Earthquake regional safety evaluation system based on data warehousing and three-dimensional visualization

Abstract

The application discloses a seismic regional safety evaluation system based on data warehousing and three-dimensional visualization, which comprises a data acquisition module, a data preprocessing module, a three-dimensional visualization establishing module, a seismic safety evaluation module and a result output module, and relates to the technical field of data processing. The seismic regional safety evaluation system based on data warehousing and three-dimensional visualization extracts image data and parameter data, establishes a geological three-dimensional model by using drilling parameters, image data and geological feature data, establishes a ground three-dimensional model by using building parameters and geological feature data, and fuses the geological three-dimensional model and the ground three-dimensional model to form a grade evaluation model for evaluating the safety of a seismic region. By means of advanced three-dimensional modeling technology, the system can intuitively display the spatial distribution characteristics of underground rock layers and simultaneously establish an intelligent correlation mechanism among multidimensional data, so that a more scientific and efficient solution strategy is provided for seismic risk evaluation work.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a regional seismic safety assessment system based on data storage and three-dimensional visualization. Background Technology

[0002] Earthquakes are natural disasters with immense destructive power, posing a serious threat to human life and property. Accurate and timely assessments of regional earthquake safety are crucial for developing scientifically sound earthquake prevention and mitigation measures.

[0003] In current earthquake regional assessment practices, conventional relational databases are mainly used. Although they can meet the basic data management needs, they have several technical shortcomings.

[0004] The reference patent is titled: "A Method and System for Implementing Seismic Safety Assessment Application Based on Cesium" (Patent Publication No.: CN118467650A, Patent Publication Date: 2024-08-09). This method uses Cesium to store seismic safety assessment data, linking spatial and non-spatial data to achieve visualized browsing, querying, and analysis of the data. Remote sensing imagery and DEM elevation data are overlaid in the Cesium scene to create a 3D visualization of the seismic safety assessment data, forming a 3D scene for the application. The method also models the geological strata, allowing for intuitive browsing of borehole geological structures. The system determines the application project by inputting its coordinates or using map picking, calculates the borehole location with the shortest distance to the application location, obtains the borehole ground motion parameters at the shortest path location, and generates a ground motion parameter report.

[0005] Based on the description in the above documents, existing evaluation systems fail to view the spatial location information of boreholes. Currently, borehole data is mostly presented in a two-dimensional planar form, making it impossible to intuitively observe the three-dimensional effect of boreholes based on soil layer information. This restricts the intuitive display of the spatial characteristics of underground rock and soil layers and lacks effective spatial correlation methods, affecting the comprehensive analysis efficiency. Therefore, this invention provides a regional seismic safety evaluation system based on data entry and three-dimensional visualization. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a regional seismic safety assessment system based on data storage and 3D visualization. This system solves the problems of existing assessment systems failing to view the spatial location information of boreholes, and the fact that current borehole data is mostly presented in a two-dimensional planar form, making it impossible to intuitively observe the three-dimensional effect of boreholes based on soil layer information. This restricts the intuitive display of the spatial characteristics of underground rock and soil layers, lacks effective spatial correlation methods, and affects the comprehensive analysis efficiency.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a regional seismic safety assessment system based on data storage and 3D visualization, comprising:

[0008] The data acquisition module is used to collect various types of earthquake-related data and divide them into image data and parameter data;

[0009] The data preprocessing module categorizes and associates the data, forms the required data entry format, and then stores the data in the district assessment database.

[0010] The 3D visualization module extracts image and parameter data to build a geological 3D model using borehole parameters, image data, and geological feature data, and builds a ground 3D model using building parameters and geological feature data. The geological 3D model and the ground 3D model are then merged to form a level assessment model for evaluating the safety of earthquake-prone areas.

[0011] The earthquake safety assessment module uses a grade assessment model and combines multi-source data to assign weights to achieve a safety grade assessment of the earthquake zone, and generates protection strategies based on the safety grade assessment.

[0012] The results output module outputs the resulting 3D model, evaluation level, and protection strategy, and displays them through a visual interface.

[0013] Preferably, the data preprocessing module performs the following operations to classify and associate the data:

[0014] Based on the collected data, determine the data categories that need to be classified, and use the data categories as entity nodes, and use the data with different timestamps under each data category as secondary nodes;

[0015] Define the edge association between entity nodes and secondary nodes. When an entity node is selected, all secondary nodes under the corresponding entity node are displayed. At the same time, the edge association between entity nodes is determined according to the subsequent model. The model relies on the common combination of data to obtain the result, which forms the collinear edge association. The model relies on the extended prediction result of the data to form the predicted edge association.

