Dynamic risk assessment method and system for historical buildings

Abstract

The present invention discloses a method and system for dynamic risk assessment of historical buildings, and relates to the technical field of risk assessment of historical buildings. The method for dynamic risk assessment of historical buildings obtains the three-dimensional coordinates, real-time image data and internal property data of the historical buildings, and comprehensively analyzes the surface and internal data of each time period to obtain the surface change risk assessment index and the internal change risk assessment index, and analyzes the comprehensive change risk assessment index. If the index exceeds the preset interval, the abnormal surface is identified. The present invention can achieve comprehensive and continuous monitoring by collecting and analyzing the dynamic data of the surface and internal structure of the historical buildings. Compared with traditional static inspections or single inspections, dynamic monitoring can more timely capture the slight changes of the building in different time periods, especially some hidden damages that are difficult to directly observe. Through this dynamic monitoring method, potential risks can be discovered in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of historical building risk assessment, and in particular to a historical building dynamic risk assessment method and system. Background Art

[0002] With the acceleration of urbanization, more and more historical buildings have gradually become an important part of cultural heritage. Due to long-term natural erosion, environmental changes, aging, and human destruction, historical buildings are facing varying degrees of damage and risks. In order to protect and inherit these precious cultural resources, it is particularly important to reasonably and timely assess the dynamic risks of historical buildings. However, the existing historical building risk assessment methods still have many limitations, and innovative technologies are urgently needed to improve the accuracy, reliability, and real-time performance of the assessment.

[0003] At present, risk assessment of historical buildings usually relies on manual inspection and regular structural inspection, but these methods have the following shortcomings: low efficiency of manual inspection, time lag, and insufficient accuracy of risk assessment. Therefore, how to achieve real-time and accurate dynamic risk assessment of historical buildings has become a research hotspot in academia and engineering.

[0004] In recent years, with the continuous development of science and technology, building risk assessment methods based on sensors, computer vision, artificial intelligence and other technologies have gradually been applied, especially in the fields of intelligent building monitoring and structural health monitoring. Although these technologies have made certain progress in application, for historical buildings, their complexity and diversity make the existing technologies still have certain limitations, especially in how to combine multiple data sources, how to comprehensively reflect the health status of buildings, and how to conduct systematic and comprehensive dynamic risk assessments. These are the focus of current technical research.

[0005] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. First, the existing technology is limited to static and periodic building inspections, relying only on the detection of surface cracks and corrosion, failing to fully monitor the dynamic changes of buildings during actual use, and lacking real-time monitoring of the internal structure of the building, which can easily lead to some hidden and gradually accumulated risks not being identified in time, thereby increasing the cost of repair and safety hazards. Secondly, most of the existing assessment methods rely on a single indicator for assessment, lacking a comprehensive analysis of multi-dimensional factors, which can easily lead to the risk assessment of buildings being limited to surface inspections, ignoring deep-seated problems such as material aging, temperature and humidity fluctuations, and hidden cracks. Buildings Changes in the internal structure often have a significant impact on long-term stability, and a single assessment method is unlikely to reveal potential structural problems, and it is easy to fail to intervene effectively before major problems occur in the building, causing a sharp increase in maintenance costs. In addition, existing technologies usually rely on fixed risk assessment thresholds and standards, and fail to adjust risk assessment models according to the dynamic changes of buildings. The environment and external factors of buildings are constantly changing during use, and fixed assessment standards are difficult to effectively respond to the gradually accumulated changes in buildings, which can easily lead to delays in the risk assessment results of buildings, thereby missing the opportunity for repairs, increasing the probability of safety problems in the building in the future, and causing adverse effects on long-term use. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a dynamic risk assessment method and system for historical buildings, which solves the problems existing in the existing technology that only rely on static inspections, ignore dynamic changes and internal structure monitoring, have a single assessment method, make it difficult to identify potential risks in a timely manner, and increase repair costs and safety hazards.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a dynamic risk assessment method for historical buildings, comprising the following steps: obtaining three-dimensional coordinate information of each location point of the historical building, and establishing a three-dimensional model of the historical building; in the three-dimensional model of the historical building, continuously obtaining real-time image data of each building surface for several time periods, and internal property data of each measurement location point, the real-time image data including several pixel points and the pixel value of each pixel point, the internal property data including environmental data, structural data, and material data, the environmental data including surface temperature value, surface humidity value, and pressure value, the structural data including vibration frequency value and stress value, the material data including hardness value, conductivity value, and surface reflectivity value; conduct a comprehensive analysis on the real-time image data of each building surface in each period and the internal property data of each measurement location point to obtain the surface change risk assessment index and the internal change risk assessment index of the historical building; conduct a comprehensive analysis on the surface change risk assessment index and the internal change risk assessment index of the historical building to obtain the comprehensive change risk assessment index of the historical building; determine whether the comprehensive change risk assessment index of the historical building is within the preset risk assessment interval, and when the comprehensive change risk assessment index of the historical building is outside the preset risk assessment interval, identify abnormal building surfaces and mark them in the three-dimensional model of the historical building; wherein, the specific formula for calculating the comprehensive change risk assessment index of the historical building is as follows: ;in, is a comprehensive change risk assessment index for historic buildings. is a natural constant, is the surface change risk assessment index for historic buildings, is the surface evaluation coefficient stored in the database, is the internal change risk assessment index for historic buildings, is the internal evaluation coefficient stored in the database, .

[0008] Furthermore, the specific steps for obtaining the surface change risk assessment index of historical buildings are as follows: performing regional division on the real-time image data of each building surface in each time period to obtain corrosion area data and crack area data of each building surface in each time period, wherein the corrosion area data includes a number of corrosion pixel points, and the crack area data includes a number of crack pixel points; performing comprehensive analysis on the corrosion area data and crack area data of each building surface in each time period to obtain corrosion area change indexes and crack area change indexes of several groups of historical buildings; performing comprehensive analysis on the corrosion area change indexes and crack area change indexes of several groups of historical buildings to obtain the surface change risk assessment index of historical buildings.

[0009] Furthermore, the specific steps for obtaining the corrosion area data and crack area data of each building surface in each time period are as follows: preprocessing the pixel values ​​of each pixel point on each building surface in each time period respectively; after the preprocessing, performing image thresholding processing on the pixel values ​​of each pixel point on each building surface in each time period to obtain a number of initial corrosion area data and initial crack area data of each building surface in each time period; performing connectivity marking processing on a number of initial corrosion area data and initial crack area data of each building surface in each time period respectively to obtain the corrosion area data and crack area data of each building surface in each time period.

[0010] Furthermore, the specific steps for obtaining the corrosion area change index and the crack area change index of several groups of historical buildings are as follows: for each time period, the number of corrosion pixels in the corrosion area data of each building surface and the number of crack pixels in the crack area data are counted respectively, and recorded as the corrosion area area value and the crack area area value of each building surface respectively; the pixel value of each corrosion pixel in the corrosion area data of each building surface in each time period and the corrosion area area value are comprehensively analyzed to obtain the corrosion area change index of several groups of historical buildings; the pixel value of each crack pixel in the crack area data of each building surface in each time period and the crack area area value are comprehensively analyzed to obtain the crack area change index of several groups of historical buildings.

