Land planning survey project surveying and mapping information data analysis method and system

Abstract

The invention discloses a surveying and mapping information data analysis method and system for a land planning investigation and survey project, particularly relates to the technical field of topographic surveying and mapping, and is used for solving the technical defect of surface point cloud missing caused by fixed laser parameters in a vegetation coverage area. The method comprises the following steps: acquiring a near-infrared spectrum image, polarization characteristics and an aircraft pitch angle of a vegetation area in real time; calculating a vegetation coverage density and inverting a leaf dip angle distribution entropy value; coupling multiple scattering effects and geometric propagation characteristics to generate an actual penetration path length; dynamically increasing pulse energy based on a coverage density threshold; improving the transmitting frequency in a grading manner according to the penetration path excess proportion; transmitting a laser beam by adopting optimized parameters to generate a high-integrity earth surface point cloud data set; the problem of terrain blind areas caused by vegetation shielding is solved, and terrain model support is provided for land planning.

Description

Technical Field

[0001] The present invention relates to the technical field of topographic surveying and mapping, and more particularly to a method and system for analyzing surveying and mapping information data for land planning, investigation and surveying projects. Background Art

[0002] In land planning, surveying, and investigation projects, the integrity of terrain data directly impacts the rationality of planning solutions. Airborne laser detection and ranging (LiDAR) technology is widely used for surface topography mapping in vegetated areas such as woodlands and grasslands due to its ability to penetrate vegetation. Existing technologies collect point cloud data using LiDAR systems with fixed parameters. Their preset laser emission energy, pulse frequency, and scanning pattern are all set according to standard environments.

[0003] In current LiDAR mapping processes, fixed hardware parameters struggle to adapt to the physical obstruction characteristics of complex vegetation. This is particularly true in areas of mixed vegetation (such as interlaced zones of trees and shrubs). Uniform parameter settings lead to insufficient laser beam penetration, missing surface point cloud data, and terrain modeling errors. This problem directly hinders the reliability of subsequent land planning analysis and cannot be fundamentally addressed through conventional data processing methods. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for analyzing surveying and mapping information data of land planning and surveying projects to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for analyzing surveying and mapping information data of a land planning, investigation and survey project, comprising:

[0007] S1. Real-time acquisition of near-infrared spectral images, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit during LiDAR flight operations.

[0008] S2. Calculate vegetation cover density based on near-infrared spectral images, and invert the entropy of leaf litter angle distribution based on multi-angle near-infrared spectral polarization characteristics;

[0009] S3. Based on the leaf tilt distribution entropy and pitch angle data, the actual penetration path length of the laser beam in the vegetation layer is generated by coupling the multiple scattering effect with the geometric propagation characteristics.

[0010] S4. When the vegetation coverage density is greater than a preset density threshold, dynamically increasing the pulse energy of the laser transmitter;

[0011] S5. When the actual penetration path length is greater than the preset reference path length, the laser pulse emission frequency is dynamically increased based on the difference ratio between the actual penetration path length and the preset reference path length;

[0012] S6. Using the increased pulse energy and the increased emission frequency, a laser beam is emitted toward the surface to generate a surface point cloud dataset for land planning, surveying and analysis.

[0013] Furthermore, near-infrared spectral images of vegetation-covered areas, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit are acquired in real time during the LiDAR flight operation, including:

[0014] The reflectance distribution image of the vegetation coverage area is collected using a multispectral imaging sensor in the visible-near infrared band as a near-infrared spectral image;

[0015] The polarization-sensitive lidar receiver records the intensity components of the laser echo signal at 0°, 45°, 90°, and 135° polarization directions to generate multi-angle near-infrared spectral polarization characteristics.

[0016] The inertial measurement unit on the aircraft outputs the tilt angle of the laser radar emission axis relative to the horizontal plane in real time as pitch angle data.

[0017] Furthermore, the vegetation cover density is calculated based on the near-infrared spectral image, and the entropy of the leaf tilt distribution is inverted based on the multi-angle near-infrared spectral polarization characteristics, including:

[0018] Compare the near-infrared band reflectance value of each pixel in the near-infrared spectral image with the preset vegetation reflectance threshold, and calculate the proportion of pixels with near-infrared band reflectance values greater than the preset vegetation reflectance threshold to the total number of pixels as the vegetation cover density;

[0019] Extract the intensity components at 0°, 45°, 90°, and 135° polarization directions from the multi-angle near-infrared spectral polarization characteristics and calculate the polarization degree parameter of each pixel;

[0020] According to the polarization degree parameter of each pixel, the corresponding leaf tilt angle value is searched in the preset polarization degree-leaf tilt angle mapping relationship;

[0021] Count the leaf inclination angle values of all pixels in a preset geographical area and construct a leaf inclination angle distribution histogram;

[0022] Based on the leaf inclination angle distribution histogram, the entropy value of the leaf inclination angle distribution of the community is output through the information entropy calculation formula.

[0023] Furthermore, based on the leaf tilt distribution entropy and pitch angle data, the actual penetration path length of the laser beam in the vegetation layer is generated through the coupling analysis of multiple scattering effects and geometric propagation characteristics, including:

[0024] According to the entropy value of the leaf inclination distribution of the community, the corresponding multiple scattering intensity level is found in the preset entropy value-scattering intensity mapping relationship;

[0025] According to the pitch angle data, the geometric propagation path extension coefficient is calculated by the inverse value of the pitch angle cosine;

[0026] Multiply the multiple scattering intensity level by the geometric propagation path extension coefficient to obtain the vegetation penetration correction factor;

[0027] The vegetation penetration correction factor is multiplied by the preset standard vertical penetration path length to generate the actual penetration path length of the laser beam in the vegetation layer.

[0028] Furthermore, the preset entropy value-scattering intensity mapping relationship is established through a vegetation layer laser transmission calibration experiment, and the preset standard vertical penetration path length is obtained through lidar altimetry data.

[0029] Furthermore, when the vegetation coverage density is greater than a preset density threshold, the pulse energy of the laser transmitter is dynamically increased, including:

[0030] Compare the currently acquired vegetation cover density with a preset density threshold, which is determined through calibration experiments on typical vegetation cover areas;

[0031] When the vegetation coverage density is greater than a preset density threshold, the corresponding pulse energy increase ratio is searched in a preset energy adjustment coefficient mapping table according to the amount by which the vegetation coverage density exceeds the preset density threshold; the preset energy adjustment coefficient mapping table is established through experiments correlating vegetation types with laser penetration efficiency;

[0032] Calculate the target pulse energy value according to the pulse energy increase ratio;

[0033] The output pulse energy of the laser transmitter is adjusted to the target pulse energy value through the laser control circuit.

[0034] Furthermore, when the actual penetration path length is greater than the preset reference path length, the laser pulse emission frequency is dynamically increased based on the difference ratio between the actual penetration path length and the preset reference path length, including:

[0035] When the actual penetration path length is greater than the preset reference path, the difference ratio between the actual penetration path length and the preset reference path is calculated, specifically, the quotient of the actual penetration path length minus the preset reference path length divided by the preset reference path length;

[0036] Searching for the pulse frequency increase ratio corresponding to the difference ratio in the preset frequency adjustment mapping relationship;

[0037] Calculate the target pulse emission frequency value according to the pulse frequency increase ratio and the current pulse emission frequency of the laser transmitter;

[0038] The pulse emission frequency of the laser transmitter is adjusted to the target pulse emission frequency value through the laser control circuit.

[0039] Furthermore, the preset reference path is determined through a vertical penetration experiment in a sparsely vegetation area, and the preset frequency adjustment mapping relationship is established through a point cloud integrity calibration experiment under different penetration difficulties.

