A method for monitoring geological disaster deformation based on airborne laser radar

Abstract

The present invention discloses a geological disaster deformation monitoring method based on airborne laser radar, which belongs to the field of geological disaster monitoring and early warning technology. The monitoring method comprises the following steps: obtaining ground point cloud data of the target area at different periods through an unmanned aerial vehicle airborne laser radar measurement system; filtering the ground point cloud data of the target area at different periods to obtain the real surface point cloud data of the target area at different periods; performing the following processing on the real surface point cloud data of the target area at different periods: generalization processing; based on the point cloud elevation difference algorithm of plane fitting and space fitting, calculating the change of the real surface point cloud data of the target area at different periods in chronological order; according to the change of the real surface point cloud data of the target area at different periods, finally obtaining the deformation area of ​​the target area at different periods. The present invention is not only highly accurate, but also more comprehensive, and provides data support for the monitoring and early warning of geological disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring and early warning, and in particular to a geological disaster deformation monitoring method based on airborne laser radar. Background Art

[0002] Geological disasters seriously threaten the safety of people's lives and property, so it is particularly important to carry out regular monitoring of geological disaster bodies with certain risks. At present, the existing geological disaster monitoring methods are relatively traditional, mainly including: (1) single-point GNSS displacement monitoring station monitoring, which has the advantage of high data accuracy, but lacks overall monitoring of the disaster body; (2) InSAR (interferometric synthetic aperture radar) monitoring of the disaster body area, which has the advantage of being able to monitor geological disasters as a whole, but because InSAR uses satellite-launched lasers to obtain data on the earth's surface, long-distance detection leads to low data accuracy. At the same time, it cannot obtain real ground elevation data and cannot meet the requirements of deformation monitoring. In general, the above two monitoring methods do not have the characteristics of high accuracy and good integrity at the same time, and it is urgent to develop a new method for geological disaster deformation monitoring. Summary of the invention

[0003] The purpose of the present invention is to provide a geological disaster deformation monitoring method based on airborne laser radar in response to the existing technical status. The present invention is not only highly accurate but also more comprehensive, providing data support for the monitoring and early warning of geological disasters.

[0004] In order to achieve the above object, the present invention adopts the following technical scheme:

[0005] A method for monitoring geological disaster deformation based on airborne laser radar comprises the following steps:

[0006] S1. Obtain ground point cloud data of the target area at different times through the UAV-borne laser radar measurement system;

[0007] S2, filtering the ground point cloud data of the target area at different periods, removing the ground point cloud data related to surface vegetation and structures, and obtaining the real surface point cloud data of the target area at different periods;

[0008] S3. Process the real surface point cloud data of the target area at different times as follows:

[0009] S3.1. Generalization processing: generalize the real surface point cloud data of the target area at different times to obtain the core surface point cloud data of the target area at different times;

[0010] S3.2, based on the point cloud elevation difference algorithm of plane fitting and space fitting, calculate the changes of the real surface point cloud data of the target area in different periods in chronological order;

[0011] S4. According to the changes of the real surface point cloud data of the target area at different periods, the deformation area of ​​the target area at different periods is finally obtained.

[0012] Furthermore, in step S3.2, for the real surface point cloud data of the target area at any two periods, the calculation steps of the point cloud elevation difference algorithm based on plane fitting and space fitting are as follows:

[0013] S3.2.1, plane fitting:

[0014] For the core surface point cloud data of the target area in the later period, randomly select a core point cloud P, take the core point cloud P as the center of the circle, select other real surface point cloud data of the same period within the range of D / 2, jointly fit a plane M, and obtain the normal vector N of the plane M;

[0015] Wherein: D is the first preset distance;

[0016] S3.2.2. Calculation of spatial fitting and point cloud variation:

[0017] Starting from plane M along the direction of normal vector N, with d / 2 as the projection radius, there is a cylinder passing through the core point cloud P and with normal vector N as the central axis. All real surface point cloud data of the latter and previous periods in the cylinder are searched, which are recorded as point cloud data sets N1 and N2 respectively;

[0018] Wherein: d is the second preset distance, d<D;

[0019] The average positions M1 and M2 of the point cloud datasets N1 and N2 are calculated along the normal vector N, respectively. The average position refers to the average elevation value of all the point clouds in the point cloud dataset;

