Horizontal displacement monitoring method of concrete arch dam based on point cloud big data

Abstract

The present invention relates to the field of data processing, and discloses a method and system for monitoring the horizontal displacement of a concrete arch dam based on point cloud big data, including: a data acquisition device acquires the current concrete arch dam corridor information, and obtains the current concrete arch dam corridor collection surface collection sequence; the current concrete arch dam corridor collection surface collection sequence is sent to the data acquisition device, and the data acquisition device performs concrete arch dam corridor collection surface data collection according to the current concrete arch dam corridor collection surface collection sequence; the concrete arch dam horizontal displacement data is obtained according to the data collected by the concrete arch dam corridor collection surface sequence; the concrete arch dam corridor horizontal displacement range is obtained according to the obtained horizontal displacement data. Through the technical solution provided by the present invention, data monitoring of the horizontal displacement of the arch dam can be realized, which is helpful to timely discover small deformations and cracks, thereby preventing potential safety hazards.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for monitoring horizontal displacement of a concrete arch dam based on point cloud big data. Background Art

[0002] As an important structure in water conservancy projects, the safety and stability of concrete arch dams are directly related to the normal operation of the entire water conservancy facility and the safety of people and property. Therefore, regular and accurate monitoring of concrete arch dams is particularly important. Traditional monitoring methods often rely on manual measurement and physical sensors, but these methods have the problems of low accuracy, low efficiency and inability to achieve comprehensive monitoring.

[0003] With the development of science and technology, especially the rise of point cloud big data technology, new possibilities have been provided for the monitoring of concrete arch dams. Point cloud big data technology can obtain a large amount of point cloud data on the surface of the arch dam through high-precision measurement equipment, and then construct an accurate three-dimensional model, providing more accurate and comprehensive data support for the deformation monitoring of the arch dam.

[0004] However, most of the monitoring systems currently on the market still rely on traditional measurement methods and cannot meet the dual requirements of accuracy and efficiency of modern water conservancy projects. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for monitoring the horizontal displacement of a concrete arch dam based on point cloud big data, comprising the following steps:

[0006] Step 1: The data acquisition device obtains the current concrete arch dam corridor information, and the cloud data server establishes a data processing container according to the current concrete arch dam corridor information. The data processing container segments the current concrete arch dam corridor collection surface according to the segmentation feature data to obtain the current concrete arch dam corridor collection surface collection sequence;

[0007] Step 2: The data processing container segments the original data of the concrete arch dam corridor collection surface according to the segmentation feature data to obtain a comparison sequence, and obtains a concrete arch dam corridor collection surface sequence group according to the current concrete arch dam corridor collection surface collection sequence and the comparison sequence; the current concrete arch dam corridor collection surface collection sequence is sent to the data collection device, and the data collection device collects concrete arch dam corridor collection surface data according to the current concrete arch dam corridor collection surface collection sequence;

[0008] Step 3, sending the collected data of the concrete arch dam corridor to the data processing container to determine whether the data collection is qualified. If qualified, proceed to step 5; otherwise, proceed to step 4;

[0009] Step 4, obtaining the serial number of the concrete arch dam gallery collection surface that failed to collect data and the collection failure information, re-collecting the data of the concrete arch dam gallery collection surface, and returning to step 3;

[0010] Step 5: According to the data collected by the concrete arch dam gallery collection surface sequence, the surface data of the current concrete arch dam gallery collection surface is obtained; according to the surface data of the current concrete arch dam gallery collection surface, the difference of the radian data of each group in the concrete arch dam gallery collection surface sequence group is obtained, and the deformation data of the current concrete arch dam gallery collection surface is obtained; according to the deformation data of the current concrete arch dam gallery collection surface, the horizontal displacement data of the concrete arch dam is obtained;

[0011] Step six, based on the horizontal displacement data, obtain the horizontal displacement range of the concrete arch dam corridor.

[0012] Furthermore, the data processing container segments the concrete arch dam gallery collection surface according to the segmentation feature data to obtain a current concrete arch dam gallery collection surface collection sequence, including:

[0013] The segmentation feature is the unit segmentation arc, and the concrete arch dam gallery collection surface is segmented according to the unit segmentation arc, and the current concrete arch dam gallery collection surface collection sequence is obtained according to the segmentation order.

