A method and system for safely supervising the flight status of an unmanned aerial vehicle

Abstract

The present invention provides a method and system for safely supervising the flight status of a drone. By analyzing a flight instability signal, obstacle data of a subsequent flight path is obtained, an obstacle stability supervision value is output, and the value is compared with a threshold value to generate a signal of whether the obstacle is stable. When the drone is unstable in flight, the present invention monitors and analyzes the stability of obstacles in the subsequent flight path of the drone, and determines the safety level of a drone with low stability when passing through a subsequent area containing obstacles, that is, an obstacle instability signal indicates that the probability of affecting the drone flight is high, and an obstacle stability signal indicates that the probability of affecting the drone flight is low.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle trajectory planning, and in particular to a method and system for safely supervising the flight status of an unmanned aerial vehicle. Background Art

[0002] When a drone is flying, obstacles on the flight path will affect its flight status. When the distance between obstacles on the flight path cannot meet the normal flight requirements of the drone, it is necessary to adjust the original flight path of the drone so that the drone can maintain a safe and stable state during the flight.

[0003] A Chinese invention patent with publication number CN116700070B discloses a method and system for safely monitoring the flight status of a drone. The method performs flight status simulation prediction based on preset status test control instructions to generate a simulated flight status. The method provides a standard reference state and drives the drone based on the same status test control instructions to obtain a first flight state of the drone. The system also determines a first flight deviation feature based on the difference between the first flight state and the simulated flight state.

[0004] However, there is no real-time collection of UAV flight data to obtain the flight stability supervision value for evaluating the stability of the UAV during flight through the changes in flight width, so as to realize the supervision of abnormal situations in UAV flight and provide historical data for subsequent adjustments to UAVs with abnormal flight. In the case of unstable flight of the UAV, there is no monitoring and analysis of the stability of the UAV in the subsequent flight path of obstacles to judge the safety level of UAVs with low stability when passing through subsequent areas containing obstacles.

[0005] To this end, the present invention provides a method and system for safely supervising the flight status of an unmanned aerial vehicle. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] A method for safely supervising the flight status of an unmanned aerial vehicle comprises the following steps:

[0009] Step 1: Collect UAV flight data in real time, output flight stability supervision value, compare it with the threshold, and generate a flight stability signal;

[0010] If the flight stability supervision value is greater than the flight stability supervision threshold, a UAV flight instability signal is generated;

[0011] Step 2: When a flight instability signal is obtained, the obstacle data of the subsequent flight path is obtained, the obstacle stability supervision value is output, and compared with the threshold to generate a signal whether the obstacle is stable;

[0012] If the obstacle stability supervision value is greater than the obstacle stability supervision threshold, an obstacle instability signal is generated;

[0013] Step 3: Based on whether the obstacle stabilizes the signal, generate corresponding execution commands for the drone and complete the corresponding control work.

[0014] The present invention further illustrates: in step 1, within the monitoring period, the flight status of the UAV is monitored and collected in real time, and the obtained flight width status risk value and flight height status risk value are summed to obtain a flight stability supervision value;

[0015] How to obtain the flight width status risk value:

[0016] Obtain a width time risk value and a width state risk value, add the width time risk value and the width state risk value together to obtain a flight width state risk value;

[0017] How to obtain the flight altitude status risk value:

[0018] Obtain the altitude time risk value and the altitude state risk value, add the altitude time risk value and the altitude state risk value together to obtain the flight altitude state risk value.

[0019] The present invention further illustrates that in step 1, the process of obtaining the width time risk value is as follows:

[0020] The monitoring period is divided into several time nodes, the flight width of each time node is obtained, marked as the actual flight width, the actual flight width is calculated to be different from the preset flight width, the absolute value is taken, and the instantaneous width deviation value is obtained;

[0021] The instantaneous width deviation value is compared with the instantaneous width deviation threshold value. If the instantaneous width deviation value is greater than or equal to the instantaneous width deviation threshold value, a width deviation abnormal signal is generated, and the time node corresponding to the generated width deviation abnormal signal is marked as a width deviation abnormal time node;

[0022] Obtain the number of width deviation abnormal time nodes, calculate the ratio of the width deviation abnormal time nodes to the total number of time nodes in the monitoring period, and obtain the width time risk value;

[0023] The process of obtaining the width state risk value is as follows:

[0024] When an abnormal width deviation signal occurs, the instantaneous width deviation value under the abnormal width deviation signal is obtained and marked as the abnormal instantaneous width deviation value. The average of all abnormal instantaneous width deviation values ​​in the monitoring period is calculated to obtain the instantaneous width deviation mean. The ratio of the instantaneous width deviation mean to the instantaneous width deviation threshold is calculated to obtain the width status risk value.

