A method and system for fail-safe flight control of an unmanned aerial vehicle sensor

Abstract

The present invention relates to the field of unmanned aerial vehicle (UAV) control. More specifically, the present invention relates to a method and system for safe flight control of UAVs in case of sensor failures. The method includes: obtaining the relative displacement changes of the wings and the three-axis data of the sensors during the flight of the UAV, wherein the relative displacement changes include: vertical height changes and inclination changes; calculating the mutation index of the UAV according to the vertical height changes of the wings, and determining whether there is an abnormality in the UAV fuselage, thereby constructing the fluctuation interval of the UAV; using historical flight data, determining the abnormal interval by calculating the similarity and applying the Otsu threshold method. At the same time, comparing the synchronization coefficients of the current and historical three-axis data of the sensors to identify sensor failures. When the optimal synchronization coefficient is lower than the preset threshold, trigger the PID control to return to the base, ensuring flight safety. The present invention improves the detection ability of UAV sensor failures by calculating the sensor synchronization coefficient, thereby ensuring the safe flight of UAVs.

Description

Technical Field

[0001] The present invention relates to the field of UAV control. More specifically, the present invention relates to a method and system for fail-safe flight control of UAV sensors. Background Art

[0002] A UAV, i.e., an unmanned aerial vehicle (UAV), is an aircraft that does not require a human pilot to board and operate. It flies autonomously through remote control or a preset program. With the progress of technology, especially breakthroughs in the fields of avionics, communication, sensors, and artificial intelligence, UAVs have become important tools in many fields such as military reconnaissance, surveillance, target strikes, and civilian fields like agricultural monitoring, logistics distribution, environmental research, film production, emergency rescue, and infrastructure inspection. As a result, more attention is paid to the flight safety of UAVs; UAV sensors are key components in the UAV system, used to collect and process UAV flight status and environmental information to ensure the safe flight of UAVs.

[0003] The existing Chinese patent application document with the publication number CN115167508A discloses a multi-rotor UAV sensor fail-safe flight control system and method, including: a sensor module for detecting the first flight status information of the UAV in the current state; a fault monitoring module for monitoring the operating status information of each sensor in the sensor module and determining whether any of the sensors is abnormal; and a control module for, after the sensor module is abnormal, acquiring the visual data of the UAV and performing flight control on the UAV based on the first flight status information and the visual data.

[0004] Through the first flight status information and visual data, this application document can still determine the state of the UAV in the event of a sensor failure, provide accurate flight control, and ensure flight safety; currently, during the flight of UAVs, sensors play a crucial role. However, sensor failures may cause UAVs to be unable to obtain accurate flight data, thereby affecting flight safety. If the real-time data feedback system of the UAV fails to promptly identify and report sensor failures, the control terminal may not be able to issue an effective warning. If the UAV continues to fly, there is a risk of accidents. Summary of the Invention

[0005] To solve the problem that the real-time data feedback system of the UAV fails to promptly identify and report sensor failures, the control terminal may not be able to issue an effective warning, and if the UAV continues to fly, there is a risk of accidents, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a method for fail-safe flight control of a drone sensor includes: obtaining the relative displacement change of the wings and the three-axis data of the sensor during the flight of the drone, wherein the relative displacement change includes: vertical height change and inclination change; calculating the mutation index of the drone according to the vertical height change of the wings from the take-off time of the drone to the current moment, determining whether the fuselage of the drone is abnormal, and constructing the fluctuation interval of the drone; traversing the historical flight data of the same model as the current drone, taking the data interval of the same time period in the intercepted historical flight data as the interval to be matched, calculating the similarity between the fluctuation interval and the interval to be matched, obtaining a plurality of height difference similarities, forming a set in descending order of the height difference similarities, using the Otsu threshold method to perform binary classification on the set, obtaining two groups, and taking the maximum mean value of the groups as the pending abnormal interval; calculating the three-axis data synchronization coefficient between the sensor of the current drone and the corresponding sensor of the historical data drone, taking the maximum value of the sensor synchronization coefficients corresponding to all the pending abnormal intervals as the optimal synchronization coefficient, and in response to the optimal synchronization coefficient being less than the preset synchronization threshold, the sensor of the current drone fails, and using PID to control the drone to return.

