reaction feature; The ratio of the time required for the drone to stabilize after completing the obstacle avoidance operation to the required time threshold is used as the second obstacle avoidance reaction feature; The sum of the first obstacle avoidance reaction characteristic and the second obstacle avoidance reaction characteristic is determined as an obstacle avoidance reaction characterization parameter.

8. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The stability analysis module is used to determine whether the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard, including: If the obstacle avoidance reaction characterization parameter is less than the obstacle avoidance reaction characterization parameter threshold, it is determined that the obstacle avoidance operation performed by the drone meets the timely obstacle avoidance standard.

9. The UAV intelligent operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The stable analysis module is used to adjust the gain and sampling rate of the sensor used for obstacle avoidance in the drone, including: Increasing the gain, wherein the amount of gain increase is positively correlated with the environmental interference characterization coefficient; The sampling rate is increased, and the amount of increase in the sampling rate is positively correlated with the environmental interference characterization coefficient.

10. The intelligent operation and maintenance system of unmanned aerial vehicle based on the Internet of Things according to claim 1, characterized in that: The stable analysis module is also used to issue an early warning signal. include, If the obstacle avoidance maneuver performed by the drone does not meet the timely obstacle avoidance criteria, a warning signal is issued.

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