[0040] Furthermore, the laser beam is emitted toward the surface using increased pulse energy and a higher emission frequency to generate a surface point cloud dataset for land planning, surveying, and analysis, including:

[0041] The laser diode is driven by a laser transmitter control circuit to emit a near-infrared laser pulse beam with increased pulse energy and increased emission frequency;

[0042] The laser receiver collects the laser echo signal reflected by the surface and records the arrival time of the photon of each echo signal;

[0043] The laser flight time is calculated based on the difference between the photon arrival time and the laser pulse emission time;

[0044] Synchronously obtain the global positioning system coordinates of the aircraft and the attitude angle data output by the inertial navigation unit at the time of laser pulse emission;

[0045] According to the laser flight time, global positioning system coordinates and attitude angle data, the three-dimensional coordinates of the surface reflection point are generated through spatial geometry solution;

[0046] Gather the three-dimensional coordinates of all surface reflection points to form a surface point cloud dataset;

[0047] The attitude angle data includes pitch angle, roll angle and heading angle data.

[0048] In another aspect, the present invention provides a land planning, surveying and investigation project surveying and mapping information data analysis system, comprising:

[0049] Multi-source detection module, used to obtain near-infrared spectral images of vegetation-covered areas, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit in real time during lidar flight operations;

[0050] The vegetation analysis module is used to calculate the vegetation cover density based on near-infrared spectral images and to invert the entropy value of the leaf litter angle distribution based on the multi-angle near-infrared spectral polarization characteristics;

[0051] The path calculation module is used to generate the actual penetration path length of the laser beam in the vegetation layer through the coupling analysis of multiple scattering effects and geometric propagation characteristics based on the entropy value of the leaf inclination distribution and the pitch angle data;

[0052] An energy control module is used to dynamically increase the pulse energy of the laser transmitter when the vegetation coverage density is greater than a preset density threshold;

[0053] A frequency control module is used to dynamically increase the laser pulse emission frequency based on the difference ratio between the actual penetration path length and the preset reference path when the actual penetration path length is greater than the preset reference path;

[0054] The point cloud generation module is used to emit laser beams toward the surface using increased pulse energy and increased emission frequency to generate surface point cloud datasets for land planning, surveying and analysis.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. By establishing a real-time response link between vegetation physical characteristics and laser transmission, using the entropy value of leaf inclination angle distribution to quantify the multiple scattering effect, and combining the pitch angle to dynamically correct the geometric path, the adaptability limitations of the traditional fixed parameter model to complex vegetation are broken through; by accurately calculating the penetration path length, the energy attenuation misjudgment caused by the random orientation of leaves is avoided from a physical perspective, and the laser beam penetration efficiency is fundamentally guaranteed; a dual-parameter coordinated control mechanism is constructed, based on the vegetation coverage density threshold to trigger the pulse energy boost, and at the same time, the emission frequency is adjusted in stages according to the penetration path excess, forming an energy-frequency three-dimensional compensation strategy. This not only solves the one-sidedness of single parameter adjustment, but also significantly improves the point cloud integrity in high-density vegetation areas and avoids the signal-to-noise ratio deterioration caused by energy overload.

[0057] 2. At the data acquisition level, the final surface point cloud dataset overcomes terrain blind spots caused by vegetation obstruction through adaptive laser parameters, thereby improving the uniformity of the spatial distribution of surface reflection points. At the data quality level, the integration of high-precision positioning and dynamic optimization of emission parameters significantly reduces the proportion of elevation anomalies. It can accurately reflect the actual surface undulation characteristics, providing a reliable data basis for slope analysis and watershed delineation in land planning. In particular, for planning scenarios that require precise terrain support, such as forest renewal and soil and water conservation projects, vegetation interference errors are eliminated at the data source end, improving the scientific nature and implementation safety of planning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for analyzing surveying and mapping information data for a land planning, investigation and survey project according to the present invention;

[0059] Figure 2The present invention is a structural diagram of a land planning, investigation and surveying project surveying and mapping information data analysis system. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] Example 1: Figure 1 The present invention provides a method for analyzing surveying and mapping information data of a land planning, investigation and surveying project, comprising:

[0062] S1. Real-time acquisition of near-infrared spectral images, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit during LiDAR flight operations.

[0063] S2. Calculate vegetation cover density based on near-infrared spectral images, and invert the entropy of leaf litter angle distribution based on multi-angle near-infrared spectral polarization characteristics;

[0064] S3. Based on the leaf tilt distribution entropy and pitch angle data, the actual penetration path length of the laser beam in the vegetation layer is generated by coupling the multiple scattering effect with the geometric propagation characteristics.

[0065] S4. When the vegetation coverage density is greater than a preset density threshold, dynamically increasing the pulse energy of the laser transmitter;

[0066] S5. When the actual penetration path length is greater than the preset reference path length, the laser pulse emission frequency is dynamically increased based on the difference ratio between the actual penetration path length and the preset reference path length;

[0067] S6. Using the increased pulse energy and the increased emission frequency, a laser beam is emitted toward the surface to generate a surface point cloud dataset for land planning, surveying and analysis.

[0068] S1. Real-time acquisition of near-infrared spectral images, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit during LiDAR flight operations. Specific implementation is as follows:

[0069] The multispectral imaging sensor, for example, can be a HySpex-VNIR-1800 imaging device, mounted on a gimbal-stabilized platform at the bottom of the aircraft. During flight operations, the multispectral imaging sensor scans and images the vegetation-covered area at a specific acquisition rate, for example, 10 frames per second. The multispectral imaging sensor operates in the visible to near-infrared range, specifically from 780 nanometers to 1000 nanometers. The reflectance distribution image of the vegetation-covered area is generated by first recording the raw radiance values of the vegetation surface in response to solar radiation. The onboard computing unit then performs radiometric calibration using preset atmospheric correction parameters to convert these radiance values into surface reflectance values. These atmospheric correction parameters are obtained through pre-flight whiteboard calibration, which uses a standard reflectance reference plate to measure ambient radiation. The resulting near-infrared spectral image contains the geographic coordinates of each pixel. These coordinates are acquired in real time by the GPS receiver module integrated into the sensor, achieving centimeter-level accuracy.

[0070] Polarization-sensitive lidar receivers, such as the RIEGL VQ-780II laser scanning system, emit near-infrared laser pulses with a wavelength of 1064 nanometers. When the laser pulse beam strikes the vegetation surface, the receiver uses a four-channel optical system to synchronously collect echo signals at four polarization directions: 0°, 45°, 90°, and 135°. The intensity component for each polarization direction is acquired by first directing the echo signal through a polarization beam-splitting prism assembly, where four independent photodetectors measure the photon count rate in each channel. The photodetectors use avalanche photodiodes with a response time of less than 1 nanosecond. The intensity components are quantized using a 12-bit analog-to-digital converter, converting the analog signals into digital values ranging from 0 to 4095. The resulting multi-angle near-infrared spectral polarization signature dataset includes precise timestamps with nanosecond resolution.

[0071] The aircraft's onboard inertial measurement unit (IMU), such as the NovAtel SPAN-IGM-S1 integrated navigation system, consists of a three-axis fiber-optic gyroscope and a three-axis accelerometer. During flight, the IMU measures the spatial orientation parameters of the lidar transmit axis in real time at a fixed sampling frequency, such as 200 Hz. The pitch angle data is generated as follows: the system first obtains angular velocity data in the vehicle coordinate system using the gyroscope, while the accelerometer measures three-dimensional linear acceleration data. The raw sensor data is fused using a loosely coupled Kalman filter, whose state vector contains parameters such as position, velocity, and attitude angle error. After 20 iterative calculations, the tilt angle between the lidar transmit axis and the horizontal plane is output, with an accuracy of 0.01 degrees. The data output interface uses the RS422 serial communication protocol, and each data record includes a time stamp in the International Atomic Time format.

[0072] The GPS clock synchronization module establishes a unified time reference by receiving the 1PPS pulse-per-second signal transmitted by GPS satellites. The timestamp alignment process involves three stages: First, each acquisition device receives the 1PPS pulse-per-second signal via a BNC interface as a hardware synchronization trigger source. Second, the device uses field-programmable gate array technology to implement a high-precision clock counter with a frequency of 100 MHz. Finally, upon the arrival of the pulse-per-second signal, the clock counters of all devices are reset to zero and restarted. A dynamic compensation mechanism is used to control time synchronization accuracy: every 60 seconds, the drift of each device's clock counter is monitored and a linear regression model is used to calculate the clock deviation compensation coefficient. The compensation formula is: the compensation value is equal to the clock drift divided by the time interval. Ultimately, all data sets output by all acquisition devices use a unified UTC format timestamp, with time synchronization accuracy controlled within 50 microseconds.