[0007] The effect is that by monitoring the relative displacement change of the wings and the three-axis data of the sensor of the drone in real time, calculating the mutation index and constructing the fluctuation interval, it is beneficial to timely discover the abnormal situation of the fuselage of the drone; by comparing with the historical flight data of the same model of the drone, calculating the similarity between the fluctuation interval and the interval to be matched, and using the Otsu threshold method for binary classification, the abnormal interval can be effectively identified, enhancing the monitoring of the drone state, improving the accuracy of fault detection, significantly improving the safety and reliability of the drone during flight, and reducing the accident risk caused by sensor failure.

[0008] Preferably, the mutation index includes:

[0009] Obtaining the vertical height difference between any two wings of the drone, taking the maximum vertical height difference as the height difference of the drone, and obtaining the sequence of height differences of the drone from the take-off moment to the current moment.

[0010] Taking the ratio of the height difference of the drone between the current moment and the previous moment in the sequence of height differences as the height change factor of the drone at the current moment, calculating the complement of the ratio between the height change factor and the mean value of the ratio of the height differences of the drone from the take-off moment to the previous moment at the current moment, and obtaining the mutation index of the height difference of the drone at the current moment.

[0011] Its effect is as follows: By comparing the vertical height differences between any two wings of the drone, the vertical changes of the drone's fuselage can be accurately captured, which helps to identify abnormal postures that may occur during the flight of the drone. By the ratio of the height difference of the fuselage between the current moment and the previous moment, the amplitude of the state change of the drone is obtained, and then the mutation index of the drone state is reflected. According to the mutation index, the flight route is adjusted or the return procedure is executed to ensure the safety of the drone.

[0012] Preferably, the mutation index further includes:

[0013] Taking the absolute value of the difference between the ratio of the height difference of the fuselage of the drone between the current moment and the previous moment as the change amplitude of the height difference of the fuselage at the current moment;

[0014] Taking the average value of the absolute value of the ratio of the height difference of the fuselage of the drone from the take-off moment to the previous moment of the current moment as the average amplitude of the drone;

[0015] Taking the ratio between the change amplitude of the height difference of the fuselage at the current moment and the average amplitude of the drone as the mutation index of the drone at the current moment.

[0016] Preferably, the construction of the fluctuation interval of the drone includes:

[0017] When the mutation index is greater than the mutation threshold, it means that the fuselage of the drone appears abnormal. The flight data of the drone from the take-off moment to the current moment is used to construct a fluctuation interval.

[0018] Its effect is as follows: By constructing a fluctuation interval, it is analyzed whether the flight state of the drone deviates from the normal range, and the abnormal situation is further analyzed and confirmed to improve the flight safety of the drone.

[0019] Preferably, the height difference similarity includes:

[0020] Calculating the Pearson correlation coefficient between the to-be-determined fluctuation interval and the to-be-matched interval to obtain the height difference similarity.

[0021] Preferably, the height difference similarity further includes:

[0022] Calculating the Pearson correlation coefficient between the to-be-determined fluctuation interval and the to-be-matched interval, and calculating the dynamic time warping distance between the to-be-determined fluctuation interval and the to-be-matched interval;

[0023] Taking the ratio between the Pearson correlation coefficient and the dynamic time warping distance as the height difference similarity.

[0024] The effect is that by calculating the ratio between the Pearson correlation coefficient and the DTW distance as the height difference similarity, abnormal patterns in the UAV flight data can be identified more accurately. This method can capture the subtle differences between data and improve the accuracy of anomaly detection. Among them, the Pearson correlation coefficient is sensitive to outliers, while the DTW distance can better handle local deformations of time series, enhancing the robustness of anomaly detection.

