Urban building information data acquisition method based on three-dimensional simulation

Abstract

The invention provides an urban building information data acquisition method based on three-dimensional simulation. The method comprises the steps that an initial image set is segmented, a building structure load transmission path is obtained, layer height related characteristic parameters of crack forms and depths are determined, and wind load environment data are acquired; according to the preliminary distribution rule, the relevance between crack width distribution and high-altitude humidity change is analyzed, and the layer height relevance prediction logic of crack morphology and depth change is determined; time window division is carried out on a building maintenance operation period through a safety assessment basis, hierarchical maintenance time node arrangement is obtained, and a priority order of maintenance opportunities is obtained through risk level assessment by using cumulative effect indexes; and generating a crack processing task list according to priority ranking, and generating a maintenance opportunity execution scheme in combination with specific characteristics of crack branch density and crack edge roughness.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a city building information data acquisition method based on three-dimensional simulation. BACKGROUND

[0002] The study of building surface cracks is a crucial part of the field of building safety, as it directly relates to the stability and service life of building structures, and has an invaluable role in ensuring personnel safety and property safety. With the acceleration of urbanization, issues such as building aging and environmental erosion are becoming increasingly prominent. Cracks, as an intuitive manifestation of building damage, have become a key indicator for assessing the health of buildings. However, current methods for monitoring and analyzing building surface cracks still have significant shortcomings. Many methods rely too heavily on the collection of surface data and lack in-depth exploration of the development characteristics of cracks in different parts, especially when faced with complex building structures, making it difficult to capture the dynamic evolution of cracks in the spatial dimension. In addition, existing technologies often overlook the correlation between crack morphology changes and the overall stress environment of the building, resulting in inaccurate predictions of crack development direction and depth, which in turn affects the reliability of safety assessments. In this context, the research field faces prominent technical challenges. The expansion trend of cracks in different building parts shows significant differences, for example, in high-rise buildings, the depth of cracks may exhibit gradient characteristics with changes in floor height. This spatial non-uniformity makes it difficult for a single monitoring method to provide comprehensive coverage. The dynamic changes in crack morphology, such as changes in length extension direction, are often closely related to the stress distribution of the building. If real-time capture and dynamic adjustment of analysis strategies cannot be achieved, it is difficult to accurately determine the impact of cracks on the structure. These two factors interweave and jointly constrain the accuracy and practicality of crack expansion research. Therefore, how to develop a three-dimensional tracking method that adjusts analysis strategies in real time based on the differences in crack expansion characteristics in different parts of the building, combined with dynamic morphological characteristics, has become a key issue in improving the accuracy of building safety assessment and determining the scientificity of maintenance timing. SUMMARY

[0003] The present application provides a city building information data acquisition method based on three-dimensional simulation, mainly including:

[0004] Collecting crack morphology data and depth data at different building floor heights, extracting crack extension direction and crack width distribution characteristics, and obtaining an initial crack morphology and depth distribution image set;

[0005] Segmenting the initial image set to obtain the building structure load transfer path, determining the floor height-related characteristic parameters of crack morphology and depth, and collecting wind load environment data;

[0006] Fusion wind load environment data, generate crack extension path at different floor height, get the preliminary distribution rule of crack extension direction and depth change according to the diffusion path, get the influence weight of surface stress concentration and interlayer shear;

[0007] According to the preliminary distribution rule, analyze the correlation between crack width distribution and high altitude humidity change, determine the layer height correlation prediction logic of crack morphology and depth change;

[0008] According to the layer height correlation prediction logic, the crack propagation path is calculated layer by layer, and the influence of high altitude humidity change on crack development is evaluated to obtain the safety evaluation basis of different layer height intervals;

[0009] Through the safety evaluation basis, the building maintenance operation cycle is divided into time window, the layered maintenance time node arrangement is generated, and the priority order of maintenance opportunity is obtained through risk level evaluation;

[0010] According to the priority order, generate maintenance opportunity execution scheme.

[0011] Further, the crack morphology data and depth data of different building floor height are collected, the crack extension direction and crack width distribution characteristics are extracted, and the initial crack morphology and depth distribution image set is obtained, including:

[0012] Divide the building floor height into low layer, middle layer and high layer interval, collect the self weight data of each interval structure, scan the wall surface, record the crack starting point and ending point coordinates, calculate the crack extension direction angle, measure the crack width, generate the width distribution data set; Calculate the mean and standard deviation of the width data set, generate the distribution density index, measure the crack depth, map the depth and layer height coefficient, fit the depth gradient function; Calculate the mean of the depth gradient function, mark the key monitoring area, construct the feature matrix containing the crack position, width, depth, extension angle and distribution density; Map the feature matrix to generate crack width thermal map and depth contour map, label the load distribution layer by layer, and generate the image set.

[0013] Further, the initial image set is segmented to obtain the load transfer path of building structure, determine the layer height related characteristic parameters of crack morphology and depth, collect wind load environment data, including:

[0014] Processing the image set, identifying the beam, column, wall contour, constructing the load transfer network diagram, extracting the connected path from the top layer to the foundation, generating the load transfer path; extracting the crack data on the path, calculating the crack length difference value and time interval ratio, generating the development rate coefficient, calculating the depth increment and initial depth ratio, generating the expansion ratio, calculating the crack contour fractal dimension, generating the shape complexity index; correlate the development rate coefficient, expansion ratio, complexity index and layer height, build parameter database, collect wind speed and wind direction, match the database, generate comprehensive data set.

[0015] Further, the fusion wind load environment data, generate the expansion path of the crack at different layer height, get the preliminary distribution rule of crack extension direction and depth change according to the diffusion path, get the influence weight of surface stress concentration and interlayer shear, including:

[0016] Matching the wind load environment data and the layer height related parameters, calculating the crack propagation speed, extracting the crack starting point and end point coordinates, connecting into the expansion path; calculate the coordinate difference value on the expansion path, generate the extension direction angle, calculate the depth difference and distance ratio, generate the depth change gradient, statistics grouping mean and distribution range, generate the distribution rule; identify the extension direction change area in the distribution rule, calculate the width growth rate ratio, generate the stress concentration coefficient, calculate the interlayer misplacement distance and height ratio, generate the shear deformation angle, extract the weight.

[0017] Further, after generating the expansion path of the crack at different layer height, including:

[0018] Arrange the crack development rate coefficient and depth expansion ratio, calculate the matrix element, determine the vertical or horizontal expansion dominant crack; calculate the crack density in the vertical expansion area, search the convergence node, extract the horizontal expansion area bifurcation point, calculate the crack extension speed; collect wind speed and load data, mark the high-rise wind pressure and low layer self weight influence area, calculate the crack depth change rate in the influence area; identify the turning point of the expansion path, calculate the direction deflection angle and depth increment gradient, map to generate the distribution rule atlas containing the deflection angle and increment gradient.

[0019] Further, according to the preliminary distribution rule, analyze the correlation between crack width distribution and high altitude humidity change, determine the layer height correlation prediction logic of crack shape and depth change, including:

[0020] Collect the crack width data and humidity monitoring value in the distribution rule, calculate the width change rate and humidity change rate correlation coefficient, generate the corresponding relationship data set; process the corresponding relationship data set, generate the prediction logic.

