Building engineering survey data management method based on BIM (Building Information Modeling) technology

Abstract

The invention discloses a BIM technology-based constructional engineering survey data management method, and relates to the technical field of survey data management. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data, topographic image data and geological profile parameters to form a multi-source survey data set; importing the data into a BIM data processing library, establishing a unified index through recursive segmentation and hierarchical coding, and constructing a survey data management model based on a block recombination model; extracting geometric indexes and geological indexes from the model; inputting the indexes into a distributed data analysis engine, calculating a geometric index deviation degree, a geological index deviation degree and a comprehensive index, and generating a risk level label; and finally evaluating the risk level according to the comprehensive risk index, and generating a corresponding management scheme. According to the method, efficient integration and accurate management of multi-source survey data are realized, engineering risks can be scientifically evaluated, a management scheme is generated in a targeted manner, and the accuracy, efficiency and engineering safety of construction engineering survey data management are improved.

Description

Technical Field

[0001] This invention relates to the field of survey data management technology, and more specifically, to a method for managing architectural engineering survey data based on BIM technology. Background Technology

[0002] In the field of construction engineering, survey data is a crucial foundation for engineering design, construction, and management. Its accuracy, completeness, and management efficiency directly impact project quality and safety. Currently, the construction engineering survey process generates various types of data, including 3D point cloud data, topographic image data, and geological profile parameters. These multi-source survey data are typically characterized by diverse formats, large volumes, and strong spatial correlations.

[0003] In current technologies, the management of survey data for construction projects largely relies on traditional file storage and manual processing, lacking a unified management platform and efficient integration methods. On the one hand, survey data in different formats is difficult to effectively correlate and share, resulting in severe data silos. This forces engineers to spend considerable time on format conversion and data matching, significantly reducing work efficiency. On the other hand, traditional management methods cannot accurately index and quickly retrieve multi-source survey data. When querying specific areas or types of data, the process is cumbersome and time-consuming. Furthermore, current technologies for analyzing survey data often focus on single indicators, failing to comprehensively consider the impact of geometric features and geological conditions on the project. This hinders the scientific and accurate assessment of project risks, making it difficult to develop targeted and effective management plans. Consequently, this may lead to safety hazards during construction, affecting the smooth progress and final quality of the project. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies in building engineering survey data management, such as data silos, low management efficiency, unscientific risk assessment, and lack of targeted management solutions, this invention provides a building engineering survey data management method based on BIM technology. This method addresses the problems of existing technologies, such as difficulty in effectively integrating and sharing multi-source survey data, low efficiency in data query and retrieval, and inability to comprehensively assess engineering risks and formulate targeted management solutions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for managing architectural engineering survey data based on BIM technology, comprising,

[0006] S1: Collect 3D point cloud data, terrain image data and geological profile parameters to form a multi-source survey dataset;

[0007] S2: Import the multi-source survey dataset into the BIM data processing library for preprocessing, establish a unified index through recursive segmentation and hierarchical coding, and construct a survey data management model based on the block-based reorganization model;

[0008] S3: Extract geometric and geological indicators based on the aforementioned survey data management model;

[0009] The geometric parameters include volumetric parameters and structural verticality deviation; the geological parameters include soil shear modulus, void ratio, and water content.

[0010] S4: Input the geometric and geological indicators into the distributed data analysis engine to calculate the geometric and geological indicator deviations. Calculate the comprehensive indicator based on the geometric and geological indicator deviations, and generate risk level labels and management plans.

[0011] The technical effects and advantages of this invention are as follows:

[0012] 1. This invention achieves efficient integration of multi-source survey data, builds a unified management platform through BIM technology, breaks down data silos, and effectively associates data of different formats after lossless import and coordinate transformation, thereby improving the efficiency of data sharing and utilization.

[0013] 2. This invention establishes a scientific indexing and management mechanism, uses recursive segmentation and hierarchical coding to achieve rapid data retrieval, and combines a block-based reorganization model with version management to ensure data traceability and accuracy, thereby reducing data query and management costs.

