House health intelligent monitoring and evaluation method and system

Abstract

The invention relates to the technical field of building structure intelligent evaluation, in particular to a house health intelligent monitoring evaluation method and system, and the method comprises the steps: setting a house structure monitoring subsystem according to the condition of a house structure, and carrying out the real-time monitoring of a house through the subsystem, and obtaining the initial detection data of the house; constructing a house monitoring data optimization mechanism, obtaining house structure three-dimensional detection information based on the house initial detection data, and processing the house structure three-dimensional detection information by using the data optimization mechanism to obtain house target detection information; establishing a house structure and environment evaluation system, and evaluating the house in combination with the house target detection information to obtain a house analysis evaluation result; and the house condition is comprehensively evaluated by combining the house analysis and evaluation result and the house target detection information, so that the intelligent monitoring and accurate evaluation of the house health condition are realized. According to the invention, intelligent monitoring and accurate evaluation of the house health condition can be realized, and the development of a smart city is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent evaluation of building structures, and in particular to a method and system for intelligent monitoring and evaluation of building health. Background Art

[0002] Traditional building structure monitoring methods rely primarily on manual inspections and regular testing. These methods suffer from limitations and issues such as long inspection cycles, low efficiency, large monitoring errors, and non-standardized evaluation processes. Furthermore, as buildings age, their structural performance gradually deteriorates, making it difficult for traditional inspection methods to promptly detect and address potential safety hazards.

[0003] With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent monitoring and assessment technologies have gradually been applied to the field of building structural health monitoring. Based on relevant technologies, various structural indicators of buildings are monitored in real time, and monitoring data are processed and analyzed using data analysis algorithms and machine learning models, which is conducive to the assessment and early warning of the structural health status of buildings.

[0004] In order to solve the problems existing in traditional detection methods and improve the monitoring efficiency and accuracy of building structures, it is necessary to optimize and upgrade the existing building monitoring and evaluation methods and related systems to provide technical support for the construction of smart cities. Summary of the Invention

[0005] In view of the shortcomings of existing methods and the needs of practical applications, in order to shorten the inspection cycle of buildings, improve work efficiency, reduce evaluation errors and standardize the evaluation process, realize efficient inspection and intelligent evaluation of building structural performance, timely identify and deal with potential safety hazards of houses, and ensure the safety and stability of buildings. On the one hand, the present invention provides a method for intelligent monitoring and evaluation of house health, which includes: setting a house structure monitoring subsystem according to the structural conditions of the house, monitoring the house in real time through the house structure monitoring subsystem to obtain initial detection data of the house; constructing a house monitoring data optimization mechanism, obtaining three-dimensional detection information of the house structure based on the initial detection data of the house, and processing the three-dimensional detection information of the house structure using the house monitoring data optimization mechanism to obtain house target detection information; establishing a house structure and environment evaluation system, combining the house structure and environment evaluation system and the house target detection information to evaluate the house and obtain a house analysis and evaluation result; combining the house analysis and evaluation result with the house target detection information to comprehensively evaluate the house condition, so as to realize intelligent monitoring and accurate evaluation of the health status of the house. The present invention realizes intelligent monitoring and accurate assessment of the health status of a building through a series of processes, which not only helps to timely discover and deal with potential housing safety hazards, but also provides a reference basis for the maintenance and management of the building, thereby improving the overall performance and service life of the building.

[0006] Optionally, setting up the housing structure monitoring subsystem based on the housing structure includes configuring sensors, arranging sensors, and designing a data transmission mode in the housing structure monitoring subsystem based on the housing structure. The present invention sets up the housing structure monitoring subsystem based on the housing structure, and rationally configures sensors, arranges sensors, and designs a data transmission mode, thereby improving the intelligence level and management effectiveness of the monitoring system.

[0007] Optionally, constructing a housing monitoring data optimization mechanism includes: establishing a housing information verification model and a housing three-dimensional data adjustment model within the housing monitoring data optimization mechanism; and combining the housing information verification model and the housing three-dimensional data adjustment model to construct the housing monitoring data optimization mechanism. The housing monitoring data optimization mechanism constructed by the present invention can improve data accuracy, optimize data processing procedures, and enhance the accuracy of assessment results, thereby ensuring housing safety and optimizing resource allocation.

[0008] Optionally, the establishing of a house information verification model and a house three-dimensional data adjustment model in the house monitoring data optimization mechanism includes: establishing a house information verification model based on three-dimensional data features and house spatial structure principles; The housing information verification model satisfies the following relationship: in, represents the average distance from the reference monitoring point to the neighboring monitoring points, An index that represents the influence of the geometric characteristics of the building surface, Indicates the number of neighboring monitoring points within the preset distance range of the reference monitoring point. Indicates the actual distance from the neighborhood monitoring point to the reference monitoring point, Indicates the total number of housing data monitoring points, Indicates the influence coefficient corresponding to the house surface color and material reflectivity; in, represents the standard deviation of the distance between the reference monitoring point and the mean, Indicates the total number of housing data monitoring points, Indicates the number of neighboring monitoring points within the preset distance range of the reference monitoring point. Indicates the actual distance from the neighborhood monitoring point to the reference monitoring point, Indicates the average distance from the reference monitoring point to the neighboring monitoring points.

[0009] The housing information inspection model and three-dimensional data adjustment model of the present invention can realize the automatic processing and analysis of monitoring data, reduce manual intervention and errors, improve data processing efficiency, and provide data support for subsequent evaluation and analysis work.