[0021] Further, the crack propagation path is calculated by the layer height correlation prediction logic, the influence of high altitude humidity change on crack development is evaluated, and safety evaluation basis of different layer height intervals is obtained, including:

[0022] According to the prediction logic, crack propagation speed and depth growth prediction value are calculated, layer height calculation intervals are divided, the depth growth prediction value and time increment product are accumulated, layered propagation paths are generated, humidity monitoring data are collected, humidity change rate and expansion length product are calculated, risk levels are divided, safety indexes are calculated, the risk levels, expansion length and depth growth prediction value are comprehensively calculated, and the safety evaluation basis is generated.

[0023] Further, the safety evaluation basis is used to divide time windows for building maintenance operation cycles, generate layered maintenance time node arrangements, and obtain priority ranking of maintenance timing through risk level evaluation, including:

[0024] The safety index descending rate in the safety evaluation basis is calculated, the time when the safety index descends to a threshold value is calculated, time windows are divided, and maintenance time windows are marked, the earliest starting time of the maintenance time window is extracted, layered maintenance time node arrangement tables are generated, and priority ranking of maintenance timing is obtained through risk level evaluation.

[0025] Further, the layered maintenance time node arrangement is generated, including:

[0026] High-risk bearing areas and medium-risk decoration areas are divided, the key stress parts of the bearing areas and the general crack areas of the decoration areas are screened, maintenance projects are marked, structure importance and damage degree scores are calculated, maintenance urgency weights are generated, and layered maintenance time node arrangements are sorted and generated.

[0027] Further, the maintenance timing execution scheme is generated according to the priority ranking, including:

[0028] Maintenance tasks in the priority ranking are extracted, crack treatment task lists containing layer height positions and processing methods are generated, safety state levels are divided according to the crack treatment task lists, safety evaluations are generated, task execution timing in the safety evaluations is allocated, inspection plans are developed, and the maintenance timing execution scheme is generated.

[0029] The technical scheme provided by the embodiment of the application can include the following beneficial effects:

[0030] The application discloses a kind of based on three-dimensional simulation city building information data acquisition method, by collecting the crack data of different floor height, establishes height gradient coefficient measurement datum, extracts crack characteristic parameter, fuses wind load environmental data, constructs floor height extension matrix, identifies crack type and extension path.Combined with altitude humidity variation, interlayer vibration frequency and material aging rate, carry out hierarchical calculation to crack propagation, obtain the basis of safety evaluation.Based on bearing capacity decay rate and structural stability coefficient, establish risk rating matrix, generate hierarchical maintenance execution list, determine maintenance time window and resource allocation priority sequence.The application realizes the accurate analysis and prediction of building crack, provides basis for formulating scientific maintenance scheme, effectively improves the safety and service life of building structure. BRIEF DESCRIPTION OF DRAWINGS

[0031] Fig. 1 It is a flow chart of the application based on three-dimensional simulation city building information data acquisition method.

[0032] Fig. 2 It is a schematic diagram of the application based on three-dimensional simulation city building information data acquisition method. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the application will be described clearly and in detail below with reference to the drawings in the embodiments of the application. The described embodiments are only some of the embodiments of the application.

[0034] As Figs. 1-2 , the application based on three-dimensional simulation city building information data acquisition method specifically can include:

[0035] Step S101, collect crack morphology data and depth data of different building floor height, extract crack extension direction and crack width distribution characteristics, establish height gradient coefficient measurement datum, obtain initial crack morphology and depth distribution image set.

[0036] The height values of each floor of the building and the structural dead load data corresponding to the floor are obtained, the height values are divided into three height intervals of low floor interval, middle floor interval and high floor interval, the average load distribution values in each interval are calculated, the wall surface of each height interval is scanned by a laser scanner, and the starting point coordinates and the ending point coordinates of the cracks are recorded, the crack extension direction angle is determined according to the coordinate difference, the image recognition technology is used to measure the width values of each position of the cracks, and the crack width data set and the crack extension direction data set of each height interval are obtained. For the crack width data set, the mean value and the standard deviation of the crack width in the same height interval are calculated, the crack distribution density index of the interval is determined according to the ratio of the standard deviation to the mean value, the depth values of each sampling point of the cracks are measured by using an ultrasonic detector, and a mapping relationship between the depth values and the corresponding floor height coefficients is established, wherein the floor height coefficient is defined as the ratio of the current floor height to the total height of the building, a linear function D = a x H + b is used to fit the relationship between the depth value D and the floor height coefficient H, wherein a is the slope and b is the intercept, and the depth gradient change function is obtained by least square method. According to the depth gradient change function, the derivative of the function is obtained to obtain the depth gradient value, and the arithmetic mean value of the depth gradient values of all sampling points in each height interval is calculated as the mean value of the depth gradient of the interval. If the mean value of the depth gradient of a certain interval exceeds a predetermined threshold, the interval is marked as a key monitoring area. Combining the crack extension direction data set, the crack distribution density index and the depth gradient change function, a crack morphology feature matrix is constructed, which contains data in five dimensions of crack position coordinates, width values, depth values, extension angles and distribution density indexes. The crack morphology feature matrix is mapped, a color-coded crack width heat map is generated according to the width values, an isodepth contour map is drawn according to the depth values, the image is layered according to the load distribution values, and a crack morphology distribution image corresponding to the height interval is formed. The distribution images of all height intervals are summarized to obtain an initial crack morphology and depth distribution image set.

[0037] Specifically, in the field of building structure health monitoring, the formation and development of cracks are often closely related to the height distribution and load conditions of the building.

[0038] It should be noted that the cracks of building structures of different heights will show obvious stratification due to the difference in self-weight and external load.

[0039] Specifically, in the height division, the low layer interval usually refers to the lower third of the total building height, which bears the maximum pressure of the overall building; the middle layer interval is the middle third, with relatively balanced load; the high layer interval is the upper third, mainly bearing wind load and its own weight. The laser scanner can accurately measure the three-dimensional coordinates of the wall surface by emitting a laser beam and receiving the reflected signal. When the laser beam sweeps across the crack, the reflected signal changes, and by analyzing the time difference and intensity change of the signal, the starting point and ending point coordinates of the crack can be determined. The angle of crack extension direction is obtained by calculating the vector direction from the starting point to the ending point, which is of great significance to determine the cause of the crack.

[0040] In one possible implementation, the crack distribution density index reflects the concentration degree of cracks in a certain area. When the ratio of standard deviation to mean is large, it indicates that the crack width distribution is uneven, and there may be local stress concentration phenomenon. The ultrasonic detector calculates the crack depth by emitting ultrasonic pulses and calculating the echo time. The principle is that ultrasonic waves will produce reflection when propagating inside the crack. By measuring the time difference between emission and reception, combined with the propagation speed of ultrasonic waves in concrete, the crack depth value can be obtained. The introduction of the height coefficient makes the crack depth at different heights comparable, facilitating the establishment of a unified evaluation standard. The fitting process of the linear function D=a x H+b is realized by the least squares method, where the slope a represents the rate of depth change with height, and the intercept b represents the reference depth value. The depth gradient value is obtained by taking the derivative of the function, which reflects the trend of crack depth change. When the average depth gradient of a certain interval exceeds the preset threshold, it indicates that the crack depth in this area changes dramatically, which may pose a potential safety hazard to the structure, and therefore needs to be monitored.

[0041] Preferably, the construction of the crack morphology feature matrix integrates multi-dimensional information, including not only geometric features such as position, width, depth, and angle, but also the distribution density index, a statistical feature. This multi-dimensional data fusion can more comprehensively describe the crack state. In generating the visualization image, the width heat map uses color gradient to represent crack width change, with red indicating larger width area and blue indicating smaller width area; the depth contour map connects points with the same depth value to form contour lines, similar to the representation method of topographic maps.

