Intelligent analysis method and system for surveying and mapping information data

Abstract

The invention discloses an intelligent analysis method and system for surveying and mapping information data, relates to the technical field of data analysis, and judges whether a scanning angle range or pulse repetition frequency of a laser radar system needs to be changed by recording local point cloud density, an energy attenuation coefficient and interlayer variance of point cloud data in real time and calculating a self-organizing rupture index. And if the change is needed, judging whether to adjust the scanning angle range or the pulse repetition frequency based on the real-time state vector, and determining the adjustment direction. And according to the adjusted parameters, updating the laser radar system and continuously surveying and mapping the earth surface data. Therefore, the system can flexibly determine whether to adjust the scanning angle range and the pulse repetition frequency or to adjust the scanning angle range and the pulse repetition frequency at the same time, thereby avoiding blind adjustment, reducing surveying and mapping precision fluctuation and ensuring stable point cloud quality. The finally generated point cloud data set has more reasonable density and precision, so that the real terrain of the earth surface is accurately reflected, and the accurate surveying and mapping result is ensured.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to an intelligent analysis method and system for surveying and mapping information data. Background Technology

[0002] In the mapping of surface topography, lidar has become an increasingly common choice. LiDAR actively emits laser pulses and receives the signals reflected back from the surface of objects, enabling the acquisition of high-density, high-precision spatial three-dimensional point cloud datasets in a short time. Each point in the three-dimensional point cloud dataset contains attributes such as spatial coordinates, elevation information, and echo intensity, reflecting minute surface undulations and detailed features of ground objects. After processing such as coordinate calculation, attitude correction, and noise filtering, the point cloud data can be further used to generate basic topographic data products such as digital elevation models, digital surface models, and digital orthophotos, providing high-precision spatial data support for subsequent topographic analysis, geological assessment, engineering planning, and ecological monitoring.

[0003] Among these factors, the point cloud density and ranging accuracy of the point cloud dataset acquired by lidar are key factors determining the quality of surface mapping data, directly affecting the spatial resolution, geometric realism, and elevation accuracy of the terrain model. Insufficient point cloud density leads to a lack of detailed surface information, making it impossible to accurately depict terrain undulations, valley boundaries, and feature outlines, resulting in an overly smooth surface and reduced spatial continuity in the digital elevation model. Conversely, insufficient ranging accuracy introduces elevation errors in the vertical direction, causing distortions such as artificial height, warping, or layer misalignment in the terrain model. During the surveying process, some key hardware parameters of the lidar system are closely related to the point cloud density and ranging accuracy of the final point cloud dataset generated by the lidar system. For example, the most critical hardware parameters—pulse repetition frequency (PRF) and field of view (FOV)—have a significant impact on the point cloud density and ranging accuracy. PRF determines the number of laser pulses emitted per unit time, and its magnitude directly affects the temporal sampling density of the point cloud and the energy distribution for single-point ranging: when PRF is too high, although the point cloud density increases, the energy of a single pulse decreases and the echo signal weakens, resulting in a decrease in ranging accuracy; when PRF is too low, the pulse energy is concentrated and the ranging accuracy improves, but the point cloud becomes sparse, resulting in a loss of spatial detail. The FOV determines the spread width and angular coverage of the laser beam on the ground, and its magnitude also has an opposite effect on point cloud density and ranging accuracy. When the field of view (FOV) is large, the laser performs an extended scan over a wider area of ​​the surface, increasing the area projected by each scan line, expanding the point cloud coverage, and increasing the laser intersection area per unit area, thereby improving the overall point cloud density. However, at the same time, excessively large edge scan angles lead to steep incident angles, resulting in severe tilting or even distortion of some echo signals, reducing the ranging accuracy in edge areas. Conversely, when the FOV decreases, the laser beam is concentrated on a smaller area, resulting in a more vertical signal and a more reasonable incident angle, which is beneficial for improving ranging accuracy. However, due to the narrower coverage area and reduced scan line overlap, the number of point clouds per unit area decreases, leading to a lower overall density. Therefore, in actual topographic mapping, adjusting the hardware parameters of the lidar system—pulse repetition frequency (PRF) and scan angle range (FOV)—is crucial to ensure that the mapped topographic point cloud data reflects the actual surface topography in a more detailed and accurate manner, thus ensuring the accuracy of the mapping. However, in practice, when it is necessary to adjust the pulse repetition frequency (PRF) or field of view (FOV) to ensure the point cloud density and ranging accuracy of the point cloud data, existing methods often simply adjust both parameters blindly at the same time. This kind of simple, linked adjustment is prone to coupling imbalance between parameters: for example, in areas with drastic terrain undulations or abrupt changes in reflectivity, increasing both PRF and FOV simultaneously may temporarily increase the point cloud coverage density, but it will further disperse the laser energy and significantly reduce the signal-to-noise ratio, resulting in the accumulation of ranging errors. Conversely, if both are reduced simultaneously on flat or low-reflectivity surfaces, it may lead to insufficient sampling points, loss of details, and the formation of data gaps. Existing methods lack a mechanism for identifying the "mutual influence direction" and "dominant source of mismatch" between PRF and FOV. They cannot accurately determine which parameter needs to be adjusted based on the dynamic feedback of the point cloud generation process, or whether both parameters need to be adjusted together and their corresponding adjustment directions. This results in significant fluctuations in mapping accuracy under complex terrain conditions, insufficient stability of point cloud quality, and ultimately inaccurate mapping results that cannot accurately reflect the true terrain of the surface. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above and provide a method and system for intelligent analysis of surveying and mapping information data.

[0005] In a first aspect of this invention, a method for intelligent analysis of surveying and mapping information data is first proposed, the method comprising: The local point cloud density, energy attenuation coefficient, and inter-layer variance at the time of point cloud dataset generation are recorded in real time as a real-time state vector; and the self-organized fracture index is calculated based on the real-time state vector. Based on the self-organized fracture index and the preset self-organized fracture index threshold, determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed. If changes are required, determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector; and determine the direction of adjustment for the scan angle range or the pulse repetition frequency. The surface data mapping continues by adjusting the lidar system parameters based on the adjusted scanning angle range or pulse repetition frequency.

[0006] Optionally, the steps for calculating local point cloud density are as follows: Set a sliding time window and obtain the set of point cloud coordinates within the time window; and divide the point cloud region into regular three-dimensional voxel units according to the spatial distribution of the point cloud coordinate set, with the voxel volume of each voxel unit set to V0. The total number of point clouds in each voxel unit is counted, and the total number of point clouds in each voxel unit is divided by the voxel volume V0. The ratio of the division is recorded as the sampling density of the corresponding voxel unit. Calculate the average sampling density of all voxel units as the local point cloud density for the corresponding sliding time window.