[0020] Calculate the change L of the real surface point cloud data at the core point cloud P in the later period. The calculation formula of the change L is as follows:

[0021] L= |M1-M2 |

[0022] If L>0, the real surface point cloud data of the later period has changes at the core point cloud P, otherwise there is no change;

[0023] S3.2.3. For the core surface point cloud data of the target area in the later period, traverse all core point clouds, find the core point clouds with changes, and circle the point cloud change area;

[0024] Furthermore, step S1 further includes the following steps:

[0025] Before obtaining the ground point cloud data of the target area at different times through the UAV-borne LiDAR measurement system, RTK base stations are deployed in the target area. The point clouds corresponding to the RTK base stations are used as checkpoints. The number of checkpoints is ;

[0026] After obtaining the ground point cloud data of the target area at different times through the UAV-mounted laser radar measurement system, the elevation error of the ground point cloud data is calculated. and plane position error , for any period of ground point cloud data, when its elevation error Greater than the preset elevation error threshold, or its plane position error When the error is greater than the preset plane position error, the acquired ground point cloud data is invalid, and step S1 is executed again; otherwise, the acquired ground point cloud data is valid, and step S2 is executed again;

[0027] Elevation error The calculation formula is as follows:

[0028]

[0029] in:

[0030] For the The elevation value of the point cloud corresponding to each checkpoint;

[0031] For the The measured elevation value of each checkpoint;

[0032] Plane position error The calculation formula is as follows:

[0033]

[0034]

[0035]

[0036] in:

[0037] is the plane position error of the ground point cloud data in the X direction;

[0038] is the plane position error of the ground point cloud data in the Y direction;

[0039] For the The plane position of the point cloud corresponding to each check point in the X direction;

[0040] For the The measured plane position of each check point in the X direction;

[0041] For the The plane position of the point cloud corresponding to each check point in the Y direction;

[0042] For the The measured plane position of each check point in the Y direction.

[0043] Furthermore, when the target area is a flat terrain, the preset elevation error threshold is 0.15, and the preset plane position error threshold is 0.3;

[0044] When the target area is a hilly terrain, the preset elevation error threshold is 0.25, and the preset plane position error threshold is 0.3;

[0045] When the target area is a mountainous terrain, the preset elevation error threshold is 0.35, and the preset plane position error threshold is 0.4;

[0046] When the target area is a mountainous terrain, the preset elevation error threshold is 0.5, and the preset plane position error threshold is 0.4.

[0047] Furthermore, when deploying RTK base stations in the target area, the number of RTK base stations shall be no less than 15.

[0048] Furthermore, step S3.2.1 further includes the following steps:

[0049] After fitting a plane M, calculate the standard deviation , if the standard deviation The relationship with D satisfies D ≥ t· , t is the preset range parameter, then the fitted plane M is valid, and step S3.2.2 is continued; otherwise, the fitted plane M is invalid, and after re-determining D, step S3.2.1 is executed;

[0050] Standard Deviation The calculation formula is as follows:

[0051]

[0052] in:

[0053] The radius D / 2 is the The distance between the point cloud and the plane M;

[0054] is the average distance between all point clouds and plane M within the radius D / 2;

[0055] It is the total amount of point cloud within the radius D / 2.

[0056] Furthermore, the preset range parameter t is 20~25.

[0057] Further, after step S3.2.2 is completed, the following steps are also included:

[0058] When the total number of point clouds in point cloud datasets N1 and N2 is not less than the preset number When L changes If the calculation result of the change L is within the interval, it is considered that the calculation result of the change L can be adopted, and step S3.2.3 is executed. Otherwise, the calculation result of the change L cannot be adopted. After re-determining d, step S3.2.2 is executed. The calculation formula is as follows:

[0059]

[0060] in:

[0061] is the preset credibility coefficient;

[0062] , are the standard deviations of the elevation values ​​of the point clouds in the point cloud datasets N1 and N2, respectively;

[0063] , are the number of point clouds in point cloud datasets N1 and N2 respectively;

[0064] is the registration error of the elevation value of the real surface point cloud data of the target area in the previous and next two periods;

[0065] When the total number of point clouds in point cloud dataset N1 or N2 is less than the preset number When , point cloud datasets N1 and N2 cannot be used. After redetermining d, execute step S3.2.2.