[0042] Step S102, segmenting the initial image set to obtain the building structure load transfer path, determining the height-related characteristic parameters of crack morphology and depth, and collecting wind load environment data.

[0043] An edge detection algorithm is used to process each image in the initial image set, identify the beam, column, and wall contours in the building structure, construct a load transfer network graph based on the contour connection relationship, wherein the nodes represent the structural members and the edges represent the load transfer direction, and obtain the complete building structure load transfer path by tracing the connected paths from the top layer to the foundation. According to the load transfer path, the crack data at each node on the path is extracted, the ratio of the crack length difference between adjacent time points to the time interval is calculated to obtain the crack development rate coefficient, the ratio of the crack depth increment to the initial depth is measured to determine the depth expansion ratio, the fractal dimension of the crack contour is calculated by the box counting method, specifically, the crack contour is covered with square grids of different scales, the number of grids containing cracks is counted, and the morphological complexity index is obtained according to the slope of the logarithmic relationship between the grid scale and the number. The crack development rate coefficient, depth expansion ratio, and morphological complexity index are associated with the corresponding layer height value to establish a layer height related characteristic parameter database, wind load monitoring equipment is deployed at each layer height position, real-time wind speed and wind direction angle are collected, collection time and duration are recorded, wind load data are matched with the corresponding layer height in the layer height related characteristic parameter database, and a comprehensive data set containing crack characteristic parameters and wind load environment data is formed.

[0044] Specifically, the edge detection algorithm plays a key role in building structure image processing, which determines the structure contour by identifying the pixel points with sharp changes in gray value in the image.

[0045] Specifically, the algorithm calculates the gray difference between each pixel point and its surrounding pixels, and marks the point as an edge point when the difference exceeds a preset threshold. By connecting these edge points, complete beam, column, and wall contour lines are formed.

[0046] It should be noted that the construction of the load transfer network graph is based on the principles of structural mechanics. In building structures, loads are transferred from the upper structure to the foundation layer by layer. The nodes in the network graph represent load-bearing members such as beam-column junctions and wall support points, and the edges represent the direction and path of load transfer. By using a depth-first search algorithm to trace all possible paths from the top layer to the foundation, a complete load transfer system can be obtained.

[0047] In one possible implementation, the calculation of the crack development rate coefficient involves time series data analysis. Assuming that a crack length L1 is measured at time t1 and a length L2 is measured at time t2, the development rate coefficient is the result of L2-L1 divided by t2-t1. This coefficient reflects the expansion speed of the crack per unit time, which is of great significance for evaluating the safety of the structure. The depth expansion ratio is obtained by comparing the crack depth changes at different times. If the initial depth is D0 and the current depth is D1, the expansion ratio is D1-D0 divided by D0. The process of calculating the fractal dimension by box counting method is worth explaining in detail. This method uses a series of square grids of different scales to cover the crack contour image.

[0048] For example, first cover with a grid of side length r1, count the number of grids containing crack parts N1; then cover with a smaller grid of side length r2, get the number N2. By plotting the log(1 / r) vs. log(N) graph, the slope is the fractal dimension. The larger the fractal dimension, the more complex the crack morphology, which may indicate more serious structural damage.

[0049] Preferably, the establishment of the layer height related characteristic parameter database realizes the integration of multi-dimensional data. The database stores the crack development rate coefficient, depth expansion ratio and morphological complexity index of each floor as an index associated with the height value of the layer. This association enables engineers to quickly query the crack characteristics of a specific height and identify the height distribution pattern of crack development. The deployment of wind load monitoring equipment takes into account the height gradient effect of the building. Wind speed and direction vary significantly at different heights, usually increasing with height. Real-time data recorded by the monitoring equipment are matched with crack characteristic parameters to reveal the development pattern of cracks under wind load.

[0050] Step S103, fuse wind load environment data, get the crack expansion path at different layer heights through the layer height related characteristic parameters of crack morphology and depth, get the preliminary distribution pattern of crack extension direction and depth change according to the diffusion path, and get the influence weight of surface stress concentration and interlayer shear.

[0051] The wind speed value and wind direction angle in the wind load environment data are matched with the layer height related characteristic parameters of crack morphology and depth in space and time. According to the corresponding relationship between the wind speed value and the crack development rate coefficient, the crack propagation speed at each spatial position is calculated by using the bilinear interpolation method. The crack initiation point and termination point coordinates are extracted from the crack image. A continuous trajectory is formed by connecting the crack initiation points to the termination points at each layer height position. The crack propagation path at different layer heights is obtained. According to the coordinate difference between adjacent points on the crack propagation path, the angle between the path tangent and the horizontal direction is calculated as the crack extension direction. The depth change gradient is obtained by dividing the depth difference between adjacent points on the path by the distance between the points. The extension direction angle and the depth change gradient are statistically grouped according to the layer height interval. The mean value and distribution range of each group are calculated to obtain the preliminary distribution law of the crack extension direction and the depth change. According to the preliminary distribution law, the area where the extension direction changes by more than a preset threshold is identified as a stress concentration point. The ratio of the crack width growth rate at the stress concentration point to the average growth rate at the layer is calculated as the surface stress concentration coefficient. The interlayer shear deformation angle is obtained by dividing the horizontal offset distance of the cracks between adjacent floors by the interlayer height. According to the correlation between the stress concentration coefficient and the crack propagation path curvature and the correlation between the shear deformation angle and the crack depth change gradient, the influence weights of surface stress concentration and interlayer shear are determined.

[0052] Specifically, the spatio-temporal matching of wind load environment data and crack characteristic parameters is a key link in understanding the crack propagation mechanism.

[0053] Specifically, the spatio-temporal matching needs to correspond the wind speed and wind direction data at a certain time with the crack development rate coefficient at the same time and the same position. This correspondence reveals the direct influence of external environmental load on crack development.

[0054] In one possible implementation, the application of bilinear interpolation method can solve the problem of limited data collection points. Assuming that the wind speed is 8 m / s and 12 m / s is measured at the height of 10 m and 20 m of the building respectively, the corresponding crack development rate coefficients are 0.5 mm / day and 0.8 mm / day respectively. For the value at the height of 15 m, the wind speed can be estimated to be about 10 m / s and the crack development rate coefficient can be estimated to be about 0.65 mm / day by bilinear interpolation. This interpolation calculation makes the data distribution continuous throughout the building height range. The construction of the crack propagation path requires accurate extraction of key information from the image. The initiation point of the crack is usually located in the stress concentration area, such as the corner of the window or the periphery of the door frame; the termination point is the farthest end of the current crack extension. After identifying these feature points by image processing technology, a spline curve fitting method is used to connect the corresponding points at each layer height to form a smooth and continuous three-dimensional propagation path. This path not only reflects the spatial distribution of the crack, but more importantly, reveals the development trend of the crack in the vertical direction.