[0010] Optionally, the establishing of the house information verification model and the house three-dimensional data adjustment model in the house monitoring data optimization mechanism includes: establishing the house three-dimensional data adjustment model based on house structural characteristics; The three-dimensional data adjustment model of the house satisfies the following relationship: in, Represents the three-dimensional data result of the house after registration, Represents the rotation coefficient of the three-dimensional data of the house, Represents the difference value of the three-dimensional data of the house, Indicates the translation factor of the 3D house data.

[0011] The present invention establishes a communication model based on the structural characteristics of the house, which can more accurately reflect the actual structure and shape of the house and help improve the accuracy of the overall evaluation results of the house.

[0012] Optionally, the processing of the three-dimensional house structure detection information using the house monitoring data optimization mechanism to obtain house target detection information includes: inspecting the three-dimensional house structure detection information using a house information inspection model in the house monitoring data optimization mechanism to obtain optimized three-dimensional house structure detection information; and adjusting the optimized three-dimensional house structure detection information using a house three-dimensional data adjustment model in the house monitoring data optimization mechanism to obtain house target detection information. The present invention realizes automated processing of house data through the house monitoring data optimization mechanism, reducing the need for manual intervention, thereby not only improving processing efficiency but also reducing the risk of errors caused by human factors.

[0013] Optionally, establishing a housing structure and environment assessment system, evaluating the housing in combination with the housing structure and environment assessment system and the housing target detection information, and obtaining a housing analysis and assessment result includes: providing a housing structure assessment layer and an indoor environmental quality assessment layer in the housing structure and environment assessment system based on the housing target detection information; and evaluating the housing based on the housing structure assessment layer, the indoor environmental quality assessment layer, and the housing target detection information to obtain a housing analysis and assessment result. The system of the present invention encompasses both a housing structure assessment layer and an indoor environmental quality assessment layer. During the assessment process, multiple aspects such as the structural safety and stability of the housing, as well as the comfort and healthiness of the indoor environment, can be comprehensively considered, helping to more accurately reflect the overall condition of the housing.

[0014] Optionally, setting a house structure evaluation layer and an indoor environment quality evaluation layer in a house structure and environment evaluation system based on the house target detection information includes: In the housing structure evaluation layer, a housing crack gap prediction model, a housing horizontal deformation calculation model, and a housing tilt degree and uneven settlement analysis model are set; The house crack gap prediction model satisfies the following relationship: in, Indicates the distance between adjacent cracks on the wall of a house. represents the constraint coefficient corresponding to the thickness of the building wall, represents the constraint coefficient corresponding to the end of the building wall, Indicates the restriction coefficient of foundation on cracks in house walls. Indicates the tensile stress value on the wall of the building. Indicates the ultimate tensile strain value of the house concrete; The building horizontal deformation calculation model satisfies the following relationship: in, Indicates the horizontal deformation value of the overall structure of the house. represents the horizontal distance of the overall structure of the house at time i, Indicates the initial horizontal distance of the overall structure of the house, Represents the error coefficient in the process of obtaining the overall structure of the house; The building tilt degree and the uneven settlement analysis model satisfy the following relationship: in, Represents the dependent variable of the building tilt and uneven settlement analysis model, Indicates the degree of building inclination and the slope of the uneven settlement analysis model. Indicates the independent variable of the building tilt and uneven settlement analysis model, The coefficient representing the degree of building tilt and the differential settlement analysis model.

[0015] The present invention sets up a house structure evaluation layer and an indoor environmental quality evaluation layer in the house structure and environment assessment system, and sets a specific mathematical model at the same time, which can improve the scientificity and accuracy of the evaluation results and help to promptly discover and deal with safety problems in the house structure in the future.

[0016] Optionally, the establishment of a housing structure and environment assessment system, combining the housing structure and environment assessment system and the housing target detection information to evaluate the housing, and obtaining housing analysis and assessment results includes: obtaining the wall crack analysis results of the housing through the housing crack gap prediction model in the housing structure assessment layer; obtaining the horizontal deformation analysis results of the housing structure according to the housing horizontal deformation calculation model in the housing structure assessment layer; obtaining the relationship analysis results of the housing tilt angle and uneven settlement using the housing tilt degree and uneven settlement analysis model in the housing structure assessment layer; obtaining the indoor environmental parameter analysis results of the housing based on the indoor environmental quality assessment layer; and obtaining the housing analysis and assessment results based on the wall crack analysis results, the horizontal deformation analysis results, the relationship analysis results, and the indoor environmental parameter analysis results. The present invention combines the assessment system and housing detection information to evaluate the housing, which not only helps to comprehensively assess the housing condition, but also can scientifically guide housing maintenance and management, thereby promoting the construction and development of smart cities.

[0017] In a second aspect, to efficiently execute the intelligent housing health monitoring and assessment method provided by the present invention, the present invention also provides an intelligent housing health monitoring and assessment system, comprising an input device, a processor, an output device, and a memory, wherein the input device, processor, output device, and memory are interconnected, the memory including the intelligent housing health monitoring and assessment method described in the first aspect of the present invention, the memory storing a computer program including program instructions, and the processor configured to invoke the program instructions. The intelligent housing health monitoring and assessment system provided by the present invention has a compact structure, strong applicability, and greatly improved operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the housing health intelligent monitoring and evaluation method of the present invention; Figure 2 Schematic diagram of the architecture of the house structure monitoring subsystem in the house health intelligent monitoring and assessment method of the present invention; Figure 3 Schematic diagram of simulation comparison between initial detection data of a house and target detection information of a house in the intelligent monitoring and evaluation method for house health of the present invention; Figure 4 This is a structural diagram of the housing health intelligent monitoring and evaluation system of the present invention. DETAILED DESCRIPTION

[0019] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0020] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0021] See Figure 1 To address the shortcomings and problems of existing building monitoring methods and meet the actual needs of building monitoring, various structural information of buildings is monitored in real time based on technologies such as the Internet of Things, big data, and artificial intelligence. The building monitoring data is processed and analyzed using data analysis algorithms and machine learning models to achieve real-time assessment and early warning of the building's structural health status, thereby efficiently and accurately detecting and evaluating the structural performance of the building. The present invention provides a method for intelligent monitoring and assessment of building health, which includes the following steps: S1. Set up a housing structure monitoring subsystem based on the housing structure. Use the above housing structure monitoring subsystem to monitor the housing in real time to obtain initial housing detection data. The specific setting steps and implementation contents are as follows: It is necessary to configure sensors, layout sensors and design data transmission modes in the building structure monitoring subsystem according to the building structure conditions.