[0073] In a specific implementation, when the aircraft is cruising at an altitude of 100 meters and a speed of 80 meters per second, the reflectivity distribution image acquired by the multispectral imaging sensor has a specific ground resolution, such as 0.1 meters. Each pixel contains reflectivity values in multiple near-infrared bands, such as 780 nanometers, 850 nanometers, 900 nanometers, and 950 nanometers. Under consistent operating conditions, the polarization-sensitive lidar receiver records laser pulses at a fixed interval, such as 0.5 meters. Each recorded point contains intensity components in four polarization directions, with the digital quantization range of the intensity components ranging from 0 to 4095. The pitch angle data recording frequency output by the inertial measurement unit is synchronized 1:1 with the laser emission frequency, ensuring that corresponding attitude data is available at each laser emission moment. The time synchronization module continuously monitors the time deviation between each device. When the detected time deviation exceeds a specific threshold, such as 100 microseconds, it automatically triggers a hardware reset circuit to re-establish clock synchronization. All collected data is transmitted in real time to the onboard data storage unit via a Gigabit Ethernet interface, using the HDF5 format for data container storage. In the storage structure, near-infrared spectral images are stored as two-dimensional floating-point arrays, multi-angle near-infrared spectral polarization features are stored as four-dimensional integer arrays, and elevation angle data are stored as one-dimensional floating-point arrays. Each array contains a unified UTC timestamp field. A lossless compression algorithm is used for data storage, with a fixed compression ratio (e.g., 2:1) to optimize storage space utilization.

[0074] S2. Calculate vegetation cover density based on near-infrared spectral images, and invert the entropy of leaf tilt distribution based on multi-angle near-infrared spectral polarization characteristics. The specific implementation is as follows:

[0075] Vegetation cover density is calculated based on near-infrared spectral imagery. The specific process is as follows: First, the near-infrared reflectance value of each pixel in the near-infrared spectral imagery is read. This reflectance value is derived from the surface reflectance value generated in step S1. The preset vegetation reflectance threshold is determined through calibration experiments using representative vegetation samples. This calibration method involves selecting various pure vegetation sample areas within the flight operation area, such as grassland, shrubs, and tree forests, and obtaining reflectance values for these areas in the 780-1000 nm band using a ground-based spectroradiometer. The measurements are performed during clear noon hours with a solar altitude greater than 45 degrees. Radiometric calibration is performed using a standard reflectance reference plate. After collecting reflectance data from at least 30 sample points, the minimum reflectance value across all samples in the 850 nm band is used as the lower threshold, and the maximum value as the upper threshold. For example, in a temperate mixed forest, the lower threshold can be set at 0.35, and the upper threshold can be set at 0.48. During the calculation process, the near-infrared reflectance value of each pixel is compared to the preset lower threshold. Pixels with reflectance values greater than the lower threshold and less than the upper threshold are considered valid vegetation pixels. The number of valid vegetation pixels is counted and divided by the total number of pixels in the image to obtain the vegetation cover density value. This value is stored in floating-point format, ranging from 0 to 1, and is accurate to four decimal places.

[0076] The degree of polarization (DOP) parameter is calculated based on the multi-angle near-infrared spectral polarization characteristics. The specific process involves extracting the intensity component values for each pixel at four polarization directions (0°, 45°, 90°, and 135°). These intensity component values are derived from the digital quantization values generated in step S1. The DOP parameter is calculated by first calculating the absolute difference between the intensity component at 45° and 135°, then calculating the sum of the intensity component at 0° and 90°, and finally dividing the absolute difference by the sum to obtain the DOP parameter. This calculation is performed in real time by the onboard processing unit. For each pixel, the calculation steps include reading the intensity values for the four directions from a four-dimensional array, performing two subtraction operations and one addition operation, and then performing a division operation. The calculation result is stored as a floating-point value between 0 and 1. In the event of an abnormal denominator of zero, the DOP parameter for that pixel is automatically assigned a value of zero, and an abnormal status code is recorded in the data quality flag.

[0077] The leaf tilt angle inversion is achieved through a preset polarization degree-leaf tilt angle mapping relationship. This mapping relationship was established through ground-based calibration experiments. The experimental method involves selecting no fewer than 100 sampling points in a typical vegetation area. At each sampling point, an electronic inclinometer is used to measure the angle between the leaf normal and the horizontal plane as the true leaf tilt angle value. Simultaneously, a polarization measurement device of the same model as the airborne equipment is used to obtain the polarization parameter at the same location. After data collection is completed, the polarization parameter is binned at fixed intervals, for example, dividing the range from 0 to 1 into 20 intervals at intervals of 0.05. The median value of the true leaf tilt angle within each interval is calculated. During implementation, the corresponding bin interval is searched in a mapping table based on the polarization parameter value of each pixel, and the leaf tilt angle value mapped to that interval is output. For example, when the polarization parameter is 0.28, the corresponding bin interval is 0.25 to 0.30, and the leaf tilt angle value mapped to this interval is 52 degrees. The leaf tilt angle values of all pixels are stored as a two-dimensional integer array with values ranging from 0 to 90 degrees.

[0078] The construction of the leaf inclination angle distribution histogram is implemented for a preset geographical area. The preset geographical area is defined as a rectangular grid of fixed size, such as a geographical range of 30 meters by 30 meters. Within the selected area, the leaf inclination angle values of all pixels are traversed and divided into intervals with fixed angle intervals, such as 5 degrees. The histogram binning rule is: the first interval covers leaf inclination angle values greater than or equal to 0 degrees and less than 5 degrees, the second interval covers leaf inclination angle values greater than or equal to 5 degrees and less than 10 degrees, and so on until the last interval covers leaf inclination angle values greater than or equal to 85 degrees and less than or equal to 90 degrees. During the statistical process, abnormal data outside the range of 0 to 90 degrees is automatically filtered out, and the total number of valid pixels is recorded. The histogram data is stored as an integer array with an array length equal to the number of angle intervals, such as 18 elements, and each element stores the pixel frequency of the corresponding interval.

[0079] The calculation of the entropy value of the leaf inclination angle distribution in the community is implemented based on the leaf inclination angle distribution histogram. First, the pixel frequency of each angle interval is divided by the total number of valid pixels to obtain the probability value of the interval. The information entropy calculation process is: take the natural logarithm of the probability value of each angle interval, multiply the logarithm by the probability value, then sum the product results of all intervals, and finally take the negative value of the sum as the entropy value. When the probability value is zero, the entropy contribution term of the interval is defined as zero. The calculation process is performed using a floating-point arithmetic unit, and the natural logarithm calculation calls the standardized implementation of the mathematical function library. The final entropy value result retains four decimal places and the value range is between 0 and 3. The entropy value calculation result of each geographical area is associated with the geographical coordinate information of the center point of the area.

[0080] In a specific implementation, when processing a near-infrared spectral image of a specific size, such as a 1000-pixel by 1000-pixel image, vegetation cover density calculations involve comparing the reflectance values of 1,000,000 pixels against a preset threshold, such as a lower threshold of 0.35. Polarization parameter calculations perform 4,000,000 arithmetic operations. Leaf inclination mapping is implemented using a 20-entry lookup table. Within a 30-meter by 30-meter geographic area, at a pixel resolution of 0.1 meter, the histogram statistics cover 90,000 pixels. Entropy calculations perform a fixed number of logarithmic operations, such as 18. All processing is performed on a dedicated image processing card in the onboard computer, with processing latency controlled within a specific timeframe, such as 100 milliseconds. The data outputs, including a vegetation cover density layer, a leaf inclination distribution layer, and an entropy matrix, are registered to a unified coordinate system using a geographic information system interface, using a specific geodetic datum, such as WGS84. A lossless compression algorithm is used during storage, and the compression parameters are set to a fixed ratio, such as a 2:1 compression ratio.