[0055] It should be noted that the calculation of the path tangent direction directly reflects the local propagation characteristics of the crack. For any point on the path, the tangent direction of the point can be obtained by calculating the vector direction formed by the adjacent points before and after the point. When the angle between the tangent and the horizontal direction is close to 90 degrees, it indicates that the crack mainly develops vertically; when the angle is small, it indicates that the crack has a tendency to expand horizontally. The calculation of the depth change gradient is similar, by dividing the depth difference of adjacent measurement points by the horizontal distance, the spatial variation rate of the depth is obtained. The identification of stress concentration points is based on the mutation characteristics of the crack extension direction. When the crack extension direction of a certain area has a large angle deflection within a short distance, it often means that there is a complex stress state at that place. The surface stress concentration coefficient quantifies the stress concentration by comparing the local and overall crack propagation rates. The larger the ratio, the more serious the stress concentration. The interlayer shear deformation angle reflects the relative displacement between floors, which will generate additional shear stress at the connection between the floor and the wall, accelerating the formation and expansion of cracks.

[0056] Preferably, the determination of the influence weight adopts a correlation analysis method. Through statistical analysis, it is found that when the surface stress concentration coefficient increases by 0.1, the curvature of the crack propagation path increases by about 15%; and when the interlayer shear deformation angle increases by 1 milliradian, the crack depth change gradient increases by about 20%. Based on these correlation data, the relative influence degree of surface stress concentration and interlayer shear on crack development can be quantitatively determined.

[0057] Based on the crack development rate coefficient and the depth expansion ratio, the vertical expansion dominant and horizontal extension dominant crack types are identified, the crack convergence nodes in the surface stress concentration area and the bifurcation paths under the action of interlayer shear are extracted, the crack extension speed difference in different floor height intervals is identified, the crack depth change trend in the high-rise wind pressure influence area and the low-rise self-weight influence area is marked, and the distribution rule atlas containing the expansion direction deflection angle and the depth increasing gradient is formed.

[0058] The crack development rate coefficients of each layer height are arranged into a row vector, the depth expansion ratios are arranged into a column vector, a layer height expansion matrix is constructed by an outer product operation, an element value in the i-th row and the j-th column of the matrix is equal to the product of the development rate coefficient of the i-th layer and the depth expansion ratio of the j-th layer, if an element on the diagonal of the matrix is greater than the average value of other elements in the same row, it is determined that the layer is a vertical expansion dominant crack, otherwise, it is a horizontal extension dominant crack. According to the distribution of the vertical expansion dominant crack and the horizontal extension dominant crack, the number of cracks per unit area in the vertical expansion dominant area is calculated as the crack density, a point with a density exceeding a preset threshold is searched as a convergence node, an intersection point of a crack main stem and a branch in the horizontal extension dominant area is identified as a branch point by an image skeleton extraction algorithm, and a crack extension speed is obtained by measuring the ratio of a crack length increment between adjacent layer heights to a time interval. For the crack extension speed distribution, wind speed monitoring data and floor load data of each layer height are obtained, a region in which the wind speed in the top one-third height range exceeds a preset threshold is marked as a high-rise wind pressure influence area, a unit area bearing capacity is obtained by dividing the floor load in the bottom two-thirds height range by the floor area, and a region in which the unit area bearing capacity exceeds a preset threshold is marked as a low-rise self-weight influence area, and a time variation rate of crack depth in the two influence areas is calculated as a crack depth variation trend, respectively. Based on the depth variation trend and the positions of the convergence node and the branch point, a position where a crack path direction changes abruptly is identified as a turning point, an included angle between path directions before and after the turning point is calculated as an expansion direction deflection angle, a depth increment gradient is obtained by measuring a depth difference between adjacent layer heights divided by a height difference, and the deflection angle and the increment gradient are two-dimensionally mapped according to spatial coordinates to form a distribution rule atlas containing the expansion direction deflection angle and the depth increment gradient.

[0059] Specifically, the construction of the layer height expansion matrix is based on the principle of outer product operation, and this mathematical method can comprehensively reflect the mutual relationship between crack characteristics of different layer heights.

[0060] Specifically, assuming that a 20-story building has a crack development rate coefficient of 0.8 mm / day at the 5th floor, 1.2 mm / day at the 10th floor, and 1.5 mm / day at the 15th floor, and corresponding depth expansion ratios are 0.3, 0.5, and 0.7, respectively. Through the outer product operation, the element value at the intersection position of the 5th floor and the 10th floor is 0.8*0.5=0.4, which reflects the correlation strength between the cracks of the two layer heights.

[0061] It should be noted that the determination of the vertical expansion dominant crack is based on the relative size of the diagonal element. When the element value on the diagonal of the matrix is significantly higher than other elements in the same row, it indicates that the crack of the layer mainly develops in the vertical direction. This phenomenon usually occurs in load-bearing walls because the vertical load is the main stress source. On the contrary, the horizontal extension dominant crack often occurs at the junction of the floor and the wall, and is greatly affected by temperature changes and material shrinkage.

[0062] In one possible implementation, the calculation of crack density involves the application of image segmentation techniques. By dividing the wall surface into 1 square meter grid cells, the number of cracks within each cell is counted. When the crack density of a certain area reaches more than 5 per square meter, the point is identified as a convergence node. These nodes often locate at geometric mutation positions such as window corners, door hole periphery, etc., which are typical manifestations of stress concentration. The image skeleton extraction algorithm plays a key role in identifying bifurcation points. This algorithm preserves the centerline structure by stripping the crack edge pixels layer by layer, and finally obtains the skeleton graph of the crack. In the skeleton graph, the position where a line segment separates into two or more is the bifurcation point. This bifurcation phenomenon reflects the multi-directional expansion characteristics of cracks under complex stress state.

[0063] Preferably, the division of wind pressure influence area and self-weight influence area takes into account the stress characteristics of the building. The wind pressure on the top of high-rise buildings increases exponentially with height, and when the wind speed reaches more than 15 meters per second, the lateral force generated will cause diagonal cracks in the wall. The low-rise area mainly bears the vertical load transmitted by the upper structure, and when the unit area bearing exceeds 80% of the design value, vertical cracks are likely to occur. The measurement of the deflection angle of the expansion direction reveals the dynamic characteristics of crack development. When the crack encounters steel bars or other obstacles, it will deflect in direction, and the deflection angle is usually between 30 degrees and 60 degrees. The depth increment gradient reflects the speed of crack development towards the interior, and the typical value is an increase of 0.5 millimeters in depth per meter in height. Mapping these parameters to a two-dimensional coordinate system, the horizontal axis represents the building height, and the vertical axis represents the parameter value, forming a graph that intuitively displays the distribution law of cracks on the entire building height.

[0064] Step S104, according to the preliminary distribution law, analyze the correlation between crack width distribution and high altitude humidity change, determine the layer height correlation prediction logic of crack morphology and depth change.

[0065] According to the crack width data in the preliminary distribution rule, the air humidity monitoring values of each layer height in the corresponding period are obtained, the Pearson correlation coefficient of the crack width change rate and the humidity change rate is calculated, the correlation coefficient is equal to the product of the covariance of two variables divided by the standard deviation, if the absolute value of the correlation coefficient exceeds the preset threshold, it is determined that there is significant correlation in the layer height interval, and the corresponding relationship data set of crack width and high altitude humidity is established. The support vector machine algorithm is used to process the corresponding relationship data set, the crack width, humidity value and layer height position are taken as the input feature vector, and the obtained influence weight of surface stress concentration and interlayer shear is taken as the output target value. The radial basis kernel function is mapped to a high-dimensional space for regression training, and a weight adjustment function is obtained. The updated influence weight value is dynamically calculated according to the real-time environmental data. Based on the updated influence weight value, a crack morphology prediction equation is constructed by a multivariate linear regression method, taking the layer height and the updated weight value as independent variables and the crack extension angle as the dependent variable. The same method is used to construct a depth change prediction equation, taking the layer height and the updated weight value as independent variables and the depth growth rate as the dependent variable, and the layer height correlation prediction logic of crack morphology and depth change is determined by the two equations. According to the dynamic parameter requirements involved in the layer height correlation prediction logic, the vibration acceleration signals of each floor are measured by an acceleration sensor, the vibration frequency spectrum is obtained by fast Fourier transform, the peak frequency is extracted as the interlayer vibration frequency, and the ultrasonic detector is used to measure the attenuation rate of concrete sound velocity with time as the material aging rate. The obtained vibration frequency and aging rate data are used to correct the time-varying parameters in the prediction logic.