[0025] Preferably, the synchronization coefficient satisfies the following polynomial:

[0026] ;

[0027] ;

[0028] In the formula, represents the synchronization coefficient of the sensor, represents the three-axis vector of the sensor, represents the rotation speed of the sensor around the axis, represents the rotation speed of the sensor around the axis, represents the rotation speed of the sensor around the axis, represents the three-axis vector of the sensor at the th moment during the th flight of the UAV, represents the three-axis vector of the sensor at the th moment during the th flight of the UAV, represents the time interval, represents the starting point of any undetermined abnormal interval, represents the cosine similarity.

[0029] The effect is that by monitoring the synchronization coefficient in real time, the control parameters can be dynamically adjusted to adapt to the changes in sensor data, thereby improving the flight stability and safety. A high synchronization coefficient indicates a high degree of consistency in sensor data at different time points or between different UAVs. If the synchronization coefficient is significantly lower than the normal level, this may indicate a sensor failure or data anomaly.

[0030] In a second aspect, a UAV sensor fault-safe flight control system includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned UAV sensor fault-safe flight control method is implemented.

[0031] The present invention has the following effects:

[0032] 1. The present invention effectively identifies the abnormal fluctuation intervals during the flight of an unmanned aerial vehicle (UAV) by comparing the similarity between historical flight data and current flight data and using the Otsu threshold method for binary classification. By calculating the sensor synchronization coefficient and further analyzing the consistency between current sensor data and historical data, the detection ability for UAV sensor failures is further enhanced.

[0033] 2. The present invention timely discovers the abnormal states of UAV sensors by real-time monitoring the relative displacement changes of the UAV's wings and the three-axis data of the sensors. By calculating the mutation index and the fluctuation interval, the system can identify the abnormal behaviors of the UAV fuselage and respond in a timely manner, improving the flight safety of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0035] Figure 1 is a flowchart of the method of steps S1 - S4 in a method for safe flight control of a UAV sensor failure according to an embodiment of the present invention.

[0036] Figure 2 is a block diagram of the structure of a UAV sensor failure safe flight control system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0038] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0039] Specific implementation scenario: For the flight state of the UAV, the UAV

[0040] Referring to Figure 1 , a method for safe flight control of a UAV sensor failure includes steps S1 - S4, specifically as follows:

[0041] S1: Obtain the relative displacement changes of the wings of the UAV during flight and the three-axis data of the sensors, wherein the relative displacement changes include: vertical height changes and inclination changes.

[0042] Exemplarily, an infrared position detector is installed at the lower part of each wing of the drone, and the infrared position detector can obtain the relative position height between the wings.

[0043] In this embodiment, the sensor is for a gyroscope to monitor the angular velocity of the drone around each axis, maintain the stability of the drone and prevent shaking, and avoid the inability to fly safely and cause accidents after the gyroscope fails.

[0044] It should be noted that in the initial stage of the drone takeoff, if the environmental wind force is small, the relative position of the vertical heights of the four rotor shafts should remain stable with a limited fluctuation range. However, the actual situation may be affected by various factors, such as wind speed influence and propeller power change, etc. To ensure the safe flight of the drone, a reasonable limit needs to be set for the fluctuation range of the relative position of the vertical heights of the four rotor shafts. When the relative height of the drone wings changes significantly, it indicates that the flight state of the drone is unstable. Therefore, further analyze the change in the vertical height of the drone to obtain the mutation index of the drone. The specific steps are as follows:

[0045] S2: Calculate the mutation index of the drone according to the change in the vertical height of the wings from the takeoff time of the drone to the current time, judge whether there is an abnormality in the drone fuselage, and construct the fluctuation interval of the drone.

[0046] The steps to obtain the mutation index include:

[0047] Obtain the vertical height difference between any two wings of the drone, take the largest vertical height difference as the height difference of the drone body, and obtain the sequence of the height differences of the drone body from the takeoff time of the drone to the current time.