[0016] The final result is a data classification graph with edge associations. When indexing, the current data graph and related data graphs are obtained by matching the content of the nodes.

[0017] Preferably, the operation of the three-dimensional visualization modeling module in establishing a three-dimensional geological model using borehole parameters, image data, and geological feature data is as follows:

[0018] By selecting locations in historical earthquake zones for drilling operations and extracting the images and parameter data acquired during the drilling operations;

[0019] After classifying the image data according to the time and location of acquisition, the image data is analyzed to distinguish between data that can be retained and data affected by lighting. The two-dimensional image data is restored to the actual parameter features and fused to form a three-dimensional geological model.

[0020] Preferably, the operation of analyzing and distinguishing between retainable data and data affected by illumination in the image data is as follows:

[0021] Image data is extracted by dividing and enlarging the image data into segments, and then converting each segment of the image data to grayscale to determine the grayscale values ​​of the features inside the image.

[0022] Based on the grayscale value change curve, the boundary of the area affected by illumination is obtained. Based on the time and orientation order, the image data retained under the boundary of each image area are stitched together to form a complete geological two-dimensional image.

[0023] Based on the region boundary of the first image data, the image region to be retained is determined. The position on the region boundary that is closest to the relative boundary of the image region to be retained is marked as the segmentation point. A first cutting line parallel to the relative boundary is drawn at the segmentation point. Then, the adjacent image data in the time sequence is processed to obtain the second cutting line. The movement distance between adjacent images is determined based on the acquisition time interval. The relative boundary of adjacent image data is determined according to the interval distance. The image region to be retained by adjacent image data is formed by the distance between the relative boundary and the first cutting line.

[0024] After processing all images, all retained image regions are obtained. Based on the acquisition location, the image regions are stitched together to form a complete two-dimensional geological image.

[0025] Preferably, the image data is subjected to a grid-based magnification operation as follows:

[0026] Extract the image data, set the horizontal length and vertical width of the grid and label them as a and b respectively, and let the horizontal length and vertical width of the image data be A and B respectively. Then A = a × c1, where c1 is the number of grids in the horizontal direction, and B = b × c2, where c2 is the number of grids in the vertical direction.

[0027] Then, the corresponding grid image is enlarged, and the image is sharpened to optimize details, keeping the pixel values ​​consistent after the image is enlarged.

[0028] Preferably, the operation of determining the boundary of the region affected by illumination based on the grayscale value change curve is as follows:

[0029] Multiple reference lines that intersect the region boundary are set at equal intervals in the grid image data. Then, the gray value change curve is determined by the gray value of the image data on the reference lines. The value up to the starting point of the reference line is used as the horizontal axis, and the gray value change is used as the vertical axis.

[0030] The grayscale threshold D of the area affected by illumination is determined by using historical data in the database. The point on the grayscale value change curve where the grayscale value first exceeds the grayscale threshold D is the boundary point.

[0031] The boundary points are connected sequentially by smooth curves to form the region boundaries, and the grid image is divided into the preserved image region and the image region affected by lighting.

[0032] Preferably, the operation of determining the movement distance between adjacent images is as follows:

[0033] The acquisition center point is kept consistent under the set time sequence, and the acquisition rotation angle between adjacent time intervals is α. Under the drilling parameters, the hole radius is r, and the formula for calculating the moving distance is:

[0034] L = 2 × r × sin(α / 2);

[0035] L is the distance traveled, and sin(α / 2) is the sine value at angle α / 2;

[0036] Then, the spacing distance is calculated, and the distance between the relative boundary and the first cutting line in the first image data is S, and S>L. Then, the spacing distance H=SL. The third cutting line is set at the distance H between the adjacent image and the relative boundary of the current image. The image area within the spacing between the second cutting line and the third cutting line is the image area to be retained in the adjacent image data.

[0037] Preferably, the operation of restoring the two-dimensional image data to actual parameter features and fusing them to form a three-dimensional geological model is as follows:

[0038] The origin of the spatial coordinate system is the center of the surface diameter. The X and Y axes are established at the center, and the Z axis is established vertically downward from the origin.

[0039] After determining the characteristics of each geological layer by combining a complete two-dimensional geological image, the distance from the origin to each geological layer is calculated sequentially, and the spatial coordinates are obtained by combining the relative positional relationship between the geological layers. The point coordinates are connected by curves, and the curves are smoothed to form a surface, finally resulting in a three-dimensional geological model.

