A water level intelligent monitoring method and system based on shore-based laser radar

Abstract

The present invention proposes a water level intelligent monitoring method and system based on shore-based laser radar, which relates to the field of water level monitoring technology. It includes extracting point cloud data of an area of ​​interest through a laser radar; obtaining the horizontal correction angle of the laser radar; using the horizontal correction angle to correct the relative horizontal angle of the selected laser channel; calculating the distance and intensity of each point on the selected laser channel; determining the functional relationship between the vertical angle and the laser beam channel sequence number, and obtaining the vertical angle of the selected laser channel; calculating the point cloud coordinates according to the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel; based on the point cloud coordinate data, using the least squares method to fit the horizontal plane, obtain the water level height and correct it, so as to realize the monitoring of the water level. The present invention analyzes the point cloud data, develops algorithms, parses the radar data by itself, fits discrete point clouds, constructs water surface equations, calculates the water level height and corrects it, so as to realize the accurate monitoring of the water level.

Description

Technical Field

[0001] The present invention belongs to the technical field of water level monitoring, and in particular relates to a water level intelligent monitoring method and system based on shore-based laser radar. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As we all know, water level monitoring is an important means of flood forecasting, flood control command, safe dispatch of water conservancy projects, and water resource management and protection. Traditional water level monitoring mainly relies on hydrological stations, ship-borne sonar, single-beam or multi-beam depth sounders, airborne depth sounders, etc. to obtain water depth maps. Such methods often consume a lot of manpower and material resources and cannot achieve real-time and accurate monitoring.

[0004] As an active sensor, LiDAR has gradually become a core sensor in various fields due to its high measurement accuracy, fast response speed and strong anti-interference ability. The point cloud data obtained by LiDAR contains the position (distance, azimuth) of the target. Its spatial resolution can reach the centimeter level, and its temporal resolution can reach the millisecond level and is not affected by lighting conditions. It can work continuously day and night. The resolution of the data is usually higher than that of millimeter-wave radar or camera, and it can accurately image and form a high-precision three-dimensional point cloud. LiDAR is usually used to detect, identify and track objects in various scenes. In order to meet the application requirements of unmanned driving, vehicle-mounted obstacle avoidance, etc., LiDAR has made a major breakthrough in large-scale real-time dynamic high-precision ranging methods, which provides a solid technical foundation for the application of LiDAR water level monitoring.

[0005] Compared with hydrological stations, lidar can be installed and deployed flexibly and at a lower cost; compared with shipborne sonar, single-beam or multi-beam depth sounders and airborne depth sounders, lidar does not need to consider corrosion or adhesion caused by contact with the water surface, which leads to inaccurate measurements. With the improvement of production technology, the price of lidar has gradually decreased, making the large-scale application of lidar possible.

[0006] The inventors found that the current water level monitoring method based on LiDAR still has many shortcomings:

[0007] (1) The use of LiDAR for water level monitoring lacks real-time performance;

[0008] (2) It is impossible to achieve accurate monitoring of the entire water area. Summary of the invention

[0009] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a water level intelligent monitoring method and system based on shore-based laser radar. Based on the point cloud data collected by the shore-based laser radar, the point cloud data is analyzed, an algorithm is developed, the radar data is parsed independently, and effective point cloud information of the water surface is extracted; discrete point clouds are fitted, the water surface equation is constructed, the water level height is calculated and corrected, so as to realize accurate monitoring of the water level.

[0010] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0011] A first aspect of the present invention provides a method for intelligent water level monitoring based on shore-based laser radar.

[0012] A water level intelligent monitoring method based on shore-based laser radar comprises the following steps:

[0013] Select the area of ​​interest on the water surface and extract the point cloud data of the area of ​​interest through lidar;

[0014] Get the horizontal correction angle of the laser radar;

[0015] Correct the relative horizontal angle of the selected laser channel using the horizontal correction angle to obtain the final relative horizontal angle;

[0016] Calculate the distance and intensity of each point on the selected laser channel;

[0017] Obtain the vertical angle of each line, determine the functional relationship between the vertical angle and the laser beam channel number, and obtain the vertical angle of the selected laser channel;

[0018] Calculate the point cloud coordinates based on the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel;

[0019] Based on the point cloud coordinate data, the least squares method is used to fit the horizontal plane, the water level height is calculated and corrected, and the corrected water level height is obtained to realize water level monitoring.