[0022] In this embodiment, a house structure monitoring subsystem is designed based on the structural characteristics of the house, aiming to meet the actual monitoring needs of the building and effectively make up for the shortcomings and defects of existing monitoring methods. The above-mentioned monitoring subsystem integrates advanced sensor technology and can capture key indicator data of the house structure in real time and accurately, providing data support for the assessment of the health status of the house structure.

[0023] The building structure monitoring subsystem uses sensor elements and data acquisition equipment to achieve real-time monitoring of hidden parameters such as internal stress, deformation, and vibration of the building structure, which helps to promptly discover and solve potential problems in the building structure, thereby effectively ensuring the safety and stability of the building.

[0024] In terms of sensor layout and data collection, the appropriate sensor type is selected based on the actual situation of the house structure, and the layout of data monitoring points is optimized, including but not limited to accelerometers, structural diagnostics, inclinometers and vibration monitors, GNSS monitoring all-in-one devices and other equipment. At the same time, the crack meter is used to monitor the expansion of cracks in the house, while the static level is used to monitor the relative settlement of the wall. The accelerometer and inclinometer are responsible for monitoring structural vibration and tilt changes respectively. In this way, the initial detection data of the house can be effectively collected based on the data monitoring points and system equipment.

[0025] The subsystem equipment provides around-the-clock, uninterrupted monitoring of key building components, ensuring real-time visibility into structural changes. Furthermore, by integrating the Internet of Things, sensor technology, data transmission, data analysis, and artificial intelligence, it enables the immediate collection, efficient transmission, and accurate analysis of initial building inspection data, providing technical support for structural health monitoring.

[0026] Furthermore, different transmission methods can be selected based on sensor type. Relevant data can be aggregated via wireless transmission devices and uploaded to the cloud service platform of the Intelligent Housing Health Monitoring and Assessment System via GPRS wireless signals. This data is received and processed by the service program and stored in a reliable database for Houxi data analysis and processing. Because the housing inspection process generates a large amount of monitoring data, the subsystem uses a more stable server and database system to ensure stable storage and access of housing inspection data. At the same time, relevant technicians can access housing monitoring data on the information platform at any time via computers or mobile phones, allowing for real-time viewing and monitoring of housing structural data.

[0027] In an optional embodiment, the operation process and related contents of the building structure monitoring subsystem are as follows: The building structure monitoring subsystem uses sensors to obtain real-time data from different monitoring points in the building structure, which is conducive to the subsequent intuitive reflection of the health status of the building structure. Appropriate sensors are selected in the subsystem and arranged in reasonable monitoring locations to ensure the accuracy and reliability of data from different monitoring points.

[0028] The house structure monitoring subsystem uses wireless transmission to ensure the real-time and accuracy of data. It is also equipped with an optimized database to achieve fast and stable storage and retrieval of initial house inspection data. At the same time, it is equipped with a reasonable energy supply device to ensure the continuous and stable operation of the house structure monitoring subsystem.

[0029] In this embodiment, the operating architecture of the building structure monitoring subsystem can be found in Figure 2 ,For the arrangement information of the monitoring subsystem sensors, please refer to Table 1.

[0030] Table 1 Layout information of the building structure monitoring subsystem By selecting appropriate sensors based on the building structure and arranging them in reasonable locations, the building structure monitoring subsystem can obtain data from different monitoring points in the building structure in real time and accurately, further ensuring the accuracy and reliability of the monitoring data and providing an information basis for subsequent analysis of the health status of the building structure.

[0031] The use of wireless transmission ensures real-time data transmission from the monitoring subsystem, reducing data transmission delays and errors. Furthermore, the optimized database configuration allows for the efficient storage and retrieval of initial house inspection data, improving the efficiency of monitoring data storage and processing.

[0032] Table 1 shows that the system's combination of multiple sensors enables the building structure monitoring subsystem to comprehensively monitor hidden parameters such as stress, deformation, and vibration. This helps promptly identify and resolve potential structural health issues, reducing the probability of safety accidents. Furthermore, the subsystem sets warning thresholds for different monitoring parameters. When monitoring data exceeds the threshold, it automatically issues a warning signal, alerting relevant personnel to take timely countermeasures.

[0033] The building structure monitoring subsystem can also be used in conjunction with visualization software and intelligent analysis platforms to intuitively display monitoring data and analysis results, enabling relevant personnel to more conveniently obtain initial building inspection data and quickly understand the health status of the building structure. The above-mentioned building structure monitoring subsystem provides strong support for the safety monitoring and health management of the building structure.

[0034] Furthermore, the method for obtaining the initial detection data of the house and the system operation steps in this embodiment are only an optional condition of the present invention. In one or some other embodiments, the method for obtaining the initial detection data of the house and the system operation steps can be adjusted and optimized according to the actual situation of the house structure and the requirements of intelligent monitoring of house health, so that the house structure monitoring data can be collected and processed more effectively, so that the present application can adapt to diverse monitoring needs and provide customized data monitoring solutions for different types of houses.