[0014] 3. This invention comprehensively assesses engineering risks using multi-dimensional indicators, while also considering geometric and geological factors. Through standardized calculations and L2 norm analysis, it accurately reflects engineering deviations, clearly classifies risk levels, and provides a reliable basis for risk management.

[0015] 4. This invention generates targeted management plans and formulates differentiated measures according to different risk levels. Low-risk projects focus on routine monitoring, medium-risk projects make precise adjustments, and high-risk projects receive emergency intervention, ensuring that engineering problems are resolved in a timely manner and improving the safety and stability of the project. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the present invention;

[0017] Figure 2 This is a flowchart illustrating the construction of the survey data model for this invention. Detailed Implementation

[0018] 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.

[0019] like Figure 1 and Figure 2 The BIM-based building survey data management method shown includes:

[0020] S1: Collect 3D point cloud data, terrain image data and geological profile parameters to form a multi-source survey dataset;

[0021] S2: Import the multi-source survey dataset into the BIM data processing library for preprocessing, establish a unified index through recursive segmentation and hierarchical coding, and construct a survey data management model based on the block-based reorganization model;

[0022] S3: Extract geometric and geological indicators based on the aforementioned survey data management model;

[0023] The geometric parameters include volumetric parameters and structural verticality deviation; the geological parameters include soil shear modulus, void ratio, and water content.

[0024] S4: Input the geometric and geological indicators into the distributed data analysis engine to calculate the geometric and geological indicator deviations. Calculate the comprehensive indicator based on the geometric and geological indicator deviations, and generate risk level labels and management plans.

[0025] It should be specifically explained that the reason for collecting 3D point cloud data, topographic image data, and geological profile parameters to form a multi-source survey dataset is as follows: 3D point cloud data records the spatial geometric information of the site in the form of high-density discrete points, which can accurately reflect the topographic relief of the site and the spatial location and outline of existing buildings, providing a foundation for geometric modeling of the BIM model; topographic image data intuitively shows the surface features of the site through high-resolution images, helping to judge the site environment and potential construction interference factors; geological profile parameters contain key information such as soil and rock types and soil mechanical properties, which directly affect core engineering decisions such as foundation design and foundation selection. The combination of the three can achieve comprehensive data coverage from above ground to underground and from geometry to attributes, meeting the requirements of BIM model for the integrity and accuracy of survey data.

[0026] It should be further noted that the tool for acquiring the three-dimensional point cloud data is a three-dimensional laser scanner; the tool for acquiring the terrain image data is an unmanned aerial survey device; and the tool for acquiring the geological profile parameters is a geological exploration instrument.

[0027] It should be further explained that the 3D point cloud data (.las format) is acquired by using a 3D laser scanner to scan the building site at multiple sites. Each site must ensure sufficient overlap (no less than 30%). The data is stitched together using a target sphere to ensure the integrity and accuracy of the point cloud data. During the scanning process, a reasonable scanning resolution is set, and the scanning density is increased for key areas (such as the area around buildings or areas with complex terrain). The terrain image data (.tif format) is acquired using UAV aerial surveying in a "flight planning + autonomous flight" mode, employing professional aerial surveying software (such as Pix4). The drone (Dmapper) pre-plans flight routes, sets flight altitude, directional overlap (not less than 80%), and lateral overlap (not less than 70%). The drone then autonomously flies along the planned route and takes photos, ensuring that the image data completely covers the entire site. The geological profile parameters (.csv format) are collected according to engineering survey specifications and a pre-set borehole layout plan. Drilling is carried out using a drilling rig, and information is collected at different depths, such as the rock and soil type, soil density, and internal friction angle of the 10 boreholes. At the same time, a ground-penetrating radar is used to scan along a pre-set survey line to obtain continuous geological profile data.

[0028] It should be further explained that all data acquisition processes record metadata such as acquisition time and equipment parameters in real time, and assign a unique identifier to each type of data to facilitate subsequent data management and traceability, and ultimately form a multi-source survey dataset covering 3D point cloud, terrain image data, and geological profile parameters.