[0040] Similarly, after obtaining the ground three-dimensional model, the orientation of the geological three-dimensional model and the ground three-dimensional model are determined by the geographical location parameter information, and the classification map is used to form a grade evaluation model.

[0041] Preferably, the operation of evaluating the safety level of an earthquake-prone area by combining multi-source data and assigning weights in the earthquake safety assessment module is as follows:

[0042] The geological activity level is determined by comparing the fault spacing of each geological layer in the three-dimensional geological model with the threshold of fault situation in historical data. That is, when the fault spacing is less than the fault threshold, the geological activity level is level one; when the fault spacing is within the fault threshold, the geological activity level is level two; and when the fault spacing is greater than the fault threshold, the geological activity level is level three. The impact of earthquakes increases from level one to level three. Therefore, the evaluation value corresponding to different geological activity levels is the corresponding level parameter value.

[0043] The seismic resistance level is determined by combining the seismic resistance of the building group on the ground three-dimensional model. That is, the number of buildings affected by different earthquake magnitudes is obtained by using building archive information. If the number of buildings affected is less than 10% of the total number, it is a level 1 seismic resistance level; if the number of buildings affected is less than 50% of the total number, it is a level 2 seismic resistance level; and if the number of buildings affected is greater than 50% of the total number, it is a level 3 seismic resistance level. Therefore, the evaluation value corresponding to different seismic resistance levels is the corresponding level parameter value.

[0044] Then, based on the level parameter values, weights are assigned to determine the safety level evaluation of the current area.

[0045] Preferably, the expression for the security level evaluation is:

[0046] F=u×M m +w×N n ;

[0047] F is the evaluation value used for security level, and M m Here are the parameter values ​​for different geological activity levels, where m = 1 indicates a geological activity level of 1 and the parameter value is 1. u represents the weighting value of the geological activity level parameter, and N... n For different seismic resistance levels, n is 1, which means the seismic resistance level is level 1 and the parameter value is 1. w is the weight value of the seismic resistance level parameter value, and m and n ∈ [1, 2, 3].

[0048] By comparing the set security level thresholds in historical data with the current security level evaluation values, risk levels of low, medium, and high risk are determined, and corresponding strategies are matched and feedback is provided according to different risks.

[0049] This invention provides a regional seismic safety assessment system based on data import and 3D visualization. Compared with existing technologies, it has the following advantages:

[0050] 1. This earthquake regional safety assessment system based on data entry and 3D visualization extracts image and parameter data, establishes a geological 3D model using borehole parameters, image data, and geological feature data, and establishes a ground 3D model using building parameters and geological feature data. The geological 3D model and the ground 3D model are then integrated to form a grade assessment model for evaluating earthquake regional safety. Through advanced 3D modeling technology, the system can intuitively display the spatial distribution characteristics of underground rock strata, and establish an intelligent correlation mechanism between multi-dimensional data, providing a more scientific and efficient solution strategy for earthquake risk assessment.

[0051] 2. This regional seismic safety assessment system, based on data entry and 3D visualization, sets multiple reference lines at equal intervals along the regional boundary in the grid image data. Then, it determines the grayscale value variation curve based on the grayscale values ​​of the image data along the reference lines, and sequentially connects the boundary points with smooth curves to form the regional boundary. The grid image is divided into preserved image areas and areas affected by illumination. The system then combines the other preserved image areas to form two-dimensional image data. This effectively avoids the drawbacks of illumination during underground acquisition, ensuring the accuracy of the obtained image data and forming a clear geological 3D model for assessment, thus improving the efficiency of seismic area assessment.

[0052] 3. This regional earthquake safety assessment system, based on data storage and 3D visualization, determines the geological activity level by comparing the fault spacing of various geological layers in the 3D geological model with the threshold of fault conditions in historical data, and determines the seismic resistance level by combining the seismic resistance of building groups on the ground 3D model. In other words, it obtains the number of buildings affected by different earthquake levels through building archive information, assigns values ​​to them to obtain the safety level evaluation value, and realizes the safety assessment operation by combining multi-source information, effectively and accurately judges the risk level of the current area, and derives corresponding strategies for feedback and prevention. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the safety evaluation system of the present invention;

[0054] Figure 2 This is a logic flowchart of the safety evaluation system of the present invention. Detailed Implementation

[0055] 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.