[0011] Furthermore, the specific formulas for calculating the corrosion area change index and crack area change index of each group of historical buildings are as follows: { FbH i = ∑ t = 1 t 0 [ α 1 * ( ∑ u = 1 u 0 | FxS itu 2 − FxS itu 1 | u 0 ) + α 2 * ( FmJ it 2 − FmJ it 1 ) ] LbH i = ∑ t = 1 t 0 [ β 1 * ( ∑ r = 1 r 0 | LxS itr 2 − LxS itr 1 | r 0 ) + β 2 * ( LmJ it 2 − LmJ it 1 ) ] ;in, For the The corrosion regional change index of the historical buildings, For the The second period in the group The corrosion area data of the building surface The pixel value of the eroded pixel, For the The first period in the group The corrosion area data of the building surface corresponding to the second period The pixel value of the eroded pixel, is the erosion pixel coefficient stored in the database, For the The second period in the group The corrosion area value of the building surface, For the The first period in the group The corrosion area value of the building surface, is the corrosion area coefficient stored in the database, , =1, 2, 3, …, , is the number of corroded pixels in the corroded area data, For the The crack area variation index of the historical buildings, For the The second period in the group The crack area data of the building surface The pixel value of the crack pixel, For the The first period in the group The crack area data of the building surface corresponding to the second period The pixel value of the crack pixel, is the crack pixel coefficient stored in the database, For the The second period in the group The area value of the crack area on the building surface, For the The first period in the group The area value of the crack area on the building surface, is the crack area coefficient stored in the database, , =1, 2, 3, …, , is the number of crack pixels in the crack area data, =1, 2, 3, …, , is the number of building surfaces of the historic building, =1, 2, 3, …, , is the total number of groups.

[0012] Furthermore, the specific formula for calculating the surface change risk assessment index of historical buildings is as follows: ;in, is the surface change risk assessment index for historic buildings, For the The corrosion regional change index of the historical buildings, is the corrosion area coefficient stored in the database, For the The crack area variation index of the historical buildings, is the crack area coefficient stored in the database, , =1, 2, 3, …, , is the total number of groups.

[0013] Furthermore, the specific steps for obtaining the internal change risk assessment index of historical buildings are as follows: comprehensively analyze the environmental data, structural data, and material data of each measurement point on each building surface in each period to obtain the environmental change index, structural change index, and material change index of several groups of historical buildings; comprehensively analyze the environmental change index, structural change index, and material change index of several groups of historical buildings to obtain the internal change risk assessment index of historical buildings; wherein, the specific formula for calculating the internal change risk assessment index of historical buildings is as follows: ;in, is the internal change risk assessment index for historic buildings, For the Environmental change index of historical buildings, is the environmental variation coefficient stored in the database, For the The structural change index of the historical buildings, is the structural variation coefficient stored in the database, For the Material change index of historical buildings, is the material variation coefficient stored in the database, , =1, 2, 3, …, , is the total number of groups.

[0014] Furthermore, the specific steps for obtaining the environmental change index, structural change index and material change index of several groups of historical buildings are as follows: read the surface temperature value, surface humidity value and pressure value of each measuring position point on the surface of each building in each time period, and perform comprehensive analysis respectively to obtain the environmental change index of several groups of historical buildings; read the vibration frequency value and stress value of each measuring position point on the surface of each building in each time period, and perform comprehensive analysis respectively to obtain the structural change index of several groups of historical buildings; read the hardness value, conductivity value and surface reflectivity value of each measuring position point on the surface of each building in each time period, and perform comprehensive analysis respectively to obtain the material change index of several groups of historical buildings.

[0015] Furthermore, the specific formulas for calculating the environmental change index, structural change index, and material change index of each group of historical buildings are as follows: ;in, For the Environmental change index of historical buildings, For the The second period in the group The building surface The surface temperature value of each measuring point, For the The first period in the group The building surface The surface temperature value of each measuring point, is the surface temperature coefficient stored in the database, For the The second period in the group The building surface The surface humidity value at each measuring point, For the The first period in the group The building surface The surface humidity value at each measuring point, is the surface humidity coefficient stored in the database, For the The second period in the group The building surface The pressure value at each measuring point, For the The first period in the group The building surface The pressure value at each measuring point, is the pressure coefficient stored in the database, , For the The structural change index of the historical buildings, For the The second period in the group The building surface The vibration frequency value of each measuring point, For the The first period in the group The building surface The vibration frequency value of each measuring point, is the vibration frequency coefficient stored in the database, For the The second period in the group The building surface The stress value at each measuring point, For the The first period in the group The building surface The stress value at each measuring point, is the stress coefficient stored in the database, , For the Material change index of historical buildings, For the The second period in the group The building surface The hardness value of each measuring point, For the The first period in the group The building surface The hardness value of each measuring point, is the hardness coefficient stored in the database, For the The second period in the group The building surface The conductivity value of each measuring point, For the The first period in the group The building surface The conductivity value of each measuring point, is the conductivity coefficient stored in the database, For the The second period in the group The building surface The surface reflectance value of each measurement location, For the The first period in the group The building surface The surface reflectance value of each measurement location, is the surface reflectivity coefficient stored in the database, , =1, 2, 3, …, , is the total number of groups, =1, 2, 3, …, , is the number of building surfaces of the historic building, =1, 2, 3, …, , The number of measurement position points.

[0016] The dynamic risk assessment system for historical buildings includes: a historical building three-dimensional model building module, a real-time acquisition module, an index analysis module, a comprehensive risk assessment module, and a judgment marking module; the historical building three-dimensional model building module is used to obtain the three-dimensional coordinate information of each location point of the historical building and establish a three-dimensional model of the historical building; the real-time acquisition module is used to continuously obtain real-time image data of each building surface for several time periods in the three-dimensional model of the historical building, and the internal property data of each measurement location point, the real-time image data includes several pixel points and the pixel value of each pixel point, the internal property data includes environmental data, structural data, and material data, the environmental data includes surface temperature value, surface humidity value, and pressure value, the structural data includes vibration frequency value, stress value, and the material The data includes hardness value, conductivity value, and surface reflectivity value; the index analysis module is used to comprehensively analyze the real-time image data of each building surface in each time period and the internal property data of each measurement location point, so as to obtain the surface change risk assessment index and the internal change risk assessment index of the historical building; the comprehensive risk assessment module is used to comprehensively analyze the surface change risk assessment index and the internal change risk assessment index of the historical building, so as to obtain the comprehensive change risk assessment index of the historical building; the judgment and marking module is used to judge whether the comprehensive change risk assessment index of the historical building is within a preset risk assessment interval, and when the comprehensive change risk assessment index of the historical building is outside the preset risk assessment interval, identify abnormal building surfaces and mark them in the three-dimensional model of the historical building.

[0017] The present invention has the following beneficial effects:

[0018] (1) This dynamic risk assessment method for historical buildings can achieve comprehensive and continuous monitoring by collecting and analyzing dynamic data on the surface and internal structure of historical buildings. Compared with traditional static inspections or single inspections, dynamic monitoring can more timely capture subtle changes in buildings over different time periods, especially some hidden damage that is difficult to directly observe. Through this dynamic monitoring method, potential risks can be discovered in advance, reducing large-scale repairs caused by ignoring early damage, thereby reducing the maintenance cost of buildings, extending the service life of buildings, and improving the overall safety of buildings.

[0019] (2) The dynamic risk assessment method for historical buildings can simultaneously consider external damage to the building surface, such as corrosion and cracks, and changes in the internal structure, such as temperature, humidity, and stress, through a comprehensive multi-dimensional analysis. This multi-level, multi-angle risk assessment method can not only reveal local damage to the building, but also assess its potential long-term risks. By adjusting the weight coefficient of the data, it can provide more accurate assessment results for different types of buildings, ensuring that the risk assessment is more scientific and comprehensive, thereby helping to formulate a more reasonable repair plan and avoid excessive or insufficient repairs.