[0081] S3. Based on the leaf tilt distribution entropy and pitch angle data, the actual penetration path length of the laser beam in the vegetation layer is generated by coupling analysis of the multiple scattering effect and geometric propagation characteristics. The specific implementation is as follows:

[0082] The determination of the multiple scattering intensity level is based on the entropy value of the leaf inclination distribution of the community. The preset entropy value-scattering intensity mapping relationship is established through a laser transmission calibration experiment in the vegetation layer. The experimental method includes selecting typical sample areas of different vegetation types, such as broad-leaved forest, coniferous forest and shrub sample areas, and selecting no less than three sample areas for each type. A laser transmission test system is set up in each sample area. The system consists of a fixed laser transmitter with a wavelength of 1064 nanometers and an array receiver. The transmitter is installed at a specific height above the vegetation canopy, such as 1 meter, and the receiver array is arranged at a specific height above the ground, such as 0.5 meters. The distance between the transmitter and the receiver is set to a fixed value, such as 10 meters. During the test, the energy attenuation data of the laser beam passing through the vegetation layer is collected. Specifically, the transmission power value and the receiving power value are recorded. Each measurement is repeated a specific number of times, such as 10 times, and the average value is taken. The energy attenuation rate is calculated by subtracting the receiving power from the transmission power, divided by the transmission power, and then multiplied by 100%. The scattering intensity level classification standard is: when the attenuation rate is less than a specific value, for example, 40%, it is defined as level 1; when the attenuation rate is within a specific range, for example, 40% to 60%, it is defined as level 2; when the attenuation rate is within a specific range, for example, 60% to 80%, it is defined as level 3; when the attenuation rate is greater than a specific value, for example, 80%, it is defined as level 4. At the same time, the entropy value of the leaf inclination distribution of the sample area is obtained through ground measurement. The measurement method is to randomly select a specific number of leaves in the sample area, for example, 50 leaves, measure the inclination of each leaf with a protractor and calculate the entropy value. Finally, a discrete mapping table of entropy values and scattering intensity levels is established. The mapping table entries are set according to specific entropy value intervals, for example, each 0.5 entropy value interval corresponds to a level. During the implementation process, according to the entropy value of the leaf inclination distribution output by step S2, the nearest entropy value interval is found in the mapping table, and the corresponding multiple scattering intensity level is output.

[0083] The geometric path extension factor is calculated based on the elevation angle data. The elevation angle data is derived from the inclination angle of the lidar's transmit axis relative to the horizontal plane, output in step S1. The calculation process is as follows: First, the elevation angle is converted from degrees to radians using the formula: radians equal the angle multiplied by the circumference constant π, then divided by 180. The circumference constant π is set to a specific precision, such as 3.14159265. The cosine function of this radian value is then calculated using the CORDIC algorithm for a specific number of iterations, such as 10. Finally, the inverse of this cosine function is taken as the geometric path extension factor. The calculation process includes outlier handling: when the absolute value of the elevation angle is greater than a specific threshold, such as 85 degrees, the extension factor is fixed to a specific upper limit, such as 5.0. When the elevation angle is 0 degrees, the extension factor is exactly 1.0. The result is output with three decimal places and stored as a single-precision floating-point number.

[0084] The vegetation penetration correction factor is generated through coupled analysis. The correction factor is obtained by multiplying the multiple scattering intensity level by the geometric path extension factor. This multiplication is performed in the floating-point unit (FPU) with well-defined input parameters: the multiple scattering intensity level is a discrete integer value within a specific range, such as 1 to 4; the geometric path extension factor is a continuous floating-point value within a specific range, such as 1.0 to 5.0. The calculation performs a scalar multiplication. For example, when the multiple scattering intensity level is 3 and the geometric path extension factor is 1.15, the correction factor = 3 × 1.15 = 3.45. The calculated result is bounded: if the product is less than a specified lower limit, such as 1.0, the correction factor is forced to 1.0; if the product is greater than a specified upper limit, such as 20.0, the correction factor is forced to 20.0. The final output value is stored as a single-precision floating-point number.

[0085] The actual penetration path length is generated based on a preset standard vertical penetration path length. This preset standard vertical penetration path length is obtained using lidar altimetry data. This is achieved by conducting vertical flight surveys in sparsely vegetated areas where the vegetation cover density is below a certain threshold, such as 0.1, with the flight altitude set to a specific value, such as 100 meters. The lidar records the distance from the top of the canopy to the ground, collecting a specific number of measurement points, such as more than 1,000 valid points. The standard value is obtained by averaging the maximum and minimum values after removing them. For example, the standard value for temperate grasslands is approximately 3.5 meters. The final calculation process involves multiplying the vegetation penetration correction factor by the standard vertical penetration path length. For example, when the correction factor is 2.4 and the standard value is 3.8 meters, the actual penetration path length = 2.4 × 3.8 = 9.12 meters. The calculation result is output in meters, with two decimal places.

[0086] In a specific implementation case, when processing a geographic unit of a specific size, such as a 30-meter-by-30-meter area: first read the entropy value of the leaf community inclination distribution of the unit, such as 1.75; search in the preset entropy value-scattering intensity mapping table, the entropy value 1.75 is located in a specific interval, such as the 1.5 to 2.0 interval, corresponding to the multiple scattering intensity level 3; at the same time, read the pitch angle data of the unit, such as 28 degrees; perform the geometric propagation path extension coefficient calculation: convert 28 degrees to a radian value of 0.4887 radians, calculate the cosine value of 0.8765, and take the inverse to get 1.1406; multiply the multiple scattering intensity level 3 by the extension coefficient 1.1406 to obtain a correction factor = 3×1.1406=3.4218; read the standard vertical penetration path length of the area, such as 4.25 meters; and finally calculate the actual penetration path length = 3.4218×4.25=14.54 meters. All calculations are performed in the navigation computer, with processing time per geographic unit controlled within a specified range, such as 5 milliseconds. Data is output as a two-dimensional floating-point array, the dimensions of which correspond to the geographic grid. Each element stores the actual penetration path length and is associated with latitude and longitude coordinates.

[0087] The laser test system's transmit power is set to a specific value, such as 10 milliwatts; the receiver sensitivity is set to a specific range, such as 1 microwatt to 100 milliwatts; measurements are conducted under consistent meteorological conditions for each plot, such as a clear day with wind speeds less than 3 meters per second; when measuring leaf pitch angles on the ground, leaves are selected to cover the upper, middle, and lower layers of the canopy; and entropy calculations are performed using the same algorithm as in step S2. The final mapping table is stored in non-volatile memory and contains a specific number of entries, such as 100 pairs of correspondences. Precision controls during the calculation process include: floating-point operations complying with the IEEE 754 standard; trigonometric calculation errors less than a specific value, such as 0.001; and input parameter range checks performed before calculations.

[0088] S4. When the vegetation coverage density is greater than a preset density threshold, the pulse energy of the laser transmitter is dynamically increased, specifically implemented as follows:

[0089] The numerical comparison process between vegetation cover density and a preset density threshold is based on real-time vegetation cover density data. The preset density threshold is determined through calibration experiments using typical vegetation cover areas. The calibration method involves selecting representative areas of different vegetation types, such as farmland, forest, and grassland, and obtaining the actual vegetation cover in each area through ground measurements. The field measurement method uses a sampling method, manually identifying the vegetation cover area ratio within a 10m x 10m sample plot, while simultaneously obtaining aerial imagery data of the corresponding area. After collecting at least 50 sets of samples, a regression model is established between the measured vegetation cover density values and the values calculated in step S2. The preset density threshold is determined by taking the minimum threshold at which the model prediction error is less than a specified value, such as 5%. For example, the threshold for broad-leaved forest areas is set at 0.65. The comparison process involves reading the vegetation cover density value of the current geographic unit (derived from the floating-point value output in step S2) and comparing it with the preset density threshold stored in non-volatile memory. When the vegetation cover density exceeds the preset density threshold, the energy regulation process is triggered.