[0025] The present invention further illustrates: in step 1, the flight speed of the UAV in the monitoring period is monitored and collected in real time, the monitoring period is divided into a number of time nodes, the flight altitude at each time node is obtained, marked as the actual flight altitude, the actual flight altitude is calculated to be different from the preset flight altitude, the absolute value is taken, and the instantaneous altitude deviation value is obtained;

[0026] The process of obtaining the high time risk value is as follows:

[0027] The instantaneous height deviation value is compared with the instantaneous height deviation threshold. The comparison process is as follows:

[0028] If the instantaneous height deviation value is greater than or equal to the instantaneous height deviation threshold, a height deviation abnormal signal is generated, and the time node corresponding to the generation of the height deviation abnormal signal is marked as the height deviation abnormal time node;

[0029] Obtain the number of abnormal time nodes of height deviation, calculate the ratio of the abnormal time node of height deviation to the total number of time nodes in the monitoring period, and obtain the height time risk value;

[0030] The process of obtaining the height state risk value is as follows:

[0031] When an abnormal height deviation signal occurs, the instantaneous height deviation value under the abnormal height deviation signal is obtained and marked as the abnormal instantaneous height deviation value. The average of all abnormal instantaneous height deviation values ​​within the monitoring period is calculated to obtain the instantaneous height deviation average. The ratio of the instantaneous height deviation average to the instantaneous height deviation threshold is calculated to obtain the height status risk value.

[0032] The present invention further illustrates that in step 2, the barrier stability supervision value is obtained in the following manner:

[0033] The barrier stability supervision value is obtained by calculating the ratio of the regional risk value to the regional change value.

[0034] The present invention further illustrates that in step 2, the process of obtaining the regional risk value is as follows:

[0035] Obtain the real-time obstacle distance value of the obstacle area within the monitoring period, and compare the real-time obstacle distance value with the obstacle distance threshold; if the obstacle distance value is less than the obstacle distance threshold, generate an obstacle area distance danger signal; mark the time node corresponding to the generation of the obstacle area distance danger signal as the obstacle area abnormal time node;

[0036] Obtain the abnormal time node of the fault area, calculate the ratio of the abnormal time node of the fault area to the total number of time nodes in the monitoring period, and obtain the regional risk value;

[0037] The process of obtaining the regional change value is as follows:

[0038] Obtain the time nodes of the danger signals of the two adjacent obstacle areas, and perform difference calculation to obtain the adjacent abnormal time difference; calculate the average of all adjacent abnormal time differences within the monitoring period to obtain the mean of the adjacent abnormal time difference, and then calculate the ratio of the mean of the adjacent abnormal time difference to the monitoring period to obtain the regional change value.

[0039] The present invention further illustrates that in step three, the process of regulation and judgment is:

[0040] Obtain the minimum time difference of adjacent width deviation anomaly time nodes and the minimum time difference of height deviation anomaly time nodes, mark them as the minimum time difference of width deviation anomaly and the minimum time difference of height deviation anomaly respectively, add them up, and get the minimum time difference of the main body anomaly.

[0041] The present invention further illustrates that: in step three, the adjacent abnormal time difference is compared with the minimum value of the main abnormal time difference;

[0042] If the adjacent abnormal time difference is greater than or equal to the minimum value of the main abnormal time difference, a speed control signal can be generated;

[0043] When the speed control signal is obtained, the path length of the UAV in the obstacle area is obtained, the minimum value of the wide deviation abnormal time difference is compared with the minimum value of the high deviation abnormal time difference, the smallest value is selected as the obstacle passing time, and the ratio of the path length to the obstacle passing time is calculated to obtain the obstacle passing speed.