[0020] (3) This dynamic risk assessment method for historical buildings can achieve early risk warning by calculating the comprehensive change risk assessment index and comparing it with the preset risk threshold. Once it is found that the risk index of the building exceeds the expected range, the abnormal area will be automatically marked to prompt maintenance personnel to intervene in time. This data-based real-time warning mechanism can effectively avoid further expansion of building damage and take timely maintenance or reinforcement measures to ensure the stability and safety of the building during use, thereby reducing the probability of safety accidents.

[0021] (4) The dynamic risk assessment system for historical buildings provides a more scientific and efficient basis for building maintenance and restoration decisions through automated data collection, analysis and risk assessment. The system can quickly calculate the surface change risk assessment index and the internal change risk assessment index based on the building surface and internal property data in different time periods, and comprehensively derive the comprehensive risk assessment index of the building. This data-driven assessment method greatly improves the accuracy and timeliness of the decision-making process and avoids the errors and lags that may be caused by human assessment. Through real-time risk assessment, decision makers can quickly identify risk areas and make targeted maintenance decisions, thereby reducing the deviation caused by human judgment and ensuring the long-term stability and safety of the building. In addition, the system can flexibly adjust the assessment threshold and risk range to adapt to the special needs of different buildings, further improving the scientificity and rationality of decision-making.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the dynamic risk assessment method for historical buildings of the present invention.

[0024] Figure 2 This is a block diagram of the dynamic risk assessment system for historical buildings of the present invention. DETAILED DESCRIPTION

[0025] The embodiments of the present application solve the problem that the existing technology is limited to static and periodic building inspections, relies only on the detection of surface cracks and corrosion, fails to fully monitor the dynamic changes of buildings during actual use, lacks real-time monitoring of the internal structure of the building, and easily leads to some hidden and gradually accumulated risks not being identified in time, thereby increasing the cost of repair and safety hazards. Secondly, most of the existing assessment methods rely on a single indicator for assessment and lack a comprehensive analysis of multi-dimensional factors, which easily leads to the risk assessment of buildings being limited to surface inspections, ignoring deep-seated problems such as material aging, temperature and humidity fluctuations, and hidden cracks. The internal structure of the building is not well-known. Structural changes often have a great impact on long-term stability, and a single assessment method is difficult to reveal potential structural problems. It is easy to fail to intervene effectively before major problems occur in the building, causing the cost of maintenance to rise sharply. In addition, existing technologies usually rely on fixed risk assessment thresholds and standards, and fail to adjust the risk assessment model according to the dynamic changes of the building. The environment and external factors of the building are constantly changing during use. Fixed assessment standards are difficult to effectively respond to the gradually accumulated changes in the building, which can easily lead to delays in the risk assessment results of the building, thereby missing the opportunity for repair, increasing the probability of safety problems in the building in the future, and causing adverse effects on long-term use.

[0026] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0027] First, the three-dimensional coordinate information of each measurement location of the historical building is obtained by a laser scanner, and a complete three-dimensional model of the building is established. In this model, the building surface image data and the internal property data of each measurement location in different time periods are continuously collected. The data include environmental data (such as temperature, humidity, pressure), structural data (such as vibration frequency, stress), and material data (such as hardness, conductivity, surface reflectivity). For each time period, the surface image data of each building is first processed to obtain the pixel points of the corrosion and crack areas, and the corresponding change index is calculated. Subsequently, the environmental, structural, and material data of each measurement location are comprehensively analyzed to obtain the corresponding change index. By comprehensively analyzing the changes in the corrosion and crack areas and the changes in the internal data, the surface change risk assessment index and the internal change risk assessment index of the historical building are finally obtained. Finally, by comprehensively analyzing the surface change risk assessment index and the internal change risk assessment index, the comprehensive change risk assessment index is calculated and compared with the preset risk assessment interval. If the index exceeds the expected threshold, the building surface abnormality is identified and marked in the three-dimensional model as a basis for subsequent repair and maintenance.

[0028] See also Figure 1The embodiment of the present invention provides a technical solution: a method for dynamic risk assessment of historical buildings, comprising the following steps: obtaining three-dimensional coordinate information of each location point of the historical building (which can be obtained by measuring with a laser scanner), and establishing a three-dimensional model of the historical building; in the three-dimensional model of the historical building, continuously obtaining real-time image data of each building surface (including external surfaces and internal surfaces) for several time periods, and internal property data of each measurement location point, the real-time image data including several pixel points and the pixel value of each pixel point, the internal property data including environmental data, structural data, and material data, the environmental data including surface temperature value, surface humidity value, and pressure value, the structural data including vibration frequency value, stress value, and material value. The material data include hardness value, conductivity value, and surface reflectivity value; the real-time image data of each building surface in each time period and the internal property data of each measurement location point are comprehensively analyzed to obtain the surface change risk assessment index and internal change risk assessment index of the historical building; the surface change risk assessment index and internal change risk assessment index of the historical building are comprehensively analyzed to obtain the comprehensive change risk assessment index of the historical building; it is determined whether the comprehensive change risk assessment index of the historical building is within the preset risk assessment range, and when the comprehensive change risk assessment index of the historical building is outside the preset risk assessment range, the abnormal building surface is identified and marked in the three-dimensional model of the historical building.

[0029] Among them, the specific formula for calculating the comprehensive change risk assessment index of historical buildings is as follows: ;in, is a comprehensive change risk assessment index for historic buildings. is a natural constant, is the surface change risk assessment index for historic buildings, is the surface evaluation coefficient stored in the database, is the internal change risk assessment index for historic buildings, is the internal evaluation coefficient stored in the database, .

[0030] The specific implementation example of calculating the comprehensive change risk assessment index of historical buildings is as follows:

[0031] Among them, the calculation data example of the comprehensive change risk assessment index of historical buildings is as follows:

[0032] Table 1 Example of calculation data for comprehensive change risk assessment index of historical buildings

[0033] ;

[0034] In Table 1, is a natural constant, and in this example it is taken as 2.718. is the surface change risk assessment index for historic buildings, is the surface evaluation coefficient stored in the database, is the internal change risk assessment index for historic buildings, is the internal evaluation coefficient stored in the database, It is the comprehensive change risk assessment index of historical buildings, and the preset risk assessment range is [0.55-0.725].

[0035] The first set of data: the natural constant is: 2.718; the surface change risk assessment index of the historical building is: 0.92; the surface assessment coefficient stored in the database is: 0.57; the internal change risk assessment index of the historical building is: 0.78; the internal assessment coefficient stored in the database is: 0.43; the comprehensive change risk assessment index of the historical building is: 0.6065, so if it is within the preset risk assessment range of [0.55-0.725], the historical building is considered normal.

[0036] The second set of data: the natural constant is: 2.718; the surface change risk assessment index of the historical building is: 0.53; the surface assessment coefficient stored in the database is: 0.57; the internal change risk assessment index of the historical building is: 0.56; the internal assessment coefficient stored in the database is: 0.43; the comprehensive change risk assessment index of the historical building is: 0.729. Therefore, if it is outside the preset risk assessment range of [0.55-0.725], the historical building will be regarded as abnormal, and the abnormal building surface will be identified and marked in the three-dimensional model of the historical building.

[0037] The third set of data: the natural constant is: 2.718; the surface change risk assessment index of the historical building is: 0.79; the surface assessment coefficient stored in the database is: 0.57; the internal change risk assessment index of the historical building is: 0.88; the internal assessment coefficient stored in the database is: 0.43; the comprehensive change risk assessment index of the historical building is: 0.618, so if it is within the preset risk assessment range of [0.55-0.725], the historical building is considered normal.