[0090] The determination of the pulse energy increase ratio is implemented through a preset energy adjustment coefficient mapping table. The preset energy adjustment coefficient mapping table is established through an experiment to associate vegetation type with laser penetration efficiency. The experimental method includes: selecting sample areas of typical vegetation types, such as high-density shrubs and medium-density tree forests, and setting different pulse energy levels in each sample area, for example, 10 mJ to 50 mJ, with an interval of 5 mJ. The echo intensity attenuation rate of the laser beam penetrating the vegetation layer is measured, and a corresponding relationship table between the excess amount and the energy increase ratio is established. Specifically: first, the excess amount is calculated to be equal to the vegetation coverage density minus the preset density threshold, and the excess amount is divided into bins at intervals of 0.05, for example, the excess amount 0-0.05 corresponds to a ratio of 1.1, and the excess amount 0.05-0.10 corresponds to a ratio of 1.2. The energy increase ratio of each bin is the energy increase value with the most significant improvement in the attenuation rate in the bin. During the implementation process, the corresponding pulse energy increase ratio is searched in the mapping table according to the calculated excess amount, and when the excess amount is at the bin boundary, the linear interpolation method is used for calculation.

[0091] The target pulse energy value is calculated based on the current pulse energy reference value and the pulse energy increase ratio. The current pulse energy reference value is derived from the preset operating parameters of the laser transmitter, such as 30 mJ. The calculation formula execution steps are as follows: the target pulse energy value is equal to the current pulse energy reference value multiplied by the pulse energy increase ratio. The calculation process is executed in the control processor. For example, when the current pulse energy reference value is 30 mJ and the pulse energy increase ratio is 1.25, the target pulse energy value = 30 × 1.25 = 37.5 mJ. The calculation result retains one decimal place, and the unit is consistent with the reference value. The calculation process includes boundary protection: when the target value exceeds the maximum allowable energy of the laser, for example, 50 mJ, it is forced to be set to 50 mJ; when the target value is lower than the minimum working energy, for example, 5 mJ, it is forced to be set to 5 mJ.

[0092] The laser transmitter's output pulse energy is adjusted via the laser control circuit. This control circuit comprises a digital-to-analog converter (DAC) and a high-voltage driver module. The specific workflow is as follows: First, the target pulse energy is converted to a corresponding control voltage. This conversion relationship is determined using a laser calibration curve. This calibration method measures the output energy at different control voltages in a laboratory environment and establishes a voltage-energy mapping table. For example, for every 1 millijoule increase in energy, the voltage increases by 0.5 volts. The DAC then outputs an analog voltage signal to the high-voltage driver module, with the DAC resolution set to a specific value, such as 12 bits. The high-voltage driver module utilizes a capacitor energy storage circuit, where the charging voltage is linearly related to the input control voltage. The linear coefficient is determined through circuit parameter calibration. Finally, the output pulse energy is varied by adjusting the charging voltage of the storage capacitor. The energy regulation response time is kept within a specific range, such as 100 milliseconds. The actual output energy is monitored in real time during the adjustment process. This monitoring method uses a spectroscope to collect a portion of the laser energy and transmit it to a photodiode. When the monitored value deviates from the target value by exceeding a specific threshold, such as 5%, a closed-loop feedback loop is triggered for readjustment.

[0093] In a specific implementation example, when processing a geographic unit with a vegetation cover density of 0.72, the preset density threshold is 0.65, and the excess is 0.72-0.65 = 0.07. The mapping table finds the excess of 0.07 in the 0.05-0.10 bin, corresponding to a pulse energy increase ratio of 1.3. The current pulse energy baseline is 25 millijoules, and the target pulse energy is 25 x 1.3 = 32.5 millijoules. The control circuit sets the output voltage of the digital-to-analog converter to a specific value (for example, 32.5 millijoules corresponds to 16.25 volts according to the calibration curve), and the high-voltage driver module completes the energy adjustment. After the adjustment, the laser transmitter operates at the new energy parameters in the next pulse cycle, for example, 100 milliseconds later.

[0094] Calibration experiments for typical vegetation cover areas were repeated under multiple seasonal conditions, such as the spring regrowth period and the summer lush period. Experiments correlating vegetation type with laser penetration efficiency considered various weather factors, such as sunny and cloudy days, with each test repeated a specific number of times, for example, ten times. Mapping tables were stored by vegetation type, with different mapping tables for coniferous and broadleaf forests, for example, and updated at a specific interval, such as quarterly. Safety protection mechanisms for the control circuit include: overvoltage protection, which automatically shuts off the circuit when the voltage exceeds a specific threshold, such as 20 volts; temperature monitoring, which reduces output power when the heat sink temperature exceeds a specific threshold, such as 60 degrees Celsius; and energy monitoring, which utilizes a photodiode sampling circuit with a specific sampling frequency, such as 1 kHz. The monitored data is digitized via an analog-to-digital converter. All parameter adjustments are recorded in a flight log file, which includes fields such as timestamp, geographic coordinates, energy value before and after adjustment, and ambient temperature. The log is stored in structured text format. Exception handling procedures include automatically switching to safe mode and issuing an alarm if the monitored value remains out of tolerance after a specific number of consecutive adjustments, such as three.

[0095] During energy adjustment, the laser pulse repetition frequency remains constant, for example, at 200 Hz. The beam divergence angle is controlled by an independent optical system and does not change with energy adjustment. The energy monitoring circuit includes a temperature compensation circuit, with the compensation coefficient determined by pre-calibration. For vegetation cover density less than or equal to a preset density threshold, the current pulse energy baseline value remains unchanged, but vegetation cover density changes are continuously monitored. When the vegetation cover density of a specific number of consecutive geographic units, for example, 10 units, exceeds the threshold, global energy adjustment mode is triggered. The control circuit firmware includes a watchdog timer that automatically resets the system if there is no response for a specific period of time, for example, 500 milliseconds.

[0096] S5. When the actual penetration path length is greater than the preset reference path length, the laser pulse emission frequency is dynamically increased based on the difference ratio between the actual penetration path length and the preset reference path length. The specific implementation is as follows:

[0097] The calculation of the difference ratio is triggered when the actual penetration path length exceeds the preset baseline path. The preset baseline path is determined through vertical penetration experiments in sparsely vegetated areas. The experimental method involves selecting a flat area with a vegetation density below a specific threshold, such as 0.1, and conducting vertical downward LiDAR detection under clear weather conditions with a midday solar altitude greater than 45 degrees. The flight altitude is set to a fixed value, such as 100 meters. A specific number of valid laser ranging points, such as at least 1,000, are collected. After removing the maximum and minimum outliers, the arithmetic mean is calculated to form the preset baseline path. For example, the baseline value measured in a temperate grassland area is 3.8 meters. The calculation process involves reading the actual penetration path length (in meters) output by step S3, subtracting the preset baseline path value, and then dividing the difference by the preset baseline path value to obtain a dimensionless difference ratio. The specific calculation steps include: the difference ratio is equal to the actual penetration path length minus the preset baseline path length, divided by the preset baseline path length. If the calculated result is less than or equal to zero, the subsequent adjustment process is not triggered, and the status flag is recorded.

[0098] The pulse frequency boost ratio is determined using a preset frequency adjustment mapping relationship. This mapping relationship was established through point cloud integrity calibration experiments under varying penetration difficulties. The experimental method involves setting different pulse transmission frequencies, such as 100 Hz to 500 Hz, in low, medium, and high vegetation density areas (covering specific values, such as 0.2, 0.5, and 0.8), and measuring the point cloud data integrity rate (the percentage of valid echo points to the theoretical maximum number of points). A table is then created that maps the difference ratio to the frequency boost ratio. Specifically, the difference ratio is binned at 0.1 intervals. For example, a difference ratio of 0 to 0.1 corresponds to a boost ratio of 1.1, and a difference ratio of 0.1 to 0.2 corresponds to a boost ratio of 1.2. The boost ratio for each bin is the minimum frequency increase required to achieve a specific target point cloud integrity rate within that bin, such as 90%. During implementation, the corresponding pulse frequency boost ratio is searched in the mapping table based on the calculated difference ratio. Linear interpolation is used when the difference ratio falls within a bin boundary.