[0035] S2. Construct a housing monitoring data optimization mechanism to obtain three-dimensional housing structure detection information based on the initial housing detection data. Then, use the housing monitoring data optimization mechanism to process the three-dimensional housing structure detection information to obtain housing target detection information. The specific steps and implementation content are as follows: The building structure monitoring subsystem mainly includes static data collection and dynamic data monitoring, which together realize the comprehensive monitoring of the health status of the building structure. However, the error in the process of building monitoring data collection is a problem that cannot be ignored.

[0036] Static monitoring data primarily focuses on local damage information for a building structure or overall physical indicators under a specific transient state. This data can be acquired using sensors at known damage locations, such as uneven structural settlement, crack width, and wall tilt. However, errors in static data primarily arise from sensor device limitations, differences in placement, and environmental factors that affect sensor performance.

[0037] Dynamic monitoring data focuses on monitoring the long-term changes in the building structure. This primarily includes non-vibration monitoring data that requires initial values, such as cracks, tilt, and settlement. In dynamic monitoring, large fluctuations in monitoring data over a short period of time indicate increasing structural damage. However, in addition to the sensor accuracy, placement, and environmental factors mentioned in static monitoring, dynamic monitoring errors also need to consider the dynamic characteristics of the building structure itself, such as natural frequency and distance range, which can influence monitoring results.

[0038] In order to reduce the monitoring error of the house structure monitoring subsystem, a house information verification model and a house three-dimensional data adjustment model are established in the house monitoring data optimization mechanism; then the house information verification model and the house three-dimensional data adjustment model are combined to construct a house monitoring data optimization mechanism.

[0039] First, the initial inspection data of the house is further processed to obtain three-dimensional inspection information of the house structure. The above process requires three-dimensional coordinate adjustment and fusion of the initial inspection data. In the embodiment, the corresponding three-dimensional data is obtained to reflect the actual condition of the house structure. Specifically, the monitoring subsystem can comprehensively capture all monitoring point cloud data within the monitoring area. The above data can be accurately positioned in the three-dimensional coordinate system (including the x-axis, y-axis, and z-axis) within the subsystem. In the monitoring system coordinate system, the x-axis represents a component in the horizontal direction, and the y-axis is perpendicular to the x-axis, together defining the horizontal plane; while the z-axis is perpendicular to this horizontal plane, pointing to the sky, representing the vertical height information. By calculating the coordinates of each monitoring point in the system's three-dimensional coordinate system, the three-dimensional inspection information of the house structure can be integrated and generated. Based on this, the spatial layout, structural characteristics, and possible abnormalities or damage of the inspected house can be depicted.

[0040] By using the monitoring subsystem to collect cloud data from different monitoring points and combining it with the internal three-dimensional coordinate system, the spatial position of each monitoring point can be accurately calculated, and a three-dimensional detection model of the house structure can be constructed, thereby obtaining the three-dimensional detection information of the house structure of the embodiment, providing data support for subsequent structural health monitoring, damage assessment and maintenance decisions.

[0041] In this embodiment, a house information verification model is established based on the three-dimensional detection information of the house structure, three-dimensional data features and the house spatial structure principle.

[0042] In order to accurately locate the relative positions of different monitoring points in the initial house inspection data, a house information inspection model was established based on the house spatial structure principle and combined with the multidimensional index structure method.

[0043] The 3D inspection information for the building structure contains 3D data for all monitoring points. Based on this dataset, the average distance of each monitoring point within its neighborhood is calculated. This specific neighborhood is composed of the points closest to the monitoring point, and the distances between these points and the monitoring point must be between 0 and the maximum distance of the building data monitoring, that is, within a preset distance range.

[0044] Based on the above process, the number of neighboring monitoring points for each monitoring point can be obtained, and the radius of the neighborhood and the average radius distance within the neighborhood can be calculated accordingly. Subsequently, the mean and standard deviation of the distance are further calculated using the average distance data, the total number of monitoring points, the number of neighboring monitoring points, and environmental factors. Based on this, anomalous monitoring point data that significantly deviates from the normal distribution range can be identified and eliminated, thus ensuring the accuracy and reliability of the building inspection data and providing a foundation for subsequent building structural analysis and health monitoring.

[0045] The above housing information verification model satisfies the following relationship: The distance mean calculation function in the housing information inspection model is used to analyze the average distance from the reference monitoring point to the neighboring monitoring points, and the following relationship is satisfied: in, represents the average distance from the reference monitoring point to the neighboring monitoring points, An index that represents the influence of the geometric characteristics of the building surface, Indicates the number of neighboring monitoring points within the preset distance range of the reference monitoring point. Indicates the actual distance from the neighborhood monitoring point to the reference monitoring point, Indicates the total number of housing data monitoring points, Indicates the influence coefficient corresponding to the house surface color and material reflectivity; The distance mean calculation function combines parameters such as the geometric characteristics of the house, the number of neighborhood monitoring points, and the neighborhood radius to calculate the average distance, that is, to obtain the average distance from the reference monitoring point to the monitoring points in its neighborhood, which can be used to evaluate the relative position relationship between the reference monitoring point and its surrounding environment.

[0046] The influence index of the geometric characteristics of the house surface reflects the degree of influence of factors such as the geometric shape, flatness, and curvature of the house surface on the average distance calculation result. Different geometric characteristics will cause the relative distance between monitoring points to change, so it is necessary to adjust it through the influence index to enhance the accuracy and feasibility of the model.

[0047] The number of neighborhood monitoring points defines how many monitoring points around the reference monitoring point are taken into consideration and can be used to calculate the average distance. The more neighborhood monitoring points there are, the more the calculated average distance can reflect the actual situation and local characteristics of the house structure.