[0007] Optionally, the calculation steps for the energy decay coefficient are as follows: Set a sliding time window and record the energy value of each laser pulse emitted within the time window and its corresponding echo received energy value; then divide the emitted energy value by the corresponding echo received energy value to obtain the energy retention rate; Calculate the average energy retention rate of all emitted laser pulses within the sliding time window, and use it as the average energy retention rate of the sliding time window. Subtract the average energy retention rate from the value of 1 to obtain the energy decay coefficient of the corresponding sliding time window.

[0008] Optionally, the steps for calculating inter-layer variance are as follows: Within the set sliding time window, acquire the z-coordinate information of all point cloud data, and divide the entire point cloud vertically into K height layers at equal intervals according to the z-value range. For each height layer, the average z-coordinate of all point clouds is calculated as the center height of the corresponding height layer; the total number of point clouds contained in each layer is counted, and the total number of point clouds contained in each layer is divided by the total number of point clouds in the sliding time window to obtain the proportion of points in the corresponding height layer. Calculate the mean z-coordinate of all point cloud data within the sliding time window, and use it as a reference height. Calculate the difference between the center height of each height layer and the reference height, and divide the difference by the reference height to obtain the height deviation of each height layer. Multiply the absolute mean of each height deviation by the proportion of points in the corresponding height layer, and sum the results of the multiplication as the inter-layer variance of the corresponding sliding time window between layers.

[0009] Optionally, the steps for calculating the self-organized fracture index are as follows: The local point cloud density, energy decay coefficient, and inter-layer variance of each sliding time window are min-max normalized and mapped to the range of values ​​0-1. The local point cloud density, energy attenuation coefficient, and inter-layer variance after normalization for each sliding time window are calculated pairwise, and the corresponding absolute differences are calculated. The absolute difference between the local point cloud density and the energy attenuation coefficient is recorded as the first tension factor, the absolute difference between the local point cloud density and the inter-layer variance is recorded as the second tension factor, and the absolute difference between the energy attenuation coefficients is recorded as the third tension factor. The first tension factor, the second tension factor, and the third tension factor are all compressed through a preset perturbation compression function to obtain the compressed first tension factor, the compressed second tension factor, and the compressed third tension factor. Calculate the squares of the first tension factor, the second tension factor, and the third tension factor after compression, respectively, add the calculated squares together, and divide the arithmetic square root of the sum of squares by 3 to obtain the self-organized fracture degree of the corresponding sliding time window. The self-organized fracture degree of each sliding time window is compared with the preset self-organized fracture degree threshold. Sliding time windows with a self-organized fracture degree not less than the preset self-organized fracture degree threshold are recorded as abnormal windows. The total number of abnormal windows is divided by the total number of sliding time windows to obtain the self-organized fracture degree index.

[0010] Optionally, the step of determining whether the scanning angle range or pulse repetition frequency of the current lidar system parameters needs to be changed based on the self-organized fracture index and a preset self-organized fracture index threshold is as follows: Compare the self-organized fracture index with the preset self-organized fracture index threshold. If the self-organized fracture index is not less than the preset self-organized fracture index threshold, it means that the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed. If the self-organized fracture index is less than the preset self-organized fracture index threshold, it means that the scanning angle range or pulse repetition frequency of the current lidar system parameters does not need to be changed; the terrain point cloud dataset will continue to be generated according to the scanning angle range and pulse repetition frequency set by the current lidar system parameters.

[0011] Optionally, the step of determining whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector is as follows: Based on the local point cloud density, energy attenuation coefficient, and interlayer variance of two adjacent sliding time windows, calculate the partial derivative of the local point cloud density with respect to the energy attenuation coefficient as the first partial derivative, calculate the partial derivative of the local point cloud density with respect to the interlayer variance as the second partial derivative, divide the first partial derivative by the second partial derivative, and take the result of the division as the mismatch field direction value of the two adjacent sliding time windows. Compare the mismatch field direction values ​​of all two adjacent sliding time windows with the value 1, and count the total number of mismatch field direction values ​​greater than 1, the total number of mismatch field direction values ​​less than 1, and the total number of mismatch field direction values ​​equal to 1. Calculate the ratio of the total number of mismatch field direction values ​​greater than 1 to the total number of mismatch field direction values, and record it as the first ratio; the ratio of the total number of mismatch field direction values ​​equal to 1 to the total number of mismatch field direction values, and record it as the second ratio; the ratio of the total number of mismatch field direction values ​​less than 1 to the total number of mismatch field direction values, and record it as the third ratio. The decision to change the scanning angle range or the pulse repetition frequency is determined based on the first, second, and third percentages.

[0012] Optionally, the steps for determining whether to change the scan angle range or the pulse repetition frequency based on the first proportion, the second proportion, and the third proportion are as follows: Compare the first, second, and third percentages according to their magnitude. If the first percentage is the largest, only the pulse repetition frequency needs to be adjusted. If the second percentage is the largest, both the scan angle range and the pulse repetition frequency need to be adjusted. If the third percentage is the largest, only the scan angle range needs to be adjusted.

[0013] Optionally, the steps for determining the adjustment direction of the scan angle range or pulse repetition frequency are as follows: Step 1: When only the pulse repetition frequency needs to be adjusted, calculate the mean of all first partial derivatives and record it as the first mean. If the first mean is not less than 0, decrease the pulse repetition frequency; if the first mean is less than 0, increase the pulse repetition frequency. Step 2: When only the scanning angle range needs to be adjusted, calculate the mean of all second partial derivatives and record it as the second mean. If the second mean is not less than 0, decrease the scanning angle range; if the second mean is less than 0, increase the scanning angle range. Step 3: When it is necessary to adjust the scanning angle range and pulse repetition frequency simultaneously, the specific adjustment direction of the scanning angle range and pulse repetition frequency is determined based on the average of the first partial derivatives and the average of the second partial derivatives in Step 1 and Step 2.