[0066] Furthermore, the preset number is 4.

[0067] Furthermore, the registration error The calculation formula is as follows:

[0068]

[0069] in:

[0070] is the root mean square error of the elevation value of the real surface point cloud data of the target area in the previous period;

[0071] It is the root mean square error of the elevation value of the real surface point cloud data of the target area in the latter period.

[0072] The beneficial effects of the present invention are:

[0073] The present invention provides a geological disaster deformation monitoring method based on airborne laser radar, firstly, the ground point cloud data of the target area at different periods are obtained by the unmanned aerial vehicle airborne laser radar measurement system, and then the ground point cloud data of the target area at different periods are filtered to obtain the real surface point cloud data of the target area at different periods, and then the real surface point cloud data of the target area at different periods are generalized, and then the point cloud elevation difference algorithm based on plane fitting and space fitting is used to calculate the change of the real surface point cloud data of the target area at different periods in chronological order, and finally, according to the change of the real surface point cloud data of the target area at different periods, the deformation area of ​​the target area at different periods is finally obtained, so as to achieve the purpose of monitoring whether there is ground deformation in the target area. Compared with the monitoring of the single-point GNSS displacement monitoring station and the InSAR monitoring of the disaster body domain, the present invention is not only highly accurate, but also more comprehensive, and provides data support for the monitoring and early warning of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of a method for monitoring geological disaster deformation based on airborne laser radar according to the present invention;

[0075] Figure 2 It is a schematic diagram of the generalized processing in a geological disaster deformation monitoring method based on airborne laser radar of the present invention;

[0076] Figure 3 It is a principle diagram of plane fitting in a geological disaster deformation monitoring method based on airborne laser radar of the present invention;

[0077] Figure 4 This is a schematic diagram of the space fitting in a geological disaster deformation monitoring method based on airborne laser radar in the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0079] Embodiment 1:

[0080] See also Figures 1 to 4 As shown, a geological disaster deformation monitoring method based on airborne laser radar includes the following steps:

[0081] S1. Obtain ground point cloud data of the target area at different times through the UAV-mounted LiDAR measurement system.

[0082] The period of ground point cloud data is set according to the monitoring frequency requirements, for example, monthly, weekly or daily.

[0083] In addition, the ground point cloud data of the target area in each period obtained by the UAV-borne LiDAR measurement system must meet certain accuracy requirements, for example, the scale is 1:500, the point cloud density is 16 points / ㎡, and in order to ensure the validity of the data, the ground point cloud data of the target area in each period obtained by the UAV-borne LiDAR measurement system must also be quality controlled. The quality control steps are as follows:

[0084] Before obtaining the ground point cloud data of the target area at different times through the UAV-mounted laser radar measurement system, an RTK base station is deployed in the target area (RTK base station is a wireless communication device commonly used in the measurement field. Its function is to send high-precision positioning signals to the measuring instrument through radio waves). The point cloud corresponding to the RTK base station is used as a checkpoint. The number of checkpoints is ;

[0085] After obtaining the ground point cloud data of the target area at different times through the UAV-mounted laser radar measurement system, the elevation error of the ground point cloud data is calculated. and plane position error , for any period of ground point cloud data, when its elevation error Greater than the preset elevation error threshold, or its plane position error When the error is greater than the preset plane position error, the acquired ground point cloud data is invalid, and step S1 is executed again; otherwise, the acquired ground point cloud data is valid, and step S2 is continued.

[0086] Elevation error The calculation formula is as follows:

[0087]

[0088] in:

[0089] For the The elevation value of the point cloud corresponding to each checkpoint;

[0090] For the The measured elevation value of each checkpoint.

[0091] Plane position error The calculation formula is as follows:

[0092]

[0093]

[0094]

[0095] in:

[0096] is the plane position error of the ground point cloud data in the X direction;

[0097] is the plane position error of the ground point cloud data in the Y direction;

[0098] For the The plane position of the point cloud corresponding to each check point in the X direction;

[0099] For the The measured plane position of each check point in the X direction;

[0100] For the The plane position of the point cloud corresponding to each check point in the Y direction;

[0101] For the The measured plane position of each check point in the Y direction.