[0044] The present invention further illustrates that: in step three, if the adjacent abnormal time difference is less than the minimum value of the main abnormal time difference, a height control signal can be generated;

[0045] When the altitude control signal is obtained, the highest point in the obstacle area can be obtained, and the horizontal plane of the highest point can be used as the subsequent flight path of the UAV.

[0046] A UAV flight status safety supervision system, the system includes the following modules:

[0047] Stability monitoring module: collects UAV flight data in real time, outputs flight stability monitoring value, compares it with the threshold, and generates a signal whether the flight is stable;

[0048] If the flight stability supervision value is greater than the flight stability supervision threshold, a UAV flight instability signal is generated;

[0049] Obstacle analysis module: When a flight instability signal is received, the obstacle data of the subsequent flight path is obtained, the obstacle stability supervision value is output, and compared with the threshold value to generate a signal whether the obstacle is stable;

[0050] If the obstacle stability supervision value is greater than the obstacle stability supervision threshold, an obstacle instability signal is generated;

[0051] Control execution module: Based on whether the obstacle is stable, it generates corresponding execution commands for the drone and completes the corresponding control work.

[0052] The beneficial effects of the present invention are as follows:

[0053] 1. The present invention collects UAV flight data in real time, outputs a flight stability supervision value, compares it with a threshold, and generates a flight stability signal; the present invention obtains a flight stability supervision value for evaluating the stability of the UAV during flight from the change in the flight width of the UAV during flight, thereby realizing the supervision of abnormal conditions in the flight of the UAV and providing historical data for the later adjustment of UAVs with abnormal flight;

[0054] 2. The present invention obtains obstacle data of the subsequent flight path by analyzing the flight instability signal, outputs the obstacle stability supervision value, compares it with the threshold, and generates a signal of whether the obstacle is stable; when the UAV flight is unstable, the present invention monitors and analyzes the stability of obstacles in the subsequent flight path of the UAV, and determines the safety level of the UAV with low stability when passing through the subsequent area containing obstacles, that is, the obstacle unstable signal indicates that the probability of affecting the UAV flight is high, and the obstacle stable signal indicates that the probability of affecting the UAV flight is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below in conjunction with the accompanying drawings.

[0056] Figure 1 It is a flow chart of the drone safety supervision method of the present invention;

[0057] Figure 2 It is a module diagram of the drone safety supervision system of the present invention. DETAILED DESCRIPTION

[0058] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods. Example 1

[0059] like Figure 1 As shown, a method for safely supervising the flight status of a drone according to an embodiment of the present invention comprises the following steps:

[0060] Step 1: Collect UAV flight data in real time, output flight stability supervision value, compare it with the threshold, and generate a flight stability signal;

[0061] Among them, the flight stability signal includes a flight unstable signal and a flight stable signal;

[0062] In some embodiments, during the monitoring period, the flight status of the drone is monitored and collected in real time, and the obtained flight width status risk value and flight height status risk value are summed to obtain a flight stability supervision value;

[0063] The flight stability control value is compared with the flight stability control threshold. The comparison process is as follows:

[0064] If the flight stability supervision value is greater than the flight stability supervision threshold, it means that the UAV has a large risk of deviating from the preset location in terms of time and width during flight, and a UAV flight instability signal is generated;

[0065] If the flight stability supervision value is less than or equal to the flight stability supervision threshold, it means that the UAV does not have a large risk of deviation from the preset location in terms of time and width during flight, and a UAV flight stability signal is generated;

[0066] How to obtain the flight width status risk value:

[0067] Obtain a width time risk value and a width state risk value, add the width time risk value and the width state risk value together to obtain a flight width state risk value;

[0068] First, specifically, the process of obtaining the width time risk value is:

[0069] The monitoring period is divided into several time nodes, the flight width of each time node is obtained, marked as the actual flight width, the actual flight width is calculated to be different from the preset flight width, the absolute value is taken, and the instantaneous width deviation value is obtained;

[0070] The instantaneous width deviation value is compared with the instantaneous width deviation threshold. The comparison process is as follows:

[0071] If the instantaneous width deviation value is greater than or equal to the instantaneous width deviation threshold, it means that the instantaneous width deviation of the drone at this time node is large, and a width deviation abnormal signal is generated. The time node corresponding to the width deviation abnormal signal is marked as the width deviation abnormal time node;