[0048] When calculating the average distance, the actual distance from each neighborhood monitoring point to the reference monitoring point is summed, thus reflecting the sum of the actual distances of different monitoring points.

[0049] The total number of housing data monitoring points reflects the size information of the entire housing monitoring dataset, and further represents the overall scale of the housing monitoring dataset.

[0050] The influence coefficient corresponding to the house surface color and material reflectivity reflects the degree of influence of the house surface color and material on the data monitoring system. Different colors and materials will cause the reflectivity of the detection equipment to change, thereby affecting the accuracy of the monitoring data. In areas where the curvature of the house surface is significant or there are obvious bumps and unevenness, the neighborhood monitoring points tend to show a more dense distribution. Because slight changes in the surface of the house in the above-mentioned area will cause the distance between the monitoring points to shorten, the number of neighborhood points taken into consideration within the same preset distance range will increase, thereby forming a denser distribution of neighborhood points. The dense distribution will cause the calculated average distance value to be smaller. The introduction of the above-mentioned influence coefficient can adjust the average distance calculation result. The embodiment takes into account the interference of the house surface properties and materials on the monitoring data, which helps to improve the accuracy of the house monitoring data.

[0051] The standard deviation calculation function in the housing information verification model is used to analyze the standard deviation of the distance between the reference monitoring point and the average distance, and the following relationship is satisfied: in, represents the standard deviation of the distance between the reference monitoring point and the mean, Indicates the total number of housing data monitoring points, Indicates the number of neighboring monitoring points within the preset distance range of the reference monitoring point. Indicates the actual distance from the neighborhood monitoring point to the reference monitoring point, Indicates the average distance from the reference monitoring point to the neighboring monitoring points.

[0052] The standard deviation of the distance between the reference monitoring point and the average distance can be used to measure the degree of dispersion between the actual distance from the neighborhood monitoring point to the reference monitoring point and the average distance. The smaller the standard deviation, the more concentrated the distance distribution of the neighborhood monitoring points is and the smaller the deviation from the average distance is. Conversely, the larger the standard deviation, the more dispersed the distance distribution is.

[0053] The relevant calculation functions in the housing information verification model incorporate multiple parameters, including the geometric characteristics of the house, the number of neighborhood monitoring points, and the actual neighborhood radius. This allows the average distance and standard deviation to better reflect the local characteristics of the house structure, enhancing the practicality of the information verification model. The standard deviation can quickly assess the degree of dispersion in the distance distribution of neighborhood monitoring points, providing a reference for subsequent steps such as outlier detection and data screening. Furthermore, the model's calculation formulas are concise and easy to implement, helping to improve data processing efficiency.

[0054] The house information verification model can provide a reference basis for the verification of house structure monitoring data. By regularly monitoring and calculating the average distance and standard deviation indicators, abnormal monitoring points in the house structure data set can be discovered in a timely manner, providing a scientific basis for the optimization and adjustment of the house structure monitoring subsystem. It can realize effective monitoring and accurate early warning of the house structure, thereby improving the intelligence level of the house monitoring method.

[0055] When comparing the standard deviation of the average distance between each reference scanning point and the points in its neighborhood, if the average distance between a reference scanning point and the points in its neighborhood exceeds the established standard deviation range, the monitoring point will be regarded as an outlier, and corresponding measures will be taken to remove it from the initial data set. Based on this, outliers in the initial house inspection data can be effectively identified and removed, and the three-dimensional inspection information of the house structure after inspection optimization can be obtained, further ensuring the accuracy of the house monitoring data.

[0056] In this embodiment, a three-dimensional data adjustment model of a house is established based on the structural characteristics of the house.

[0057] After obtaining optimized 3D inspection information about the building structure, further 3D spatial adjustments are performed. Within the 3D monitoring space of the building, the monitoring system can keenly capture key features such as edges, vertices, and angles. To further improve the accuracy of building inspection data and provide data support for intelligent analysis of building health, the embodiment introduces a Gaussian smoothing analysis method based on adjacent scales. This method uses Gaussian convolution operations to optimize the differences in 3D building inspection information at different scales.

[0058] The differential values of the optimized 3D inspection data are analyzed. These differential values reveal the varying characteristics of the monitoring data at different scales, providing a basis for adjusting the 3D inspection data. This effectively smooths out noise and minor fluctuations in the data, resulting in smoother and more accurate adjusted monitoring data.

[0059] The differential value of the three-dimensional monitoring data of the house is analyzed based on the three-dimensional detection information of the house structure after inspection optimization, and the following relationship is satisfied: in, Represents the difference value of the three-dimensional monitoring data of the house, represents the average distance from the reference monitoring point to the neighboring monitoring points, represents the Gaussian kernel, Represents the average grayscale value of the three-dimensional monitoring data of the house, represents the weight coefficient corresponding to the standard deviation, Indicates the standard deviation of the distance between the reference monitoring points and the mean.

[0060] In three-dimensional space coordinates, The difference value of the three-dimensional monitoring data of the house at a coordinate point reflects the degree of difference between the three-dimensional detection information of the house structure at that coordinate point and the reference monitoring point or the expected state.

[0061] In Gaussian smoothing analysis, the Gaussian kernel can smooth the monitoring data to reduce the impact of noise and small fluctuations. The shape and size of the Gaussian kernel, that is, the standard deviation, determines the degree of data smoothing.

[0062] The average grayscale value of a house's three-dimensional monitoring data refers to the brightness value of each pixel or point in the monitoring image or scan data. The average grayscale value reflects the average brightness level of the entire data set. In three-dimensional house monitoring, the grayscale value is related to the material, color, and other properties of the house surface.