[0056] Please see Figures 1-2 This invention provides two technical solutions:

[0057] Example 1: A regional seismic safety assessment system based on data import and 3D visualization, comprising:

[0058] The data acquisition module is used to collect various types of earthquake-related data and divide them into image data and parameter data. Parameter data is the data other than image data, and the specific category of parameter data is collected according to the subsequent requirements.

[0059] The data preprocessing module categorizes and associates the data, forms the required data entry format, and then stores the data in the district assessment database.

[0060] The 3D visualization module extracts image and parameter data to build a geological 3D model using borehole parameters, image data, and geological feature data, and builds a ground 3D model using building parameters and geological feature data. The geological 3D model and the ground 3D model are then merged to form a level assessment model for evaluating the safety of earthquake-prone areas.

[0061] The earthquake safety assessment module uses a grade assessment model and combines multi-source data to assign weights to achieve a safety grade assessment of the earthquake zone, and generates protection strategies based on the safety grade assessment.

[0062] The results output module outputs the resulting 3D model, evaluation level, and protection strategy, and displays them through a visual interface.

[0063] The geological 3D model is constructed using borehole data (coordinates, depth, lithology, seismic wave velocity). GOCAD software is used for 3D strata visualization to reflect the characteristics of the underground geological structure. Based on building parameters (coordinates, height, structural type, seismic resistance level), parametric modeling technology is used to express the 3D form of the urban building complex. Existing coordinate system alignment technology is used to overlay the geological model with the ground model to form a 3D scene under a unified spatial framework, intuitively showing the spatial relationship between the geological structure and buildings. This includes, but is not limited to, geological parameters such as seismic wave velocity, strata lithology, and fault distribution, as well as building parameters such as structural type, height, and seismic resistance level.

[0064] The "drilling parameters" details the location, depth, diameter, and sampling data of the borehole, which are the foundation for building a three-dimensional geological model. The "geological feature data" covers geological structural information such as stratigraphic interfaces, faults, and folds, which are crucial for accurately building a three-dimensional geological model. The "building parameters" specify the geometric dimensions, material properties, and seismic design parameters of the building, which are key to building a three-dimensional ground model and evaluating its seismic performance.

[0065] By extracting image and parameter data, a geological 3D model is established using borehole parameters, image data, and geological feature data. A ground 3D model is then established using building parameters and geological feature data. The geological and ground 3D models are merged to form a level assessment model for evaluating the safety of earthquake-prone areas. Through advanced 3D modeling technology, the system can intuitively display the spatial distribution characteristics of underground rock strata and establish an intelligent correlation mechanism between multi-dimensional data, providing a more scientific and efficient solution strategy for earthquake risk assessment.

[0066] In this embodiment of the invention, the data preprocessing module performs the following operations to classify and associate the data:

[0067] Based on the collected data, determine the data categories that need to be classified, and use the data categories as entity nodes, and use the data with different timestamps under each data category as secondary nodes;

[0068] Define the edge association between entity nodes and secondary nodes. When an entity node is selected, all secondary nodes under the corresponding entity node are displayed. At the same time, the edge association between entity nodes is determined according to the subsequent model. The model relies on the common combination of data to obtain the result, which forms the collinear edge association. The model relies on the extended prediction result of the data to form the predicted edge association.

[0069] The final result is a data classification graph with edge associations. When indexing, the current data graph and related data graphs are obtained by matching the content of the nodes.

[0070] Based on their source and purpose, the data is divided into two main categories: image data and parametric data. Image data is further subdivided into geological exploration images, remote sensing images, etc., while parametric data is subdivided according to fields such as geology and architecture. Regarding edge relationships between entity nodes: edge relationships are established based on the logical relationships between data (such as time series, spatial location, causal relationships, etc.). For example, in a geological 3D model, the contact relationship between different strata is represented by edge relationships; in a ground 3D model, the adjacency relationship between buildings is also reflected by edge relationships.

[0071] In geological 3D models, geological structural information such as stratigraphic interfaces and faults is extracted. 3D modeling software (such as GOCAD) is used to simulate these interfaces and faults, forming geological bodies with collinear edge relationships. Historical earthquake data and geological structural information are used, and machine learning algorithms (such as support vector machines and neural networks) are employed to predict future seismic activity trends. These predictions are reflected in the model, forming predictive edge relationships. A graph database (such as Neo4j) is used to store and manage the data maps, and a node content matching algorithm is used to quickly retrieve related data maps. During indexing, the current and related data maps are derived based on node content matching, providing comprehensive and accurate data support for earthquake safety assessment.