[0020] A second aspect of the present invention provides a water level intelligent monitoring system based on shore-based laser radar.

[0021] An intelligent water level monitoring system based on shore-based laser radar, comprising:

[0022] The point cloud data acquisition module is configured to: select an area of ​​interest on the water surface and extract point cloud data of the area of ​​interest through a laser radar;

[0023] The correction angle acquisition module is configured to: obtain the horizontal correction angle of the laser radar;

[0024] The correction module is configured to: correct the relative horizontal angle of the selected laser channel using the horizontal correction angle to obtain a final relative horizontal angle;

[0025] The distance intensity calculation module is configured to: calculate the distance and intensity of each point on the selected laser channel;

[0026] The vertical angle calculation module is configured to: obtain the vertical angle of each line, determine the functional relationship between the vertical angle and the laser beam channel number, and obtain the vertical angle of the selected laser channel;

[0027] The point cloud coordinate calculation module is configured to: calculate the point cloud coordinates according to the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel;

[0028] The fitting correction module is configured to: fit the horizontal plane based on the point cloud coordinate data using the least squares method, calculate the water level height and correct it, obtain the corrected water level height, and realize water level monitoring.

[0029] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the water level intelligent monitoring method based on shore-based laser radar as described in the first aspect of the present invention.

[0030] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the intelligent water level monitoring method based on shore-based lidar as described in the first aspect of the present invention are implemented.

[0031] One or more of the above technical solutions have the following beneficial effects:

[0032] 1. The present invention proposes a water level intelligent monitoring method and system based on shore-based laser radar, which realizes the acquisition and real-time storage of .pcap file point cloud data, processes the water surface point cloud data of the area of ​​interest, uses the horizontal correction angle to correct the relative horizontal angle of the selected laser channel, obtains the final relative horizontal angle, calculates the distance and intensity of each point on the selected laser channel, and the vertical angle of the selected laser channel, calculates the point cloud coordinates according to the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel, finally fits the water surface equation to calculate the target frame water surface height and corrects the height, further improves the accuracy of water level intelligent perception for different water flow and water level conditions, and can obtain accurate water surface height prediction results.

[0033] 2. The present invention divides the water surface into regions of interest based on azimuth (horizontal angle and vertical angle), which facilitates the extraction of water surface point cloud features, while reducing the amount of calculation and improving the calculation speed.

[0034] 3. The present invention analyzes the UDP communication protocol, develops a self-parsing algorithm for lidar data, combines the water surface point cloud features to save valid point cloud information, and greatly reduces the amount of data calculation.

[0035] 4. The present invention uses the least square method to perform plane fitting on the collected water surface point cloud, calculates the water surface height and corrects it, thereby completing the water surface height monitoring.

[0036] 5. After testing in real scenarios, the results show that the present invention has good effectiveness and stability.

[0037] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] Figure 1 This is a flow chart of the method of the first embodiment of the present invention.

[0040] Figure 2 It is a structural schematic diagram of the shore-based laser radar data acquisition platform of the present invention.

[0041] Figure 3 The figure is a schematic diagram of capturing a pcap file for real-time storage of data of the present invention.

[0042] Figure 4 This is a schematic diagram of point cloud visualization of the present invention.

[0043] Figure 5 This is a schematic diagram of the water surface fitted by the present invention.

[0044] Figure 6 This is a schematic diagram of the water level within 300 frames. DETAILED DESCRIPTION

[0045] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0046] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0047] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0048] The overall idea proposed by the present invention is:

[0049] In view of the problem that traditional water level monitoring methods consume a lot of manpower and material resources and are difficult to meet the real-time monitoring requirements in harsh environments such as night and rainy days, the present invention proposes a water level intelligent monitoring method and system based on shore-based laser radar. By building a laser radar data acquisition platform on the shore, the area of ​​interest is selected with relative horizontal angle and vertical azimuth as parameters; an algorithm is developed to analyze the user datagram protocol (UDP), parse radar data by itself, and extract effective point cloud information on the water surface; discrete point clouds are fitted, water surface equations are constructed, water level height is calculated, and corrections are made. Finally, accurate water level prediction results are obtained, and water level is intelligently monitored.