[0063] The weight coefficient corresponding to the standard deviation can be used to adjust the influence of the standard deviation in the formula. By adjusting the weight coefficient, the influence of the difference value on the standard deviation can be changed, thereby adjusting the final analysis result.

[0064] Based on the above differential calculation results, the three-dimensional spatial data of the house is further adjusted and registered. The registered three-dimensional data of the house is obtained based on the three-dimensional data adjustment model. The adjusted three-dimensional data of the house satisfies the following relationship: in, Represents the three-dimensional data result of the house after registration, Represents the rotation coefficient of the three-dimensional data of the house, Represents the difference value of the three-dimensional data of the house, Indicates the translation factor of the 3D house data.

[0065] The registered three-dimensional data result of the house refers to the three-dimensional monitoring data of the house that is aligned with the reference coordinate system after transformations such as rotation and translation.

[0066] The rotation coefficient of the 3D data of the house can be a The rotation matrix is used to describe the rotation transformation of the house's three-dimensional data in three-dimensional space. The above matrix contains the rotation angles (or equivalent rotation parameters) around the X-axis, Y-axis, and Z-axis. The original three-dimensional data can be rotated to a new position and orientation through matrix multiplication. In practical applications, the rotation matrix can be obtained through the registration algorithm to further realize the alignment of the registered three-dimensional data with the reference coordinate system.

[0067] The translation factor for 3D house data is a three-dimensional vector that describes the translation transformation of the house's 3D data in 3D space. This vector includes the translation amounts in the X, Y, and Z axes. Vector addition can be used to translate the original 3D data to a new position. During data registration, the translation factor is primarily used to adjust the relative position between two datasets to ensure spatial consistency and uniformity of the house's 3D monitoring data.

[0068] The 3D data adjustment model aligns the 3D data of the building with the reference coordinate system through transformations such as rotation and translation, enabling registration and adjustment of the 3D monitoring data. Registration of 3D scan data ensures data integrity, accuracy, and consistency, providing technical support for the data fusion process, making it more efficient and accurate.

[0069] The 3D House Data Adjustment Model aligns the 3D house monitoring data with the reference coordinate system, enabling high-precision registration of the 3D monitoring data. This ensures the integrity, accuracy, and consistency of the house monitoring data, making subsequent data integration smoother, more efficient, and more accurate. This foundation allows for further target detection information about the house structure, providing data support for house structural analysis, assessment, and repair work.

[0070] In an optional embodiment, the three-dimensional detection information of the house structure is processed using a house monitoring data optimization mechanism to obtain house target detection information.

[0071] The three-dimensional detection information of the house structure is tested through the house information verification model in the house monitoring data optimization mechanism to obtain the three-dimensional detection information of the house structure after the inspection optimization.

[0072] The normality of the data is evaluated based on the standard deviation of the average distance from each reference scanning point to the points in its neighborhood. When the average distance of a reference scanning point exceeds the standard deviation range, it is regarded as an outlier. The identified outlier will be deleted from the initial data set to ensure the overall quality and accuracy of the data. By eliminating outliers, optimized three-dimensional data of the house is obtained, that is, the three-dimensional detection information of the house structure after inspection and optimization is obtained, based on which the actual situation of the house structure can be more truly reflected.

[0073] The 3D data adjustment model within the housing monitoring data optimization mechanism is used to adjust the optimized 3D structure detection information to obtain housing target detection information. The 3D data adjustment model within the housing monitoring data optimization mechanism is used to further adjust the optimized 3D structure detection information. By using spatial transformations such as rotation and translation, the 3D data is aligned and adjusted with the reference coordinate system to obtain housing target detection information. This information provides strong data support for subsequent housing structure analysis, safety assessment, and decision-making.

[0074] For the comparison between the initial house detection data and the house target detection information in the embodiment, please refer to Figure 3 .

[0075] Furthermore, the optimization steps and analysis methods of the house target detection information in this embodiment are only an optional condition of the present invention. In one or some other embodiments, the optimization steps and analysis methods of the house target detection information can be optimized according to the optimization requirements of the house monitoring data and the actual conditions of the house structure, which helps to more accurately obtain the changes and abnormalities of the house structure, improve the accuracy of the target detection information, and thus improve the applicability and practicality of the house health intelligent monitoring and evaluation method.

[0076] S3. Establish a housing structure and environment assessment system, combine the above housing structure and environment assessment system with housing target detection information to evaluate the housing, and obtain housing analysis and assessment results. The specific implementation content is as follows: Firstly, based on the house target detection information, the house structure evaluation layer and the indoor environment quality evaluation layer were set up in the house structure and environment assessment system.

[0077] In the housing structure and environment assessment system, two core evaluation levels are set up based on the housing target detection information: the housing structure evaluation level and the indoor environmental quality evaluation level. The above two levels together constitute the basic framework for evaluating the housing status.

[0078] The house structure evaluation system constructed based on the target data set can accurately reflect the health status of the house. This system not only covers the house structure evaluation layer, but also includes the house indoor environment quality evaluation layer, thereby realizing multi-dimensional evaluation and analysis of the overall condition of the house.

[0079] The building structure evaluation layer mainly includes crack conditions, overall building tilt, and uneven structural settlement. The relevant contents are as follows: Crack situation: Detect and record cracks in the house structure, analyze their location, length, width and other characteristics to assess the impact of cracks on the safety of the house structure.

[0080] Overall tilt of the house: Based on monitoring data and measurement technology, determine the vertical tilt of the house to determine whether the house is stable.

[0081] Uneven structural settlement: By monitoring the settlement of the building foundation and main load-bearing structures, the potential threat of uneven settlement to the structural integrity and safety of the building can be identified and assessed.