[0066] Specifically, the Pearson correlation coefficient plays a key role in analyzing the correlation between crack width and humidity. This statistical indicator quantifies the linear correlation between two variables, revealing the influence mechanism of environmental factors on structural damage.

[0067] Specifically, when the calculated correlation coefficient is close to 1, it indicates that the increase in crack width is positively correlated with the increase in humidity; when it is close to -1, it is negatively correlated; and when it is close to 0, it means that the two are basically unrelated. In practical applications, when the absolute value of the correlation coefficient exceeds 0.7, it is generally considered to have strong correlation.

[0068] It should be noted that the application principle of support vector machine algorithm in weight dynamic adjustment is based on structural risk minimization. The algorithm maps the original data to a high-dimensional feature space through a radial basis kernel function, and finds the optimal hyperplane in this space to realize regression prediction. The role of the radial basis kernel function is to handle nonlinear relationships, which can capture the complex interactions between crack width, humidity value and layer height position. During the training process, the algorithm automatically learns the influence pattern of each factor on the weight, forming a dynamic adjustment mechanism.

[0069] In one possible implementation, the process of constructing the prediction equation by the multiple linear regression method involves the principle of least squares. Taking the crack extension angle prediction as an example, assume that 100 sets of historical data are collected, each set containing the layer height, the updated weight value, and the corresponding extension angle. By minimizing the sum of squared errors between the predicted value and the actual value, the coefficients of each term in the equation are determined. The advantage of this method is that it can quantitatively describe the comprehensive influence of multiple factors on crack development. Fast Fourier Transform plays a key role in vibration frequency analysis. Acceleration sensors collect time-domain signals, which contain a mixture of various frequency components. Fourier Transform converts these time-domain signals into the frequency domain, making different frequency components appear separately. The peak frequency usually corresponds to the inherent vibration characteristics of the structure. When the building cracks, its stiffness decreases, and the natural frequency will correspondingly decrease. By monitoring the frequency change, the structural damage degree can be indirectly evaluated.

[0070] Preferably, the principle of ultrasonic detection of concrete aging is based on the relationship between sound velocity and material elastic modulus. The sound velocity of newly poured concrete is high, and as time goes by, the internal micro-cracks increase, the porosity increases, and the sound velocity gradually decreases. By regularly measuring the sound velocity value at the same position, the decay rate can be calculated, which can quantify the aging process of the material. This aging rate data is crucial for the prediction model, as it directly affects the long-term development trend of cracks. The correction mechanism of time-varying parameters embodies the dynamic adaptability of the prediction logic. Vibration frequency reflects the real-time health status of the structure, and aging rate represents the long-term degradation trend of the material. Incorporating these two parameters into the prediction logic enables the model to adaptively adjust according to the actual status of the building.

[0071] For example, when a sudden drop in the vibration frequency of a certain floor is detected, the prediction model will correspondingly increase the crack propagation risk assessment value of that area, realizing the transition from static prediction to dynamic prediction.

[0072] Step S105, for the layer height associated prediction logic, integrate the interlayer vibration frequency and the material aging rate, perform layer-by-layer calculation on the crack propagation path, combine the influence evaluation of high-altitude humidity change on crack development, and obtain the safety evaluation basis for different layer height intervals.

[0073] The reciprocal of the interlayer vibration frequency is taken as a structural damage coefficient, and the material aging rate is taken as a time attenuation coefficient, which are multiplied by the corresponding item coefficients of the original equation to obtain a modified prediction equation that integrates dynamic parameters, and the crack propagation speed prediction value and depth growth prediction value of each floor height are calculated. According to the crack propagation speed prediction value and the depth growth prediction value, a calculation interval is divided every preset height of the building floor height, the crack propagation path in each interval is calculated independently, the product of the propagation speed prediction value and the time increment of each measuring point in the interval is obtained to obtain the predicted propagation length of the interval, and the calculation results of each interval are connected to form a complete layered propagation path. Based on the predicted propagation length of the layered propagation path, the high-altitude humidity monitoring data of the corresponding period is obtained, the product of the humidity change rate and the predicted propagation length is taken as a humidity influence factor, and each floor height interval is divided into three risk levels of low, medium and high according to the influence factor value, and if the influence factor exceeds the high-risk threshold, the interval is marked as a high-risk level. The risk level, the predicted propagation length of the layered propagation path, and the depth growth prediction value output by the modified prediction equation are comprehensively considered to define a safety index calculation formula, wherein the safety index is equal to the full score value minus the risk level value multiplied by the first weight coefficient, minus the predicted propagation length multiplied by the second weight coefficient, minus the depth growth prediction value multiplied by the third weight coefficient, to obtain the safety evaluation basis of different floor height intervals.

[0074] Specifically, the introduction of the structural damage coefficient reflects the inherent relationship between vibration characteristics and crack development.

[0075] Specifically, when the building structure is intact, its natural vibration frequency remains near the design value; when cracks appear, the structural stiffness decreases, resulting in a decrease in vibration frequency. The reciprocal of the vibration frequency is used as the damage coefficient because the lower the frequency, the larger the reciprocal, which directly reflects the increase in damage degree.

[0076] For example, the initial vibration frequency of a floor is 10 Hz, which decreases to 8 Hz after cracks appear, and the damage coefficient increases from 0.1 to 0.125, which is directly reflected in the modification of the prediction equation.

[0077] It should be noted that the mechanism of the time attenuation coefficient is based on the time-varying characteristics of material properties. Concrete materials are affected by carbonation, chloride ion erosion, freeze-thaw cycles and other factors during service, resulting in a gradual decrease in strength and elastic modulus. The material aging rate is obtained through ultrasonic detection, with a typical value of 2% to 3% per year. Taking this rate as the time attenuation coefficient can dynamically adjust the prediction equation to reflect the accelerating effect of material performance degradation on crack development.

[0078] In one possible implementation, the necessity of hierarchical calculation stems from the stress feature differences of different heights of the building. The bottom layer mainly bears compressive stress, the middle layer has a combination of compression and bending, and the top layer is mainly in bending and tensile stress. By dividing a calculation interval every 5 meters or 10 meters, these stress distribution characteristics can be accurately captured. In each interval, the product of the crack propagation speed prediction value and the time increment represents the propagation distance in that period, and the total propagation length is obtained after accumulation. This hierarchical processing method avoids the roughness of analyzing the entire building as a single object. The calculation of the humidity influence factor reveals the catalytic effect of environmental factors on structural damage. High-altitude humidity changes affect the stress state inside the concrete through capillary action and dry-wet cycles. When the humidity rises from 60% to 90%, the concrete swells by absorbing water; when the humidity decreases, shrinkage stress occurs. This repeated swelling and shrinking process accelerates the propagation of existing cracks. The influence factor is quantified by the product of the humidity change rate and the predicted propagation length, and when the product exceeds the critical value, it indicates that the crack development in this area is significantly affected by the environment.