[0014] In a second aspect of this invention, a surveying and mapping information data intelligent analysis system is proposed, the system comprising: The fracturing module records the local point cloud density, energy attenuation coefficient, and inter-layer variance as a real-time state vector during the generation of the point cloud dataset; and calculates the self-organized fracturing index based on the real-time state vector. Judgment module: Based on the self-organized fracture index and the preset self-organized fracture index threshold, determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed; Modify module: If changes are required, determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector; and determine the direction of adjustment for the scan angle range or the pulse repetition frequency. Mapping and Analysis Module: The module continues mapping the surface data by adjusting the lidar system parameters based on the adjusted scanning angle range or pulse repetition frequency. Beneficial Effects of this Invention: This invention proposes an intelligent analysis method and system for surveying and mapping information data. It uses real-time recording of local point cloud density, energy attenuation coefficient, and inter-layer variance as a real-time state vector during point cloud dataset generation. Based on this real-time state vector, it calculates the self-organized fracture index to determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters needs to be changed. If a change is needed, it determines whether to adjust the scanning angle range or the pulse repetition frequency based on the real-time state vector, and then determines the direction of adjustment. The lidar system parameters are then adjusted according to the adjusted scanning angle range or pulse repetition frequency to continue mapping the surface data. This allows for specific determination of whether to adjust the scanning angle range, the pulse repetition frequency, or both, and the direction of parameter adjustment. This avoids blindly adjusting both the scanning angle range and the pulse repetition frequency simultaneously, resulting in smaller fluctuations in lidar mapping accuracy, higher point cloud quality stability, and more reasonable density and measurement accuracy of the generated point cloud dataset. Ultimately, this leads to accurate surface topography mapping results that accurately reflect the true surface topography. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 A flowchart of a method for intelligent analysis of surveying and mapping information data; Figure 2 This is a framework diagram of an intelligent analysis system for surveying and mapping information data. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides an intelligent analysis method for surveying and mapping information data. See also... Figure 1 , Figure 1 A flowchart illustrating an intelligent analysis method for surveying and mapping information data provided in an embodiment of the present invention. The method includes the following steps: S1: Real-time recording of local point cloud density, energy attenuation coefficient, and inter-layer variance during point cloud dataset generation as a real-time state vector; and calculation of self-organized fracture index based on the real-time state vector. S2: Based on the self-organized fracture index and the preset self-organized fracture index threshold, determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed; S3: If changes are required, determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector; and determine the direction of adjustment for the scan angle range or the pulse repetition frequency. S4: Adjust the lidar system parameters according to the adjusted scanning angle range or pulse repetition frequency to continue mapping the surface data.

[0019] Based on the intelligent analysis method for surveying and mapping information provided in this invention, the above-mentioned method can specifically determine whether to adjust the scanning angle range, the pulse repetition frequency, or both, and determine the specific direction of parameter adjustment. In this way, the scanning angle range and the pulse repetition frequency are not blindly adjusted together, resulting in smaller fluctuations in the mapping accuracy of the lidar, higher stability of the point cloud quality, and more reasonable density and measurement accuracy of the generated point cloud dataset. This makes the final mapping result of the surface topography accurate and can accurately reflect the true surface topography.

[0020] In one embodiment, S1: Real-time recording of local point cloud density, energy attenuation coefficient, and interlayer variance during point cloud dataset generation as a real-time state vector; and calculating the self-organized fracture index based on the real-time state vector. In one implementation, the local point cloud density, energy attenuation coefficient, and inter-layer variance are recorded in real time as a real-time state vector when the point cloud dataset is generated. Specifically, the calculation steps for the local point cloud density are as follows: a sliding time window is set, and the set of point cloud coordinates within the time window is obtained; and according to the spatial distribution of the point cloud coordinate set, the point cloud region is divided into regular three-dimensional voxel units (mesh structure), and the voxel volume of each voxel unit is set to V0; this mesh division is used to perform localized statistics on the point cloud in space, so that the density calculation can reflect the actual sampling conditions of different regions; The total number of point clouds for each voxel is counted. The total number of point clouds for each voxel is divided by the voxel volume V0. The ratio of the division is recorded as the sampling density of the corresponding voxel. The average sampling density of all voxel units is calculated as the local point cloud density of the corresponding sliding time window.

[0021] It should be noted that when calculating local point cloud density, the continuously acquired point cloud data in the time dimension is first limited to a sliding time window. The length of this time window, ΔT, is typically set based on the pulse repetition frequency (PRF) of the lidar, generally between 0.1 and 1 second, to ensure that each window contains a sufficient number of point clouds for density assessment while maintaining continuous and stable temporal resolution. In the spatial dimension, a three-dimensional Cartesian grid is used to divide the point cloud region into regular voxel units of equal volume. That is, by setting a uniform voxel side length l, the scanned area is divided into blocks in the x, y, and z directions, generating a voxel grid structure; where the volume of each voxel is V0 = l³. The setting of the voxel side length l depends on the spatial resolution required for the mapping task. If micro-topographic features need to be identified, it can be set to 0.2–0.5 meters; if used for wide-area modeling, it can be set to more than 1 meter. Furthermore, the set of point cloud coordinates and their spatial distribution within the sliding time window mainly rely on the synchronous data acquisition and built-in positioning mechanism of the lidar system. Specifically, after each laser pulse is emitted and the echo is received, the lidar records the three-dimensional coordinates (x, y, z) and corresponding timestamp t of the echo point in real time. These points constitute the set of point cloud coordinates within the time window ΔT. To obtain its spatial distribution, the system combines the lidar's own attitude information (such as pitch, y, and roll angles provided by the IMU) and position information (such as latitude, longitude, and altitude coordinates provided by GNSS) to convert all the original polar coordinate format point cloud data into a three-dimensional Cartesian coordinate system under a unified reference frame, forming a structured point cloud dataset. This voxel partitioning method not only facilitates the spatial positioning of point cloud distribution but also significantly improves the robustness and computational efficiency of local density statistics. In one implementation, the steps for calculating the energy attenuation coefficient are as follows: Set a sliding time window and record the energy value of each laser pulse emitted within the time window and its corresponding echo received energy value; then divide the emitted energy value by the corresponding echo received energy value to obtain the energy retention rate; Calculate the average energy retention rate of all emitted laser pulses within the sliding time window, and use it as the average energy retention rate of the sliding time window. Subtract the average energy retention rate from the value of 1 to obtain the energy decay coefficient of the corresponding sliding time window.

[0022] It should be noted that in a lidar system, the emitted energy value of each laser pulse and its corresponding received echo energy value are automatically measured and synchronously recorded by the system's built-in laser emission module and echo receiving module. Specifically, the laser emission module, through an electrically driven control unit, can precisely control and monitor the emission power and duration of each pulse in real time, thereby calculating the emitted energy value. Simultaneously, the echo receiving module (usually an avalanche photodiode, APD, or photomultiplier) converts the laser signal reflected back from the target into an electrical signal. The system calculates the corresponding echo energy value by analyzing the voltage amplitude, pulse area, or integrated power of the echo signal. These data are all timestamped and written to the system cache, ensuring that each pair of emitted and received energy values ​​corresponds one-to-one, thus guaranteeing the accuracy of subsequent energy retention rate calculations.