[0082] At the same time, a number of key evaluation indicators are set in the indoor environmental quality evaluation layer to comprehensively reflect the health of the indoor environment. The relevant contents are as follows: Acoustic Environment: Measure and evaluate indoor noise levels to ensure that residents are protected from noise disturbances and enjoy a quiet living environment.

[0083] Indoor air quality: By detecting the concentration of pollutants in the indoor air, such as formaldehyde, TVOCs, etc., we ensure that the indoor air is fresh and healthy.

[0084] Light environment: Analyze the distribution and intensity of indoor natural light and artificial lighting, optimize lighting design, and improve occupants' visual comfort and work efficiency.

[0085] Thermal comfort: This system comprehensively assesses the comfort of the indoor thermal environment by combining factors such as indoor temperature, humidity, and airflow velocity to ensure that residents can enjoy a pleasant living environment in all seasons.

[0086] The house structure and environment assessment system can more accurately monitor the health of the house structure and the indoor environment, providing a reference for house assessment, maintenance or upgrade.

[0087] Furthermore, in the house structure evaluation layer, a house crack gap prediction model, a house horizontal deformation calculation model, and a house tilt degree and uneven settlement analysis model are set up.

[0088] A house crack gap prediction model is introduced in the house structure evaluation layer. This model is used to analyze the formation and spacing prediction of house cracks. When the maximum constraint strain in the middle of the wall reaches the ultimate tensile strain of the concrete, the wall concrete will crack, and the crack spacing formed after the crack stabilizes.

[0089] Throughout the wall cracking process, the horizontal reinforcement at the cracks tends to remain continuous, effectively transmitting the restraining force between adjacent wall segments. Taking into account relevant practical circumstances, the embodiment sets a constraint coefficient corresponding to the wall thickness during the wall cracking process. Under this constraint, the wall between the cracks will reach a state of equilibrium under the combined action of foundation constraints and end constraints, thereby satisfying specific equilibrium equations and boundary conditions.

[0090] In order to more accurately calculate the crack spacing of the long wall of a house, a specific house crack gap prediction model is adopted in the embodiment. This model fully considers the above-mentioned constraints and equilibrium state, so that the relationship between the crack spacing and various influencing factors can be accurately analyzed.

[0091] The above-mentioned house crack gap prediction model satisfies the following relationship: in, Indicates the distance between adjacent cracks on the wall of a house. represents the constraint coefficient corresponding to the thickness of the building wall, represents the constraint coefficient corresponding to the end of the building wall, Indicates the restriction coefficient of foundation on cracks in house walls. Indicates the tensile stress value on the wall of the building. Indicates the ultimate tensile strain value of the house concrete; The house crack gap prediction model not only takes into account the physical mechanism of wall cracking, but also incorporates the wall constraint effect and the influence of foundation and end conditions. It can achieve accurate prediction of the spacing of house cracks and provide strong support for the safety assessment of house structures.

[0092] The straight-line distance between two adjacent cracks on a house wall can be used to evaluate the crack distribution of the wall and the overall stability of the house structure.

[0093] The constraint coefficient corresponding to the thickness of the house wall reflects the degree of influence of the wall thickness on the formation and development of cracks. The thicker the wall, the stronger its ability to resist the formation of cracks. Therefore, the constraint coefficient is positively correlated with the wall thickness.

[0094] The constraint coefficient corresponding to the end of a building wall measures the restraining effect of the wall end (such as the corner or the connection between the wall and the structure) on cracks. The constraint conditions at the wall end affect the expansion path and spacing of the cracks. Therefore, the constraint coefficient corresponding to the wall end plays an important role in predicting the spacing of cracks.

[0095] Factors such as foundation stability, stiffness and deformation characteristics will affect the generation and development of wall cracks. The restriction coefficient of the foundation on cracks in the walls of a house reflects the strength of the foundation's restraining effect on wall cracks.

[0096] Tensile stress is one of the main factors causing wall cracking, so the magnitude of tensile stress is directly related to the generation and distribution of cracks.

[0097] The ultimate tensile strain value of house concrete refers to the maximum strain value at which the concrete can remain intact without cracking when subjected to tensile stress. When the tensile stress on the wall causes the concrete strain to exceed this limit, cracks will occur.

[0098] In summary, the above parameters work together in the house crack gap prediction model to predict the distance between adjacent cracks on the wall through mathematical relationships, providing a scientific basis for the assessment of the stability and safety of the house structure, as well as crack prevention and repair.

[0099] A house horizontal deformation calculation model is set in the house structure evaluation layer.

[0100] During the evaluation of house structures, it is necessary not only to pay attention to the microstructural changes of cracks, but also to consider the macroscopic deformation of the overall structure of the house. In the embodiment, a house horizontal deformation calculation model is set up to quantify the horizontal deformation of the house under long-term use or external loads.

[0101] The above-mentioned calculation model for horizontal deformation of the house satisfies the following relationship: in, Indicates the horizontal deformation value of the overall structure of the house. represents the horizontal distance of the overall structure of the house at time i, Indicates the initial horizontal distance of the overall structure of the house, Represents the error coefficient in the process of obtaining the overall structure of the house; The horizontal deformation value of the overall structure of the house can be used to evaluate the stability and safety of the house structure.

[0102] The horizontal distance of the overall structure of the house at different moments refers to the horizontal distance of the overall structure of the house at a specific point in time or measurement period, thereby reflecting the actual size of the house in its current state.

[0103] The initial horizontal distance refers to the initial horizontal distance of the overall structure of the house, that is, the original size of the house when it is completed or not affected by external forces.