[0079] Preferably, the three-level classification of risk levels provides an intuitive evaluation of the safety state. A low risk level corresponds to an influence factor less than 0.3, indicating slow and controllable crack development; a medium risk level corresponds to a range of 0.3 to 0.7, requiring enhanced monitoring; a high risk level exceeds 0.7, meaning that the crack may rapidly expand and immediate reinforcement measures need to be taken. This classification method converts continuous values into discrete management decision-making basis. The multi-parameter comprehensive calculation of the safety index achieves a comprehensive structural safety evaluation. The setting of three weight coefficients reflects the relative importance of different factors: the risk level weight is usually the largest, as it integrates multiple influence factors; the propagation length weight is second, directly reflecting the geometric development of the crack; the depth growth weight is relatively small, but is crucial for the early warning of penetrating cracks. By deducting from the full score value item by item, the higher the safety index, the safer the structure, providing quantitative support for maintenance decision-making.

[0080] Step S106, divide the building maintenance operation period into time windows according to the safety evaluation basis, obtain hierarchical maintenance time node arrangement, and use the cumulative effect index to obtain the priority ranking of the maintenance timing through risk level evaluation.

[0081] The safety index value obtained in the safety evaluation basis is used to calculate the index difference value between adjacent time points divided by the time interval to obtain the index decline rate, the time required for the safety index to drop to the preset maintenance threshold is calculated according to the decline rate, the continuous time axis is divided into a plurality of time windows according to quarters, and if the safety index of a certain floor height interval is expected to drop below the maintenance threshold in a certain time window, the time window is marked as the maintenance time window of the interval. According to the distribution of the maintenance time window, the earliest appearing maintenance time window start time of each floor height interval is extracted as a maintenance node, the maintenance nodes are arranged in order from low to high according to the floor height, the floor height interval number and the corresponding maintenance start time are recorded, and a hierarchical maintenance time node arrangement table is formed. For each floor height interval in the hierarchical maintenance time node arrangement table, the current bearing capacity is obtained through a load test, the design bearing capacity is extracted from the design file, the difference between the two is divided by the building service life to obtain the bearing capacity decay rate, the actual deformation of the structure is measured by using a displacement sensor, the allowable deformation is obtained from the specification, and the ratio of the two is taken as the structural stability coefficient. The product of the decay rate and the stability coefficient is defined as the cumulative effect index. Based on the cumulative effect index and the risk level value of each floor height interval, a priority scoring formula is constructed, the score value is equal to the cumulative effect index multiplied by the risk level value, and each floor height interval in the hierarchical maintenance time node arrangement table is reordered according to the score value from high to low to obtain the priority order result of the maintenance opportunity.

[0082] Specifically, the calculation of the safety index decline rate reflects the time characteristics of the degradation of the structure performance.

[0083] Specifically, the safety index data points obtained by continuous monitoring can be used to draw a curve of the index change over time. Assuming that the safety index at the end of the first quarter is 85 and the index drops to 82 at the end of the second quarter, the quarterly decline rate is 3 units. Based on this rate, it can be predicted when the index will drop to the maintenance threshold, for example, if the threshold is set to 70, it is expected to take about 5 quarters. This prediction method makes the maintenance plan forward-looking and avoids passive response.

[0084] It should be noted that the quarterly division of the time window takes into account the seasonal factors of construction. In northern regions, low temperatures in winter are not suitable for concrete repair work; in southern regions, the quality of construction during the rainy season is difficult to guarantee. By dividing the time axis into quarterly windows, maintenance work can be reasonably arranged to avoid unfavorable seasons. When it is predicted that a certain floor height interval will reach the maintenance threshold in winter, the maintenance time can be advanced to the autumn window to ensure the quality of construction.

[0085] In one possible implementation, the arrangement of hierarchical maintenance time nodes follows the law of force transmission of the structure. The bottom layer bears the load of the entire building, and its maintenance priority is usually high; but if the top layer cracks rapidly, it may cause waterproofing failure and also needs to be handled in priority. By arranging the maintenance nodes in order of layer height, the arrangement table formed intuitively shows the maintenance timing of each layer, facilitating the overall arrangement of human and material resources. The process of load test to obtain the current bearing capacity involves step-by-step loading and deformation monitoring. During the test, an increasing load is applied to the structure, and the corresponding deformation is measured. When the deformation rate suddenly increases or obvious crack propagation occurs, the load value at this time is the current bearing capacity. Compared with the original bearing capacity in the design document, the decay rate of bearing capacity can be calculated.

[0086] For example, the designed bearing capacity is 500 kN / m2, and after 20 years of use, the actual measurement is 450 kN / m2, so the decay rate is 2.5 kN / m2 per year.

[0087] Preferably, the structural stability coefficient is obtained in real time through a network of displacement sensors. These sensors are arranged at key nodes such as beam-column connections and mid-span locations of large-span beams. The actual deformation refers to the displacement value measured under normal use load, while the allowable deformation comes from the design specification, usually 1 / 250 to 1 / 300 of the span. When the measured deformation approaches the allowable value, the stability coefficient approaches 1, indicating that the structure is in a critical state. The introduction of the cumulative effect index takes into account both the bearing capacity degradation and the deformation accumulation. The product of the two reflects the overall decline of the structure's performance. Bearing capacity decay affects the safety reserve of the structure, while deformation accumulation affects the use function and comfort. By combining the two, the structure's state can be more comprehensively evaluated. The construction of the priority score formula realizes quantitative maintenance decision-making. The region with high risk level and high cumulative effect index has the highest score value, indicating that this region urgently needs maintenance. Converting engineering judgment into comparable numerical values enables maintenance resources to be prioritized to the most needed locations, improving maintenance efficiency and the effectiveness of the use of funds.

[0088] By combining the bearing capacity decay rate and the structural stability coefficient, identify high-risk bearing structure areas and medium-risk non-bearing decorative areas, extract key stress parts with urgent maintenance needs and general crack areas suitable for preventive maintenance, set maintenance urgency weights according to structural importance and crack hazard degree, sort and generate a hierarchical execution list, and determine the maintenance time window and resource allocation priority sequence corresponding to different risk levels.

[0089] The risk rating matrix is constructed by taking the decay rate as the horizontal axis of the matrix and the stability coefficient as the vertical axis of the matrix. If the decay rate exceeds a preset high value and the stability coefficient exceeds a preset critical value, the corresponding region is marked as a high-risk grade bearing structure region. If both values are in an intermediate range, the region is marked as a medium-risk non-bearing decorative region, and the region risk classification is completed. According to the high-risk grade bearing structure region and the medium-risk non-bearing decorative region, beam-column joints, shear wall bottoms and overhanging member roots are screened as key stress positions in the bearing structure region and marked as emergency maintenance requirement projects. Positions with surface cracks and a crack depth not exceeding a preset depth value of the concrete protective layer are identified as general crack regions in the decorative region and marked as preventive maintenance projects. For the emergency maintenance requirement projects and the preventive maintenance projects, a structure importance score is given according to the force transmission path length of the component from the foundation. The crack width is measured, and a corresponding hazard degree score is obtained according to the specification limit table. The structure importance score and the hazard degree score are multiplied to obtain a maintenance urgency weight. The projects are sorted in descending order of the weight to form a hierarchical execution list. Projects with a weight exceeding a preset upper limit are listed as immediate processing tasks, and the remaining projects are listed as planned maintenance tasks. Based on the hierarchical execution list of the immediate processing tasks and the planned maintenance tasks, a time window of the last seven days is assigned to the immediate processing tasks, and subsequent monthly time windows are assigned to the planned maintenance tasks in descending order of the weight. The number of repair personnel and the configuration order of equipment types are determined according to the task urgency weight, and a correspondence table of risk grades and time windows and a resource configuration priority list are established.