[0072] In this embodiment of the invention, the operation of the three-dimensional visualization creation module in creating a three-dimensional geological model using borehole parameters, image data, and geological feature data is as follows:

[0073] By selecting locations in historical earthquake zones for drilling operations and extracting the images and parameter data acquired during the drilling operations;

[0074] After classifying the image data according to the time and location of acquisition, the image data is analyzed to distinguish between data that can be retained and data affected by lighting. The two-dimensional image data is restored to the actual parameter features and fused to form a three-dimensional geological model.

[0075] In this embodiment of the invention, the operation of analyzing and distinguishing between image data that can be retained and data affected by illumination is as follows:

[0076] Image data is extracted by dividing and enlarging the image data into segments, and then converting each segment of the image data to grayscale to determine the grayscale values ​​of the features inside the image.

[0077] Based on the grayscale value change curve, the boundary of the area affected by illumination is obtained. Based on the time and orientation order, the image data retained under the boundary of each image area are stitched together to form a complete geological two-dimensional image.

[0078] Based on the region boundary of the first image data, the image region to be retained is determined. The position on the region boundary that is closest to the relative boundary of the image region to be retained is marked as the segmentation point. A first cutting line parallel to the relative boundary is drawn at the segmentation point. Then, the adjacent image data in the time sequence is processed to obtain the second cutting line. The movement distance between adjacent images is determined based on the acquisition time interval. The relative boundary of adjacent image data is determined according to the interval distance. The image region to be retained by adjacent image data is formed by the distance between the relative boundary and the first cutting line.

[0079] After processing all images, all retained image regions are obtained. Based on the acquisition location, the image regions are stitched together to form a complete two-dimensional geological image.

[0080] The process involves converting the image data to grayscale and establishing multiple reference lines intersecting with the region boundaries. Boundary points are determined based on the grayscale value variation curves of the image data along these reference lines. A grayscale threshold for the illuminated area is determined using historical data from a database. The boundary point is located at the first point on the grayscale value variation curve where the grayscale value exceeds the threshold value D. Image data preserved below the boundaries of each image region are stitched together according to the acquisition time and orientation sequence. A feature-based stitching algorithm is employed, extracting and matching feature points (such as corner points and edge points) from the image to form a complete two-dimensional geological image.

[0081] In this embodiment of the invention, the image data is subjected to a grid-based magnification operation as follows:

[0082] Extract the image data, set the horizontal length and vertical width of the grid and label them as a and b respectively, and let the horizontal length and vertical width of the image data be A and B respectively. Then A = a × c1, where c1 is the number of grids in the horizontal direction, and B = b × c2, where c2 is the number of grids in the vertical direction.

[0083] Then, the corresponding grid image is enlarged, and the image is sharpened to optimize details, keeping the pixel values ​​consistent after the image is enlarged.

[0084] Image sharpening compensates for the contours of an image, enhances the edges and grayscale transitions, making the image clearer. It is divided into two categories: spatial domain processing and frequency domain processing. Image sharpening aims to highlight the edges and contours of ground features or the characteristics of certain linear target elements in an image, thereby facilitating better judgment of regional boundaries in subsequent processing.

[0085] In this embodiment of the invention, the operation of determining the boundary of the region affected by illumination based on the grayscale value change curve is as follows:

[0086] Multiple reference lines that intersect the region boundary are set at equal intervals in the grid image data. Then, the gray value change curve is determined by the gray value of the image data on the reference lines. The value up to the starting point of the reference line is used as the horizontal axis, and the gray value change is used as the vertical axis.

[0087] The grayscale threshold D of the area affected by illumination is determined by using historical data in the database. The point on the grayscale value change curve where the grayscale value first exceeds the grayscale threshold D is the boundary point.

[0088] The boundary points are connected sequentially by smooth curves to form the region boundaries, and the grid image is divided into the preserved image region and the image region affected by lighting.

[0089] By setting multiple reference lines at equal intervals within the grid image data that intersect the region boundary, and then determining the grayscale value change curve based on the grayscale values ​​of the image data on the reference lines, the boundary points are sequentially connected by smooth curves to form the region boundary. The grid image is divided into preserved image areas and image areas affected by illumination. The other preserved image areas are then combined to form two-dimensional image data. This effectively avoids the drawbacks of illumination during underground acquisition, ensures the accuracy of the obtained image data, and forms a clear geological three-dimensional model for assessment operations, thereby improving the assessment efficiency of seismic areas.