[0099] The target pulse emission frequency is calculated based on the current pulse emission frequency and the pulse frequency boost ratio. The current pulse emission frequency is derived from the real-time operating parameters of the laser transmitter, for example, 200 Hz. The calculation formula is as follows: The target pulse emission frequency equals the current pulse emission frequency multiplied by the pulse frequency boost ratio. This calculation is performed in the flight control processor. For example, when the current frequency is 200 Hz and the boost ratio is 1.25, the target frequency = 200 × 1.25 = 250 Hz. The result is rounded to the nearest ten (e.g., 250 Hz) and is subject to hardware limits: if the target value exceeds the maximum allowable laser frequency, for example, 500 Hz, it is forced to 500 Hz; if the target value is below the minimum operating frequency, for example, 50 Hz, it is forced to 50 Hz. The calculation performs floating-point to integer rounding.

[0100] The laser transmitter's pulse frequency is adjusted via the laser control circuit. This circuit comprises a digital signal processor and a voltage-controlled oscillator (VCO). The specific workflow is as follows: First, the target pulse frequency is converted to a corresponding control voltage. This conversion relationship is determined using a frequency-voltage calibration curve. This calibration method involves measuring the output frequency at different control voltages in the laboratory and establishing a linear fit equation. For example, a 100 Hz increase in frequency corresponds to a 1.0 volt increase in voltage. Then, a digital-to-analog converter (DAC) outputs an analog voltage signal to the VCO, with the DAC resolution set to a specific value, such as 12 bits. The VCO linearly adjusts the crystal drive current based on the input voltage, thereby changing the pulse frequency. The frequency switching response time is controlled within a specific range, such as 50 milliseconds. The switching process uses a ramped approach (the frequency change rate per millisecond does not exceed a specific value, such as 10 Hz / ms) to avoid current surges. After adjustment, the output value is verified in real time using a frequency counter with a sampling period of a specific value, such as 100 milliseconds. Recalibration is triggered when the deviation exceeds a specific threshold, such as 5%.

[0101] In a specific implementation example, when processing a geographic unit with an actual penetration path length of 7.2 meters: the preset baseline path is 5.0 meters, and the difference ratio = (7.2-5.0) ÷ 5.0 = 0.44. The difference ratio of 0.44 is found in the 0.4 to 0.5 bin in the mapping table, corresponding to a boost ratio of 1.8. The current pulse transmission frequency is 150 Hz, and the target frequency = 150 × 1.8 = 270 Hz. The control circuit sets the output voltage of the digital-to-analog converter to a specific value, driving the voltage-controlled oscillator to complete the frequency adjustment. After the adjustment, the laser transmitter operates at the new frequency in the next scanning cycle (for example, after 20 milliseconds).

[0102] Additional notes on preset parameter establishment: The point cloud integrity calibration experiment considers various terrain factors, such as flat areas with slopes less than 5 degrees and slopes with slopes between 15 and 25 degrees. Each area is tested a specific number of times, for example, 10 times. Frequency adjustment mapping tables are stored by season, with different mapping tables used for growing and non-growing seasons. The update mechanism is set to automatically download new calibration data monthly. The control circuit's protection mechanisms include: overcurrent protection, which automatically limits the current when the drive current exceeds a specific threshold, such as 2 amps; frequency surge protection, which activates buffered regulation when the rate of frequency change between consecutive pulse cycles exceeds a specific value, such as 50%; and temperature drift compensation, which uses a built-in thermistor to correct the voltage output in real time. The compensation coefficient is pre-calibrated (a specific voltage offset, such as 0.01 volt, for every 1°C increase in temperature). All adjustment operations are recorded in the system log, with entries including timestamp, geographic coordinates, original frequency, target frequency, and actual output frequency. Data is stored using a circular buffer structure with a specific buffer size, such as 1000 records.

[0103] If the actual penetration path length data is invalid (for example, if the abnormal flag output in step S3 is triggered), the current frequency is maintained and an error code is recorded. If the difference ratio for a specific number of consecutive geographic units, such as five units, exceeds 0.5, emergency mode is activated to increase the frequency to its maximum value. The voltage-controlled oscillator uses phase-locked loop technology for frequency stabilization, with phase detection accuracy set to a specific value, such as 0.1 degrees. The drive power supply is isolated to prevent ground interference. If the difference ratio is less than or equal to zero, retesting is performed at specific intervals, such as 60 seconds, until the adjustment conditions are met. The hardware watchdog circuit performs a soft reset if the control signal remains unresponsive for a specific period, such as 200 milliseconds.

[0104] During the frequency increase process, the pulse width is maintained constant, for example, at 5 nanoseconds. Beam quality control is achieved through independent optical paths. Point cloud completeness is assessed using a fixed grid method, dividing a 1-cubic-meter space into 0.1-meter-by-0.1-meter-by-0.1-meter cubes, and the percentage of valid grids is calculated. The mapping table is stored in radiation-hardened memory with a read / write cycle time of less than a specified value, such as 10 microseconds. Difference ratio calculations use double-precision floating-point arithmetic, and zero protection is implemented before division operations. When the preset reference path is less than a specified value, such as 0.1 meters, a default value, such as 3.0 meters, is used instead. Geographic unit processing is performed in a raster scan mode, with a specific east-west step distance, such as 30 meters, followed by switching to the next scan zone.

[0105] Calibration experiment environmental control: The lidar wavelength is set to a specific value, such as 1064 nanometers; the scanning angle range is set to a specific value, such as ±15 degrees; and the point cloud completeness calculation excludes invalid points with a signal-to-noise ratio below a specific threshold, such as 20 decibels. The frequency adjustment mapping table creation process includes data smoothing: a sliding average filter is applied to the raw test data, with a window size of a specific value, such as three data sets. The control circuit firmware includes a priority interrupt mechanism, with frequency adjustment instructions set as high-priority tasks and response delays less than a specific value, such as 5 milliseconds. Output frequency stability is monitored using a variance calculation method. When the period variance of a specific number of consecutive pulses, such as 100 pulses, exceeds a specific threshold, such as 0.1 milliseconds², a system self-test is triggered.

[0106] S6. Using the increased pulse energy and the increased emission frequency to emit a laser beam toward the ground surface, generating a surface point cloud dataset for land planning, survey and analysis. The specific implementation is as follows:

[0107] The laser transmitter control circuit drives the laser diode to emit a laser beam. This process is based on the target pulse energy value determined in step S4 and the target pulse emission frequency parameters determined in step S5. Specifically, the target pulse energy value is converted to a corresponding drive current value using a digital-to-analog converter. The conversion relationship is determined using a pre-calibrated curve. The calibration method is to measure the output energy values at different drive currents in a laboratory environment and establish a linear mapping relationship. For example, for every increase in energy of 1 millijoule, the drive current should be increased by 0.1 ampere. A programmable timer generates a pulse trigger signal at the target frequency. The timer clock source uses a crystal oscillator with a specific frequency, such as 100 MHz. The frequency division factor is dynamically calculated based on the target frequency. The calculation process is to divide the frequency division factor by the target pulse emission frequency. The laser diode uses a gallium arsenide semiconductor device with a specific wavelength, such as 1064 nanometers, and the pulse width is controlled to a specific value, such as 5 nanoseconds. The emission optical system includes a beam expander assembly to compress the beam divergence angle to a specific range, such as 0.5 milliradians.

[0108] The laser receiver collects surface-reflected echo signals using an avalanche photodiode array, which consists of a specific number of detector elements, such as 256. Each detector element is equipped with a time-to-digital converter. The following process works as follows: the echo signal is focused by the receiving optical system and projected onto the detector array. A response is triggered when the photon energy exceeds a specific threshold, such as 0.1 picojoules. The time-to-digital converter records the arrival time of the photon (the offset relative to the system reference clock) with a specific resolution, such as 10 picoseconds. The detector element number is also recorded to identify the signal's incident location. Signal preprocessing includes noise filtering (signal intensity is marked as invalid when it falls below a specific multiple of the ambient light intensity, such as 2 times) and multi-echo separation (continuous signals within a single pulse cycle separated by a specific time interval, such as 3 nanoseconds, are segmented and recorded).