[0050] Embodiment 1

[0051] This embodiment discloses a water level intelligent monitoring method based on shore-based laser radar.

[0052] like Figure 1 As shown, a water level intelligent monitoring method based on shore-based laser radar includes the following steps:

[0053] (1) Build a lidar data collection platform;

[0054] like Figure 2 As shown in the figure, the acquisition platform consists of a 12V lithium battery, a 12V to 220V inverter, a Leishen C32 laser radar, a tripod, a laptop and a network cable. The laser radar has a cylindrical shape, and 32 laser beams are emitted from the side of the cylinder in sequence. When encountering obstacles, they return and obtain the surrounding environment point cloud data based on the time-of-flight method. The measurement rate reaches about 600,000 points / second.

[0055] (2) Selection of regions of interest;

[0056] In order to reduce the amount of calculation, the area of ​​interest on the water surface is selected according to the actual scene, where the horizontal angle range is assumed to be: (θ 1 ,θ 2 ), the vertical angle is selected by the laser beam number, and the selected laser beam number is Φ={N 1 ,N 2 …N i}(0≤i≤31), the geometry of the region of interest is a sector.

[0057] (3) Real-time data storage;

[0058] like Figure 3 , Figure 4As shown in the figure, the laptop and the laser radar are connected via a network cable. After configuring the same IP address, the host computer can visualize the 3D point cloud data. The wireshark software can capture and send UDP protocol data packets online and store them as .pcap files.

[0059] (4) Data processing;

[0060] This study is based on self-parsing of the point cloud data packets captured in the area of ​​interest to obtain valid data.

[0061] Furthermore, in step (4), the specific implementation steps of data processing include:

[0062] S1: point cloud data capture;

[0063] In this embodiment, the data packet is composed of 12 data blocks; for a single data block, it carries the point cloud information of 32 laser reflections, assuming that the data block number is m (0-11) and the point number is j (0-31); the hexadecimal conversion function is h(); the hexadecimal byte string of each data packet block is B, such as B[3] represents the third byte of the data packet; the device packet byte string is a, such as a[3] represents the third byte of the device packet;

[0064] The calculation steps for extracting the data of the jth point P in the mth data block are as follows:

[0065] S2: Analyze the device package and obtain the horizontal correction angle A 1 and A 2 :

[0066] Horizontal correction angle A 1 , A 2 The calculation formula is:

[0067] A 1 =(16 2 a

[228] +a

[229] ) / 100,

[0068] A 2 =(16 2 a

[232] +a

[233] ) / 100;

[0069] The A1 and A2 parameters are fixed analytical parameters in each radar, and the A1 and A2 analytical formulas of each radar model are different. This embodiment uses the Leishen C32 laser radar, and the calculation formula is fixed.

[0070] S3: Parse the data packet, read and calculate the relative horizontal angle of the selected laser channel in decimal, and correct it to obtain the final relative horizontal angle θ.

[0071] According to the data packet storage data rules, the corrected relative horizontal angle θ is calculated as:

[0072] When (j%4=0)or(j%4=1),

[0073] θ=16 2 h(B[3])+h(B[2])+A 2 ;

[0074] When (j%4=2)or(j%4=3),

[0075] θ=16 2 h(B[3])+h(B[2])+A 1 ;

[0076] The horizontal angle of the .pcap data packet is a relative value. Since the transmitter of the C32 radar consists of multiple columns, the horizontal correction angle value of the column where this channel is located needs to be added to calculate the absolute horizontal angle. The correction angle value is positive clockwise and negative counterclockwise. The horizontal correction angle value is defined in the device package and is fixed for each radar.

[0077] S4: Read the selected laser channel and calculate the distance D and intensity I of each point in decimal.

[0078] D=16 2 h(B[5+3j])+h(B[4+3j])

[0079] I = h(B[6+3j]);

[0080] S5: Obtain the vertical angle V of each line according to the laser radar beam distribution, and find the functional relationship between the vertical angle and the laser beam channel number.