[0014] Furthermore, the data processing container segments the original data of the concrete arch dam gallery collection surface according to the segmentation feature data to obtain a comparison sequence, and obtains a concrete arch dam gallery collection surface sequence group according to the current concrete arch dam gallery collection surface collection sequence and the comparison sequence, including:

[0015] The data processing container segments the original data of the concrete arch dam gallery collection surface according to the unit segmentation arc, and obtains a segmentation sequence in the same order as the current concrete arch dam gallery collection surface collection sequence, which is the comparison sequence;

[0016] The current concrete arch dam gallery collection surface collection sequence and the collection surface with the same sequence number as the comparison sequence constitute the concrete arch dam gallery collection surface sequence group.

[0017] Furthermore, the obtaining of the serial number of the concrete arch dam gallery collection surface with unqualified collection and the unqualified collection information, and re-collection of the concrete arch dam gallery collection surface data, includes:

[0018] According to the concrete arch dam corridor collection surface corresponding to the serial number of the concrete arch dam corridor collection surface with unqualified collection, data collection of the concrete arch dam corridor collection surface is re-performed according to the unqualified type; the unqualified type includes that the collection coverage area of ​​the concrete arch dam corridor collection surface is unqualified.

[0019] Furthermore, the surface data of the current concrete arch dam gallery collection surface is obtained based on the data collected from the concrete arch dam gallery collection surface sequence, including:

[0020] Point cloud data is spliced ​​based on the data collected from the concrete arch dam gallery collection surface sequence to obtain surface data of the current concrete arch dam gallery collection surface; the surface data includes the curvature data of the concrete arch dam gallery collection surface.

[0021] Furthermore, the method of obtaining the difference of the radian data of each group in the sequence group of the concrete arch dam gallery collection surface according to the surface data of the current concrete arch dam gallery collection surface, and obtaining the deformation data of the current concrete arch dam gallery collection surface, includes:

[0022] The radian data of the original concrete arch dam gallery collection surface is obtained according to the original surface data of the concrete arch dam gallery collection surface, and the difference of the radian data of each group in the concrete arch dam gallery collection surface sequence group is obtained. The difference of the radian data of each group constitutes the deformation data of the current concrete arch dam gallery collection surface.

[0023] Furthermore, the horizontal displacement data of the concrete arch dam is obtained according to the deformation data of the current concrete arch dam corridor collection surface, including:

[0024] The maximum value of the difference between the radian data of each group in the concrete arch dam gallery acquisition surface sequence group and the corresponding radius difference are used to obtain the horizontal displacement data.

[0025] Furthermore, the horizontal displacement range of the concrete arch dam corridor is obtained according to the obtained horizontal displacement data, including:

[0026] Taking the concrete arch dam gallery collection surface sequence group corresponding to the maximum value in the horizontal displacement data as the center, the current concrete arch dam gallery collection surface in the concrete arch dam gallery collection surface sequence group included in the horizontal displacement data range on both sides constitutes the horizontal displacement range of the concrete arch dam gallery.

[0027] The beneficial effects of the present invention are:

[0028] The present invention uses point cloud big data technology to obtain point cloud data of the concrete arch dam surface through high-precision measurement equipment, and can build an accurate three-dimensional model. It improves the data monitoring of the horizontal displacement of the arch dam, helps to detect small deformations and cracks in time, and thus prevent potential safety hazards.

[0029] The present invention improves the monitoring efficiency through the automated data collection, processing and analysis process. The system can automatically judge the quality of data collection and perform supplementary collection when necessary, thus reducing manual intervention.

[0030] The method and system of the present invention can achieve comprehensive coverage monitoring of concrete arch dams. Through continuous data collection and analysis, the state changes of the arch dams can be monitored in real time, providing managers with immediate feedback and helping to make quick and accurate decisions.