[0029] It should be specifically noted that the method of importing the source survey dataset into the BIM data processing library is as follows: for different data formats, corresponding interface programs are used to achieve lossless import, including: 3D point cloud data is lightly processed by professional software such as Autodesk ReCap and converted into a format that BIM software can recognize; topographic image data is imported into the BIM platform after being calibrated with geographic coordinates using ArcGIS; geological profile parameters are mapped from structured CSV table data to the geological information attributes of the BIM model through a customized data interface, such as the soil type of borehole 1 being "silty clay" and the soil density being "1.8 g / cm³".

[0030] It should be further explained that, in order to ensure the collaboration of multi-source data under the same spatial reference, a unified engineering coordinate system (such as the CGCS2000 National Geodetic Coordinate System) is used for coordinate transformation, and preprocessing is performed separately for the three types of data: The preprocessing method for the three-dimensional point cloud data is based on a recursive segmentation algorithm with an adaptive noise threshold. The point cloud data is recursively segmented into multiple sub-regions (such as a 10m×10m cube) according to the spatial range. The spatial distribution density of points in each sub-region is calculated, and isolated points with a density lower than 30% of the global average are identified as noise points and deleted. (For example, after segmenting by a 10m×10m cube, in the third block of area B, it is found that the point density of a certain sub-region is only 20% of that of other regions. 100 isolated points in this sub-region are identified as noise points and deleted.)

[0031] The preprocessing method for the terrain image data is to use image enhancement algorithms to dehaze and sharpen the images to improve image clarity, remove salt-and-pepper noise from the images using median filtering, and use histogram matching to unify the color and brightness of multiple source images to avoid data deviations caused by differences in shooting conditions (e.g., for 100 images taken in foggy weather, a dehazing algorithm is used to enhance clarity, median filtering is used to remove salt-and-pepper noise from 50 of the images, and histogram matching is used to unify the color of all images to make the grass color consistent).

[0032] The preprocessing method for the geological profile parameters involves first checking the data integrity, then filling missing values ​​with a weighted average of data from nearby boreholes, identifying and removing outliers using the 3σ principle to prevent extreme data from interfering with subsequent analysis, and standardizing the parameters by converting parameters of different dimensions (such as soil density and internal friction angle) to the [0,1] interval for easier unified analysis. For example, for the missing internal friction angle data in borehole 5, a distance-weighted average method is used to fill the missing data, assuming the Euclidean distance between borehole 4 and borehole 5 is 1. The measured internal friction angle value is The Euclidean distance between borehole 6 and borehole 5 is The measured internal friction angle value is The formula for calculating the fill value is: Calculations show that If the soil density measurement value of borehole 8 is found to be " This is clearly an anomaly. Outlier detection is performed based on the 3σ principle, and the calculation formula is as follows: Where μ is the sample mean and σ is the sample standard deviation, the results were obtained by calculating the soil density from 10 borehole samples. , The normal range is Therefore, it is determined that " "Identified as an outlier and removed; corrected to " after remeasurement. All geological parameters were standardized using a normalization formula: Where x is the original parameter value, and These are the minimum and maximum values ​​of the parameter across all samples, such as the soil density "2.0 g / cm³". , The standardized value is Meanwhile, by utilizing the same feature points of the point cloud and the terrain image, the ICP (Iterative Closest Point) algorithm is used to achieve precise alignment between the image and the point cloud model, eliminating spatial deviation.

[0033] It should be further explained that the standardization of geological parameters to the [0,1] interval in this stage is only used for preliminary cross-index comparison of parameters with different dimensions (such as soil density and internal friction angle) to facilitate data quality screening.