[0102] When deploying RTK base stations in the target area, the number of RTK base stations shall be no less than 15.

[0103] According to different terrains, the preset elevation error threshold and the preset plane position error threshold are set as follows:

[0104] When the target area is flat terrain, the preset elevation error threshold is 0.15, and the preset plane position error threshold is 0.3;

[0105] When the target area is hilly, the preset elevation error threshold is 0.25, and the preset plane position error threshold is 0.3;

[0106] When the target area is mountainous terrain, the preset elevation error threshold is 0.35, and the preset plane position error threshold is 0.4;

[0107] When the target area is a mountainous terrain, the preset elevation error threshold is 0.5 and the preset plane position error threshold is 0.4.

[0108] It should also be noted that the elevation value refers to the height of the point cloud relative to the zero point (Yellow Sea level).

[0109] S2. Filter the ground point cloud data of the target area at different times, remove the ground point cloud data related to surface vegetation (shrubs, trees, etc.) and structures (houses, high-voltage lines, etc.), and obtain the real surface point cloud data of the target area at different times. Surface vegetation (shrubs, trees, etc.), structures (houses, high-voltage lines, etc.).

[0110] S3. Process the real surface point cloud data of the target area at different times as follows:

[0111] S3.1. Generalization processing: Generalize the real surface point cloud data of the target area at different periods to obtain the core surface point cloud data of the target area at different periods.

[0112] Since the amount of point cloud data collected in the field is very large, it is necessary to generalize the point cloud data to improve the computing efficiency and significantly shorten the computing time. Specifically, generalization is to generalize the messy point cloud data into a point cloud with low density and uniform distribution, which is called the core point cloud.

[0113] S3.2, the point cloud elevation difference algorithm based on plane fitting and space fitting calculates the changes of the real surface point cloud data of the target area in different periods in chronological order. Among them, for the real surface point cloud data of the target area in any two periods, the calculation steps of the point cloud elevation difference algorithm based on plane fitting and space fitting are as follows:

[0114] S3.2.1, plane fitting:

[0115] For the core surface point cloud data of the target area in the later period, randomly select a core point cloud P, take the core point cloud P as the center of the circle, select other real surface point cloud data of the same period within the range of D / 2, jointly fit a plane M, and obtain the normal vector N of the plane M;

[0116] Wherein: D is the first preset distance.

[0117] To ensure the validity of plane M, step S3.2.1 further includes the following steps:

[0118] After fitting a plane M, calculate the standard deviation , if the standard deviation The relationship with D satisfies D ≥ t· , t is the preset range parameter, then the fitted plane M is valid, and step S3.2.2 is continued; otherwise, the fitted plane M is invalid, and after re-determining D, step S3.2.1 is executed;

[0119] Standard Deviation The calculation formula is as follows:

[0120]

[0121] in:

[0122] The radius D / 2 is the The distance between the point cloud and the plane M;

[0123] is the average distance between all point clouds and plane M within the radius D / 2;

[0124] It is the total amount of point cloud within the radius D / 2.

[0125] In this embodiment, illustratively, the preset range parameter t is 20-25.

[0126] S3.2.2. Calculation of spatial fitting and point cloud variation:

[0127] Starting from plane M along the direction of normal vector N, with d / 2 as the projection radius, there is a cylinder passing through the core point cloud P and with normal vector N as the central axis. All real surface point cloud data of the latter and previous periods in the cylinder are searched, which are recorded as point cloud data sets N1 and N2 respectively;

[0128] Wherein: d is the second preset distance, d<D;

[0129] The average positions M1 and M2 of the point cloud datasets N1 and N2 are calculated along the normal vector N, respectively. The average position refers to the average elevation value of all the point clouds in the point cloud dataset;

[0130] Calculate the change L of the real surface point cloud data at the core point cloud P in the later period. The calculation formula of the change L is as follows:

[0131] L= |M1-M2 |

[0132] If L>0, the real surface point cloud data of the later period changes at the core point cloud P, otherwise there is no change.