[0081]

[0082] S6: Calculate the point cloud (X, Y, Z) coordinates based on θ, D and V,

[0083]

[0084] S7: Select the area of ​​interest and save the water surface point cloud coordinates as an .xls file;

[0085] S8: According to the region of interest defined in Section 2.1, the algorithm pseudo code for extracting water surface point cloud data is as follows:

[0086]

[0087] In the above pseudo code:

[0088] i represents the data packet number; T represents the data packet header; p represents the .pcap packet byte string; t represents the data block header; m represents the data block number; j represents the laser beam number; B represents the hexadecimal byte string of each data packet block; r represents the re.findall function in Python; and R represents an array.

[0089] (5) Based on the data obtained in step 4, fit the horizontal plane and then calculate the water level;

[0090] The fitting is performed based on the principle of least squares method, wherein the principle of least squares method is as follows.

[0091] Assuming the plane equation

[0092] z=ax+by+c,

[0093] Construct the least squares error function of the fitting plane, the formula is:

[0094]

[0095] Treat a, b, and c as independent variables and take the derivative to obtain the system of equations

[0096]

[0097] Transpose the terms and express the above equations with matrices, that is,

[0098]

[0099] Solve for a, b, c, that is

[0100]

[0101] At this time, the plane equation z=ax+by+c fitted on the water surface in the laser radar's own coordinate system is obtained. Under ideal conditions, the solution of the equation is

[0102] a=0,b=0,z=m(constant),

[0103] Water level

[0104] h=mH 0 ,

[0105] In the formula, H 0 Set the height for the LiDAR.

[0106] In reality, a and b are not 0, but a decimal close to 0, that is, the plane is not completely parallel to the XOY plane, so the water level height needs to be corrected. The correction method is as follows.

[0107] Under ideal conditions, the water surface normal vector is

[0108]

[0109] The water surface normal vector in the laser radar coordinate system is

[0110]

[0111] The angle between two normal vectors

[0112]

[0113] Corrected water surface height

[0114]

[0115] (6) Evaluation parameters

[0116] This study uses the mean absolute error e1, root mean square error e2 and mean percentage error e3 to evaluate the performance of the water level intelligent monitoring algorithm. According to the formula definition, e1 refers to the average of the absolute value of the difference between the predicted value and the true value. Since only the absolute value of the error is considered, it is not sensitive to abnormal values ​​and better reflects the overall error of the measured water level prediction results; e2 calculates the square root of the square mean of the error. Data with larger errors will have a greater impact on the overall error. It is sensitive to large and small values ​​and can be used to analyze the stability of the prediction results; e3 can reflect the degree of deviation, but it is easily affected by the true value of the water surface. The indicator formula is:

[0117]

[0118]

[0119]

[0120] In the formula, h 0 Indicates the true water surface height, h i represents the water surface height predicted in the i-th frame, and N represents the total number of frames in the test set.

[0121] In this embodiment, more specifically:

[0122] (1) Build a lidar data collection platform;

[0123] The lake shore of Daming Lake in Jinan City, Shandong Province was selected to build a laser radar data acquisition platform, which consists of a 12V lithium battery, a 12V to 220V inverter, a Leishen C32 laser radar, a tripod, a laptop and a network cable. Among them, the laser radar has a cylindrical three-dimensional geometric shape, and 32 laser beams are emitted from the side of the cylinder in a timed sequence. When encountering obstacles, they return and obtain the surrounding environment point cloud data according to the time of flight method. The measurement rate reaches about 600,000 points / second.

[0124] (2) Selection of regions of interest;

[0125] In order to reduce the amount of calculation, the area of ​​interest on the water surface is selected according to the actual scene, where:

[0126] 1) The laser vertical angle (V) range is (-11°, -16°);

[0127] 2) Z axis value is less than H 0 ;

[0128] 3) The relative horizontal angle range (V) of laser emission is (θ 1 ,θ 2 ) The specific area is (350°, 360°) ∪ (0, 80°)

[0129] (3) Real-time data storage;

[0130] Connect the laptop and LiDAR through a network cable. After configuring the same IP address, the host computer can visualize the 3D point cloud data. The wireshark software can capture and send UDP protocol data packets online and store them as .pcap files.

[0131] (4) Data processing;

[0132] This study is based on self-parsing of the point cloud data packets captured in the area of ​​interest to obtain valid data.