[0072] If the instantaneous width deviation value is less than the instantaneous width deviation threshold, it means that the instantaneous width deviation of the drone at this time node is small, and a normal width deviation signal is generated, and the time node corresponding to the normal width deviation signal is marked as a normal width deviation time node;

[0073] Obtain the number of width deviation abnormal time nodes, calculate the ratio of the width deviation abnormal time nodes to the total number of time nodes in the monitoring period, and obtain the width time risk value;

[0074] Second, specifically, the process of obtaining the width state risk value is:

[0075] When the width deviation is abnormal, the instantaneous width deviation value under the width deviation abnormal signal is obtained and marked as the abnormal instantaneous width deviation value. The average of all abnormal instantaneous width deviation values ​​in the monitoring period is calculated to obtain the instantaneous width deviation average. The ratio of the instantaneous width deviation average to the instantaneous width deviation threshold is calculated to obtain the width status risk value.

[0076] Add the width time risk value and the width state risk value to obtain the flight width state risk value;

[0077] Similarly, during the monitoring period, the flight speed of the drone is monitored and collected in real time, and the monitoring period is divided into several time nodes. The flight altitude at each time node is obtained and marked as the actual flight altitude. The actual flight altitude is calculated to be different from the preset flight altitude, and the absolute value is taken to obtain the instantaneous altitude deviation value.

[0078] First, specifically, the process of obtaining the height time risk value is:

[0079] The instantaneous height deviation value is compared with the instantaneous height deviation threshold. The comparison process is as follows:

[0080] If the instantaneous height deviation value is greater than or equal to the instantaneous height deviation threshold, it means that the instantaneous height deviation of the drone at this time node is large, and a height deviation abnormal signal is generated. The time node corresponding to the generation of the height deviation abnormal signal is marked as the height deviation abnormal time node;

[0081] If the instantaneous height deviation value is less than the instantaneous height deviation threshold, it means that the instantaneous height deviation of the drone at this time node is small, and a normal height deviation signal is generated. The time node corresponding to the normal height deviation signal is marked as a normal height deviation time node;

[0082] Obtain the number of abnormal time nodes of height deviation, calculate the ratio of the abnormal time node of height deviation to the total number of time nodes in the monitoring period, and obtain the height time risk value;

[0083] Second, specifically, the process of obtaining the height state risk value is:

[0084] When an abnormal height deviation signal is generated, the instantaneous height deviation value under the abnormal height deviation signal is obtained and marked as the abnormal instantaneous height deviation value. The average of all abnormal instantaneous height deviation values ​​within the monitoring period is calculated to obtain the instantaneous height deviation average value. The ratio of the instantaneous height deviation average value to the instantaneous height deviation threshold value is calculated to obtain the height state risk value.

[0085] The altitude time risk value and the altitude state risk value are added together to obtain the flight altitude state risk value;

[0086] The specific scheme of the embodiment of the present invention is: real-time collection of UAV flight data, output of flight stability supervision value, comparison with the threshold, and generation of a flight stability signal; the present invention obtains the flight stability supervision value for evaluating the stability of the UAV during flight through the change of the flight width of the UAV during flight, thereby realizing the supervision of abnormal conditions in the flight of the UAV and providing historical data for the later adjustment of UAVs with abnormal flights. Example 2

[0087] The method for safely supervising the flight status of a drone according to an embodiment of the present invention further includes the following steps:

[0088] Step 2: When a flight instability signal is obtained, the obstacle data of the subsequent flight path is obtained, the obstacle stability supervision value is output, and compared with the threshold to generate a signal whether the obstacle is stable;

[0089] Among them, the obstacle stability signal includes an obstacle stability signal and an obstacle instability signal;

[0090] In some embodiments, when a flight instability signal is obtained, an obstacle area of ​​the UAV under the remaining flight path is obtained, and an area risk value and an area change value of the obstacle area are obtained;

[0091] The ratio of the regional risk value to the regional change value is calculated to obtain the obstacle stability supervision value;

[0092] If the obstacle stability supervision value is greater than the obstacle stability supervision threshold, it means that the obstacle area in the subsequent flight path is small and the area range is prone to change, generating an obstacle instability signal;