[0090] In this embodiment of the invention, the operation of determining the movement distance between adjacent images is as follows:

[0091] The acquisition center point is kept consistent under the set time sequence, and the acquisition rotation angle between adjacent time intervals is α. Under the drilling parameters, the hole radius is r, and the formula for calculating the moving distance is:

[0092] L = 2 × r × sin(α / 2);

[0093] L is the distance traveled, and sin(α / 2) is the sine value at angle α / 2;

[0094] Then, the spacing distance is calculated, and the distance between the relative boundary and the first cutting line in the first image data is S, and S>L. Then, the spacing distance H=SL. The third cutting line is set at the distance H between the adjacent image and the relative boundary of the current image. The image area within the spacing between the second cutting line and the third cutting line is the image area to be retained in the adjacent image data.

[0095] Based on the acquisition time interval and movement distance, the relative boundary between adjacent images is determined. The image region retained by the adjacent image data is formed by the distance between the relative boundary and the first cutting line. The specific steps include: drawing the first cutting line parallel to the relative boundary at the segmentation point, processing the adjacent image data in the time sequence to obtain the second cutting line, and determining the movement distance and distance between adjacent images according to the acquisition time interval.

[0096] In this embodiment of the invention, the operation of restoring two-dimensional image data to actual parameter features and fusing them to form a three-dimensional geological model is as follows:

[0097] The origin of the spatial coordinate system is the center of the surface diameter. The X and Y axes are established at the center, and the Z axis is established vertically downward from the origin.

[0098] After determining the characteristics of each geological layer by combining a complete two-dimensional geological image, the distance from the origin to each geological layer is calculated sequentially, and the spatial coordinates are obtained by combining the relative positional relationship between the geological layers. The point coordinates are connected by curves, and the curves are smoothed to form a surface, finally resulting in a three-dimensional geological model.

[0099] Similarly, after obtaining the ground three-dimensional model, the orientation of the geological three-dimensional model and the ground three-dimensional model are determined by the geographical location parameter information, and the classification map is used to form a grade evaluation model.

[0100] In this embodiment of the invention, the operation of evaluating the safety level of an earthquake-prone area by combining multi-source data and assigning weights in the earthquake safety assessment module is as follows:

[0101] The geological activity level is determined by comparing the fault spacing of each geological layer in the three-dimensional geological model with the threshold of fault situation in historical data. That is, when the fault spacing is less than the fault threshold, the geological activity level is level one; when the fault spacing is within the fault threshold, the geological activity level is level two; and when the fault spacing is greater than the fault threshold, the geological activity level is level three. The impact of earthquakes increases from level one to level three. Therefore, the evaluation value corresponding to different geological activity levels is the corresponding level parameter value.

[0102] The seismic resistance level is determined by combining the seismic resistance of the building group on the ground three-dimensional model. That is, the number of buildings affected by different earthquake magnitudes is obtained by using building archive information. If the number of buildings affected is less than 10% of the total number, it is a level 1 seismic resistance level; if the number of buildings affected is less than 50% of the total number, it is a level 2 seismic resistance level; and if the number of buildings affected is greater than 50% of the total number, it is a level 3 seismic resistance level. Therefore, the evaluation value corresponding to different seismic resistance levels is the corresponding level parameter value.

[0103] Then, based on the level parameter values, weights are assigned to determine the safety level evaluation of the current area.

[0104] In this embodiment of the invention, the expression for security level evaluation is:

[0105] F=u×M m +w×N n ;

[0106] F is the evaluation value used for security level, and M m Here are the parameter values ​​for different geological activity levels, where m = 1 indicates a geological activity level of 1 and the parameter value is 1. u represents the weighting value of the geological activity level parameter, and N... n For different seismic resistance levels, n is 1, which means the seismic resistance level is level 1 and the parameter value is 1. w is the weight value of the seismic resistance level parameter value, and m and n ∈ [1, 2, 3].

[0107] By comparing the set security level thresholds in historical data with the current security level evaluation values, risk levels of low, medium, and high risk are determined, and corresponding strategies are matched and feedback is provided according to different risks.

[0108] Wherein, the safety level threshold is set as [s, t]. Then, when F < [s, t], it is a low risk level; when F ∈ [s, t], it is a medium risk level; and when F > [s, t], it is a high risk level.

[0109] The geological activity level is determined by comparing the fault spacing of various geological layers in the 3D geological model with the threshold of fault conditions in historical data. The seismic resistance level is determined by combining the seismic resistance of building groups on the ground 3D model. In other words, the number of buildings affected by different earthquake levels is obtained through building archive information, and values ​​are assigned to obtain the safety level evaluation value. This enables safety assessment operations to be carried out by combining multi-source information, effectively and accurately judging the risk level of the current area, and deriving corresponding strategies for feedback and prevention.