[0034] It needs further explanation that the method of establishing a unified index through recursive segmentation and hierarchical coding is as follows: First, the project site is divided into two construction zones, A and B, according to the overall plan. Zone B is further divided into a foundation treatment zone (B01) and a main building zone (B02). Taking the foundation treatment zone of Zone B as an example, the specific method of recursive segmentation is to adopt a three-layer recursive segmentation strategy within the B01 block: The first layer divides the data into three types of data subspaces according to data type: 3D point cloud (PT), terrain image (IM), and geological parameters (GP). The second layer spatially divides the 3D point cloud data into 10m×10m×10m cubes, and the terrain image data into 500×500 pixel grids. The geological parameter data is grouped by borehole number in the grid division. The third layer further subdivides the above division results. For example, the three-dimensional point cloud sub-cubes are further divided according to the reflection intensity range of the points. The specific method of hierarchical coding is to adopt a tree-like coding structure. The coding rule is "construction zone code - functional block number - data type identifier - subspace code". For example, the three-dimensional point cloud data of the foundation treatment area of ​​zone B is coded as "B-01-PT-03-05", which means that in the three-dimensional point cloud data of the first functional block of zone B, the fifth sub-interval of the third 10m×10m×10m cube can be quickly retrieved from the BIM data processing library through this code.

[0035] It needs further explanation that the specific method for constructing a survey data management model based on the block-based reorganization model is as follows: The 3D point cloud, topographic imagery, geological parameters, and other data within each functional block are integrated to form "data blocks." Utilizing the parametric modeling capabilities of the BIM platform, the data blocks are mapped to corresponding components (such as foundation caps and pile foundations) in the BIM model. Version management is implemented for each data block, recording data update time, modification personnel, and other information to ensure data traceability and accuracy. For example, in the foundation treatment area (B01) of Zone B, based on the above approach, "B01-PT" is... Point cloud data, “B01-IM” image data, and “B01-GP” geological parameters are integrated into a single data block. Assuming that 10 pile foundations are planned for construction in the area, the center coordinates of each pile foundation component are associated with the nearest borehole location in the geological profile parameters through spatial coordinate mapping. The center coordinates of pile foundation A are calculated to be closest to the location of borehole 3. The system automatically establishes the correspondence between the two. Using the attribute linking function of the BIM model, the attribute table of pile foundation A is bound to parameters such as soil type, soil density (“1.9g / cm³”), and internal friction angle (30°) of borehole 3. When the engineer selects pile foundation A in the BIM platform, the geological parameters of borehole 3 can be displayed in real time in the attribute bar on the right. At the same time, through point cloud slicing technology, the three-dimensional point cloud data slices of the location of pile foundation A are extracted and embedded into the visualization display of the BIM model.

[0036] It should be further explained that when data blocks are associated with BIM components (such as pile foundations and pile caps), the 'spatial distance threshold determination method' is used. That is, when the Euclidean distance between the center coordinates of the component and the borehole location is ≤0.5m, the geological parameters of the borehole are associated; if the distance is >0.5m, a micro borehole (diameter ≤100mm) needs to be added within 3m around the component to obtain the corresponding geological data, so as to ensure that the associated geological parameters can truly reflect the geological conditions of the area where the component is located.

[0037] It should be specifically noted that geometric and geological indicators are extracted based on the survey data management model. The geometric indicators include volume parameters and structural verticality deviation; the geological indicators include soil shear modulus, void ratio, and water content.

[0038] It should be further explained that the specific method for extracting the aforementioned volume parameters involves utilizing the 3D data integration capabilities of the survey data management model to retrieve point cloud data from the model and converting it into a triangular mesh model using the Boolean operations and mesh generation functions of the BIM platform. The volume parameters are then extracted using the mesh volume integral formula. Calculate the volume, where The volume of a single triangular mesh cell is calculated by summing the faces of the triangular mesh using the formula. Calculate the surface area, where The area of ​​a single triangular mesh face; the specific method for extracting the structural verticality deviation is as follows: in the BIM platform, based on the component association index of the survey data management model, select the edge contour lines of existing buildings or proposed structural components in the model, extract the three-dimensional coordinates of the start and end points of the contour lines, calculate the angle between its vertical vector and the standard vertical vector [0,0,1], and use... To express.

[0039] It should be further explained that the specific method for extracting the soil shear modulus G, void ratio e, and water content w is to directly read the geotechnical test data of each borehole from the geological profile parameter block of the survey data management model. This model uses a data indexing mechanism to accurately associate the test data of each borehole with the actual geographical location.