[0038] The fourth set of data: the natural constant is: 2.718; the surface change risk assessment index of the historical building is: 1.04; the surface assessment coefficient stored in the database is: 0.57; the internal change risk assessment index of the historical building is: 0.44; the internal assessment coefficient stored in the database is: 0.43; the comprehensive change risk assessment index of the historical building is: 0.635, so it is within the preset risk assessment range of [0.55-0.725], and the historical building is considered normal.

[0039] The fifth set of data: the natural constant is: 2.718; the surface change risk assessment index of the historical building is: 1.15; the surface assessment coefficient stored in the database is: 0.57; the internal change risk assessment index of the historical building is: 0.91; the internal assessment coefficient stored in the database is: 0.43; the comprehensive change risk assessment index of the historical building is: 0.543. Therefore, if it is outside the preset risk assessment range of [0.55-0.725], the historical building will be regarded as abnormal, and the abnormal building surface will be identified and marked in the three-dimensional model of the historical building.

[0040] The specific steps to build a 3D model of a historical building are as follows:

[0041] The three-dimensional coordinate information of each location point of the historical building is registered to ensure that all scanned data can be aligned to a unified coordinate system, which can be aligned using a registration algorithm (such as the ICP algorithm).

[0042] The three-dimensional coordinate information of each location point of the historical building is subjected to noise removal and streamlining processing to remove invalid points or interference that may occur during the scanning process.

[0043] Utilize the 3D coordinate information of each location point of the historical building to carry out 3D modeling (commonly used software include AutoCAD, Revit, Blender, etc.), and generate a 3D geometric model of the historical building through point cloud interpolation, surface reconstruction and other technologies.

[0044] The final output 3D model can be used for subsequent risk assessment, dynamic monitoring, maintenance planning and other analysis tasks. The output file is usually in *.obj, .fbx, .stl and other formats.

[0045] The pixel value of a pixel point can be obtained by measuring it using a high-resolution camera.

[0046] The surface temperature value can be obtained by measuring using a temperature sensor.

[0047] The surface humidity value can be obtained by measuring using a humidity sensor.

[0048] The pressure value can be obtained by measuring using a pressure sensor.

[0049] The vibration frequency value can be obtained by measuring using a vibration sensor (accelerometer).

[0050] Stress values ​​can be obtained by measuring using stress sensors (strain gauges).

[0051] The hardness value can be obtained by measuring with a hardness sensor.

[0052] The conductivity value can be obtained by measuring using a conductivity sensor.

[0053] The surface reflectance value can be obtained by measuring using a surface reflectance sensor.

[0054] Specifically, the specific steps for obtaining the surface change risk assessment index of historical buildings are as follows: the real-time image data of each building surface in each time period are divided into regions respectively to obtain the corrosion area data and crack area data of each building surface in each time period, the corrosion area data includes a number of corrosion pixel points, and the crack area data includes a number of crack pixel points; the corrosion area data and crack area data of each building surface in each time period are comprehensively analyzed to obtain the corrosion area change index and crack area change index of several groups of historical buildings; the corrosion area change index and crack area change index of several groups of historical buildings are comprehensively analyzed to obtain the surface change risk assessment index of historical buildings.

[0055] The specific formula for calculating the surface change risk assessment index of historic buildings is as follows: ;in, is the surface change risk assessment index for historic buildings, For the The corrosion regional change index of the historical buildings, is the corrosion area coefficient stored in the database, For the The crack area variation index of the historical buildings, is the crack area coefficient stored in the database, , =1, 2, 3, …, , is the total number of groups.

[0056] In this implementation scheme, by dividing the building surface images in each time period into regions, the corrosion areas and crack areas are identified separately to ensure the accuracy of risk assessment. Corrosion and cracks are common signs of building aging and damage. Monitoring these areas separately can effectively reflect the health status of the building surface. This highly targeted analysis method helps to detect changes in the building surface in a timely manner, thereby providing early warning for subsequent maintenance and repair. By comprehensively analyzing the corrosion and crack area data, the corrosion area change index and the crack area change index are further calculated, and then these indices are synthesized into a surface change risk assessment index. This process can quantify the changes in the building surface. The use of indexed calculation methods not only improves the standardization and objectivity of the assessment, but also more accurately reflects the degree of deterioration of the building. This quantitative assessment method can provide a scientific basis for building protection and reduce the main body of manual assessment. By regularly acquiring real-time image data of buildings and comparing it with historical data in the database, dynamic monitoring of the building surface can be achieved. This continuous time series analysis can reveal the evolution trend of building surface damage and help managers evaluate long-term changes. More importantly, this method can not only detect current damage, but also reveal potential risks, so as to make predictions and adjustments in advance, maximize the service life of buildings and reduce the occurrence of sudden disasters. The corrosion area coefficient and crack area coefficient in the formula can be adjusted according to the characteristics and protection needs of different buildings. This flexibility enables the system to adapt to the assessment of different types of buildings, ensuring that all types of historical buildings can receive tailor-made risk assessment plans. Building managers can adjust risk thresholds according to actual conditions, optimize the applicability of assessment results, and further improve the accuracy and reliability of building management.

[0057] Specifically, the specific steps for obtaining the corrosion area data and crack area data of each building surface in each time period are as follows: preprocessing the pixel values ​​of each pixel point on each building surface in each time period respectively; after the preprocessing, performing image thresholding processing on the pixel values ​​of each pixel point on each building surface in each time period to obtain a number of initial corrosion area data and initial crack area data of each building surface in each time period, the initial corrosion area data including a number of corrosion pixel points, and the initial crack area data including a number of crack pixel points; performing connected labeling processing on the several initial corrosion area data and initial crack area data of each building surface in each time period respectively to obtain the corrosion area data and crack area data of each building surface in each time period.

[0058] Among them, preprocessing includes grayscale processing (i.e. converting the image into a grayscale image) and smoothing filtering processing (applying simple filtering (such as mean filtering) to remove noise in the image).

[0059] The specific image thresholding process is as follows: select a suitable threshold, and consider the pixels in the image above the threshold as corrosion areas or crack areas, and the pixels below the threshold as normal areas. If there is a significant brightness change in the corrosion or crack area, a lower threshold can be selected; if the change is small, a higher threshold can be selected.

[0060] For example, suppose that the pixel value of the corrosion area is low, and a threshold T is set, and the pixels less than T are considered as the corrosion area. The pixel value of the crack area changes greatly, and another threshold T′ is set, and the pixels greater than T′ are considered as the crack area.

[0061] The specific process of connected labeling is as follows: connected pixels in the image are grouped into the same area. If some small corrosion or crack areas are detected, they will be processed as a whole area. The specific steps are as follows: use connected domain analysis (such as depth-first search or breadth-first search) to mark all connected pixels. Each connected area represents a corrosion or crack area.