[0031] Based on the analysis results of point cloud big data, the system can intelligently identify abnormal deformation patterns and automatically issue an early warning when the preset warning threshold is reached. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the horizontal displacement monitoring method of concrete arch dam based on point cloud big data;

[0033] Figure 2 It is a schematic diagram of the principle of the horizontal displacement monitoring system of concrete arch dam based on point cloud big data;

[0034] Figure 3 This is a schematic diagram of the dam point cloud data. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0036] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0037] like Figure 1 As shown in FIG. 1 , the horizontal displacement monitoring method of a concrete arch dam based on point cloud big data includes the following steps:

[0038] Step 1: The data acquisition device obtains the current concrete arch dam corridor information, and the cloud data server establishes a data processing container according to the current concrete arch dam corridor information. The data processing container segments the current concrete arch dam corridor collection surface according to the segmentation feature data to obtain the current concrete arch dam corridor collection surface collection sequence;

[0039] Step 2: The data processing container segments the original data of the concrete arch dam corridor collection surface according to the segmentation feature data to obtain a comparison sequence, and obtains a concrete arch dam corridor collection surface sequence group according to the current concrete arch dam corridor collection surface collection sequence and the comparison sequence; the current concrete arch dam corridor collection surface collection sequence is sent to the data collection device, and the data collection device collects concrete arch dam corridor collection surface data according to the current concrete arch dam corridor collection surface collection sequence;

[0040] Step 3, sending the collected data of the concrete arch dam corridor to the data processing container to determine whether the data collection is qualified. If qualified, proceed to step 5; otherwise, proceed to step 4;

[0041] Step 4, obtaining the serial number of the concrete arch dam gallery collection surface that failed to collect data and the collection failure information, re-collecting the data of the concrete arch dam gallery collection surface, and returning to step 3;

[0042] Step 5: According to the data collected by the concrete arch dam gallery collection surface sequence, the surface data of the current concrete arch dam gallery collection surface is obtained; according to the surface data of the current concrete arch dam gallery collection surface, the difference of the radian data of each group in the concrete arch dam gallery collection surface sequence group is obtained, and the deformation data of the current concrete arch dam gallery collection surface is obtained; according to the deformation data of the current concrete arch dam gallery collection surface, the horizontal displacement data of the concrete arch dam is obtained;

[0043] Step six, based on the horizontal displacement data, obtain the horizontal displacement range of the concrete arch dam corridor.

[0044] The data processing container segments the concrete arch dam gallery collection surface according to the segmentation feature data to obtain the current concrete arch dam gallery collection surface collection sequence, including:

[0045] The segmentation feature is the unit segmentation arc, and the concrete arch dam gallery collection surface is segmented according to the unit segmentation arc, and the current concrete arch dam gallery collection surface collection sequence is obtained according to the segmentation order.

[0046] The data processing container segments the original data of the concrete arch dam gallery collection surface according to the segmentation feature data to obtain a comparison sequence, and obtains a concrete arch dam gallery collection surface sequence group according to the current concrete arch dam gallery collection surface collection sequence and the comparison sequence, including:

[0047] The data processing container segments the original data of the concrete arch dam gallery collection surface according to the unit segmentation arc, and obtains a segmentation sequence in the same order as the current concrete arch dam gallery collection surface collection sequence, which is the comparison sequence;

[0048] The current concrete arch dam gallery collection surface collection sequence and the collection surface with the same sequence number as the comparison sequence constitute the concrete arch dam gallery collection surface sequence group.

[0049] The obtaining of the serial number of the concrete arch dam gallery collection surface with unqualified collection and the unqualified collection information, and re-collection of the concrete arch dam gallery collection surface data, includes:

[0050] According to the concrete arch dam corridor collection surface corresponding to the serial number of the concrete arch dam corridor collection surface with unqualified collection, data collection of the concrete arch dam corridor collection surface is re-performed according to the unqualified type; the unqualified type includes that the collection coverage area of ​​the concrete arch dam corridor collection surface is unqualified.

[0051] The data collected from the concrete arch dam gallery collection surface sequence to obtain the surface data of the current concrete arch dam gallery collection surface includes:

[0052] Point cloud data is spliced ​​based on the data collected from the concrete arch dam gallery collection surface sequence to obtain surface data of the current concrete arch dam gallery collection surface; the surface data includes the curvature data of the concrete arch dam gallery collection surface.

[0053] The method of obtaining the difference of the radian data of each group in the sequence group of the concrete arch dam gallery collection surface according to the surface data of the current concrete arch dam gallery collection surface, and obtaining the deformation data of the current concrete arch dam gallery collection surface, includes:

[0054] The radian data of the original concrete arch dam gallery collection surface is obtained according to the original surface data of the concrete arch dam gallery collection surface, and the difference of the radian data of each group in the concrete arch dam gallery collection surface sequence group is obtained. The difference of the radian data of each group constitutes the deformation data of the current concrete arch dam gallery collection surface.