[0090] Specifically, the construction of the risk rating matrix is based on a two-dimensional evaluation idea, and the bearing capacity decay rate and the structural stability coefficient are comprehensively judged as two independent dimensions.

[0091] Specifically, the horizontal axis of the matrix represents the bearing capacity decay rate, and the value increases from left to right. The vertical axis represents the structural stability coefficient, and the value increases from bottom to top. The matrix is divided into nine regions. The upper right corner region represents a high-risk state with severe decay and deformation close to the limit value. The lower left corner represents a safe state with good performance. This visualization method enables engineers to quickly locate the risk grade of each structural region.

[0092] It should be noted that the identification of key stress positions follows the principles of structural mechanics. Beam-column joints are key connection points of frame structures, bearing bending moment, shear force and axial force, and once damaged, they can cause local or even overall collapse. The bottom of the shear wall bears the maximum horizontal shear force and overturning moment, and is the key part of seismic design. The root of the overhanging member has stress concentration and lacks redundant force transmission paths. The development of cracks in these positions directly threatens the safety of the structure and must be given priority.

[0093] In one possible implementation, the role of the concrete protective layer is not only to prevent steel corrosion, but also to be an important indicator of crack severity. The protective layer thickness is usually 20 to 50 mm. When the crack depth does not exceed this range, the steel is not exposed, and the structural bearing capacity is basically not affected. Preventive measures such as surface sealing can be used. Once the crack penetrates the protective layer to the steel surface, the steel begins to rust and expand, which will accelerate the crack propagation, forming a vicious cycle. The calculation of the force transmission path length reflects the importance of the component in the overall structure. Starting from the foundation, tracing upwards along the load transmission direction, the more components passed, the longer the path. If the component upstream of the force transmission path is damaged, it will affect the stress state of all downstream components.

[0094] For example, the force transmission path length of a load-bearing column is 1, the beam supported on the column is 2, the secondary beam on the beam is 3, and so on. The shorter the path, the more important the component, and the higher the maintenance priority.

[0095] Preferably, the specification limit table provides a correspondence between crack width and damage level. For reinforced concrete components in general environments, the damage level is grade 1 when the crack width is less than 0.2 mm, grade 2 when the crack width is 0.2 to 0.3 mm, and grade 3 when the crack width exceeds 0.3 mm. In corrosive environments, the limit is more stringent. This grading method converts continuous crack width into discrete damage levels, facilitating quantitative assessment. The differentiated allocation of time windows reflects the urgency of maintenance work. Immediate tasks usually involve structural safety and must be repaired within seven days of discovering the problem to avoid the risk of expanding.

[0096] Step S107, according to the priority ranking, generate a crack treatment task list, combine the specific characteristics of crack branch density and crack edge roughness, and generate a maintenance opportunity execution scheme.

[0097] According to the priority ranking result, the maintenance tasks of each interval are extracted in order of layer height from low to high, the layer height position, crack type and processing method determined according to the crack type of each task are recorded, a layered crack processing task list is formed, and the list contains three contents of task number, belonging layer height interval and corresponding processing method. For each task in the task list, the crack branch number is divided by the crack trunk length to obtain the crack branch density, the edge roughness is calculated by using the box counting method to calculate the log(N) of the covering box number of the crack edge contour at different scales, the crack network complexity is obtained by counting the number of intersection points in unit area, the edge irregularity is obtained by squaring and taking the square root of the perpendicular distance from the crack edge point to the fitting straight line and dividing by the number of edge points, and the branch node density is obtained by counting the number of crack bifurcation points and dividing by the total length of the crack. Based on the five characteristic parameters, five weight coefficients are preset according to the influence degree of each parameter on the safety of the structure, a crack damage comprehensive score formula is constructed, the score value is equal to the branch density multiplied by the first coefficient plus the edge roughness multiplied by the second coefficient plus the network complexity multiplied by the third coefficient plus the irregularity multiplied by the fourth coefficient plus the node density multiplied by the fifth coefficient, the safety state is divided into three levels of safe, basically safe and unsafe according to the score value, and the target safety evaluation result is obtained. Combined with the target safety evaluation result, the task list and the processing method, the execution opportunity within seven days is allocated for the task of the unsafe level, the monthly change rate of the characteristic parameters is calculated for the task of the basically safe level, and if the change rate exceeds the preset threshold, the monitoring period is shortened and the execution opportunity is advanced, and the quarterly inspection plan is made for the task of the safe level. A maintenance opportunity execution scheme containing task number, execution opportunity and processing method is generated.

[0098] Specifically, the layered crack processing task list embodies the structured maintenance management concept.

[0099] Specifically, the arrangement in order of layer height enables the maintenance team to reasonably arrange the use of high-altitude operation equipment and avoid repeated erection of scaffolding. The crack type determines the selection of processing method, such as using brushing sealant for surface micro-cracks and pressure grouting for penetrating cracks. This corresponding relationship ensures that each task has a clear technical solution and improves the maintenance efficiency.

[0100] It should be noted that the principle of calculating fractal dimension by box counting method is based on self-similarity theory. The crack edge image is covered with square grids of different side lengths, and the number of grids containing cracks is counted. When the grid side length is r, the covering number is N(r), and the relationship between log(1 / r) and log(N) is plotted in a double logarithmic coordinate system, and the slope is the fractal dimension. The larger the fractal dimension, the rougher the crack edge, which usually means that the stress state during crack formation is more complex.

[0101] In one possible implementation, the calculation of edge irregularity involves a statistical method. The crack edge is first digitized to obtain a series of coordinate points, and then the best straight line is fitted to these points using the least squares method. The perpendicular distance of each edge point to the fitted straight line is calculated, and the standard deviation of these distances reflects the irregularity of the edge. Regular cracks are often caused by a single stress source, while irregular cracks may involve multiple composite stresses. The complexity of the crack network is quantified by the intersection density. In a unit area, the more nodes formed by the intersection of cracks, the more complex the stress distribution. This complexity often occurs in the stress concentration areas of the structure, such as the periphery of the opening and the sudden change of the cross section. A high complexity of the crack network is more difficult to repair than a single crack, as the mutual influence between multiple cracks needs to be considered.

[0102] Preferably, the weight coefficients are set based on engineering experience and theoretical analysis. The weight of the branch density is usually the highest, as more branches mean more possible paths for crack propagation; the edge roughness is second, reflecting the complexity of the stress state; the network complexity, irregularity, and node density have relatively small weights, but may also become dominant factors in specific cases. This weighted scoring method combines multiple independent indicators into a single numerical value, facilitating decision-making. The monitoring of the monthly change rate provides a basis for dynamic management. By comparing the characteristic parameter values of adjacent months, it can be determined whether the crack is in an active expansion period. When the change rate exceeds 5%, it usually indicates that the structure is rapidly deteriorating, and the monitoring period needs to be shortened to every two weeks, and early maintenance may be considered. This trend-based management method is more forward-looking than simply relying on the current state, allowing for measures to be taken before the problem becomes serious, significantly reducing maintenance costs and safety risks.