[0133] Furthermore, in step (4), the specific implementation steps of data processing are carried out according to the aforementioned method, which will not be repeated here. The acquired water surface point cloud data information is as follows:

[0134] Table 1: Water surface point cloud data

[0135] Point cloud data of water surface

[0136]

[0137] Extract water surface point cloud data based on the delineated area of ​​interest.

[0138] (5) Based on the data obtained in step (4), fit the horizontal plane and then calculate the water level height.

[0139] The fitting is based on the principle of least squares method, and the fitting formula is as follows:

[0140] z=-0.0049x+0.0346y-2.0825

[0141] The plan view is as follows Figure 5 As shown, the water level information of 300 frames in 5 minutes is as follows Figure 6 shown.

[0142] (6) Evaluation parameters

[0143] The test results are shown in Table 2:

[0144]

[0145]

[0146] After experimental verification, it was found that the water level intelligent perception technology based on lidar can effectively monitor the water level, with an average absolute error of 0.057m, a root mean square error of 0.0735m, and an average percentage error of 7.588%.

[0147] Embodiment 2

[0148] This embodiment discloses a water level intelligent monitoring system based on shore-based laser radar.

[0149] An intelligent water level monitoring system based on shore-based laser radar, comprising:

[0150] The point cloud data acquisition module is configured to: select an area of ​​interest on the water surface and extract point cloud data of the area of ​​interest through a laser radar;

[0151] The correction angle acquisition module is configured to: obtain the horizontal correction angle of the laser radar;

[0152] The correction module is configured to: correct the relative horizontal angle of the selected laser channel using the horizontal correction angle to obtain a final relative horizontal angle;

[0153] The distance intensity calculation module is configured to: calculate the distance and intensity of each point on the selected laser channel;

[0154] The vertical angle calculation module is configured to: obtain the vertical angle of each line, determine the functional relationship between the vertical angle and the laser beam channel number, and obtain the vertical angle of the selected laser channel;

[0155] The point cloud coordinate calculation module is configured to: calculate the point cloud coordinates according to the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel;

[0156] The fitting correction module is configured to: fit the horizontal plane based on the point cloud coordinate data using the least squares method, calculate the water level height and correct it, obtain the corrected water level height, and realize water level monitoring.

[0157] Embodiment 3

[0158] The purpose of this embodiment is to provide a computer-readable storage medium.

[0159] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for intelligent water level monitoring based on shore-based laser radar as described in Example 1 of the present disclosure.

[0160] Embodiment 4

[0161] The purpose of this embodiment is to provide an electronic device.

[0162] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the water level intelligent monitoring method based on shore-based laser radar as described in Example 1 of the present disclosure are implemented.

[0163] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0164] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0165] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A water level intelligent monitoring method based on shore-based laser radar, characterized in that: The following steps are involved: Select the area of ​​interest on the water surface, store the data in real time, and extract the point cloud data of the area of ​​interest through LiDAR; Get the horizontal correction angle of the laser radar; The calculation formulas for the horizontal correction angles A1 and A2 are: ； It is the device packet byte string; Parse the data packet, read and calculate the relative horizontal angle of the selected laser channel in decimal and correct it to obtain the final relative horizontal angle θ; According to the data packet storage data rules, the corrected relative horizontal angle θ is calculated as: when , when , ； The horizontal angle of the data packet is a relative value. To calculate the absolute horizontal angle, you need to add the horizontal correction angle value of the column where this channel is located. The correction angle value is positive clockwise and negative counterclockwise. The horizontal correction angle value is defined in the device package and is fixed for each radar. The data block number is m, and the point number is j. The hexadecimal conversion function is h(). The hexadecimal byte string of each data packet block is B. Correct the relative horizontal angle of the selected laser channel using the horizontal correction angle to obtain the final relative horizontal angle; Calculate the distance D and intensity I of each point on the selected laser channel; ； Obtain the vertical angle of each line, determine the functional relationship between the vertical angle V and the laser beam channel number, and obtain the vertical angle of the selected laser channel; ； Calculate the point cloud coordinates based on the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel; Calculate the point cloud (X, Y, Z) coordinates based on θ, D and V, ； Based on the point cloud coordinate data, the horizontal plane is fitted to obtain the water level height. The least square method is used to fit the horizontal plane, obtain the water level height and correct it to obtain the corrected water level height, so as to realize the monitoring of the water level. Based on the principle of least squares method, assume that the plane equation is: ； Construct the least squares error function of the fitting plane, the formula is: Treat a, b, and c as independent variables and take derivatives to obtain the plane equation of the water surface fitted in the laser radar's own coordinate system: , under ideal conditions, the solution of the equation is ， Water level In the formula, H0 is the installation height of the laser radar; Obtain the angle between the water surface normal vector under ideal conditions and the water surface normal vector under the laser radar coordinate system, and correct the water level based on the angle. The correction method is as follows; Under ideal conditions, the water surface normal vector is ， The water surface normal vector in the laser radar coordinate system is The angle between two normal vectors Corrected water surface height 。 2. The water level intelligent monitoring method based on shore-based laser radar according to claim 1 is characterized in that: The geometric shape of the selected region of interest is a sector, the selected horizontal angle range is (θ1, θ2), and the vertical angle selection is represented by the laser beam number. The selected laser beam number is }(0≤i≤31).