[0093] If the obstacle stability supervision is less than or equal to the obstacle stability supervision threshold, it means that the obstacle area in the subsequent flight path is large and the area range is not easy to change, and an obstacle stability signal is generated;

[0094] First, specifically, the process of obtaining the regional risk value is as follows:

[0095] Obtain the real-time obstacle distance value of the obstacle area within the monitoring period, and compare the real-time obstacle distance value with the obstacle distance threshold (the obstacle distance threshold can be set to the maximum flight width of the drone within the monitoring period);

[0096] If the obstacle distance value is less than the obstacle distance threshold, it means that the space between obstacles is narrow, which may cause the UAV to collide with obstacles during flight, generating an obstacle distance danger signal; the time node corresponding to the generation of the obstacle distance danger signal is marked as the obstacle abnormal time node;

[0097] If the obstacle distance value is greater than or equal to the obstacle distance threshold, it means that the space between obstacles is not narrow, and the drone will not collide with obstacles during flight, and an obstacle distance safety signal is generated;

[0098] Obtain the abnormal time node of the fault area, calculate the ratio of the abnormal time node of the fault area to the total number of time nodes in the monitoring period, and obtain the regional risk value;

[0099] The process of obtaining the regional change value is as follows:

[0100] Obtain the time nodes of the danger signals of the two adjacent obstacle areas, and perform difference calculation to obtain the adjacent abnormal time difference; perform mean calculation on all adjacent abnormal time differences within the monitoring period to obtain the mean of the adjacent abnormal time difference, and then perform ratio calculation on the mean of the adjacent abnormal time difference and the monitoring period to obtain the regional change value;

[0101] It should be noted that the obstacle area is: taking the horizontal plane of the flight path as the reference plane, obtaining obstacles on both sides of the reference plane, and the reference plane area between the obstacles on both sides is recorded as the obstacle area;

[0102] The obstacle area includes non-fixed obstacles on both sides, or one of the two sides is a non-fixed obstacle; the non-fixed obstacle may be a tree, for example;

[0103] The real-time obstacle distance value of the obstacle area during the monitoring period can be measured by remote sensing technology;

[0104] The technical solution of the embodiment of the present invention is as follows: when a flight instability signal is obtained, obstacle data of a subsequent flight path is obtained, an obstacle stability supervision value is output, and the obstacle stability signal is generated after comparison with a threshold value. When the flight of a UAV is unstable, the present invention monitors and analyzes the stability of obstacles in the subsequent flight path of the UAV, and determines the safety level of a UAV with low stability when passing through a subsequent area containing obstacles, that is, an obstacle instability signal indicates that the probability of affecting the flight of the UAV is high, and an obstacle stability signal indicates that the probability of affecting the flight of the UAV is low. Example 3

[0105] A method for safely supervising the flight status of an unmanned aerial vehicle according to an embodiment of the present invention comprises the following steps:

[0106] Step 3: Based on whether the obstacle is stable, generate corresponding execution commands for the drone and complete the corresponding control work;

[0107] Among them, the corresponding execution commands include the continue flight command and the obstacle control command;

[0108] In some embodiments, when an obstacle stabilization signal is obtained, a continue flight command is generated so that the drone flies according to a preset flight path;

[0109] When an obstacle instability signal is received, a control judgment is made, an obstacle control command is generated, and the flight path of the drone is controlled;

[0110] Specifically, the process of regulation and judgment is:

[0111] Obtain the minimum time difference of adjacent width deviation anomaly time nodes and the minimum time difference of height deviation anomaly time nodes, mark them as minimum time difference of width deviation anomaly and minimum time difference of height deviation anomaly respectively, add them up, and obtain the minimum time difference of main body anomaly;

[0112] Compare the adjacent anomaly time difference with the minimum value of the main anomaly time difference;

[0113] If the adjacent abnormal time difference is greater than or equal to the minimum value of the main abnormal time difference, a speed control signal can be generated;

[0114] When the speed control signal is obtained, the path length of the UAV in the obstacle area is obtained, the minimum value of the wide deviation abnormal time difference is compared with the minimum value of the high deviation abnormal time difference, and the smallest value is selected as the obstacle passing time. The ratio of the path length and the obstacle passing time is calculated to obtain the obstacle passing speed;