[0040] It should be noted that the geometric and geological indicators are then input into a distributed data analysis engine (such as Apache Spark) to calculate the geometric and geological indicator deviations. Based on the geometric and geological indicator deviations, a comprehensive indicator is calculated, and a risk level label is generated.

[0041] It should be further explained that the calculation method for the geometric index deviation is to convert the geometric index into a dimensionless standardized value to eliminate the influence of dimensional differences: , , Where V is the measured sub-region volume (unit: ), For reference volume in design specifications, only when hour ,like , (No risk bias); S is the measured surface area of ​​the sub-region (unit: ), For reference surface area in design specifications, only when hour, ,like , ; The measured verticality deviation of the structure (unit: °) The maximum allowable verticality deviation for the project (as defined in the "Code for Acceptance of Construction Quality of Concrete Structures") shall only apply when... hour, ,like , This ensures that the deviation only reflects "adverse risks"; the geometric deviation is calculated using the L2 norm to determine the overall degree of deviation of geometric indicators. This value comprehensively reflects the coordinated deviation of volume, surface area, and verticality. The larger the value, the more serious the deviation between the geometric features and the design expectations.

[0042] It should be further explained that the calculation method for the geological deviation is to standardize geological indicators and strengthen risk orientation: , , , Where G is the measured soil shear modulus (unit: MPa). ε is the standard shear modulus of this type of soil (taken from the "Code for Design of Building Foundations"); e is the measured void ratio (dimensionless). is the critical void ratio for this type of soil layer; w is the measured water content (unit: %). The optimum moisture content for this type of soil layer; for example, silty clay: standard shear modulus. The critical void ratio is 25-30 MPa. The optimum moisture content is 0.8-0.85. 22%–28%; Medium sand: Standard shear modulus The critical void ratio is 40-50 MPa. The optimum moisture content is 0.65-0.7. 15%–20%; Silt: Standard shear modulus The critical void ratio is 18-22 MPa. The optimum moisture content is 0.75-0.8. The deviation is 18%–22%; the overall deviation of geological indicators is calculated using the L2 norm. This value comprehensively reflects the deviation between the mechanical properties and physical state of the soil layer. The larger the value, the more unfavorable the geological conditions are for engineering stability.

[0043] It should be further explained that the comprehensive index is calculated using a formula. As a comprehensive risk indicator, it directly amplifies the superposition effect of geometric deviation and geological deviation through an exponential function. and This is the standardized one-dimensional bias, with values ​​ranging from [value range missing]. The sum of them It reflects the cumulative degree of overall deviation; the base e of the exponential function is a natural constant (approximately 2.718), ensuring that the risk amplifies non-linearly as deviation accumulates.

[0044] It should be further explained that the risk level label is generated in the following way: low risk: hour, ,match( At this point, both geometric and geological deviations are relatively small; medium risk: At this point, there is a certain accumulation of deviations (e.g., large geometric deviations but slight geological deviations, or both with moderate deviations); High risk: The accumulated deviation has triggered an explosive growth in the exponential function.

[0045] It should be further explained that generating corresponding management solutions based on the risk level labels includes: firstly, in the BIM platform, calling the risk level labels (low risk, medium risk, high risk) generated by the distributed data analysis engine; then, automatically matching and generating differentiated management solutions based on different risk levels.

[0046] It should be further explained that the low-risk management plan applies when the comprehensive indicators are below the threshold. When the risk level is low, the management plan generated by the system is "routine maintenance," which includes: retaining the existing design and construction plan without making any additional adjustments, setting a periodic monitoring plan on the BIM platform (such as updating and scanning the point cloud data once a quarter and collecting geological test data once every six months), and automatically pushing reminders to management personnel to ensure long-term stability.

[0047] It should be further explained that the management plan for medium-risk levels is as follows: when the comprehensive indicators are between the threshold... and When the geometric deviation is between, it is judged as medium risk, and when the geometric deviation is between, it is judged as medium risk. or geological deviation When the deviation is large, it is judged as 'large deviation'; large verticality deviation. Prioritize adjusting construction techniques (such as increasing the frequency of verticality monitoring and optimizing formwork fixing methods) to address large volume / surface area deviations. or Prioritize optimizing component design dimensions (such as fine-tuning the foundation volume to within the reference range).