[0055] The method of obtaining the horizontal displacement data of the concrete arch dam according to the deformation data of the current concrete arch dam corridor collection surface includes:

[0056] The maximum value of the difference between the radian data of each group in the concrete arch dam gallery acquisition surface sequence group and the corresponding radius difference are used to obtain the horizontal displacement data.

[0057] The horizontal displacement range of the concrete arch dam corridor is obtained according to the obtained horizontal displacement data, including:

[0058] Taking the concrete arch dam gallery collection surface sequence group corresponding to the maximum value in the horizontal displacement data as the center, the current concrete arch dam gallery collection surface in the concrete arch dam gallery collection surface sequence group included in the horizontal displacement data range on both sides constitutes the horizontal displacement range of the concrete arch dam gallery.

[0059] like Figure 2 As shown, a concrete arch dam horizontal displacement monitoring system based on point cloud big data applies a concrete arch dam horizontal displacement monitoring method based on point cloud big data, including a data acquisition device, a feature acquisition device, a cloud data server, a data processing device and a communication device;

[0060] The data acquisition device, feature acquisition device and communication device are respectively connected to the data processing device; the communication device is connected to the cloud data server.

[0061] Specifically, this method mainly realizes comprehensive monitoring of the horizontal displacement of concrete arch dams through four core steps: data collection, data processing, data comparison and displacement analysis.

[0062] First, the current information of the concrete arch dam corridor is obtained through the data acquisition device. This information includes but is not limited to the shape, size, location, etc. of the corridor. Then, the cloud data server establishes a data processing container based on this information for subsequent data processing and analysis.

[0063] In the data processing container, the current concrete arch dam corridor acquisition surface is finely segmented according to the preset segmentation feature data (such as unit segmentation arc), thereby obtaining an orderly acquisition sequence. This sequence will serve as the benchmark for subsequent data acquisition and comparison.

[0064] Next, the data processing container will segment the original data of the concrete arch dam corridor acquisition surface according to the same segmentation feature data to obtain a comparison sequence. This comparison sequence has the same order and segmentation method as the current acquisition sequence, which is convenient for subsequent data comparison and analysis.

[0065] By comparing the current acquisition sequence with the comparison sequence one by one, a series of concrete arch dam corridor acquisition surface sequence groups can be formed. These sequence groups will serve as the basic units for subsequent data analysis.

[0066] After completing the data collection, the quality of the collected data needs to be judged. If the data collection is qualified, proceed to the next step; if it is unqualified, it is necessary to obtain the collection surface number and collection information of the concrete arch dam corridor where the collection is unqualified, and re-collect the data.

[0067] The key to this step is to ensure the accuracy and completeness of data collection and provide a reliable basis for subsequent data analysis.

[0068] After confirming that the data collection is correct, the data analysis phase begins. First, based on the data collected from the concrete arch dam corridor collection surface sequence, the complete surface data of the current concrete arch dam corridor collection surface is obtained through point cloud data splicing technology. These data include key information such as the curvature and shape of the corridor.

[0069] Then, by comparing the current surface data with the original surface data, the difference in the radian data of each group in the concrete arch dam corridor acquisition surface sequence group can be calculated. These differences directly reflect the deformation of the corridor at different positions.

[0070] Finally, based on these deformation data, the horizontal displacement of the concrete arch dam can be further analyzed. Specifically, by finding the maximum value of the radian data difference and combining it with the corresponding radius difference, accurate horizontal displacement data can be obtained.

[0071] After obtaining the horizontal displacement data, it is necessary to further determine the horizontal displacement range of the concrete arch dam corridor in order to ensure comprehensive monitoring of the arch dam and timely discover and deal with potential safety hazards.

[0072] The specific method is: taking the concrete arch dam gallery collection surface sequence group corresponding to the maximum value in the horizontal displacement data as the center, a certain range is expanded to both sides. The current concrete arch dam gallery collection surfaces in all concrete arch dam gallery collection surface sequence groups included in this range constitute the horizontal displacement range of the concrete arch dam gallery.