[0023] In one implementation, the steps for calculating inter-layer variance are as follows: Within the set sliding time window, the z-coordinate information of all point cloud data is acquired, and the entire point cloud is divided into K height layers at equal intervals in the vertical direction according to the range of z values ​​(i.e. the difference between the maximum height and the minimum height). The purpose of this division is to structurally group the point cloud data according to different vertical distribution levels, thereby capturing the point cloud density and elevation fluctuation in different height ranges. For each height layer, the average z-coordinate of all point clouds is calculated as the center height of the corresponding height layer; the total number of point clouds contained in each layer is counted, and the total number of point clouds contained in each layer is divided by the total number of point clouds in the sliding time window to obtain the proportion of points in the corresponding height layer. Calculate the mean z-coordinate of all point cloud data within the sliding time window, and use it as a reference height. Calculate the difference between the center height and the reference height of each height layer, and divide the difference by the reference height to obtain the height deviation of each height layer. Multiply the absolute mean of each height deviation by the proportion of points in the corresponding height layer, and sum the results as the inter-layer variance of the corresponding sliding time window. It should be noted that when dividing the point cloud into K height layers at equal intervals in the vertical direction, the value of K usually depends on the overall height range of the point cloud (i.e., the difference between the maximum and minimum heights), the vertical distribution density of the point cloud, and the terrain resolution required for the mapping task. In practical applications, the typical value of K is between 5 and 20. If the terrain is undulating and multiple structures (such as buildings, vegetation, and the ground surface) need to be analyzed, a larger K (such as 15 or 20) can be used; if the terrain is relatively flat or the point cloud is sparse, a smaller K (such as 5 or 8) can be used to avoid statistical failure due to insufficient samples within the layer. The specific choice of K value can be determined according to the approximate ground surface type of the mapping task, and is not limited or elaborated here. In one implementation method, the steps for calculating the self-organized fracture index are as follows: The local point cloud density, energy decay coefficient, and inter-layer variance of each sliding time window are min-max normalized and mapped to the range of values ​​0-1. For each sliding time window, the normalized local point cloud density, energy attenuation coefficient, and inter-layer variance are calculated pairwise, and the corresponding absolute differences are calculated. The absolute difference between the local point cloud density and the energy attenuation coefficient is denoted as the first tension factor, the absolute difference between the local point cloud density and the inter-layer variance is denoted as the second tension factor, and the absolute difference between the energy attenuation coefficients is denoted as the third tension factor. Second tension factor Third tension factor All are compressed using a preset perturbation function Compression yields the first tension factor after compression. The second tension factor after compression The third tension factor after compression ; Calculate the first tension factor after compression respectively The second tension factor after compression The third tension factor after compression The square of the sum of the squares is calculated, and the arithmetic square root of the sum of the squares is divided by 3 to obtain the self-organized fracture degree of the corresponding sliding time window. The self-organized fracture degree of each sliding time window is compared with the preset self-organized fracture degree threshold. Sliding time windows with a self-organized fracture degree not less than the preset self-organized fracture degree threshold are recorded as abnormal windows. The total number of abnormal windows is divided by the total number of sliding time windows to obtain the self-organized fracture degree index.

[0024] It should be noted that the calculation of local point cloud density, energy attenuation coefficient, and inter-layer variance using the above method is to accurately quantify surface features and provide basic data for subsequent parameter adjustments. The purpose and benefits of each calculation step are as follows: The calculation of local point cloud density, based on voxel partitioning, divides the space into equal-volume units and quantifies the density of the point cloud in a local area by counting the number of points within each voxel. The advantage of this method is that it can accurately reflect terrain details, especially in areas with significant terrain undulations, effectively capturing changes in surface details. Using a regular three-dimensional voxel grid ensures the stability and consistency of the point cloud density calculation, avoiding calculation deviations or inhomogeneities that may occur with other methods. This method also has the advantage of being unaffected by uneven point cloud distribution, providing reliable data under different terrain conditions. The calculation of the energy attenuation coefficient reflects the degree of laser energy loss during propagation by recording the emission and echo energy of each laser pulse. This method calculates the ratio of the echo to the emitted signal and then performs normalization to eliminate the initial energy differences in laser emission under different environmental conditions, making the calculation results universal and adaptable to various measurement environments and conditions. By tracking energy attenuation, the reflection characteristics of the laser beam and the ground surface can be identified in a timely manner. For example, the impact of vegetation, ground roughness, and meteorological conditions on signal propagation can be determined, providing a basis for dynamically adjusting lidar parameters (such as PRF and FOV) and ensuring measurement accuracy. The calculation of inter-layer variance, by analyzing the height distribution of point cloud data, quantifies the differences between different height layers, effectively reflecting the complexity of terrain undulations. Through inter-layer variance calculation, areas with significant elevation changes can be identified, especially in environments with numerous buildings, vegetation, or complex terrain, capturing the impact of these features on the surface point cloud distribution. The advantage of this method is that it not only provides detailed information in the vertical direction but also combines it with point cloud density on the horizontal plane to comprehensively assess the spatial structural complexity of the ground surface.

[0025] Therefore, the advantage of the above method lies in its ability to comprehensively and accurately characterize the three-dimensional information of the surveyed area by combining multiple features of point cloud spatial distribution, energy attenuation, and height differences. This approach can comprehensively capture the characteristics of terrain changes, environmental influences, and the measurement system itself, thus providing a more scientific and stable data foundation for subsequent adjustment of lidar system parameters.

[0026] It's important to note that the advantage of calculating the self-organized fracture index using the above method lies in its comprehensive consideration of three key parameters: local point cloud density, energy attenuation coefficient, and inter-layer variance. These three parameters reflect the spatial distribution, signal quality, and vertical structural characteristics of the point cloud during lidar scanning from different dimensions. Min-max normalization converts these parameters with different dimensions into a unified numerical range (0-1), ensuring the universality and comparability of the calculation results. By calculating the absolute differences pairwise, the "tension" between different features can be captured—that is, whether these parameters are spatially incompatible or mismatched in measurement, reflecting potential system mismatch or instability. For example, the difference between local point cloud density and energy attenuation coefficient can reveal whether the point cloud sampling is sufficiently dense in areas with significant signal attenuation, or whether sample sparsity has occurred; the difference between local point cloud density and inter-layer variance can reveal whether the terrain structure is too flat or has excessive undulations, affecting the accuracy and reliability of the measurement. By compressing these tension factors using a perturbation compression function, the sensitivity to abnormal changes is kept within a reasonable range, preventing excessive system response due to extreme local changes. Finally, the self-organized fracturing degree is obtained by taking the square root of the sum of the squares of each tension factor and dividing by 3. This value comprehensively reflects the "disorder" or "instability" of the system in its current state. By comparing it with a preset self-organized fracturing degree threshold, if the self-organized fracturing degree exceeds the threshold, it indicates that the system is in an unstable state, and the parameters of the lidar (such as the scanning angle range FOV or pulse repetition frequency PRF) need to be adjusted. The advantage of this calculation method is that it can dynamically and accurately reflect the working state of the lidar in a specific terrain environment, promptly detect system parameter mismatches or inadequacies, and thus optimize the measurement results by adjusting the lidar parameters, thereby improving mapping accuracy and efficiency.