[0048] Take the ratio of the height difference of the drone body between the current time and the previous time in the sequence of height differences of the drone body as the height change factor of the drone at the current time, and calculate the complement of the ratio between the height change factor and the mean value of the ratio of the height difference of the drone body from the takeoff time to the previous time at the current time to obtain the mutation index of the height difference of the drone body at the current time.

[0049] Specifically, the mutation index satisfies the following polynomial:

[0050] ;

[0051] ;

[0052] In the formula, represents the mutation index of the drone at the th moment, represents the ratio of the height difference of the drone body at the th flight at the th moment, represents the The height difference of the UAV at the moment during the th flight, the height difference of the UAV at the moment during the th flight, the ratio of the height difference of the UAV at the moment during the th flight, the ratio of the height difference of the UAV at the moment during the th flight, representing the mean function.

[0053] That is to say, represents the change speed of the height difference of the UAV at the moment. When the change speed at the moment is greater than the previous average speed, the mutation index is greater than 1, indicating that the fuselage of the UAV has mutated at this time, that is, it has suddenly tilted.

[0054] In addition, in another embodiment, it further includes:

[0055] Taking the absolute value of the difference between the ratio of the height difference of the UAV at the current moment and the previous moment as the change amplitude of the height difference of the UAV at the current moment;

[0056] Taking the mean value of the absolute values of the ratio of the height difference of the UAV from the take-off moment to the previous moment of the current moment as the average amplitude of the UAV;

[0057] Taking the ratio between the change amplitude of the height difference of the UAV at the current moment and the average amplitude of the UAV as the mutation index of the UAV at the current moment.

[0058] Specifically, the mutation index satisfies the following relational expression:

[0059] ;

[0060] In the formula, represents the mutation index of the UAV at the moment, represents the ratio of the height difference of the UAV at the moment during the th flight, represents the ratio of the height difference of the UAV at the moment during the th flight, represents the ratio of the height difference of the UAV at the moment during the th flight, represents the ratio of the height difference of the UAV at the The ratio of the height difference of the UAV at the time during the flight, the ratio of the height difference of the UAV at the time during the

[0061] That is to say, It represents the ratio of the height difference of the UAV at the time during the flight, which is calculated by comparing the vertical height of the UAV at the current time with the vertical height at a previous time, and is used to measure the rate of change of the UAV's height. It reflects the severity of the height change of the UAV between two adjacent times. When the mutation index

[0062] is greater than 1, there are fluctuations in the wings of the UAV during flight, resulting in tilting. The tilting may be due to sensor failure or external influence, making the UAV unable to fly normally.

[0063] Furthermore, to further determine whether the current tilt is normal or abnormal, the system will search for data segments in the historical database that are similar to the current height difference change of the UAV. This is achieved by calculating the similarity between the height difference sequence of the current flight and the height difference sequence of the historical flight.

[0064] S3: Traverse the historical flight data of the same model as the current UAV, take the data interval of the same time period in the intercepted historical flight data as the interval to be matched, calculate the similarity between the fluctuation interval and the interval to be matched to obtain multiple height difference similarities, form a set of the height difference similarities in descending order, use the Otsu threshold method to perform binary classification on the set to obtain two groups, and take the maximum mean value of the groups as the pending abnormal interval.

[0065] The steps to obtain the height difference similarity include:

[0066] Calculate the Pearson correlation coefficient between the pending fluctuation interval and the interval to be matched to obtain the height difference similarity.

[0067] Specifically, the height difference similarity satisfies the following relational expression:

[0068] ;

[0069] In the formula, It represents the time from ​Height difference similarity of the UAV at a certain moment Indicates the UAV at the th flight, from the moment to the moment, the height difference of the UAV body Indicates the UAV at the th flight, from the moment to the moment, the height difference of the UAV body Indicates the Pearson correlation coefficient Indicates the time interval

[0070] That is to say The closer the

[0071] value is to 1, the stronger the linear relationship between the two sequences, that is, the change in the height difference of the UAV body during the current flight is similar to the change in the height difference of the UAV body during the historical flight

[0072] In addition, in another embodiment, it further includes:

[0073] Calculate the Pearson correlation coefficient between the undetermined fluctuation interval and the interval to be matched, and calculate the dynamic time warping distance between the undetermined fluctuation interval and the interval to be matched

[0074] Specifically, the height difference similarity satisfies the following relational expression

[0075] ;

[0076] In the formula Indicates the height difference similarity of the UAV from the moment to the moment Indicates the UAV at the th flight, from the moment to the moment, the height difference of the UAV body Indicates the UAV at the th flight, from the moment to the moment, the height difference of the UAV body Indicates the value of the dynamic time warping algorithm Indicates the Pearson correlation coefficient Indicates the time interval

[0077] That is to say, the greater the height difference similarity of the UAV, the greater the height difference of the UAV body from the moment to the moment and the height difference of the UAV body from the th flight, from the From the moment to the more similar the change in the height difference of the fuselage is from the moment to the

[0078] Further analysis shows that calculating the synchronization coefficient between the sensors of the UAV in the to-be-determined abnormal interval and the sensors of the UAV in the historical data is to analyze the similarity between the sensors of the current flight and the sensors of the historical flight data, and then significant deviations can be identified, which may be caused by sensor failures. The specific steps are as follows:

[0079] S4: Calculate the three-axis data synchronization coefficient between the sensors of the current UAV and the corresponding sensors of the UAV in the historical data. Take the maximum value of the synchronization coefficients of the corresponding sensors in all to-be-determined abnormal intervals as the optimal synchronization coefficient. When the optimal synchronization coefficient is less than the preset synchronization threshold, the sensors of the current UAV malfunction, and use PID to control the UAV to return.

[0080] Specifically, the synchronization coefficient satisfies the following polynomial:

[0081] ;

[0082] ;

[0083] In the formula, represents the synchronization coefficient of the sensor, represents the three-axis vector of the sensor, represents the rotation speed of the sensor around the axis, represents the rotation speed of the sensor around the axis, represents the rotation speed of the sensor around the axis, represents the three-axis vector of the sensor at the th moment during the th flight of the UAV, represents the three-axis vector of the sensor at the th moment during the th flight of the UAV, represents the time interval, represents the starting point of any to-be-determined abnormal interval, represents the cosine similarity.

[0084] That is to say, the degree of similarity is reflected by calculating the cosine similarity, and the value range of the cosine similarity is between . When the synchronization coefficient of the sensor approaches , it means that the current data is more similar to the historical data in terms of direction. On the contrary, when it approaches , it means that they are completely dissimilar.

[0085] In addition, in another embodiment, it further includes:

[0086] The synchronization coefficient satisfies the following polynomial:

[0087] ;

[0088] ;

[0089] In the formula, represents the synchronization coefficient of the sensor, represents the Euclidean distance between the three-axis vectors of the sensors at corresponding moments during the th flight and the th flight of the UAV, represents the rotational speed of the axis of the sensor at the th moment, represents the rotational speed of the axis of the sensor at the th moment, represents the rotational speed of the axis of the sensor at the th moment, represents the rotational speed of the axis of the sensor at the th moment, represents the rotational speed of the axis of the sensor at the th moment, represents the rotational speed of the axis of the sensor at the th moment, represents the time interval.

[0090] The present invention also provides a UAV sensor fault-safe flight control system. As Figure 2 shown, the system includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a UAV sensor fault-safe flight control method according to the first aspect of the present invention is implemented.

[0091] The system further includes a communication bus, a communication interface, and other components well-known to those skilled in the art. Their settings and functions are known in the art, so they will not be elaborated here.

[0092] In the present invention, the aforementioned memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random-access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0093] In the description of this specification, "a plurality of" and "several" mean at least two, such as two, three, or more, etc., unless otherwise specifically defined.