[0048] It should be further explained that the management plan for high-risk levels is as follows: when the comprehensive indicators exceed the threshold... When a situation is deemed high-risk, the system generates an "emergency intervention" management plan, which includes: when the comprehensive indicators... When the system automatically triggers an early warning notification (without manual confirmation), the notification recipients include the project manager and chief supervisor; the locked area is a 10m×10m sub-area where the high-risk component is located, not the entire construction zone, to avoid excessive control affecting the construction of other areas.

[0049] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0050] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A method for managing architectural engineering survey data based on BIM technology, characterized in that, include: S1: Collect 3D point cloud data, terrain image data and geological profile parameters to form a multi-source survey dataset; S2: Import the multi-source survey dataset into the BIM data processing library for preprocessing, establish a unified index through recursive segmentation and hierarchical coding, and construct a survey data management model based on the block-based reorganization model; S3: Extract geometric and geological indicators based on the aforementioned survey data management model; The geometric parameters include volumetric parameters and structural verticality deviation; the geological parameters include soil shear modulus, void ratio, and water content. S4: Input the geometric and geological indicators into the distributed data analysis engine to calculate the geometric and geological indicator deviations. Calculate the comprehensive indicator based on the geometric and geological indicator deviations, and generate risk level labels and management plans.

2. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The 3D point cloud data was acquired using a 3D laser scanner, and the data format is .las; the terrain image data was acquired using UAV aerial survey equipment, and the data format is .tif; the geological profile parameters were acquired using geological exploration instruments, and the data format is .csv.

3. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The preprocessing methods for the multi-source survey dataset include: 3D point cloud data preprocessing: dividing the data into sub-regions based on a recursive segmentation algorithm and removing isolated noise points with a density lower than 30% of the global average; topographic image data preprocessing: removing haze and sharpening, median filtering to remove salt-and-pepper noise, and histogram matching to unify color brightness; geological profile parameter preprocessing: filling in missing values, removing outliers using the 3σ principle, and standardizing parameters of different dimensions to the [0,1] interval.

4. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The unified index is established by first dividing the construction area into zones, then dividing it into functional blocks. Within each functional block, the data is recursively divided into three levels based on data type, spatial / grid / drilling grouping, and subdivided into sub-intervals. Subsequently, a tree-like hierarchical coding rule of "construction zone code - functional block number - data type identifier - subspace code" is used to assign a unique index code to each data subspace.

5. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The survey data management model is constructed by using functional blocks divided by a unified index as units. The multi-source survey data in each functional block is integrated into data blocks through a block-based reorganization model. Then, the spatial distance threshold determination method is used to accurately associate the data blocks with the corresponding components of the BIM model. At the same time, version management is performed on the data blocks, and finally, the survey data management model is constructed.

6. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The extraction method for the geometric indicators is based on the three-dimensional point cloud data of the survey data management model to generate a triangular mesh model to calculate the volume parameters. The coordinates of the component outline are extracted by relying on the "data block-BIM component" association index and the angle between it and the standard vertical vector is calculated to obtain the structural verticality deviation. The extraction method for the geological indicators is based on the data indexing mechanism of the survey data management model. The data block associated with the target BIM component is located, and the geotechnical test data of the borehole in the area where the component is located is read from the corresponding geological profile parameter submodule to obtain the soil shear modulus, void ratio and water content.

7. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The calculation method for the geometric index deviation and geological index deviation is to first convert the geometric index and geological index into standard values, and then calculate the geometric index deviation and geological index deviation through the L2 norm.

8. The method for managing architectural engineering survey data based on BIM technology according to claim 1, characterized in that: The risk level label is obtained by calculating a comprehensive index based on the deviation of geometric indicators and the deviation of geological indicators using a nonlinear superposition method. Based on the comprehensive index, the risk level label is analyzed and generated using the "deviation accumulation - index mapping threshold classification" method.

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