[0110] Example 2 differs from Example 1 in that it extracts data from multiple areas where earthquakes have occurred in the past (with known final risk levels). It then uses existing safety assessment systems and, unlike existing systems, incorporates image processing and a 3D model to analyze the extracted data. The final results are compared with known results, and the processing time is recorded. Specific results are shown in Table 1.

[0111] Table 1 Test Record Sheet

[0112]

[0113] In summary, when the safety evaluation system of this invention, which incorporates image processing and constructs a three-dimensional model, is used for evaluation, the time required to obtain the results is shorter and the final result is more accurate. Therefore, the safety evaluation system of this invention can be better applied in practical operations.

[0114] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0116] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A regional seismic safety assessment system based on data import and 3D visualization, characterized by: include: The data acquisition module is used to collect various types of earthquake-related data and divide them into image data and parameter data; The data preprocessing module categorizes and associates the data, forms the required data entry format, and then stores the data in the district assessment database. The 3D visualization module extracts image and parameter data to build a geological 3D model using borehole parameters, image data, and geological feature data, and builds a ground 3D model using building parameters and geological feature data. The geological 3D model and the ground 3D model are then merged to form a level assessment model for evaluating the safety of earthquake-prone areas. The earthquake safety assessment module uses a grade assessment model and combines multi-source data to assign weights to achieve a safety grade assessment of the earthquake zone, and generates protection strategies based on the safety grade assessment. The results output module outputs the resulting 3D model, evaluation level, and protection strategy and displays them through a visual interface; The operation of the 3D visualization creation module to create a 3D geological model using borehole parameters, image data, and geological feature data is as follows: By selecting locations in historical earthquake zones for drilling operations and extracting the images and parameter data acquired during the drilling operations; After classifying the image data according to the time and location of acquisition, the image data is analyzed to distinguish between data that can be retained and data affected by lighting. The two-dimensional image data is restored to the actual parameter features and fused to form a three-dimensional geological model. The operation of analyzing and distinguishing between the image data that can be retained and the data affected by lighting is as follows: Image data is extracted by dividing and enlarging the image data into segments, and then converting each segment of the image data to grayscale to determine the grayscale values ​​of the features inside the image. Based on the grayscale value change curve, the boundary of the area affected by illumination is obtained. Based on the time and orientation order, the image data retained under the boundary of each image area are stitched together to form a complete geological two-dimensional image. Based on the region boundary of the first image data, the image region to be retained is determined. The position on the region boundary that is closest to the relative boundary of the image region to be retained is marked as the segmentation point. A first cutting line parallel to the relative boundary is drawn at the segmentation point. Then, the adjacent image data in the time sequence is processed to obtain the second cutting line. The movement distance between adjacent images is determined based on the acquisition time interval. The relative boundary of adjacent image data is determined according to the interval distance. The image region to be retained by adjacent image data is formed by the distance between the relative boundary and the first cutting line. After processing all images, all retained image regions are obtained. Based on the acquisition location, the image regions are stitched together to form a complete two-dimensional geological image. The operation of determining the boundary of the region affected by illumination based on the grayscale value change curve is as follows: Multiple reference lines that intersect the region boundary are set at equal intervals in the grid image data. Then, the gray value change curve is determined by the gray value of the image data on the reference lines. The value up to the starting point of the reference line is used as the horizontal axis, and the gray value change is used as the vertical axis. The grayscale threshold D of the area affected by illumination is determined by using historical data in the database. The point on the grayscale value change curve where the grayscale value first exceeds the grayscale threshold D is the boundary point. The boundary points are connected sequentially by smooth curves to form the region boundaries, and the grid image is divided into the preserved image region and the image region affected by lighting.

2. The seismic regional safety assessment system based on data import and three-dimensional visualization according to claim 1, characterized in that: The data preprocessing module performs the following operations to classify and associate the data: Based on the collected data, determine the data categories that need to be classified, and use the data categories as entity nodes, and use the data with different timestamps under each data category as secondary nodes; Define the edge association between entity nodes and secondary nodes. When an entity node is selected, all secondary nodes under the corresponding entity node are displayed. At the same time, the edge association between entity nodes is determined according to the subsequent model. The model relies on the common combination of data to obtain the result, which forms the collinear edge association. The model relies on the extended prediction result of the data to form the predicted edge association. The final result is a data classification graph with edge associations. When indexing, the current data graph and related data graphs are obtained by matching the content of the nodes.