[0062] In this implementation scheme, when processing the building surface image, firstly, the noise is removed by graying and simple filtering (such as mean filtering), which can significantly improve the accuracy of subsequent processing. Graying helps to reduce the interference of color information and focus on the texture features of the building surface, while filtering effectively removes the noise in the image and avoids the interference of noise on the corrosion and crack detection results. This can ensure that the image processing is more stable, reduce false detection, and improve the reliability of the algorithm in practical applications. Image thresholding can effectively identify corrosion areas and crack areas by selecting appropriate thresholds to distinguish different areas. Corrosion and cracks often have different pixel value characteristics. The thresholding method can accurately classify building surface problems by accurately dividing different areas, thereby providing clear basic data for subsequent change analysis. This method is flexible and can adjust the threshold according to different building materials and damage characteristics, ensuring the universality of the method. Through connected labeling processing, connected corrosion or crack pixels in the image can be classified into the same area, thereby avoiding the problem of small-scale damage being ignored. Connected labels not only help integrate fragmented damage information, but also can accurately locate the area and distribution of damage, which is helpful for more Comprehensively evaluate the health status of the building surface. In addition, the connected domain analysis method can identify and mark large or small damaged areas, thereby providing more detailed regional data for risk assessment, further improving the accuracy and effectiveness of the assessment. The automated processing of this series of steps (such as graying, filtering, thresholding, and connected marking) greatly improves the efficiency and accuracy of detection, avoiding situations that are easily overlooked or misassessed during traditional manual inspections. Through automated means, the building surface can be monitored on a large scale and at a high frequency, greatly reducing the workload of manual monitoring, and can timely discover potential building risks, ensuring that necessary repair and protection measures are taken early, thereby reducing the cost and risk of building maintenance. These processing steps provide strong data support for real-time monitoring and long-term tracking of building surface changes. As time periods change, the data of corrosion areas and crack areas can be continuously updated to form a dynamic assessment system for the health of building surfaces. For historical buildings, the building surface may be damaged to varying degrees over time. Through this processing flow, its changing trend can be continuously monitored, providing reliable data support for long-term protection and restoration decisions, and extending the service life of the building.

[0063] Specifically, the specific steps for obtaining the corrosion area change index and the crack area change index of several groups of historical buildings are as follows: for each time period, the number of corrosion pixels in the corrosion area data of each building surface and the number of crack pixels in the crack area data are counted respectively, and recorded as the corrosion area area value and the crack area area value of each building surface respectively; the pixel value of each corrosion pixel in the corrosion area data of each building surface in each time period and the corrosion area area value are comprehensively analyzed to obtain the corrosion area change index of several groups of historical buildings; the pixel value of each crack pixel in the crack area data of each building surface in each time period and the crack area area value are comprehensively analyzed to obtain the crack area change index of several groups of historical buildings.

[0064] The specific formula for calculating the corrosion area change index and crack area change index of each group of historical buildings is as follows: { FbH i = ∑ t = 1 t 0 [ α 1 * ( ∑ u = 1 u 0 | FxS itu 2 − FxS itu 1 | u 0 ) + α 2 * ( FmJ it 2 − FmJ it 1 ) ] LbH i = ∑ t = 1 t 0 [ β 1 * ( ∑ r = 1 r 0 | LxS itr 2 − LxS itr 1 | r 0 ) + β 2 * ( LmJ it 2 − LmJ it 1 ) ] ;in, For the The corrosion regional change index of the historical buildings, For the The second period in the group The corrosion area data of the building surface The pixel value of the eroded pixel, For the The first period in the group The corrosion area data of the building surface corresponding to the second period The pixel value of the eroded pixel, is the erosion pixel coefficient stored in the database, For the The second period in the group The corrosion area value of the building surface, For the The first period in the group The corrosion area value of the building surface, is the corrosion area coefficient stored in the database, , =1, 2, 3, …, , is the number of corroded pixels in the corroded area data, For the The crack area variation index of the historical buildings, For the The second period in the group The crack area data of the building surface The pixel value of the crack pixel, For the The first period in the group The crack area data of the building surface corresponding to the second period The pixel value of the crack pixel, is the crack pixel coefficient stored in the database, For the The second period in the group The area value of the crack area on the building surface, For the The first period in the group The area value of the crack area on the building surface, is the crack area coefficient stored in the database, , =1, 2, 3, …, , is the number of crack pixels in the crack area data, =1, 2, 3, …, , is the number of building surfaces of the historic building, =1, 2, 3, …, , is the total number of groups.

[0065] It should be explained that the weighted calculation of the corrosion area change index and the crack area change index of each group of historical buildings is carried out after standardization (i.e., removing the unit), so the weighted calculation can be performed directly.

[0066] For the two time periods in each group, they are adjacent time periods, and since the corrosion area and crack area of ​​the building surface will change after each time period, when analyzing pixel changes, the corrosion area of ​​the second time period, i.e., the latter time period, is used as a benchmark to obtain the corresponding corrosion area of ​​the first time period, i.e., the previous time period, including the corrosion area of ​​the first time period and the corrosion area of ​​the building surface that occurs between the first time period and the second time period.

[0067] In this implementation scheme, by calculating the change index of the corrosion area and the crack area, the dynamic changes of the building surface damage can be accurately quantified. This method provides detailed tracking of building damage by counting the number of corrosion and crack pixel points, area changes and other details. By comparing data from multiple time periods, it is not only possible to identify the scale changes of surface damage, but also to quantitatively evaluate the expansion trend of damage, thereby providing accurate data support for subsequent risk assessment and decision-making. By using data from adjacent time periods for comparison, the evolution of building surface damage can be continuously monitored. The changes in the area and pixel values ​​of the corrosion and crack areas reflect the growth trend of building surface damage. By analyzing these changes, the development speed and law of building surface damage can be identified. It provides a scientific basis for building maintenance and repair. In addition, since the data covers multiple time periods, it can effectively discover potential damage trends and take repair measures in advance, thereby extending the service life of the building. In the calculation process of the change index of the corrosion area and the crack area, the standardization process ensures the unification of various data units, so that different types of parameters can be weighted calculated in the same indicator system, which not only improves the accuracy and consistency of the calculation, but also avoids the calculation deviation caused by different units and dimensions. The standardized data can be directly used for weighted processing to ensure that the various damage characteristics are reasonably integrated to generate a comprehensive and reliable change index. The data of the corrosion area and the crack area are processed separately, and the The influence of multiple factors such as corrosion pixel value, crack pixel value, and regional area. This multi-dimensional analysis method allows different types of damage on the building surface (such as corrosion, cracks, etc.) to be independently evaluated, and the impact of each type of damage on the overall change can be fully reflected. Therefore, the obtained change index not only reflects the overall change trend of the building surface, but also reveals the contribution of different types of damage, helping to more accurately evaluate the health of the building. Through the calculation of the corrosion area change index and the crack area change index, it can provide a basis for building maintenance and repair decisions. If the corrosion or cracks in certain areas develop rapidly, it may mean that the area will face higher risks in the future. Through the changes in these indices, decision makers can be helped to prioritize For areas with higher risks, more scientific maintenance plans can be formulated to optimize the allocation and use of resources while ensuring the safety of buildings. In the process of data collection and analysis over multiple time periods, an evolution model of building surface damage can be established through historical data to predict future damage trends. This long-term monitoring can not only detect abnormal changes in a timely manner, but also provide forward-looking references for building risk management. Through trend prediction, maintenance personnel can prepare repair plans in advance, reduce the occurrence of sudden damage, and reduce repair costs and construction risks. The entire process, from image processing to detection of corrosion and crack areas, to calculation and analysis of indexes, is an automated process, which greatly improves monitoring efficiency and avoids omissions and errors in manual inspections.It can also quickly respond to changes in building damage at different times. With the development of technology, this automated monitoring system can be further combined with artificial intelligence and machine learning to enhance the real-time analysis and prediction capabilities of building health status.