[0133] In order to ensure the validity of the change amount L (to improve the accuracy and credibility of the results and avoid misjudgment of change identification caused by various errors), after step S3.2.2 is completed, the following steps are also included:

[0134] When the total number of point clouds in point cloud datasets N1 and N2 is not less than the preset number When L changes If the calculation result of the change L is within the interval, it is considered that the calculation result of the change L can be adopted, and step S3.2.3 is executed. Otherwise, the calculation result of the change L cannot be adopted. After re-determining d, step S3.2.2 is executed. The calculation formula is as follows:

[0135]

[0136] in:

[0137] is the preset credibility coefficient;

[0138] , are the standard deviations of the elevation values ​​of the point clouds in the point cloud datasets N1 and N2, respectively;

[0139] , are the number of point clouds in point cloud datasets N1 and N2 respectively;

[0140] is the registration error of the elevation value of the real surface point cloud data of the target area in the previous and next two periods;

[0141] When the total number of point clouds in point cloud dataset N1 or N2 is less than the preset number When , point cloud datasets N1 and N2 cannot be used. After redetermining d, execute step S3.2.2.

[0142] In this embodiment, for example, when the reliability of the calculation result of the change amount L reaches 95% or above, the reliability coefficient The value is 1.98.

[0143] Specifically, the registration error The calculation formula is as follows:

[0144]

[0145] in:

[0146] is the root mean square error of the elevation value of the real surface point cloud data of the target area in the previous period;

[0147] It is the root mean square error of the elevation value of the real surface point cloud data of the target area in the latter period.

[0148] In this embodiment, for example, the preset number is 4.

[0149] S3.2.3. For the core surface point cloud data of the target area in the later period, traverse all core point clouds, find out the core point clouds with changes, and circle the point cloud change area.

[0150] S4. According to the changes of the real surface point cloud data of the target area at different periods, the deformation area of ​​the target area at different periods is finally obtained. In theory, the changes of the real surface point cloud data of the target area at different periods can directly indicate whether there are cracks, subsidence and other deformations on the surface.

[0151] Embodiment 2:

[0152] On the basis of Example 1, in order to ensure the accuracy of the monitoring results, in step S4, before finally obtaining the deformation areas of the target area at different periods, the following steps are also included:

[0153] Combined with comprehensive comparison and analysis of image data collected in the target area, on-site surveys and verifications are carried out at the same time.

[0154] The present invention is not limited to the above-mentioned specific implementation modes. A person skilled in the art can implement the present invention in various other specific implementation modes based on the contents disclosed in the present invention. Therefore, any design that adopts the design structure and concept of the present invention and makes some simple changes or modifications shall fall within the scope of protection of the present invention.

Claims

1. A method for monitoring geological disaster deformation based on airborne laser radar, characterized in that: The following steps are involved: S1. Obtain ground point cloud data of the target area at different times through the UAV-borne laser radar measurement system; S2, filtering the ground point cloud data of the target area at different periods, removing the ground point cloud data related to surface vegetation and structures, and obtaining the real surface point cloud data of the target area at different periods; S3. Process the real surface point cloud data of the target area at different times as follows: S3.

1. Generalization processing: generalize the real surface point cloud data of the target area at different times to obtain the core surface point cloud data of the target area at different times; S3.2, the point cloud elevation difference algorithm based on plane fitting and space fitting calculates the changes of the real surface point cloud data of the target area in different periods in chronological order. Among them, for the real surface point cloud data of the target area in any two periods, the calculation steps of the point cloud elevation difference algorithm based on plane fitting and space fitting are as follows: S3.2.1, plane fitting: For the core surface point cloud data of the target area in the later period, randomly select a core point cloud P, take the core point cloud P as the center of the circle, select other real surface point cloud data of the same period within the range of D / 2, jointly fit a plane M, and obtain the normal vector N of the plane M; Wherein: D is the first preset distance; S3.2.

2. Calculation of spatial fitting and point cloud variation: Starting from plane M along the direction of normal vector N, with d / 2 as the projection radius, there is a cylinder passing through the core point cloud P and with normal vector N as the central axis. All real surface point cloud data of the latter and previous periods in the cylinder are searched, which are recorded as point cloud data sets N1 and N2 respectively; Wherein: d is the second preset distance, d<D; The average positions M1 and M2 of the point cloud datasets N1 and N2 are calculated along the normal vector N, respectively. The average position refers to the average elevation value of all the point clouds in the point cloud dataset; Calculate the change L of the real surface point cloud data at the core point cloud P in the later period. The calculation formula of the change L is as follows: L= |M1-M2 | If L>0, the real surface point cloud data of the later period has changes at the core point cloud P, otherwise there is no change; S3.2.