3. The seismic regional safety assessment system based on data import and three-dimensional visualization according to claim 1, characterized in that: The image data is subjected to a grid-based magnification operation as follows: Extract the image data, set the horizontal length and vertical width of the grid and label them as a and b respectively, and let the horizontal length and vertical width of the image data be A and B respectively. Then A = a × c1, where c1 is the number of grids in the horizontal direction, and B = b × c2, where c2 is the number of grids in the vertical direction. Then, the corresponding grid image is enlarged, and the image is sharpened to optimize details, keeping the pixel values ​​consistent after the image is enlarged.

4. The seismic regional safety assessment system based on data import and three-dimensional visualization according to claim 1, characterized in that: The operation to determine the movement distance between adjacent images is as follows: The acquisition center point is kept consistent under the set time sequence, and the acquisition rotation angle between adjacent time intervals is α. Under the drilling parameters, the hole radius is r, and the formula for calculating the moving distance is: L = 2 × r × sin(α / 2); L is the distance traveled, and sin(α / 2) is the sine value at angle α / 2; Then, the spacing distance is calculated, and the distance between the relative boundary and the first cutting line in the first image data is S, and S>L. Then, the spacing distance H=SL. The third cutting line is set at the distance H between the adjacent image and the relative boundary of the current image. The image area within the spacing between the second cutting line and the third cutting line is the image area to be retained in the adjacent image data.

5. The seismic regional safety assessment system based on data import and three-dimensional visualization according to claim 1, characterized in that: The operation of restoring the two-dimensional image data to actual parameter features and fusing them to form a three-dimensional geological model is as follows: The origin of the spatial coordinate system is the center of the surface diameter. The X and Y axes are established at the center, and the Z axis is established vertically downward from the origin. After determining the characteristics of each geological layer by combining a complete two-dimensional geological image, the distance from the origin to each geological layer is calculated sequentially, and the spatial coordinates are obtained by combining the relative positional relationship between the geological layers. The point coordinates are connected by curves, and the curves are smoothed to form a surface, finally resulting in a three-dimensional geological model. Similarly, after obtaining the ground three-dimensional model, the orientation of the geological three-dimensional model and the ground three-dimensional model are determined by the geographical location parameter information, and the classification map is used to form a grade evaluation model.

6. The seismic regional safety assessment system based on data import and three-dimensional visualization according to claim 1, characterized in that: The earthquake safety assessment module performs the following operation to evaluate the safety level of an earthquake-prone area after weighting and combining multi-source data: The geological activity level is determined by comparing the fault spacing of each geological layer in the three-dimensional geological model with the threshold of fault situation in historical data. That is, when the fault spacing is less than the fault threshold, the geological activity level is level one; when the fault spacing is within the fault threshold, the geological activity level is level two; and when the fault spacing is greater than the fault threshold, the geological activity level is level three. The impact of earthquakes increases from level one to level three. Therefore, the evaluation value corresponding to different geological activity levels is the corresponding level parameter value. The seismic resistance level is determined by combining the seismic resistance of the building group on the ground three-dimensional model. That is, the number of buildings affected by different earthquake magnitudes is obtained by using building archive information. If the number of buildings affected is less than 10% of the total number, it is a level 1 seismic resistance level; if the number of buildings affected is less than 50% of the total number, it is a level 2 seismic resistance level; and if the number of buildings affected is greater than 50% of the total number, it is a level 3 seismic resistance level. Therefore, the evaluation value corresponding to different seismic resistance levels is the corresponding level parameter value. Then, based on the level parameter values, weights are assigned to determine the safety level evaluation of the current area.

7. The seismic regional safety assessment system based on data import and three-dimensional visualization according to claim 6, characterized in that: The expression for the security level evaluation is: F=u×M m +w×N n ; F is the evaluation value used for security level, and M m Here are the parameter values ​​for different geological activity levels, where m = 1 indicates a geological activity level of 1 and the parameter value is 1. u represents the weighting value of the geological activity level parameter, and N... n For different seismic resistance levels, n is 1, which means the seismic resistance level is level 1 and the parameter value is 1. w is the weight value of the seismic resistance level parameter value, and m and n ∈ [1, 2, 3]. By comparing the set security level thresholds in historical data with the current security level evaluation values, risk levels of low, medium, and high risk are determined, and corresponding strategies are matched and feedback is provided according to different risks.

Images