[0109] Laser time-of-flight calculations are performed using a time-difference measurement circuit. Input parameters include the laser pulse emission time (time-stamped by the transmitting circuit with a specified accuracy, such as 1 nanosecond) and the photon arrival time (time-stamped by the receiving circuit). The calculation process is as follows: the time-of-flight equals the photon arrival time minus the laser pulse emission time. This calculation is performed using a high-precision counter with a clock frequency set to a specified value, such as 10 GHz. The measurement result is converted to a floating-point nanosecond value. Data validation includes invalidating the time-of-flight if it exceeds a physical limit (e.g., 2000 nanoseconds for a maximum range of 300 meters).

[0110] The simultaneous acquisition of aircraft positioning and attitude data is achieved through multi-source sensor fusion. The GPS receiver outputs three-dimensional coordinates (longitude, latitude, and altitude) in the WGS84 coordinate system at a specific frequency, such as 10 Hz. Positioning accuracy is improved to a specific value, such as 2 cm, using real-time dynamic differencing technology. The inertial navigation unit, consisting of a three-axis gyroscope and accelerometer, outputs attitude angle data (pitch, roll, and heading) at a specific frequency, such as 200 Hz, with an angular measurement accuracy of 0.01 degrees. Time synchronization mechanisms include using the GPS timing signal as the system time base; adding a unified timestamp to all sensor data (with a specific accuracy, such as 1 microsecond); and triggering an interrupt signal when a laser emission event triggers a snapshot of the sensor data at the current moment.

[0111] The three-dimensional coordinates of the surface reflection point are calculated through spatial geometric transformation. Input parameters include: laser flight time (in nanoseconds), GPS coordinates (longitude, latitude, and altitude), and attitude angle data (pitch, roll, and heading). The calculation is performed in three steps: First, the flight time is converted to a distance value. The distance is equal to the flight time multiplied by the speed of light constant, divided by 2. The speed of light constant is 299,792,458 meters per second. Next, a rotation matrix is constructed from the vehicle coordinate system to the ground coordinate system. The matrix elements are calculated using the sine and cosine functions of the attitude angles. For example, the rotation matrix element corresponding to a pitch angle θ is cosθ. Finally, the coordinates of the target point are calculated through coordinate transformation: the target point longitude is equal to the aircraft longitude coordinate plus the distance value multiplied by the element in the first row and first column of the rotation matrix; the target point latitude is equal to the aircraft latitude coordinate plus the distance value multiplied by the element in the first row and second column of the rotation matrix; and the target point elevation is equal to the aircraft altitude plus the distance value multiplied by the element in the first row and third column of the rotation matrix. The calculation process uses double-precision floating-point operations, and matrix operations are implemented by calling the linear algebra library.

[0112] Surface point cloud datasets are generated through data aggregation and structuring. The aggregation process involves creating a data record for each valid echo point. The record fields contain three-dimensional coordinate values (X, Y, and Z coordinates, all in meters), reflection intensity values (8-bit integers), and timestamps (64-bit integers). These records are then cached in a memory buffer in chronological order. Structuring involves dividing the geographic space into grids with a specific grid size, such as 1 meter by 1 meter; performing a weighted average of multiple points falling within the same grid, with weights set based on signal strength (for example, the weight increases by 0.1 for every 10-unit increase in intensity); and generating a regularly gridded point cloud array. The data output format uses a standardized point cloud format, consisting of a file header (recording coordinate system parameters and scan time range) and a point data volume (storing 16 bytes of data per point).

[0113] The laser diode temperature is controlled by a thermoelectric cooler, maintaining temperature stability within a specified range, such as ±0.1°C. The receiving optical system's bandpass filter maintains a specific central wavelength, such as 1064 nanometers, and a specific bandwidth, such as 2 nanometers. The time synchronization circuit utilizes an anti-interference design (signal transmission utilizes shielded twisted-pair cable, with a time delay calibration accuracy of a specified value, such as 10 picoseconds). Elevation corrections in coordinate calculations include atmospheric refraction correction (using a temperature and humidity table to obtain a correction factor, such as 0.25 meters per kilometer under standard atmospheric conditions) and tidal corrections (using astronomical almanac data to account for the effects of solid tides). Point cloud data post-processing includes outlier removal (deleting a point if its elevation difference from the neighborhood average exceeds a specified threshold, such as 1 meter) and generation of a digital elevation model (using inverse distance weighted interpolation with a specified search radius, such as 2 meters).

[0114] The coaxiality of the transmitting and receiving optical paths is calibrated monthly (to a specific accuracy, such as 5 microradians). The inertial navigation unit performs zero-bias compensation every flight hour (compensation data is collected through static warm-up). Point cloud density is controlled to ensure no fewer than a specific number of points per square meter, such as 10. Data storage utilizes a segmented write strategy, generating a separate file and adding a spatial index for each specific amount of data collected, such as 1 gigabyte. Quality control metrics include: planar positioning error less than a specific value, such as 0.1 meter (verified by ground control points); elevation error less than a specific value, such as 0.05 meter (verified by differential global positioning system measurements). Exception handling procedures include: automatically switching to a backup navigation data source when a specific number of consecutive points, such as 1,000, fail to resolve; and activating a lossy compression algorithm (with a compression ratio set to a specific value, such as 4 to 1) when the remaining storage capacity falls below a specific percentage, such as 10%.

[0115] Operational timing control instructions: The timing synchronization accuracy between laser emission events and data acquisition is controlled to a specific value, such as within 5 nanoseconds; the point cloud generation delay is controlled to a specific time range, such as within 100 milliseconds; and the time alignment error between the GPS data and the laser flight time is less than a specific value, such as 1 microsecond. In a specific implementation example, when the target pulse energy value is 35 millijoules and the target pulse emission frequency is 250 Hz: the laser diode drive current is set to a specific value, such as 3.5 amps; the pulse repetition period is 4 milliseconds; a single flight mission can cover a specific area, such as 10 square kilometers, and the generated point cloud data volume is approximately a specific value, such as 50 gigabytes. All hardware operating parameters are recorded in the device log, including real-time monitoring values of temperature, voltage, and current, with a sampling frequency of a specific value, such as 100 Hz.

[0116] The solution of this embodiment establishes a dynamic response mechanism for vegetation structural characteristics, geometric paths, and emission parameters. Specifically, step S2 establishes a quantitative mapping between the entropy value of the leaf inclination distribution and the multiple scattering intensity. Step S3 calculates the geometric path extension coefficient in real time through the pitch angle, and couples it with the scattering level to generate a penetration correction factor, solving the inherent problem of inaccurate path modeling in complex terrain. Furthermore, steps S4-S5 establish a dual-parameter collaborative adjustment mechanism for pulse energy and frequency based on the vegetation cover density and the excess of the actual penetration path. It achieves adaptive optimization through bin mapping and boundary protection, which is significantly different from the single-variable control mode of the existing technology. Finally, step S6 combines the dynamically optimized laser parameters with high-precision spatiotemporal synchronization technology to form a closed-loop point cloud generation system. Through the coupling of multi-level physical models, the technical contradiction of the inability to achieve both integrity and accuracy of point cloud data in vegetation-occluded environments is resolved.

[0117] Example 2: Figure 2 The present invention provides a structural diagram of a land planning, surveying and investigation project surveying and mapping information data analysis system, which includes:

[0118] Multi-source detection module, used to obtain near-infrared spectral images of vegetation-covered areas, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit in real time during lidar flight operations;

[0119] The vegetation analysis module is used to calculate the vegetation cover density based on near-infrared spectral images and to invert the entropy value of the leaf litter angle distribution based on the multi-angle near-infrared spectral polarization characteristics;

[0120] The path calculation module is used to generate the actual penetration path length of the laser beam in the vegetation layer through the coupling analysis of multiple scattering effects and geometric propagation characteristics based on the entropy value of the leaf inclination distribution and the pitch angle data;

[0121] An energy control module is used to dynamically increase the pulse energy of the laser transmitter when the vegetation coverage density is greater than a preset density threshold;

[0122] A frequency control module is used to dynamically increase the laser pulse emission frequency based on the difference ratio between the actual penetration path length and the preset reference path when the actual penetration path length is greater than the preset reference path;

[0123] The point cloud generation module is used to emit laser beams toward the surface using increased pulse energy and increased emission frequency to generate surface point cloud datasets for land planning, surveying and analysis.