[0068] Specifically, the specific steps for obtaining the internal change risk assessment index of historical buildings are as follows: comprehensively analyze the environmental data, structural data, and material data of each measurement location point on the surface of each building in each time period to obtain the environmental change index, structural change index, and material change index of several groups of historical buildings; comprehensively analyze the environmental change index, structural change index, and material change index of several groups of historical buildings to obtain the internal change risk assessment index of historical buildings.

[0069] The specific formula for calculating the internal change risk assessment index of historical buildings is as follows: ;in, is the internal change risk assessment index for historic buildings, For the Environmental change index of historical buildings, is the environmental variation coefficient stored in the database, For the The structural change index of the historical buildings, is the structural variation coefficient stored in the database, For the Material change index of historical buildings, is the material variation coefficient stored in the database, , =1, 2, 3, …, , is the total number of groups.

[0070] In this implementation plan, through the comprehensive analysis of the environmental data, structural data and material data of historical buildings, the risk changes inside the building can be comprehensively evaluated. Environmental data (such as temperature, humidity, pressure, etc.), structural data (such as stress, vibration frequency, etc.) and material data (such as hardness, conductivity, etc.) reflect the state of the building and its possible changes from different dimensions. Integrating these data into the internal change risk assessment index will help to accurately determine the potential risk areas inside the building, thereby improving the preventive management of building safety. By separately calculating the environmental change index, structural change index and material change index, quantitative evaluation can be performed on different aspects of the building. Each data item can accurately reflect the different dimensional states of the building, thereby providing more More detailed and actionable data support, for example, environmental changes may affect the temperature and humidity changes on the surface or structure of the building, structural changes will directly affect the stability of the building, and material changes are related to the durability and safety of the building. By combining these change indicators, an overall internal change risk assessment index is obtained, which can provide a more scientific and comprehensive basis for maintenance and repair. The environmental, structural, and material data are analyzed independently, and different weighting coefficients are assigned to each change index (such as environmental change coefficient, structural change coefficient, material change coefficient). The weights can be flexibly adjusted according to the degree of influence of various types of data on the internal health status of the building, thereby improving the accuracy of the assessment. Different needs of different buildings may require adjustment. The weights of different parameters, this weighting mechanism helps to dynamically and accurately evaluate building risks, avoiding a single indicator that is too one-sided or overly biased in one aspect. The internal change risk assessment index can reflect the changes in the internal state of the building at different time points, so that the risk management of the building can be dynamically monitored. As time goes by, the change data of the environment, structure and materials will be continuously updated, so that the health changes inside the building can be reflected in real time, providing timely decision support. If some indexes exceed the preset risk threshold, managers can take repair measures in advance to avoid potential catastrophic consequences. By comprehensively analyzing various data of the building, the internal change risk assessment index obtained can manage risks in a refined manner and help make repair decisions more scientific, such as Environmental changes in certain locations may lead to local stress concentration or material degradation, thus causing more serious structural problems. Through these assessment results, relevant decision-makers can develop more targeted repair plans to optimize resource allocation, ensure efficient and accurate repair work, and reduce resource waste. The internal change risk assessment index is not only an assessment tool for the current building condition, but also helps analyze the long-term trend of internal changes in buildings by tracking multi-period data. For example, through long-term monitoring and trend analysis, the risk of internal changes in buildings in the future can be predicted, and preventive repairs or protection can be carried out in advance. This trend prediction function provides building managers with a more forward-looking perspective and helps to achieve healthy management of the building life cycle.The entire internal change risk assessment process is data-driven and can automatically collect and analyze various types of building data, reducing errors and omissions caused by manual intervention. In addition, with the development of intelligent technology, this method can be combined with artificial intelligence, machine learning and other technologies to further enhance data processing and prediction capabilities. Intelligent assessment can provide real-time feedback on the health status of buildings, helping decision makers quickly identify potential risks and make adjustments.

[0071] Specifically, the specific steps for obtaining the environmental change index, structural change index and material change index of several groups of historical buildings are as follows: read the surface temperature value, surface humidity value and pressure value of each measuring position point on the surface of each building in each time period, and perform comprehensive analysis respectively to obtain the environmental change index of several groups of historical buildings; read the vibration frequency value and stress value of each measuring position point on the surface of each building in each time period, and perform comprehensive analysis respectively to obtain the structural change index of several groups of historical buildings; read the hardness value, conductivity value and surface reflectivity value of each measuring position point on the surface of each building in each time period, and perform comprehensive analysis respectively to obtain the material change index of several groups of historical buildings.

[0072] The specific formulas for calculating the environmental change index, structural change index, and material change index for each group of historical buildings are as follows: ;in, For the Environmental change index of historical buildings, For the The second period in the group The building surface The surface temperature value of each measuring point, For the The first period in the group The building surface The surface temperature value of each measuring point, is the surface temperature coefficient stored in the database, For the The second period in the group The building surface The surface humidity value at each measuring point, For the The first period in the group The building surface The surface humidity value at each measuring point, is the surface humidity coefficient stored in the database, For the The second period in the group The building surface The pressure value at each measuring point, For the The first period in the group The building surface The pressure value at each measuring point, is the pressure coefficient stored in the database, , For the The structural change index of the historical buildings, For the The second period in the group The building surface The vibration frequency value of each measuring point, For the The first period in the group The building surface The vibration frequency value of each measuring point, is the vibration frequency coefficient stored in the database, For the The second period in the group The building surface The stress value at each measuring point, For the The first period in the group The building surface The stress value at each measuring point, is the stress coefficient stored in the database, , For the Material change index of historical buildings, For the The second period in the group The building surface The hardness value of each measuring point, For the The first period in the group The building surface The hardness value of each measuring point, is the hardness coefficient stored in the database, For the The second period in the group The building surface The conductivity value of each measuring point, For the The first period in the group The building surface The conductivity value of each measuring point, is the conductivity coefficient stored in the database, For the The second period in the group The building surface The surface reflectance value of each measurement location, For the The first period in the group The building surface The surface reflectance value of each measurement location, is the surface reflectivity coefficient stored in the database, , =1, 2, 3, …, , is the total number of groups, =1, 2, 3, …, , is the number of building surfaces of the historic building, =1, 2, 3, …, , The number of measurement position points.