3. The water level intelligent monitoring method based on shore-based laser radar according to claim 1 is characterized in that: Calculate the distance and intensity of each point in decimal for the selected laser channel.

4. The water level intelligent monitoring method based on shore-based laser radar according to claim 1 is characterized in that: The vertical angle of each channel data of the laser radar is obtained by table lookup method; The functional relationship between the vertical angle and the laser beam channel number is determined when the laser beam channel number is an odd number and an even number, respectively.

5. A water level intelligent monitoring system based on shore-based laser radar, characterized in that: include: The point cloud data acquisition module is configured to: select the area of ​​interest on the water surface, store the data in real time, and extract the point cloud data of the area of ​​interest through the laser radar; The correction angle acquisition module is configured to: obtain the horizontal correction angle of the laser radar; The calculation formulas for the horizontal correction angles A1 and A2 are: ； It is the device packet byte string; Parse the data packet, read and calculate the relative horizontal angle of the selected laser channel in decimal and correct it to obtain the final relative horizontal angle θ; According to the data packet storage data rules, the corrected relative horizontal angle θ is calculated as: when , when , ； The horizontal angle of the data packet is a relative value. To calculate the absolute horizontal angle, you need to add the horizontal correction angle value of the column where this channel is located. The correction angle value is positive clockwise and negative counterclockwise. The horizontal correction angle value is defined in the device package and is fixed for each radar. The data block number is m, and the point number is j. The hexadecimal conversion function is h(). The hexadecimal byte string of each data packet block is B. The correction module is configured to: correct the relative horizontal angle of the selected laser channel using the horizontal correction angle to obtain a final relative horizontal angle; The distance intensity calculation module is configured to: calculate the distance D and intensity I of each point on the selected laser channel; ； A vertical angle calculation module is configured to: obtain the vertical angle of each line, determine the functional relationship between the vertical angle and the laser beam channel number, and obtain the vertical angle of the selected laser channel; ； The point cloud coordinate calculation module is configured to: calculate the point cloud coordinates based on the final relative horizontal angle, the distance of each point, and the vertical angle of the selected laser channel; calculate the point cloud (X, Y, Z) coordinates based on θ, D and V, ； The fitting correction module is configured to: fit the horizontal plane based on the point cloud coordinate data, and then obtain the water level height, fit the horizontal plane using the least square method, obtain the water level height and correct it, obtain the corrected water level height, and realize the monitoring of the water level; Based on the principle of least squares method, assume that the plane equation is: ； Construct the least squares error function of the fitting plane, the formula is: Treat a, b, and c as independent variables and take derivatives to obtain the plane equation of the water surface fitted in the laser radar's own coordinate system: , under ideal conditions, the solution of the equation is ， Water level In the formula, H0 is the installation height of the laser radar; Obtain the angle between the water surface normal vector under ideal conditions and the water surface normal vector under the laser radar coordinate system, and correct the water level based on the angle. The correction method is as follows; Under ideal conditions, the water surface normal vector is ， The water surface normal vector in the laser radar coordinate system is The angle between two normal vectors Corrected water surface height 。 6. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the water level intelligent monitoring method based on shore-based laser radar as described in any one of claims 1 to 4 are implemented.

7. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the water level intelligent monitoring method based on shore-based lidar as described in any one of claims 1-4 are implemented.

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