[0115] If the time difference between adjacent anomalies is less than the minimum time difference between the main anomalies, a height control signal can be generated;

[0116] When the altitude control signal is obtained, the highest point in the obstacle area can be obtained, and the horizontal plane of the highest point is used as the subsequent flight path of the drone;

[0117] The technical solution of the embodiment of the present invention is as follows: based on whether the obstacle is a stable signal, a corresponding execution command is generated for the UAV, and the corresponding control work is completed; by integrating and analyzing the supervision status of the UAV flight body and the supervision status under the flight path, the flight status of the subsequent UAV is determined to ensure that the UAV can complete the flight mission more safely. Example 4

[0118] Based on the first and second embodiments, please refer to Figure 2 As shown, a drone flight status safety monitoring system according to an embodiment of the present invention includes the following modules:

[0119] Stability monitoring module: collects UAV flight data in real time, outputs flight stability monitoring value, compares it with the threshold, and generates a signal whether the flight is stable;

[0120] If the flight stability supervision value is greater than the flight stability supervision threshold, a UAV flight instability signal is generated;

[0121] Obstacle analysis module: When a flight instability signal is received, the obstacle data of the subsequent flight path is obtained, the obstacle stability supervision value is output, and compared with the threshold value to generate a signal whether the obstacle is stable;

[0122] If the obstacle stability supervision value is greater than the obstacle stability supervision threshold, an obstacle instability signal is generated;

[0123] Control execution module: Based on whether the obstacle is stable, it generates corresponding execution commands for the drone and completes the corresponding control work.

[0124] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for safely supervising the flight status of an unmanned aerial vehicle, characterized in that: The following steps are involved: Step 1: Collect UAV flight data in real time, output the flight stability supervision value, compare it with the flight stability supervision threshold, and generate a flight stability signal; If the flight stability supervision value is greater than the flight stability supervision threshold, a UAV flight instability signal is generated; In step 1, during the monitoring period, the flight status of the drone is monitored and collected in real time, and the obtained flight width status risk value and flight height status risk value are summed to obtain the flight stability supervision value; How to obtain the flight width status risk value: Obtain a width time risk value and a width state risk value, add the width time risk value and the width state risk value together to obtain a flight width state risk value; The process of obtaining the width time risk value is as follows: The monitoring period is divided into several time nodes, the flight width of each time node is obtained, marked as the actual flight width, the actual flight width is calculated to be different from the preset flight width, the absolute value is taken, and the instantaneous width deviation value is obtained; The instantaneous width deviation value is compared with the instantaneous width deviation threshold value. If the instantaneous width deviation value is greater than or equal to the instantaneous width deviation threshold value, a width deviation abnormal signal is generated, and the time node corresponding to the generated width deviation abnormal signal is marked as a width deviation abnormal time node; Obtain the number of width deviation abnormal time nodes, calculate the ratio of the width deviation abnormal time nodes to the total number of time nodes in the monitoring period, and obtain the width time risk value; The process of obtaining the width state risk value is as follows: When the width deviation is abnormal, the instantaneous width deviation value under the width deviation abnormal signal is obtained and marked as the abnormal instantaneous width deviation value. The average of all abnormal instantaneous width deviation values ​​in the monitoring period is calculated to obtain the instantaneous width deviation average. The ratio of the instantaneous width deviation average to the instantaneous width deviation threshold is calculated to obtain the width status risk value. How to obtain the flight altitude status risk value: Obtain the altitude time risk value and the altitude state risk value, add the altitude time risk value and the altitude state risk value together to obtain the flight altitude state risk value; In step 1, the flight speed of the drone within the monitoring period is monitored and collected in real time, the monitoring period is divided into several time nodes, the flight altitude at each time node is obtained, marked as the actual flight altitude, the actual flight altitude is calculated to be different from the preset flight altitude, the absolute value is taken, and the instantaneous altitude deviation value is obtained; The process of obtaining the high time risk value is as follows: The instantaneous height deviation value is compared with the instantaneous height deviation threshold. The comparison process is as follows: If the instantaneous height deviation value is greater than or equal to the instantaneous height deviation threshold, a height deviation abnormal signal is generated, and the time node corresponding to the generation of the height deviation abnormal signal is marked as the height deviation abnormal time node; Obtain the number of abnormal time nodes of height deviation, calculate the ratio of the abnormal time node of height deviation to the total number of time nodes in the monitoring period, and obtain the height time risk value; The process of obtaining the height state risk value is as follows: When an abnormal height deviation signal is generated, the instantaneous height deviation value under the abnormal height deviation signal is obtained and marked as the abnormal instantaneous height deviation value. The average of all abnormal instantaneous height deviation values ​​within the monitoring period is calculated to obtain the instantaneous height deviation average value. The ratio of the instantaneous height deviation average value to the instantaneous height deviation threshold value is calculated to obtain the height state risk value. Step 2: When a flight instability signal is obtained, the obstacle data of the subsequent flight path is obtained, the obstacle stability supervision value is output, and compared with the threshold to generate a signal whether the obstacle is stable; If the obstacle stability supervision value is greater than the obstacle stability supervision threshold, an obstacle instability signal is generated; In step 2, the barrier stability supervision value is obtained as follows: Calculate the ratio of regional risk value to regional change value to obtain the obstacle stability supervision value; In step 2, the process of obtaining the regional risk value is as follows: Obtain the real-time obstacle distance value of the obstacle area within the monitoring period, and compare the real-time obstacle distance value with the obstacle distance threshold; If the obstacle distance value is less than the obstacle distance threshold, an obstacle distance danger signal is generated; the time node corresponding to the generation of the obstacle distance danger signal is marked as an obstacle abnormal time node; Obtain the abnormal time node of the fault area, calculate the ratio of the abnormal time node of the fault area to the total number of time nodes in the monitoring period, and obtain the regional risk value; The process of obtaining the regional change value is: Obtain the time nodes of the danger signals of the two adjacent obstacle areas, and perform difference calculation to obtain the adjacent abnormal time difference; perform mean calculation on all adjacent abnormal time differences within the monitoring period to obtain the mean of the adjacent abnormal time difference, and then perform ratio calculation on the mean of the adjacent abnormal time difference and the monitoring period to obtain the regional change value; Step 3: Based on whether the obstacle stabilizes the signal, generate corresponding execution commands for the drone and complete the corresponding control work.