[0073] In order to realize the above monitoring method, a concrete arch dam horizontal displacement monitoring system based on point cloud big data is also proposed. The system consists of a data acquisition device, a feature acquisition device, a cloud data server, a data processing device, and a communication device. The various parts work together through efficient data transmission and interaction mechanisms to ensure the accuracy and real-time nature of the monitoring process.

[0074] Example 1: Horizontal displacement monitoring of a large concrete arch dam, such as Figure 3 Schematic diagram of the dam point cloud data shown.

[0075] In a large hydropower station project, a method for monitoring the horizontal displacement of a concrete arch dam based on point cloud big data was used to monitor the deformation of the arch dam. A high-precision laser scanner was used as a data acquisition device to conduct a comprehensive scan of the concrete arch dam corridor. The scanned data was transmitted to the data processing device in real time through a communication device.

[0076] The cloud data server automatically creates a data processing container based on the received scan data, and segments the corridor collection surface according to the preset unit segmentation arc (for example, every 10 degrees is a segmentation unit) to generate the current concrete arch dam corridor collection surface collection sequence. The data processing container compares the current collection sequence with the original data (i.e., the scan data at the initial stage of the arch dam construction) to form a concrete arch dam corridor collection surface sequence group.

[0077] After automatic analysis, it was found that some of the data on the acquisition surface had occlusion or reflection problems, resulting in incomplete data. The system automatically marked these unqualified areas and directed the data acquisition device to rescan these areas. The qualified data was further processed and the complete corridor surface data was obtained by splicing the point cloud data. Compared with the original data, the curvature difference of each sequence group was calculated, and then the deformation data of the arch dam was obtained. The system analyzed the deformation data and determined the radius difference corresponding to the maximum curvature difference, thereby obtaining the horizontal displacement data.

[0078] Based on the maximum horizontal displacement data, the system determines a horizontal displacement range including the maximum displacement point and its adjacent areas, providing precise positioning for subsequent maintenance and repair work.

[0079] Example 2: Regular monitoring of a concrete arch dam of a medium-sized reservoir

[0080] In order to ensure the safe operation of a concrete arch dam of a medium-sized reservoir, the management department uses a monitoring method based on point cloud big data to conduct regular horizontal displacement monitoring. Every quarter, drones equipped with high-precision cameras are used to collect data from the corridor. The drones fly along the preset route and take pictures of the corridor from all angles.

[0081] The collected image data is uploaded to the cloud data server. The server automatically establishes a data processing container and segments the corridor image according to the set segmentation features (such as one segmentation unit every 15 degrees) to form an acquisition sequence.

[0082] The server compares the current acquisition sequence with historical data to generate a sequence group. At the same time, the image recognition technology is used to check the quality of data acquisition, mark the image areas that are blurred, blocked or poorly lit, and perform additional acquisition in subsequent flights.

[0083] The quality-controlled image data is further processed and a complete 3D model of the corridor is obtained through 3D reconstruction technology. The deformation of the arch dam is analyzed and the horizontal displacement data is calculated by comparing it with the 3D model of the last monitoring. Finally, the horizontal displacement range is determined based on the displacement data to provide decision support for the maintenance and safety management of the arch dam.

[0084] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for monitoring the horizontal displacement of a concrete arch dam based on point cloud big data is applied to a system for monitoring the horizontal displacement of a concrete arch dam based on point cloud big data, and is characterized in that: It includes a data collection device, a feature collection device, a cloud data server, a data processing device and a communication device; The data collection device, feature collection device and communication device are respectively connected to the data processing device; the communication device is connected to the cloud data server; The steps include: Step 1: The data acquisition device obtains the current concrete arch dam corridor information, and the cloud data server establishes a data processing container according to the current concrete arch dam corridor information. The data processing container segments the current concrete arch dam corridor collection surface according to the segmentation feature data to obtain the current concrete arch dam corridor collection surface collection sequence; Step 2: The data processing container segments the original data of the concrete arch dam corridor collection surface according to the segmentation feature data to obtain a comparison sequence, and obtains a concrete arch dam corridor collection surface sequence group according to the current concrete arch dam corridor collection surface collection sequence and the comparison sequence; the current concrete arch dam corridor collection surface collection sequence is sent to the data collection device, and the data collection device collects concrete arch dam corridor collection surface data according to the current concrete arch dam corridor collection surface collection sequence; Step 3, sending the collected data of the concrete arch dam corridor to the data processing container to determine whether the data collection is qualified. If qualified, proceed to step 5; Otherwise, go to step 4; Step 4, obtaining the serial number of the concrete arch dam gallery collection surface that failed to collect data and the collection failure information, re-collecting the data of the concrete arch dam gallery collection surface, and returning to step 3; Step 5: According to the data collected by the concrete arch dam gallery collection surface sequence, the surface data of the current concrete arch dam gallery collection surface is obtained; according to the surface data of the current concrete arch dam gallery collection surface, the difference of the radian data of each group in the concrete arch dam gallery collection surface sequence group is obtained, and the deformation data of the current concrete arch dam gallery collection surface is obtained; according to the deformation data of the current concrete arch dam gallery collection surface, the horizontal displacement data of the concrete arch dam is obtained; Step six, based on the horizontal displacement data, obtain the horizontal displacement range of the concrete arch dam corridor.

2. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 1 is characterized in that: The data processing container segments the concrete arch dam gallery collection surface according to the segmentation feature data to obtain the current concrete arch dam gallery collection surface collection sequence, including: The segmentation feature is the unit segmentation arc, and the concrete arch dam gallery collection surface is segmented according to the unit segmentation arc, and the current concrete arch dam gallery collection surface collection sequence is obtained according to the segmentation order.

3. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 2 is characterized in that: The data processing container segments the original data of the concrete arch dam gallery collection surface according to the segmentation feature data to obtain a comparison sequence, and obtains a concrete arch dam gallery collection surface sequence group according to the current concrete arch dam gallery collection surface collection sequence and the comparison sequence, including: The data processing container segments the original data of the concrete arch dam gallery collection surface according to the unit segmentation arc, and obtains a segmentation sequence in the same order as the current concrete arch dam gallery collection surface collection sequence, which is the comparison sequence; The current concrete arch dam gallery collection surface collection sequence and the collection surface with the same sequence number as the comparison sequence constitute the concrete arch dam gallery collection surface sequence group.

4. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 3 is characterized in that: The obtaining of the serial number of the concrete arch dam gallery collection surface with unqualified collection and the unqualified collection information, and re-collection of the concrete arch dam gallery collection surface data, includes: According to the concrete arch dam corridor collection surface corresponding to the serial number of the concrete arch dam corridor collection surface with unqualified collection, data collection of the concrete arch dam corridor collection surface is re-performed according to the unqualified type; the unqualified type includes that the collection coverage area of ​​the concrete arch dam corridor collection surface is unqualified.

5. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 4 is characterized in that: The data collected from the concrete arch dam gallery collection surface sequence to obtain the surface data of the current concrete arch dam gallery collection surface includes: Point cloud data is spliced ​​based on the data collected from the concrete arch dam gallery collection surface sequence to obtain surface data of the current concrete arch dam gallery collection surface; the surface data includes the curvature data of the concrete arch dam gallery collection surface.

6. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 5 is characterized in that: The method of obtaining the difference of the radian data of each group in the sequence group of the concrete arch dam gallery collection surface according to the surface data of the current concrete arch dam gallery collection surface, and obtaining the deformation data of the current concrete arch dam gallery collection surface, includes: The radian data of the original concrete arch dam gallery collection surface is obtained according to the original surface data of the concrete arch dam gallery collection surface, and the difference of the radian data of each group in the concrete arch dam gallery collection surface sequence group is obtained. The difference of the radian data of each group constitutes the deformation data of the current concrete arch dam gallery collection surface.

7. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 6 is characterized in that: The method of obtaining the horizontal displacement data of the concrete arch dam according to the deformation data of the current concrete arch dam corridor collection surface includes: The maximum value of the difference between the radian data of each group in the concrete arch dam gallery acquisition surface sequence group and the corresponding radius difference are used to obtain the horizontal displacement data.

8. The method for monitoring horizontal displacement of concrete arch dams based on point cloud big data according to claim 7 is characterized in that: The horizontal displacement range of the concrete arch dam corridor is obtained according to the obtained horizontal displacement data, including: Taking the concrete arch dam gallery collection surface sequence group corresponding to the maximum value in the horizontal displacement data as the center, the current concrete arch dam gallery collection surface in the concrete arch dam gallery collection surface sequence group included in the horizontal displacement data range on both sides constitutes the horizontal displacement range of the concrete arch dam gallery.

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