[0103] The above only lists some preferred embodiments of the present application, but the present application is not limited thereto, and many improvements and changes can be made. Any improvements and changes made on the basis of the basic principles of the present application should be considered to fall within the scope of protection of the present application.

Claims

1. A method for collecting city building information data based on three-dimensional simulation, characterized in that, The method comprises: Collecting crack morphology data and depth data at different building story heights, extracting crack extension direction and crack width distribution characteristics, and obtaining an initial crack morphology and depth distribution image set; Segmenting the initial image set to obtain a building structure load transfer path, determining story height related characteristic parameters of crack morphology and depth, and collecting wind load environment data; Fusing the wind load environment data to generate a crack propagation path at different story heights, obtaining a preliminary distribution rule of crack extension direction and depth change according to the diffusion path, and obtaining the influence weight of surface stress concentration and interlayer shear; According to the preliminary distribution rule, analyzing the correlation between crack width distribution and high-altitude humidity change, and determining the story height correlation prediction logic of crack morphology and depth change; According to the story height correlation prediction logic, performing layered calculation on the crack propagation path, combining the influence evaluation of high-altitude humidity change on crack development, and obtaining safety evaluation basis of different story height intervals; Through the safety evaluation basis, dividing the building maintenance operation cycle into time windows to generate layered maintenance time node arrangement, and obtaining priority ranking of maintenance opportunity through risk level evaluation; Generating a maintenance opportunity execution scheme according to the priority ranking.

2. The method for collecting urban building information data based on three-dimensional simulation according to claim 1, characterized in that, The collecting crack morphology data and depth data at different building story heights, extracting crack extension direction and crack width distribution characteristics, and obtaining an initial crack morphology and depth distribution image set comprises: Dividing the building story height into low, middle and high layer intervals, collecting the self-weight data of each interval structure, scanning the wall surface, recording the crack start and end point coordinates, calculating the crack extension direction angle, measuring the crack width, generating the width distribution data set; calculating the mean and standard deviation of the width data set, generating the distribution density index, measuring the crack depth, mapping the depth and story height coefficient, fitting the depth gradient function; calculating the mean of the depth gradient function, marking the key monitoring area, constructing the feature matrix containing the crack position, width, depth, extension angle and distribution density; mapping the feature matrix to generate a crack width thermal map and a depth contour map, layering the load distribution, and generating the image set.

3. The method of claim 1, wherein the method is characterized by: The segmenting the initial image set to obtain a building structure load transfer path, determining story height related characteristic parameters of crack morphology and depth, and collecting wind load environment data comprises: Processing the image set, identifying the beam, column and wall contour, constructing a load transfer network diagram, extracting a connected path from the top layer to the foundation, and generating the load transfer path; extracting crack data on the path, calculating the crack length difference and time interval ratio, generating a development rate coefficient, calculating the depth increment and initial depth ratio, generating an expansion ratio, calculating the crack contour fractal dimension, and generating a morphology complexity index; correlating the development rate coefficient, expansion ratio, complexity index and story height, constructing a parameter database, collecting wind speed and direction, matching the database, and generating a comprehensive data set.

4. The method for collecting urban building information data based on three-dimensional simulation according to claim 1, characterized in that, The fusing wind load environment data to generate a crack propagation path at different story heights, obtaining a preliminary distribution rule of crack extension direction and depth change according to the diffusion path, and obtaining the influence weight of surface stress concentration and interlayer shear comprises: Matching the wind load environment data with the floor height related parameters, calculating the crack propagation speed, extracting the crack starting point and end point coordinates, and connecting them into the propagation path; calculating the coordinate difference value on the propagation path, generating the extension direction angle, calculating the depth difference value and distance ratio, generating the depth variation gradient, and generating the distribution rule by statistical grouping mean and distribution range; identifying the extension direction change area in the distribution rule, calculating the width growth rate ratio, generating the stress concentration coefficient, calculating the interlayer displacement distance and height ratio, generating the shear deformation angle, and extracting the weight.

5. The method of claim 1, wherein the method is characterized by: After generating the propagation path of the crack at different floor heights, the method comprises: Arranging the crack development rate coefficient and the depth expansion ratio, calculating the matrix element, and determining the vertical or horizontal expansion dominant crack; calculating the crack density in the vertical expansion area, searching for the convergence node, extracting the horizontal expansion area bifurcation point, and calculating the crack extension speed; collecting wind speed and load data, marking the high-rise wind pressure and the self-weight influence area of the middle and low layers, and calculating the crack depth change rate in the influence area; identifying the turning point of the propagation path, calculating the direction deflection angle and the depth increment gradient, and mapping to generate a distribution rule map containing the deflection angle and the increment gradient.

6. The method of claim 1, wherein the method is a method of collecting urban building information data based on three-dimensional simulation, characterized by, According to the preliminary distribution rule, the correlation between the crack width distribution and the altitude humidity change is analyzed to determine the floor height correlation prediction logic of the crack morphology and depth change, which comprises: Collecting the crack width data and humidity monitoring value in the distribution rule, calculating the width change rate and humidity change rate correlation coefficient, and generating a corresponding relationship data set; processing the corresponding relationship data set to generate the prediction logic.

7. The method of claim 1, wherein the method is a method of collecting urban building information data based on three-dimensional simulation, characterized by, According to the floor height correlation prediction logic, the crack propagation path is calculated, the influence of altitude humidity change on crack development is evaluated, and the safety evaluation basis of different floor height intervals is obtained, which comprises: According to the prediction logic, the crack propagation speed and depth growth prediction value are calculated; the floor height calculation interval is divided, the depth growth prediction value and time increment product are accumulated to generate a layered propagation path; humidity monitoring data is collected, the humidity change rate and extension length product are calculated, and the risk level is divided; the safety index is calculated, the risk level, extension length and depth growth prediction value are comprehensively considered to generate the safety evaluation basis.

8. The method of claim 1, wherein the method is a method of collecting urban building information data based on three-dimensional simulation, characterized by, Through the safety evaluation basis, the building maintenance operation cycle is divided into time windows, a layered maintenance time node arrangement is generated, and the priority ranking of the maintenance opportunity is obtained through risk level evaluation, which comprises: The safety index descending rate in the safety evaluation basis is calculated, the time when the safety index descends to a threshold value is calculated, the time window is divided, and the maintenance time window is marked; the earliest starting time of the maintenance time window is extracted to generate a layered maintenance time node arrangement table; the priority ranking of the maintenance opportunity is obtained through risk level evaluation.

9. The method of claim 1, wherein the method is a method of collecting urban building information data based on three-dimensional simulation, characterized by, The layered maintenance time node arrangement comprises: Dividing the high-risk load-bearing area and the medium-risk decoration area; screening the key stress parts of the load-bearing area and the general crack area of the decoration area, and marking the maintenance projects; calculating the structure importance and hazard degree scores to generate the maintenance urgency weight, and sorting to generate the layered maintenance time node arrangement.

10. The method of claim 1, wherein the method is a method of collecting urban building information data based on three-dimensional simulation, characterized by, The maintenance opportunity execution scheme is generated according to the priority ranking, and the method comprises the following steps: The maintenance tasks in the priority ranking are extracted, a crack treatment task list containing layer height positions and treatment methods is generated, a safety state level is divided according to the crack treatment task list, a safety evaluation is generated, a task execution opportunity in the safety evaluation is allocated, an inspection plan is made, and the maintenance opportunity execution scheme is generated.

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