3. For the core surface point cloud data of the target area in the later period, traverse all core point clouds, find the core point clouds with changes, and circle the point cloud change area; S4. According to the changes of the real surface point cloud data of the target area at different periods, the deformation area of ​​the target area at different periods is finally obtained.

2. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 1, characterized in that: Step S1 also includes the following steps: Before obtaining the ground point cloud data of the target area at different times through the UAV-borne LiDAR measurement system, RTK base stations are deployed in the target area. The point clouds corresponding to the RTK base stations are used as checkpoints. The number of checkpoints is ; After obtaining the ground point cloud data of the target area at different times through the UAV-mounted laser radar measurement system, the elevation error of the ground point cloud data is calculated. and plane position error , for any period of ground point cloud data, when its elevation error Greater than the preset elevation error threshold, or its plane position error When the error is greater than the preset plane position error, the acquired ground point cloud data is invalid, and step S1 is executed again; otherwise, the acquired ground point cloud data is valid, and step S2 is continued; Elevation error The calculation formula is as follows: in: For the The elevation value of the point cloud corresponding to each checkpoint; For the The measured elevation value of each checkpoint; Plane position error The calculation formula is as follows: in: is the plane position error of the ground point cloud data in the X direction; is the plane position error of the ground point cloud data in the Y direction; For the The plane position of the point cloud corresponding to each check point in the X direction; For the The measured plane position of each check point in the X direction; For the The plane position of the point cloud corresponding to each check point in the Y direction; For the The measured plane position of each check point in the Y direction.

3. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 2 is characterized in that: When the target area is flat terrain, the preset elevation error threshold is 0.15, and the preset plane position error threshold is 0.3; When the target area is a hilly terrain, the preset elevation error threshold is 0.25, and the preset plane position error threshold is 0.3; When the target area is a mountainous terrain, the preset elevation error threshold is 0.35, and the preset plane position error threshold is 0.4; When the target area is a mountainous terrain, the preset elevation error threshold is 0.5, and the preset plane position error threshold is 0.

4.

4. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 2 is characterized in that: When deploying RTK base stations in the target area, the number of RTK base stations shall be no less than 15.

5. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 1, characterized in that: Step S3.2.1 further includes the following steps: After fitting a plane M, calculate the standard deviation , if the standard deviation The relationship with D satisfies D ≥ t· , t is the preset range parameter, then the fitted plane M is valid, and step S3.2.2 is continued; otherwise, the fitted plane M is invalid, and after re-determining D, step S3.2.1 is executed; Standard Deviation The calculation formula is as follows: in: The radius D / 2 is the The distance between the point cloud and the plane M; is the average distance between all point clouds and plane M within the radius D / 2; It is the total amount of point cloud within the radius D / 2.

6. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 5 is characterized in that: The preset range parameter t is 20~25.

7. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 1, characterized in that: After step S3.2.2 is completed, the following steps are also included: When the total number of point clouds in point cloud datasets N1 and N2 is not less than the preset number When the change L is If the calculation result of the change L is within the interval, the calculation result of the change L is considered to be credible, and step S3.2.3 is executed. Otherwise, the calculation result of the change L is not credible, and after re-determining d, step S3.2.2 is executed. The calculation formula is as follows: in: is the preset credibility coefficient; , are the standard deviations of the elevation values ​​of the point clouds in the point cloud datasets N1 and N2, respectively; , are the number of point clouds in point cloud datasets N1 and N2 respectively; is the registration error of the elevation value of the real surface point cloud data of the target area in the previous and next two periods; When the total number of point clouds in point cloud dataset N1 or N2 is less than the preset number When , point cloud datasets N1 and N2 cannot be used. After redetermining d, execute step S3.2.

2.

8. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 7, characterized in that: The preset number is 4.

9. The method for monitoring geological disaster deformation based on airborne laser radar according to claim 7, characterized in that: The registration error The calculation formula is as follows: in: is the root mean square error of the elevation value of the real surface point cloud data of the target area in the previous period; It is the root mean square error of the elevation value of the real surface point cloud data of the target area in the latter period.

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