[0073] In this implementation plan, by reading and analyzing data of multiple dimensions (such as environmental data, structural data, and material data), a comprehensive integrated risk assessment is provided for each building surface measurement location point in each time period. These data include surface temperature, humidity, pressure, vibration frequency, stress, hardness, conductivity, and reflectivity, etc. They can reflect the state changes of the building from multiple angles, including environmental data (such as temperature, humidity, and pressure) reflecting the impact of external climate conditions on the building, and can effectively assess the potential damage caused by environmental factors; structural data (such as vibration frequency and stress) can reflect the stability and bearing capacity of the building, and judge the building risk from the perspective of structural health; material data (such as hardness, conductivity, and surface reflectivity) can assess the aging and corrosion of building materials. Or physical degradation process, especially the changes in the surface layer. Through this multi-dimensional analysis, it is possible to accurately capture the different impacts of changes in the internal and external environment of the building on the building safety, ensuring that the evaluation results are more comprehensive and accurate. During the evaluation process, the change index of each group of historical buildings is calculated based on the data of two consecutive time periods. By comparing the change data of each time period, the change trend inside or outside the building can be identified. Through the time-by-time analysis of the environment, structure, and materials, possible risk sources and their aggravation process can be identified, providing predictive support for future maintenance and restoration work. By comparing data from multiple time periods, it is possible to evaluate whether the building is in a state of deterioration, timely discover potential structural problems or changes caused by external conditions, and enhance the dynamic monitoring ability of the building. Force, this method assigns corresponding coefficients (such as surface temperature coefficient, vibration frequency coefficient, etc.) to each data item (temperature, humidity, pressure, vibration, etc.). These coefficients reflect the degree of influence of different factors on building changes, and can dynamically adjust the weights according to actual conditions to ensure more accurate risk assessment. By regularly reading and analyzing the data of each time period, this method can monitor the health status of the building for a long time. As the building ages, the change index of each time period can be used to determine whether the building has entered a degraded or dangerous state, so as to make repair and protection decisions in advance. For example, if the temperature, humidity, stress, etc. in certain areas change too much, a repair plan can be made in advance to prevent more serious damage. Based on the comprehensive analysis of various data, it can Accurately identify potential risk sources on and inside the building. For example, changes in temperature and humidity may affect the degradation of surface materials, while structural stress and vibration data may indicate a decrease in the building's bearing capacity. Through a comprehensive assessment of these factors, managers can carry out repairs or reinforcements in a more targeted manner, reduce unnecessary repair work, and improve resource utilization efficiency. By automatically reading, processing, and analyzing various data of historical buildings, this method can greatly improve the intelligence of building management and reduce errors and omissions caused by human intervention. Combined with modern sensor technology, it can achieve continuous monitoring of the entire life cycle of the building and capture any abnormal changes in a timely manner. This intelligent data analysis method can help managers quickly respond to changes in the health of the building.Avoid risks caused by delayed detection or repair. Historical buildings are not only engineering structures, but also an important part of cultural heritage. Through this scientific and systematic internal risk assessment method, it is possible to effectively identify risk factors that may damage cultural value in historical buildings, such as aging of materials or deformation of structures, and provide timely repair and protection measures for these buildings, which can not only ensure their structural safety, but also continue their historical and cultural value.

[0074] See also Figure 2 The embodiment of the present invention provides a technical solution: a dynamic risk assessment system for historical buildings, including: a historical building three-dimensional model building module, a real-time acquisition module, an index analysis module, a comprehensive risk assessment module, and a judgment marking module; the historical building three-dimensional model building module is used to obtain the three-dimensional coordinate information of each location point of the historical building and establish a three-dimensional model of the historical building; the real-time acquisition module is used to continuously obtain real-time image data of each building surface in a number of time periods in the three-dimensional model of the historical building, and the internal property data of each measurement location point, the real-time image data includes a number of pixel points and the pixel value of each pixel point, the internal property data includes environmental data, structural data, and material data, the environmental data includes surface temperature value, surface humidity value, and pressure value, and the structural data includes vibration frequency value, stress value, material data includes hardness value, conductivity value, surface reflectivity value; index analysis module, for comprehensive analysis of real-time image data of each building surface in each time period, and internal property data of each measurement location point, to obtain surface change risk assessment index and internal change risk assessment index of historical buildings; comprehensive risk assessment module, for comprehensive analysis of surface change risk assessment index and internal change risk assessment index of historical buildings, to obtain comprehensive change risk assessment index of historical buildings; judgment and marking module, for judging whether the comprehensive change risk assessment index of historical buildings is within a preset risk assessment interval, and when the comprehensive change risk assessment index of historical buildings is outside the preset risk assessment interval, identifying abnormal building surfaces and marking them in the three-dimensional model of historical buildings.

[0075] In summary, this application has at least the following effects:

[0076] Comprehensive and continuous monitoring can be achieved through dynamic data collection and analysis of the surface and internal structure of historical buildings. Compared with traditional static inspections or single inspections, dynamic monitoring can capture subtle changes in buildings in different time periods more promptly, especially some hidden damage that is difficult to observe directly. Through this dynamic monitoring method, potential risks can be discovered in advance and large-scale repairs caused by ignoring early damage can be reduced, thereby reducing the maintenance cost of buildings, extending the service life of buildings, and improving the overall safety of buildings.

[0077] Through multi-dimensional comprehensive analysis, it is possible to simultaneously consider external damage to the building surface, such as corrosion and cracks, and changes in the internal structure, such as temperature, humidity, and stress. This multi-level, multi-angle risk assessment method can not only reveal local damage to the building, but also assess its potential long-term risks. By adjusting the weight coefficient of the data, it can provide more accurate assessment results for different types of buildings, ensuring that the risk assessment is more scientific and comprehensive, thereby helping to formulate a more reasonable repair plan and avoid excessive or insufficient maintenance.

[0078] By calculating the comprehensive change risk assessment index and comparing it with the preset risk threshold, early risk warning can be achieved. Once the risk index of the building is found to exceed the expected range, the abnormal area will be automatically marked to prompt maintenance personnel to intervene in time. This data-based real-time warning mechanism can effectively avoid further expansion of building damage, take timely maintenance or reinforcement measures, ensure the stability and safety of the building during use, and thus reduce the probability of safety accidents.

[0079] Through automated data collection, analysis and risk assessment, a more scientific and efficient basis is provided for building maintenance and restoration decisions. The system can quickly calculate the surface change risk assessment index and the internal change risk assessment index based on the building surface and internal property data in different time periods, and comprehensively derive the comprehensive risk assessment index of the building. This data-driven assessment method greatly improves the accuracy and timeliness of the decision-making process, avoids the errors and lags that may be caused by human assessment, and through real-time risk assessment, decision makers can quickly identify risk areas and make targeted maintenance decisions, thereby reducing the deviation caused by human judgment and ensuring the long-term stability and safety of the building. In addition, the system can flexibly adjust the assessment threshold and risk range to adapt to the special needs of different buildings, further improving the scientificity and rationality of decision-making.

[0080] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0081] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A dynamic risk assessment method for historical buildings, characterized in that: The following steps are involved: Obtain the three-dimensional coordinate information of each location point of the historical building and establish a three-dimensional model of the historical building; In the three-dimensional model of the historical building, the real-time image data of each building surface in several time periods and the internal property data of each measurement position point are continuously acquired and analyzed to obtain the surface change risk assessment index and the internal change risk assessment index of the historical building, and a comprehensive analysis is performed to obtain the comprehensive change risk assessment index of the historical building, and it is judged whether it is within the preset risk assessment interval, the real-time image data includes several pixels and the pixel value of each pixel, the internal property data includes environmental data, structural data, and material data, the environmental data includes surface temperature value, surface humidity value, and pressure value, the structural data includes vibration frequency value and stress value, and the material data includes hardness value, conductivity value, and surface reflectivity value; When the comprehensive change risk assessment index of a historic building is outside the preset risk assessment range, identify abnormal building surfaces and mark them in the three-dimensional model of the historic building; Among them, the specific formula for calculating the comprehensive change risk assessment index of historical buildings is as follows: ； in, is a comprehensive change risk assessment index for historic buildings. is a natural constant, is the surface change risk assessment index for historic buildings, is the surface evaluation coefficient stored in the database, is the internal change risk assessment index for historic buildings, is the internal evaluation coefficient stored in the database, ; The specific formula for calculating the surface change risk assessment index of historic buildings is as follows: ； in, is the surface change risk assessment index for historic buildings, For the The corrosion regional change index of the historical buildings, is the corrosion area coefficient stored in the database, For the The crack area variation index of the historical buildings, is the crack area coefficient stored in the database, , is the total number of groups; The specific steps to obtain the internal change risk assessment index of historical buildings are as follows: Comprehensively analyze the environmental data, structural data, and material data of each measurement point on each building surface in each period, and obtain the environmental change index, structural change index, and material change index of several groups of historical buildings; Comprehensively analyze the environmental change index, structural change index, and material change index of several groups of historical buildings to obtain the internal change risk assessment index of historical buildings; The specific formula for calculating the internal change risk assessment index of historical buildings is as follows: ； in, is the internal change risk assessment index for historic buildings, For the Environmental change index of historical buildings, is the environmental variation coefficient stored in the database, For the The structural change index of the historical buildings. is the structural variation coefficient stored in the database, For the Material change index of historical buildings, is the material variation coefficient stored in the database, , is the total number of groups.