[0027] In one embodiment, S2: The step of determining whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed based on the self-organized fracture index and a preset self-organized fracture index threshold is as follows: Compare the self-organized fracture index with the preset self-organized fracture index threshold. If the self-organized fracture index is not less than the preset self-organized fracture index threshold, it means that the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed. If the self-organized fracture index is less than the preset self-organized fracture index threshold, it means that the scanning angle range or pulse repetition frequency of the current lidar system parameters does not need to be changed; the point cloud dataset of the terrain will continue to be generated according to the scanning angle range or pulse repetition frequency set by the current lidar system parameters.

[0028] It should be noted that, based on the comparison between the self-organized fracturing index and the preset self-organized fracturing index threshold, the system can dynamically determine whether to adjust the LiDAR's scanning angle range (FOV) or pulse repetition frequency (PRF). Specifically, if the currently calculated self-organized fracturing index is not less than the preset threshold, it indicates that the point cloud data has entered an unstable state under the current LiDAR system settings, potentially leading to insufficient information or decreased accuracy. For example, the point cloud density may be too low, or the measurement error may be large due to excessive surface undulation. In this case, the system will consider the current LiDAR operating state to be unsuitable, and therefore needs to restore the system's balance by adjusting the scanning angle range (FOV) or pulse repetition frequency (PRF) to improve the quality of the point cloud data and ensure measurement accuracy. For example, suppose that in a certain LiDAR scan, the point cloud density in the measurement area is low, and the ground undulation is large. The calculated self-organized fracturing index exceeds the preset threshold. In this case, the system determines that the current scanning angle or pulse repetition frequency is not adapted to the terrain features. Therefore, the system might decide to reduce the field of view (FOV) to concentrate the laser beam's scanning area, increasing the point cloud density per unit area, or reduce the pulse repetition frequency (PRF) to reduce signal attenuation and improve ranging accuracy, thereby enhancing the system's adaptability and measurement precision. Conversely, if the self-organized breakage index is less than a preset threshold, it indicates that the current lidar system is operating in a relatively stable state, with parameters such as point cloud density, energy attenuation, and inter-layer variance meeting requirements, and the system's sampling density and ranging accuracy sufficient to meet measurement needs. In this case, the system will continue to use the currently set FOV and PRF to avoid unnecessary parameter adjustments, thereby improving system operating efficiency and reducing resource waste. Through this mechanism, the system can dynamically adjust lidar parameters based on real-time data changes during terrain mapping, ensuring the quality of measurement data while improving the system's adaptability and efficiency.

[0029] In one embodiment, S3: If a change is required, determine whether to change the scanning angle range or the pulse repetition frequency based on the real-time state vector, and determine the adjustment direction of the scanning angle range or the pulse repetition frequency. In one implementation, the step of determining whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector is as follows: based on the local point cloud density of two adjacent sliding time windows. Energy attenuation coefficient and inter-layer variance Calculate local point cloud density Energy decay coefficient partial derivatives ,remember Using the first partial derivative, calculate the local point cloud density. inter-layer variance partial derivatives ,remember The second partial derivative is used as the first partial derivative. The first partial derivative is divided by the second partial derivative, and the result of the division is used as the mismatch field direction value of two adjacent sliding time windows. The mismatch field direction values ​​of all two adjacent sliding time windows are compared with the value 1. The total number of mismatch field direction values ​​greater than 1, the total number of mismatch field direction values ​​less than 1, and the total number of mismatch field direction values ​​equal to 1 are counted. The ratio of the total number of mismatch field direction values ​​greater than 1 to the total number of mismatch field direction values ​​is calculated and recorded as the first ratio. The ratio of the total number of mismatch field direction values ​​equal to 1 to the total number of mismatch field direction values ​​is recorded as the second ratio. The ratio of the total number of mismatch field direction values ​​less than 1 to the total number of mismatch field direction values ​​is recorded as the third ratio. Compare the first, second, and third proportions in order of magnitude. If the first proportion is the largest, it indicates a mismatch dominated by the energy domain, in which case only the pulse repetition frequency needs to be adjusted. If the second proportion is the largest, it indicates a mismatch between the two domains, in which case both the scan angle range and the pulse repetition frequency need to be adjusted. If the third proportion is the largest, it indicates a mismatch dominated by the geometric domain, in which case only the scan angle range needs to be adjusted.