[0124] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0125] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0126] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission. Wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission methods include infrared, microwave, etc. The computer-readable storage medium can be any available medium accessible by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0127] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0129] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0131] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0132] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0133] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing surveying and mapping information data for land planning, investigation and survey projects, characterized in that: include: S1. Real-time acquisition of near-infrared spectral images, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit during LiDAR flight operations. S2. Calculate vegetation cover density based on near-infrared spectral images, and invert the entropy of leaf litter angle distribution based on multi-angle near-infrared spectral polarization characteristics; S3. Based on the leaf tilt distribution entropy and pitch angle data, the actual penetration path length of the laser beam in the vegetation layer is generated by coupling the multiple scattering effect with the geometric propagation characteristics. S4. When the vegetation coverage density is greater than a preset density threshold, dynamically increasing the pulse energy of the laser transmitter; S5. When the actual penetration path length is greater than the preset reference path length, the laser pulse emission frequency is dynamically increased based on the difference ratio between the actual penetration path length and the preset reference path length; S6. Using the increased pulse energy and the increased emission frequency, a laser beam is emitted toward the surface to generate a surface point cloud dataset for land planning, surveying and analysis.

2. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 1, characterized in that: Real-time acquisition of near-infrared spectral images of vegetation-covered areas, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit during LiDAR flight operations, including: The reflectance distribution image of the vegetation coverage area is collected using a multispectral imaging sensor in the visible-near infrared band as a near-infrared spectral image; The polarization-sensitive lidar receiver records the intensity components of the laser echo signal at 0°, 45°, 90°, and 135° polarization directions to generate multi-angle near-infrared spectral polarization characteristics. The inertial measurement unit on the aircraft outputs the tilt angle of the laser radar emission axis relative to the horizontal plane in real time as pitch angle data.

3. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 2, characterized in that: The vegetation cover density is calculated based on the near-infrared spectral image, and the entropy of the leaf tilt distribution is inverted based on the multi-angle near-infrared spectral polarization characteristics, including: Compare the near-infrared band reflectance value of each pixel in the near-infrared spectral image with the preset vegetation reflectance threshold, and calculate the proportion of pixels with near-infrared band reflectance values greater than the preset vegetation reflectance threshold to the total number of pixels as the vegetation cover density; Extract the intensity components at 0°, 45°, 90°, and 135° polarization directions from the multi-angle near-infrared spectral polarization characteristics and calculate the polarization degree parameter of each pixel; According to the polarization degree parameter of each pixel, the corresponding leaf tilt angle value is searched in the preset polarization degree-leaf tilt angle mapping relationship; Count the leaf inclination angle values of all pixels in a preset geographical area and construct a leaf inclination angle distribution histogram; Based on the leaf inclination angle distribution histogram, the entropy value of the leaf inclination angle distribution of the community is output through the information entropy calculation formula.

4. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 3, characterized in that: Based on the leaf tilt distribution entropy and pitch angle data, the actual penetration path length of the laser beam in the vegetation layer is generated through the coupling analysis of multiple scattering effects and geometric propagation characteristics, including: According to the entropy value of the leaf inclination distribution of the community, the corresponding multiple scattering intensity level is found in the preset entropy value-scattering intensity mapping relationship; According to the pitch angle data, the geometric propagation path extension coefficient is calculated by the inverse value of the pitch angle cosine; Multiply the multiple scattering intensity level by the geometric propagation path extension coefficient to obtain the vegetation penetration correction factor; The vegetation penetration correction factor is multiplied by the preset standard vertical penetration path length to generate the actual penetration path length of the laser beam in the vegetation layer.

5. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 4, characterized in that: The preset entropy value-scattering intensity mapping relationship is established through the vegetation layer laser transmission calibration experiment, and the preset standard vertical penetration path length is obtained through lidar altimetry data.

6. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 4, characterized in that: When the vegetation coverage density is greater than the preset density threshold, the pulse energy of the laser transmitter is dynamically increased, including: Compare the currently acquired vegetation cover density with a preset density threshold, which is determined through calibration experiments on typical vegetation cover areas; When the vegetation coverage density is greater than a preset density threshold, the corresponding pulse energy increase ratio is searched in a preset energy adjustment coefficient mapping table according to the amount by which the vegetation coverage density exceeds the preset density threshold; the preset energy adjustment coefficient mapping table is established through experiments correlating vegetation types with laser penetration efficiency; Calculate the target pulse energy value according to the pulse energy increase ratio; The output pulse energy of the laser transmitter is adjusted to the target pulse energy value through the laser control circuit.

7. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 6, characterized in that: When the actual penetration path length is greater than the preset reference path length, the laser pulse emission frequency is dynamically increased based on the difference ratio between the actual penetration path length and the preset reference path length, including: When the actual penetration path length is greater than the preset reference path, the difference ratio between the actual penetration path length and the preset reference path is calculated, specifically, the quotient of the actual penetration path length minus the preset reference path length divided by the preset reference path length; Searching for the pulse frequency increase ratio corresponding to the difference ratio in the preset frequency adjustment mapping relationship; Calculate the target pulse emission frequency value according to the pulse frequency increase ratio and the current pulse emission frequency of the laser transmitter; The pulse emission frequency of the laser transmitter is adjusted to the target pulse emission frequency value through the laser control circuit.

8. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 7, characterized in that: The preset reference path is determined through vertical penetration experiments in sparse vegetation areas, and the preset frequency adjustment mapping relationship is established through point cloud integrity calibration experiments under different penetration difficulties.

9. A method for analyzing surveying and mapping information data of a land planning and surveying project according to claim 7, characterized in that: The laser beam is emitted toward the ground surface using increased pulse energy and a higher transmission frequency to generate a surface point cloud dataset for land planning, surveying, and analysis, including: The laser diode is driven by a laser transmitter control circuit to emit a near-infrared laser pulse beam with increased pulse energy and increased emission frequency; The laser receiver collects the laser echo signal reflected by the surface and records the arrival time of the photon of each echo signal; The laser flight time is calculated based on the difference between the photon arrival time and the laser pulse emission time; Synchronously obtain the global positioning system coordinates of the aircraft and the attitude angle data output by the inertial navigation unit at the time of laser pulse emission; According to the laser flight time, global positioning system coordinates and attitude angle data, the three-dimensional coordinates of the surface reflection point are generated through spatial geometry solution; Gather the three-dimensional coordinates of all surface reflection points to form a surface point cloud dataset; The attitude angle data includes pitch angle, roll angle and heading angle data.

10. A land planning, investigation and surveying project surveying and mapping information data analysis system, used to implement a land planning, investigation and surveying project surveying and mapping information data analysis method according to any one of claims 1 to 9, characterized in that: include: Multi-source detection module, used to obtain near-infrared spectral images of vegetation-covered areas, multi-angle near-infrared spectral polarization characteristics, and pitch angle data of the aircraft's inertial navigation unit in real time during lidar flight operations; The vegetation analysis module is used to calculate the vegetation cover density based on near-infrared spectral images and to invert the entropy value of the leaf litter angle distribution based on the multi-angle near-infrared spectral polarization characteristics; The path calculation module is used to generate the actual penetration path length of the laser beam in the vegetation layer through the coupling analysis of multiple scattering effects and geometric propagation characteristics based on the entropy value of the leaf inclination distribution and the pitch angle data; An energy control module is used to dynamically increase the pulse energy of the laser transmitter when the vegetation coverage density is greater than a preset density threshold; A frequency control module is used to dynamically increase the laser pulse emission frequency based on the difference ratio between the actual penetration path length and the preset reference path when the actual penetration path length is greater than the preset reference path; The point cloud generation module is used to emit laser beams toward the surface using increased pulse energy and a higher emission frequency to generate surface point cloud datasets for land planning, surveying, and analysis.

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