2. A method for safely supervising the flight status of an unmanned aerial vehicle according to claim 1, characterized in that: In step three, the process of regulation and judgment is: Obtain the minimum time difference of adjacent width deviation anomaly time nodes and the minimum time difference of height deviation anomaly time nodes, mark them as the minimum time difference of width deviation anomaly and the minimum time difference of height deviation anomaly respectively, add them up, and get the minimum time difference of the main body anomaly.

3. A method for safely supervising the flight status of an unmanned aerial vehicle according to claim 2, characterized in that: In step three, the adjacent anomaly time difference is compared with the minimum value of the main anomaly time difference; If the adjacent abnormal time difference is greater than or equal to the minimum value of the main abnormal time difference, a speed control signal can be generated; When the speed control signal is obtained, the path length of the UAV in the obstacle area is obtained, the minimum value of the wide deviation abnormal time difference is compared with the minimum value of the high deviation abnormal time difference, the smallest value is selected as the obstacle passing time, and the ratio of the path length to the obstacle passing time is calculated to obtain the obstacle passing speed.

4. A method for safely supervising the flight status of an unmanned aerial vehicle according to claim 3, characterized in that: In step 3, if the adjacent abnormal time difference is less than the minimum value of the main abnormal time difference, a height control signal can be generated; When the altitude control signal is obtained, the highest point in the obstacle area can be obtained, and the horizontal plane of the highest point can be used as the subsequent flight path of the UAV.