[0030] It's important to note that when the first proportion is largest—that is, the proportion of mismatch field direction values ​​greater than 1 is largest—it means that within two adjacent sliding time windows, the local point cloud density has a significant impact on the energy attenuation coefficient. In other words, changes in point cloud density in this case primarily affect the energy attenuation characteristics of the laser signal, rather than the spatial distribution or terrain undulations. In this situation, adjusting the pulse repetition frequency (PRF) is most appropriate because the PRF directly affects the laser pulse emission frequency and energy distribution. Increasing the PRF can increase the number of laser pulses emitted per unit time, but an excessively high PRF can lead to uneven pulse energy distribution, affecting ranging accuracy. Therefore, increasing or decreasing the PRF can help regulate energy attenuation, allowing for more effective processing of the laser signal and thus improving the accuracy of the point cloud data. Example: Suppose in an area with sparse vegetation cover, an increase in local point cloud density may exacerbate laser signal attenuation. In this case, the system will detect a mismatch field direction value greater than 1, with energy domain mismatch dominating. Adjusting the pulse repetition frequency (PRF) by appropriately adjusting the laser pulse emission frequency optimizes the signal energy distribution and avoids measurement errors caused by excessive attenuation. When the second proportion is the largest, i.e., the proportion of mismatch field direction values ​​equal to 1 is the largest, it indicates that within two adjacent sliding time windows, the effects of local point cloud density on the energy attenuation coefficient and inter-layer variance are intertwined, meaning that mismatches exist simultaneously in the energy domain and the geometric domain. In this case, the system mismatch is mainly determined by two factors: the impact of point cloud density changes on energy attenuation and the impact of point cloud density changes on terrain undulations (inter-layer variance). At this point, adjusting PRF or FOV alone may not effectively solve the system mismatch problem; both parameters need to be adjusted simultaneously. Example: Suppose measurements are conducted in mountainous areas where the surface is not only highly undulating, but the laser signal is also affected by vegetation, leading to signal attenuation. When point cloud density increases, it increases energy attenuation and may also cause errors in capturing terrain features. In this case, the energy domain and the geometric domain are mismatched simultaneously, requiring adjustments to PRF and FOV to comprehensively optimize signal energy distribution and terrain detail capture, avoiding distortion or incomplete data. When the third proportion is the largest, i.e., the proportion of mismatch field direction values ​​less than 1 is the largest, it means that within two adjacent sliding time windows, the impact of local point cloud density on inter-layer variance is significant. This indicates that changes in point cloud density primarily affect the vertical features of the terrain, i.e., the accurate capture of terrain undulations and feature outlines, rather than the energy attenuation characteristics of the signal. In this case, adjusting the field of view (FOV) is more appropriate, as the FOV directly affects the projection range and angle of the laser beam, thus influencing the spatial distribution and detail capture of the point cloud. An excessively large FOV will result in an overly wide scan coverage area. While this may increase point cloud density, the steep incident angle of the laser beam will reduce ranging accuracy in edge areas, affecting elevation accuracy.A small field of view (FOV) can improve the perpendicularity of the incident angle and reduce distortion, thus improving ranging accuracy. However, it leads to a smaller coverage area, reducing point cloud density and terrain coverage. Example: Suppose a measurement is being conducted in a relatively flat area with minimal terrain variation, but precise depiction of ground details is required. Due to the dominance of geometric domain mismatch, point cloud density has a significant impact on terrain. In this case, adjusting the FOV makes the laser beam scan more focused, helping to improve the capture of point cloud details while reducing unnecessary redundant information and computational resource consumption.

[0031] Therefore, the first mismatch (energy domain dominant): adjust the pulse repetition frequency (PRF) to optimize energy distribution. The second mismatch (dual-domain coupling): simultaneously adjust the PRF and field of view (FOV) to comprehensively optimize energy and spatial distribution. The third mismatch (geometric domain dominant): adjust the FOV to optimize point cloud spatial distribution and detail capture. Through statistical analysis of the mismatch field direction values ​​and their proportions, the system can intelligently identify the source of mismatch and accurately adjust the lidar parameters to ensure high-efficiency mapping accuracy.

[0032] It's important to note that the advantage of determining whether to change the scan angle range (FOV) or pulse repetition frequency (PRF) using the above method lies in its precise analysis based on the interrelationships between local point cloud density, energy attenuation coefficient, and inter-layer variance, combined with the real-time status of the sliding time window, rather than relying on traditional, simple heuristic adjustment methods. The core advantage of this method is that by calculating partial derivatives, it can accurately capture the impact of changes in local point cloud density on the energy attenuation coefficient and inter-layer variance. Then, by using the ratio of mismatch field direction values, it can dynamically locate whether the root cause of the problem originates in the energy domain (PRF) or the geometric domain (FOV). This refined judgment mechanism avoids the problems of simple "blind adjustment" or "single-direction adjustment," enabling the system to flexibly and accurately adjust the lidar parameters under different terrain and measurement conditions to ensure the quality of point cloud data and the accuracy of mapping. In this way, the system can not only adapt to different terrain undulations but also make timely adjustments based on dynamic feedback, avoiding possible over-adjustment or parameter coupling imbalance, thereby improving the efficiency and accuracy of mapping.

[0033] In one embodiment, the step of determining the adjustment direction of the scanning angle range or the pulse repetition frequency is as follows: Step 1: When only the pulse repetition frequency needs to be adjusted, calculate the mean of all first partial derivatives and record it as the first mean. If the first mean is not less than 0, then decrease the pulse repetition frequency; if the first mean is less than 0, then increase the pulse repetition frequency. Step 2: When only the scanning angle range needs to be adjusted, calculate the mean of all second partial derivatives and record it as the second mean. If the second mean is not less than 0, decrease the scanning angle range; if the second mean is less than 0, increase the scanning angle range. Step 3: When it is necessary to adjust the scanning angle range and pulse repetition frequency simultaneously, the specific adjustment direction of the scanning angle range and pulse repetition frequency is determined based on the average of the first partial derivatives and the average of the second partial derivatives in Step 1 and Step 2.

[0034] It should be noted that by calculating the mean of the first and second partial derivatives, the direction for adjusting the pulse repetition frequency (PRF) and field of view (FOV) can be determined. These adjustments are made to optimize the density of the point cloud data and the ranging accuracy. Each adjustment is based on specific logic, aiming to improve measurement accuracy while ensuring data quality. First, if the first mean is greater than 0, it indicates that the local point cloud density has a positive impact on the energy attenuation coefficient, meaning that energy attenuation increases with point cloud density. In this case, increasing the pulse repetition frequency (PRF) may result in more point cloud data, but it may also cause excessive energy dispersion, leading to a decrease in ranging accuracy. Therefore, the system will choose to decrease the PRF to avoid a weak signal or decreased accuracy, ensuring data reliability. In this case, decreasing the PRF effectively controls the distribution of the laser signal, improving ranging accuracy. If the first mean is less than 0, it indicates that increasing the local point cloud density leads to a decrease in the energy attenuation coefficient, meaning that signal attenuation is smaller and reflection is stronger. Increasing the PRF can increase point cloud density, covering a wider range of terrain details without sacrificing accuracy. At this point, by increasing the PRF (Pressure Point Fiber), the system can increase the density of data points while ensuring measurement accuracy, thus reflecting surface details more accurately. If the first mean is equal to 0, it indicates that the local point cloud density has no effect on the energy attenuation coefficient, meaning that changes in point cloud density do not cause changes in energy attenuation. To optimize ranging accuracy, reducing the PRF is a reasonable choice, as it helps improve the signal-to-noise ratio and reduce ranging errors. Maintaining measurement accuracy is still important even if point cloud density no longer affects energy attenuation. Next, if the second mean is not less than 0, it indicates that the local point cloud density has a positive effect on inter-layer variance. That is, as point cloud density increases, inter-layer variance (topographic relief) increases, meaning the terrain is complex and measurement errors increase. To improve accuracy, reducing the field of view (FOV) can help concentrate the laser beam, improve ranging accuracy, and avoid accuracy reduction due to an excessively large scanning area. Therefore, reducing the FOV is reasonable and ensures more accurate measurements. If the second mean is less than 0, it indicates that the increase in local point cloud density leads to a decrease in inter-layer variance, and the terrain becomes flatter or more uniformly reflective. In this case, increasing the FOV can expand the scanning range, capture more terrain information, and increase the comprehensiveness of the data without significantly affecting ranging accuracy. Therefore, increasing the FOV is a favorable choice, improving coverage and terrain data acquisition. If the second mean equals 0, it indicates that the local point cloud density has no significant impact on the variation of inter-layer variance, which may typically occur under relatively stable terrain conditions. In this case, decreasing the FOV is still a reasonable choice because it ensures more accurate ranging, reduces errors in edge areas, and ensures stable system operation. In this situation, regardless of the variation in inter-layer variance, maintaining a smaller scanning angle range helps improve measurement accuracy and avoid unnecessary measurement errors.Finally, when both PRF and FOV need to be adjusted simultaneously, the adjustment direction is determined by the mean of the first and second partial derivatives. If both means are positive, the system needs to increase both to cope with more complex terrain and energy attenuation; if both means are negative, the system needs to decrease both to improve accuracy and reduce data sparsity. If the two means have opposite signs, the system will adjust PRF and FOV separately to ensure that each parameter is optimized according to its characteristics, thereby better adapting to the needs of different measurement conditions. In this way, the parameters of the lidar system can be flexibly adjusted to balance point cloud density and ranging accuracy, thereby achieving efficient and accurate surface mapping. In one embodiment, S4: The lidar system parameters are changed according to the adjusted scanning angle range or pulse repetition frequency to continue mapping the surface data.