2. The method for dynamic risk assessment of historical buildings according to claim 1 is characterized in that: The specific steps to obtain the surface change risk assessment index of historical buildings are as follows: The real-time image data of each building surface in each time period are respectively divided into regions to obtain corrosion region data and crack region data of each building surface in each time period, wherein the corrosion region data includes a plurality of corrosion pixel points, and the crack region data includes a plurality of crack pixel points; Comprehensively analyze the corrosion area data and crack area data of each building surface in each period to obtain the corrosion area change index and crack area change index of several groups of historical buildings; A comprehensive analysis was conducted on the corrosion area change index and crack area change index of several groups of historical buildings to obtain the surface change risk assessment index of historical buildings.

3. The method for dynamic risk assessment of historical buildings according to claim 2 is characterized in that: The specific steps to obtain the corrosion area data and crack area data of each building surface in each period are as follows: Preprocess the pixel values ​​of each pixel point on each building surface in each time period respectively; After preprocessing, the pixel value of each pixel point on each building surface in each time period is subjected to image threshold processing to obtain a number of initial corrosion area data and initial crack area data on each building surface in each time period; Connectivity marking processing is performed on a plurality of initial corrosion area data and initial crack area data of each building surface in each time period to obtain corrosion area data and crack area data of each building surface in each time period.

4. The method for dynamic risk assessment of historical buildings according to claim 2 is characterized in that: The specific steps for obtaining the corrosion area change index and crack area change index of several groups of historical buildings are as follows: For each time period, the number of corrosion pixels in the corrosion area data of each building surface and the number of crack pixels in the crack area data are counted respectively, and recorded as the corrosion area value and crack area value of each building surface respectively; Comprehensively analyze the pixel value of each corrosion pixel point and the area value of the corrosion area in the corrosion area data of each building surface in each period to obtain the corrosion area change index of several groups of historical buildings; A comprehensive analysis is performed on the pixel value of each crack pixel point in the crack area data of each building surface in each period, as well as the crack area value, to obtain the crack area change index of several groups of historical buildings.

5. The method for dynamic risk assessment of historical buildings according to claim 4 is characterized in that: The specific formula for calculating the corrosion area change index and crack area change index of each group of historical buildings is as follows: ； in, For the The corrosion regional change index of the historical buildings, For the The second period in the group The corrosion area data of the building surface The pixel value of the eroded pixel, For the The first period in the group The corrosion area data of the building surface corresponding to the second period The pixel value of the eroded pixel, is the erosion pixel coefficient stored in the database, For the The second period in the group The corrosion area value of the building surface, For the The first period in the group The corrosion area value of the building surface, is the corrosion area coefficient stored in the database, , is the number of corroded pixels in the corroded area data, For the The crack area variation index of the historical buildings, For the The second period in the group The crack area data of the building surface The pixel value of the crack pixel, For the The first period in the group The crack area data of the building surface corresponding to the second period The pixel value of the crack pixel, is the crack pixel coefficient stored in the database, For the The second period in the group The area value of the crack area on the building surface, For the The first period in the group The area value of the crack area on the building surface, is the crack area coefficient stored in the database, , , is the number of crack pixels in the crack area data, is the number of building surfaces of the historic building, is the total number of groups.

6. The method for dynamic risk assessment of historical buildings according to claim 1 is characterized in that: The specific steps to obtain the environmental change index, structural change index, and material change index of several groups of historical buildings are as follows: Read the surface temperature, surface humidity and pressure values ​​of each measuring point on each building surface in each period, and conduct comprehensive analysis to obtain the environmental change index of several groups of historical buildings; The vibration frequency and stress values ​​of each measuring point on each building surface in each period are read, and comprehensive analysis is performed to obtain the structural change index of several groups of historical buildings; The hardness value, conductivity value, and surface reflectivity value of each measuring point on each building surface in each period are read, and a comprehensive analysis is performed to obtain the material change index of several groups of historical buildings.

7. The method for dynamic risk assessment of historical buildings according to claim 6 is characterized in that: The specific formulas for calculating the environmental change index, structural change index, and material change index for each group of historical buildings are as follows: ； in, For the Environmental change index of historical buildings, For the The second period in the group The building surface The surface temperature value of each measuring point, For the The first period in the group The building surface The surface temperature value of each measuring point, is the surface temperature coefficient stored in the database, For the The second period in the group The building surface The surface humidity value at each measuring point, For the The first period in the group The building surface The surface humidity value at each measuring point, is the surface humidity coefficient stored in the database, For the The second period in the group The building surface The pressure value at each measuring point, For the The first period in the group The building surface The pressure value at each measuring point, is the pressure coefficient stored in the database, , For the The structural change index of the historical buildings. For the The second period in the group The building surface The vibration frequency value of each measuring point, For the The first period in the group The building surface The vibration frequency value of each measuring point, is the vibration frequency coefficient stored in the database, For the The second period in the group The building surface The stress value at each measuring point, For the The first period in the group The building surface The stress value at each measuring point, is the stress coefficient stored in the database, , For the Material change index of historical buildings, For the The second period in the group The building surface The hardness value of each measuring point, For the The first period in the group The building surface The hardness value of each measuring point, is the hardness coefficient stored in the database, For the The second period in the group The building surface The conductivity value of each measuring point, For the The first period in the group The building surface The conductivity value of each measuring point, is the conductivity coefficient stored in the database, For the The second period in the group The building surface The surface reflectance value of each measurement location, For the The first period in the group The building surface The surface reflectance value of each measurement location, is the surface reflectivity coefficient stored in the database, , is the total number of groups, is the number of building surfaces of the historic building, The number of measurement position points.

8. A historical building dynamic risk assessment system, using the historical building dynamic risk assessment method according to any one of claims 1 to 7, characterized in that: include: Historical building three-dimensional model building module, real-time acquisition module, index analysis module, comprehensive risk assessment module, and judgment marking module; The historical building three-dimensional model building module is used to obtain the three-dimensional coordinate information of each location point of the historical building and build a three-dimensional model of the historical building; The real-time acquisition module is used to continuously acquire real-time image data of each building surface in a plurality of time periods and internal property data of each measurement position point in the three-dimensional model of the historical building, wherein the real-time image data includes a plurality of pixel points and a pixel value of each pixel point, and the internal property data includes environmental data, structural data, and material data. The environmental data includes a surface temperature value, a surface humidity value, and a pressure value. The structural data includes a vibration frequency value and a stress value. The material data includes a hardness value, a conductivity value, and a surface reflectivity value. The index analysis module is used to comprehensively analyze the real-time image data of each building surface in each time period and the internal property data of each measurement location point to obtain the surface change risk assessment index and internal change risk assessment index of the historical building; The comprehensive risk assessment module is used to comprehensively analyze the surface change risk assessment index and the internal change risk assessment index of the historical building to obtain the comprehensive change risk assessment index of the historical building; The judgment and marking module is used to judge whether the comprehensive change risk assessment index of the historical building is within the preset risk assessment range, and when the comprehensive change risk assessment index of the historical building is outside the preset risk assessment range, identify abnormal building surfaces and mark them in the three-dimensional model of the historical building.

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