5. A UAV flight status safety monitoring system, characterized by: The system is used to execute the method described in any one of claims 1 to 4 above, and the system includes the following modules: Stability monitoring module: collects UAV flight data in real time, outputs flight stability monitoring value, compares it with the flight stability monitoring threshold, and generates a signal on whether the flight is stable; If the flight stability supervision value is greater than the flight stability supervision threshold, a UAV flight instability signal is generated; During the monitoring period, the flight status of the drone is monitored and collected in real time, and the flight width status risk value and the flight height status risk value are summed to obtain the flight stability supervision value; How to obtain the flight width status risk value: Obtain a width time risk value and a width state risk value, add the width time risk value and the width state risk value together to obtain a flight width state risk value; The process of obtaining the width time risk value is as follows: The monitoring period is divided into several time nodes, the flight width of each time node is obtained, marked as the actual flight width, the actual flight width is calculated to be different from the preset flight width, the absolute value is taken, and the instantaneous width deviation value is obtained; The instantaneous width deviation value is compared with the instantaneous width deviation threshold value. If the instantaneous width deviation value is greater than or equal to the instantaneous width deviation threshold value, a width deviation abnormal signal is generated, and the time node corresponding to the generated width deviation abnormal signal is marked as a width deviation abnormal time node; Obtain the number of width deviation abnormal time nodes, calculate the ratio of the width deviation abnormal time nodes to the total number of time nodes in the monitoring period, and obtain the width time risk value; The process of obtaining the width state risk value is as follows: When the width deviation is abnormal, the instantaneous width deviation value under the width deviation abnormal signal is obtained and marked as the abnormal instantaneous width deviation value. The average of all abnormal instantaneous width deviation values ​​in the monitoring period is calculated to obtain the instantaneous width deviation average. The ratio of the instantaneous width deviation average to the instantaneous width deviation threshold is calculated to obtain the width status risk value. How to obtain the flight altitude status risk value: Obtain the altitude time risk value and the altitude state risk value, add the altitude time risk value and the altitude state risk value together to obtain the flight altitude state risk value; Real-time monitoring and collection of the UAV flight speed within the monitoring period, dividing the monitoring period into several time nodes, obtaining the flight altitude at each time node, marking it as the actual flight altitude, performing a difference calculation between the actual flight altitude and the preset flight altitude, taking the absolute value, and obtaining the instantaneous altitude deviation value; The process of obtaining the high time risk value is as follows: The instantaneous height deviation value is compared with the instantaneous height deviation threshold. The comparison process is as follows: If the instantaneous height deviation value is greater than or equal to the instantaneous height deviation threshold, a height deviation abnormal signal is generated, and the time node corresponding to the generation of the height deviation abnormal signal is marked as the height deviation abnormal time node; Obtain the number of abnormal time nodes of height deviation, calculate the ratio of the abnormal time node of height deviation to the total number of time nodes in the monitoring period, and obtain the height time risk value; The process of obtaining the height state risk value is as follows: When an abnormal height deviation signal is generated, the instantaneous height deviation value under the abnormal height deviation signal is obtained and marked as the abnormal instantaneous height deviation value. The average of all abnormal instantaneous height deviation values ​​within the monitoring period is calculated to obtain the instantaneous height deviation average value. The ratio of the instantaneous height deviation average value to the instantaneous height deviation threshold value is calculated to obtain the height state risk value. Obstacle analysis module: When a flight instability signal is received, the obstacle data of the subsequent flight path is obtained, the obstacle stability supervision value is output, and compared with the threshold value to generate a signal whether the obstacle is stable; If the obstacle stability supervision value is greater than the obstacle stability supervision threshold, an obstacle instability signal is generated; The barrier stability supervision value is obtained as follows: Calculate the ratio of regional risk value to regional change value to obtain the obstacle stability supervision value; The process of obtaining regional risk value is as follows: Obtain the real-time obstacle distance value of the obstacle area within the monitoring period, and compare the real-time obstacle distance value with the obstacle distance threshold; If the obstacle distance value is less than the obstacle distance threshold, an obstacle distance danger signal is generated; the time node corresponding to the generation of the obstacle distance danger signal is marked as an obstacle abnormal time node; Obtain the abnormal time node of the fault area, calculate the ratio of the abnormal time node of the fault area to the total number of time nodes in the monitoring period, and obtain the regional risk value; The process of obtaining the regional change value is: Obtain the time nodes of the danger signals of the two adjacent obstacle areas, and perform difference calculation to obtain the adjacent abnormal time difference; perform mean calculation on all adjacent abnormal time differences within the monitoring period to obtain the mean of the adjacent abnormal time difference, and then perform ratio calculation on the mean of the adjacent abnormal time difference and the monitoring period to obtain the regional change value; Control execution module: Based on whether the obstacle is stable, it generates corresponding execution commands for the drone and completes the corresponding control work.

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