[0035] It should be noted that the adjustment direction and specific adjustment range have been determined in step S3. If only the pulse repetition frequency needs to be adjusted, and the mean of the first partial derivative is positive or negative, it is sufficient to decrease or increase the pulse repetition frequency. The absolute difference between the mean of the first partial derivative and 0 can be calculated, and this difference can be divided by a preset baseline value for the mean of the first partial derivative. The resulting ratio is used as the pulse repetition frequency multiplied by the original pulse repetition frequency of the lidar system as the adjustment range value. The lidar system parameters continue to map the surface data based on the adjusted pulse repetition frequency. When the mean of the first partial derivative is 0, the pulse repetition frequency of the lidar system parameters is directly adjusted according to the preset pulse repetition frequency baseline adjustment range to continue mapping the surface data. If only the scanning angle range needs to be adjusted, and the mean of the second partial derivative is positive or negative, it is sufficient to decrease or increase the scanning angle range. The absolute difference between the mean of the second partial derivative and 0 can be calculated. The difference is divided by the mean of the preset second partial derivative, and the resulting ratio is used as the scanning angle range multiplied by the original scanning angle range of the lidar system as the adjustment amplitude value. The lidar system parameters continue to map the surface data based on the adjusted scanning angle range. When the mean of the second partial derivative is 0, the scanning angle range of the lidar system parameters is directly adjusted according to the preset scanning angle range benchmark adjustment amplitude to continue mapping the surface data. When it is necessary to adjust both the scanning angle range and the pulse repetition frequency, the scanning angle range and pulse repetition frequency can be adjusted according to the specific adjustment direction and the above adjustment amplitude value. Other adjustment methods are also possible, which are not limited or elaborated. Through this dynamic adjustment mechanism, the system can automatically optimize the measurement parameters in complex environments to ensure the efficient and stable operation of the lidar.

[0036] Based on the same inventive concept, this invention also provides an intelligent analysis system for surveying and mapping information data. See also... Figure 2 , Figure 2This invention provides a framework diagram of an intelligent analysis system for surveying and mapping information data, comprising: The fracturing module records the local point cloud density, energy attenuation coefficient, and inter-layer variance as a real-time state vector during the generation of the point cloud dataset; and calculates the self-organized fracturing index based on the real-time state vector. Judgment module: Based on the self-organized fracture index and the preset self-organized fracture index threshold, determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed; Modify module: If changes are required, determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector; and determine the direction of adjustment for the scan angle range or the pulse repetition frequency. Mapping and Analysis Module: Based on the adjusted scanning angle range or pulse repetition frequency, the lidar system parameters are changed to continue mapping the surface data.

[0037] Based on the intelligent analysis system for surveying and mapping information provided in this invention, the above-described method can specifically determine whether to adjust the scanning angle range, the pulse repetition frequency, or both, and determine the specific direction of parameter adjustment. This avoids blindly adjusting both the scanning angle range and the pulse repetition frequency simultaneously, resulting in smaller fluctuations in the mapping accuracy of the lidar, higher stability of the point cloud quality, and more reasonable density and measurement accuracy of the generated point cloud dataset. Ultimately, this leads to accurate mapping results of the surface topography, accurately reflecting the true topography of the land surface. The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for intelligent analysis of surveying and mapping information data, characterized in that, Includes the following steps: The local point cloud density, energy attenuation coefficient, and inter-layer variance at the time of point cloud dataset generation are recorded in real time as a real-time state vector; and the self-organized fracture index is calculated based on the real-time state vector. Based on the self-organized fracture index and the preset self-organized fracture index threshold, determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed. If changes are required, determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector; and determine the direction of adjustment for the scan angle range or the pulse repetition frequency. The surface data mapping continues by adjusting the lidar system parameters based on the adjusted scanning angle range or pulse repetition frequency.

2. The intelligent analysis method for surveying and mapping information data according to claim 1, characterized in that, The steps for calculating local point cloud density are as follows: Set a sliding time window and obtain the set of point cloud coordinates within the time window; and divide the point cloud region into regular three-dimensional voxel units according to the spatial distribution of the point cloud coordinate set, with the voxel volume of each voxel unit set to V0. The total number of point clouds in each voxel unit is counted, and the total number of point clouds in each voxel unit is divided by the voxel volume V0. The ratio of the division is recorded as the sampling density of the corresponding voxel unit. Calculate the average sampling density of all voxel units as the local point cloud density for the corresponding sliding time window.

3. The intelligent analysis method for surveying and mapping information data according to claim 1, characterized in that, The steps for calculating the energy decay coefficient are as follows: Set a sliding time window and record the energy value of each laser pulse emitted within the time window and its corresponding echo received energy value; then divide the emitted energy value by the corresponding echo received energy value to obtain the energy retention rate; Calculate the average energy retention rate of all emitted laser pulses within the sliding time window, and use it as the average energy retention rate of the sliding time window. Subtract the average energy retention rate from the value of 1 to obtain the energy decay coefficient of the corresponding sliding time window.