[0094] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

Claims

1. A method for controlling unmanned aerial vehicle sensor failure-safe flight, characterized in that: include: Acquire the relative displacement change of the wing and the three-axis data of the sensor during the flight of the UAV, wherein the relative displacement change includes: vertical height change and inclination change; According to the vertical height change of the drone's wing from takeoff to the current moment, the mutation index of the drone is calculated, and it is determined whether the drone fuselage is abnormal, and the fluctuation range of the drone is constructed; Obtain the vertical height difference between any two wings of the drone, and take the maximum vertical height difference as the height difference of the drone's fuselage; traverse the historical flight data of the same model as the current drone, and take the data interval of the same period in the historical flight data as the interval to be matched, calculate the similarity between the fluctuation interval and the interval to be matched, and obtain multiple height difference similarities, and form a set of height difference similarities in descending order, and use the Otsu threshold method to classify the set into two groups, and take the maximum mean of the group as the abnormal interval to be determined; Calculate the three-axis data synchronization coefficient between the sensor of the current drone and the corresponding sensor of the historical data drone, and take the maximum value of the synchronization coefficient of the sensors corresponding to all pending abnormal intervals as the optimal synchronization coefficient. When the optimal synchronization coefficient is less than the preset synchronization threshold, the sensor of the current drone fails, and the drone is controlled to return using PID.

2. The method for controlling a drone sensor failure-safe flight according to claim 1, characterized in that: The mutation index includes: Obtain the vertical height difference between any two wings of the drone, take the maximum vertical height difference as the height difference of the drone's fuselage, and obtain the height difference sequence of the drone's fuselage from the take-off moment to the current moment; The ratio of the fuselage height difference of the drone between the current moment and the previous moment in the fuselage height difference sequence is used as the height change factor of the drone at the current moment, and the complement of the ratio between the height change factor and the average of the fuselage height difference ratios of the drone from the take-off moment to the moment before the current moment is calculated to obtain the mutation index of the fuselage height difference at the current moment.

3. The method for controlling a drone sensor failure-safe flight according to claim 1, characterized in that: The mutation index also includes: The absolute value of the difference between the height difference ratio of the drone at the current moment and the previous moment is taken as the change amplitude of the height difference of the drone at the current moment; The average of the absolute values ​​of the height difference ratios between the moment when the drone takes off and the moment before the current moment is taken as the average amplitude of the drone; The ratio between the change amplitude of the fuselage height difference at the current moment and the average amplitude of the UAV is taken as the mutation index of the UAV at the current moment.

4. The method for controlling a drone sensor failure-safe flight according to claim 1, characterized in that: The construction of the fluctuation range of the drone includes: In response to the mutation index being greater than the mutation threshold, the fuselage of the UAV is abnormal, and the flight data of the UAV from the take-off time to the current time is used to construct a fluctuation range.

5. The method for controlling a drone sensor failure-safe flight according to claim 1, characterized in that: The height difference similarity includes: The Pearson correlation coefficient between the fluctuation interval to be determined and the interval to be matched is calculated to obtain the height difference similarity.

6. The method for controlling a drone sensor failure-safe flight according to claim 1, characterized in that: The height difference similarity also includes: Calculate the Pearson correlation coefficient between the pending fluctuation interval and the interval to be matched, and calculate the dynamic time warping distance between the pending fluctuation interval and the interval to be matched; The ratio between the Pearson correlation coefficient and the dynamic time warping distance is taken as the height difference similarity.

7. The method for controlling a drone sensor failure-safe flight according to claim 1, characterized in that: The synchronization coefficient satisfies the following polynomial: ； ； In the formula, represents the synchronization coefficient of the sensor, represents the three-axis vector of the sensor, Indicates that the sensor is around The rotation speed of the shaft, Indicates that the sensor is around The rotation speed of the shaft, Indicates that the sensor is around The rotation speed of the shaft, Indicates that the drone is The first flight The three-axis vector of the sensor at the moment, Indicates that the drone is The first flight The three-axis vector of the sensor at the moment, Indicates the time interval, represents the starting point of any pending abnormal interval, Represents cosine similarity.

8. A UAV sensor failure safe flight control system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the unmanned aerial vehicle sensor failure safety flight control method according to any one of claims 1 to 7 is implemented.

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