4. The intelligent analysis method for surveying and mapping information data according to claim 1, characterized in that, The steps for calculating inter-layer variance are as follows: Within the set sliding time window, acquire the z-coordinate information of all point cloud data, and divide the entire point cloud vertically into K height layers at equal intervals according to the z-value range. For each height layer, the average z-coordinate of all point clouds is calculated as the center height of the corresponding height layer; The total number of point clouds contained in each layer is counted, and the total number of point clouds contained in each layer is divided by the total number of point clouds in the sliding time window to obtain the percentage of points in the corresponding height layer. Calculate the mean z-coordinate of all point cloud data within the sliding time window, and use it as a reference height. Calculate the difference between the center height of each height layer and the reference height, and divide the difference by the reference height to obtain the height deviation of each height layer. Multiply the absolute mean of each height deviation by the proportion of points in the corresponding height layer, and sum the results of the multiplication as the inter-layer variance of the corresponding sliding time window between layers.

5. The intelligent analysis method for surveying and mapping information data according to claim 1, characterized in that, The steps for calculating the self-organized fracture index are as follows: The local point cloud density, energy decay coefficient, and inter-layer variance of each sliding time window are min-max normalized and mapped to the range of values ​​0-1. The local point cloud density, energy attenuation coefficient, and inter-layer variance after normalization for each sliding time window are calculated pairwise, and the corresponding absolute differences are calculated. The absolute difference between the local point cloud density and the energy attenuation coefficient is recorded as the first tension factor, the absolute difference between the local point cloud density and the inter-layer variance is recorded as the second tension factor, and the absolute difference between the energy attenuation coefficients is recorded as the third tension factor. The first tension factor, the second tension factor, and the third tension factor are all compressed through a preset perturbation compression function to obtain the compressed first tension factor, the compressed second tension factor, and the compressed third tension factor. Calculate the squares of the first tension factor, the second tension factor, and the third tension factor after compression, respectively, add the calculated squares together, and divide the arithmetic square root of the sum of squares by 3 to obtain the self-organized fracture degree of the corresponding sliding time window. The self-organized fracture degree of each sliding time window is compared with the preset self-organized fracture degree threshold. Sliding time windows with a self-organized fracture degree not less than the preset self-organized fracture degree threshold are recorded as abnormal windows. The total number of abnormal windows is divided by the total number of sliding time windows to obtain the self-organized fracture degree index.

6. The intelligent analysis method for surveying and mapping information data according to claim 1, characterized in that, The steps to determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed, based on the self-organized fracture index and the preset self-organized fracture index threshold, are as follows: Compare the self-organized fracture index with the preset self-organized fracture index threshold. If the self-organized fracture index is not less than the preset self-organized fracture index threshold, it means that the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed. If the self-organized fracture index is less than the preset self-organized fracture index threshold, it means that the scanning angle range or pulse repetition frequency of the current lidar system parameters does not need to be changed; the terrain point cloud dataset will continue to be generated according to the scanning angle range and pulse repetition frequency set by the current lidar system parameters.

7. The intelligent analysis method for surveying and mapping information data according to claim 1, characterized in that, The steps to determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector are as follows: Based on the local point cloud density, energy attenuation coefficient, and interlayer variance of two adjacent sliding time windows, calculate the partial derivative of the local point cloud density with respect to the energy attenuation coefficient as the first partial derivative, calculate the partial derivative of the local point cloud density with respect to the interlayer variance as the second partial derivative, divide the first partial derivative by the second partial derivative, and take the result of the division as the mismatch field direction value of the two adjacent sliding time windows. Compare the mismatch field direction values ​​of all two adjacent sliding time windows with the value 1, and count the total number of mismatch field direction values ​​greater than 1, the total number of mismatch field direction values ​​less than 1, and the total number of mismatch field direction values ​​equal to 1. Calculate the ratio of the total number of mismatch field direction values ​​greater than 1 to the total number of mismatch field direction values, and record it as the first ratio; the ratio of the total number of mismatch field direction values ​​equal to 1 to the total number of mismatch field direction values, and record it as the second ratio; the ratio of the total number of mismatch field direction values ​​less than 1 to the total number of mismatch field direction values, and record it as the third ratio. The decision to change the scanning angle range or the pulse repetition frequency is determined based on the first, second, and third percentages.

8. The intelligent analysis method for surveying and mapping information data according to claim 7, characterized in that, The steps to determine whether to change the scan angle range or the pulse repetition frequency based on the first, second, and third percentages are as follows: Compare the first, second, and third percentages according to their magnitude. If the first percentage is the largest, only the pulse repetition frequency needs to be adjusted. If the second percentage is the largest, both the scan angle range and the pulse repetition frequency need to be adjusted. If the third percentage is the largest, only the scan angle range needs to be adjusted.

9. The intelligent analysis method for surveying and mapping information data according to claim 8, characterized in that, The steps to determine the adjustment direction of the scan angle range or pulse repetition frequency are as follows: Step 1: When only the pulse repetition frequency needs to be adjusted, calculate the mean of all first partial derivatives and record it as the first mean. If the first mean is not less than 0, decrease the pulse repetition frequency; if the first mean is less than 0, increase the pulse repetition frequency. Step 2: When only the scanning angle range needs to be adjusted, calculate the mean of all second partial derivatives and record it as the second mean. If the second mean is not less than 0, decrease the scanning angle range; if the second mean is less than 0, increase the scanning angle range. Step 3: When it is necessary to adjust the scanning angle range and pulse repetition frequency simultaneously, the specific adjustment direction of the scanning angle range and pulse repetition frequency is determined based on the average of the first partial derivatives and the average of the second partial derivatives in Step 1 and Step 2.

10. A surveying and mapping information data intelligent analysis system, used to implement the surveying and mapping information data intelligent analysis method according to any one of claims 1-9, characterized in that, The system includes: The fracturing module records the local point cloud density, energy attenuation coefficient, and inter-layer variance as a real-time state vector during the generation of the point cloud dataset; and calculates the self-organized fracturing index based on the real-time state vector. Judgment module: Based on the self-organized fracture index and the preset self-organized fracture index threshold, determine whether the scanning angle range or pulse repetition frequency of the current lidar system parameters need to be changed; Modify module: If changes are required, determine whether to change the scan angle range or the pulse repetition frequency based on the real-time state vector; and determine the direction of adjustment for the scan angle range or the pulse repetition frequency. Mapping and Analysis Module: Based on the adjusted scanning angle range or pulse repetition frequency, the lidar system parameters are changed to continue mapping the surface data.

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