[0104] In order to more accurately calculate the horizontal deformation value, the embodiment introduces the error coefficient in the process of obtaining the overall structure of the house, which represents the error coefficient of the overall structure of the house during the measurement or data acquisition process. It can then comprehensively consider the influence of various factors such as the accuracy of the measuring instrument, the measurement environment, and the operating technology level on the horizontal measurement results, thereby ensuring the accuracy and reliability of the calculation results.

[0105] The error coefficient in the process of obtaining the overall structure of the house needs to satisfy the following relationship: in, Represents the error coefficient in the process of obtaining the overall structure of the house, Indicates the reading error index of 3D data, represents the fluctuation index of the building structure monitoring subsystem, represents the environmental disturbance coefficient during the house structure monitoring process, Indicates the horizontal distance after the overall structure of the house changes. Indicates the initial horizontal distance of the overall structure of the house.

[0106] The above-mentioned house horizontal deformation calculation model is not only suitable for the initial assessment of newly built houses, but can also be used for regular monitoring and evaluation of existing houses. By comparing the horizontal deformation values at different time points, abnormal conditions of the house structure can be discovered in a timely manner, providing a scientific basis for subsequent house assessment, repair, reinforcement or renovation.

[0107] In the building structure evaluation layer, a building tilt degree and uneven settlement analysis model is set up.

[0108] First, computer simulation and analysis of the house inspection data are performed based on the house target detection information, based on which the intrinsic relationship between the house building foundation settlement and the superstructure tilt can be revealed. Then, the foundation elevation of the house building foundation at the two end points in the main settlement direction is obtained based on the house target detection information. and , and the distance between the two end points .

[0109] Then, based on the collected foundation elevation data and the distance between the two end points, the inclination angle of the overall settlement of the house is calculated. , the above calculation process needs to follow the basic trigonometric function relationship and satisfy the following relationship: in, Indicates the inclination angle of the overall settlement of the building. Indicates the foundation elevation corresponding to one end point in the main settlement direction of the building structure. Indicates the foundation elevation corresponding to the other end point of the main settlement direction of the building structure. Indicates the horizontal distance between two points of the building structure.

[0110] Then, after obtaining the foundation inclination angle, the inclination of the superstructure is further analyzed. The inclination angle of the superstructure needs to be measured and calculated to accurately analyze the inclination angle of the superstructure of the overall settlement of the house. , and satisfy the following relationship: in, Indicates the inclination angle of the upper structure of the overall settlement of the house, Indicates the horizontal distance between the vertical projection of the top of the building structure and the bottom. Indicates the height of the superstructure of the house.

[0111] The inclination angle of the overall settlement of the house The inclination angle of the upper structure with the overall settlement of the house A comparative analysis is conducted to reveal the correlation and mutual influence between the two.

[0112] When further analyzing the mathematical relationship between the uneven settlement of the building structure and the tilt angle, computer simulation analysis method was mainly used, and regression analysis method was combined to explore the intrinsic connection between the uneven settlement of the foundation and the tilt of the superstructure.

[0113] Analyze in Example and The mathematical relationship between uneven settlement and tilt angle can be revealed by the relationship between (dependent variable represents the change in tilt angle) and There is a linear relationship between (the independent variable represents another tilt angle or a settlement indicator related to it).

[0114] On this basis, the regression coefficient is calculated (slope) and (intercept), the correlation coefficient reflects Follow The trend and extent of change, (slope) and The calculation principle of (intercept) is based on the least squares method or other optimization algorithms, which aims to find the best linear fit that minimizes the difference between the predicted values and the actual observed values.

[0115] Therefore, the relationship between uneven settlement and tilt angle can be reasonably expressed as follows: Through regression analysis, we get and The linear regression equation of one variable is (slope) and (Intercept) is a coefficient that describes the linear trend of the tilt angle changing with uneven settlement. It not only helps to gain a deeper understanding of the behavioral characteristics of the building structure during settlement, but also provides a theoretical basis for subsequent building structure safety assessment and maintenance and reinforcement design.

[0116] The above-mentioned building tilt degree and uneven settlement analysis model satisfy the following relationship: in, Represents the dependent variable of the building tilt and uneven settlement analysis model, Indicates the degree of building inclination and the slope of the uneven settlement analysis model. Indicates the independent variable of the building tilt and uneven settlement analysis model, The coefficient representing the degree of building tilt and the differential settlement analysis model.

[0117] Analyzing the mechanisms by which differential settlement affects structural tilt, combined with relevant data, facilitates analysis of the causes and distribution of differential settlement, as well as its impact on the stability and safety of the superstructure. This analysis reveals a quantitative relationship between the degree of tilt and differential settlement, as well as the specific impact of this relationship on structural stability and safety.

[0118] The relevant implementation contents of the indoor environmental quality assessment layer are as follows: First, the monitoring parameters of the indoor environmental quality evaluation layer are selected and analyzed.

[0119] Acoustic environment is selected as an environmental quality monitoring parameter. The primary focus is on monitoring the sound pressure level of indoor noise, further differentiating between daytime and nighttime noise levels, and the distribution of noise within specific frequency ranges. This allows for a comprehensive assessment of the comfort level of the indoor acoustic environment and the potential impact on daily activities.

[0120] Indoor air quality is selected as an environmental quality monitoring parameter. Monitoring content includes, but is not limited to, carbon dioxide (CO2) concentration to assess indoor ventilation efficiency; carbon monoxide (CO) concentration to monitor potential gas leaks; fine particulate matter (PM2.5) and respirable particulate matter (PM10) concentrations to reflect the degree of indoor air pollution; formaldehyde concentration, a key indicator of harmful gas release from decoration materials; and volatile organic compound (VOC) and total volatile organic compound (TVOC) concentrations to comprehensively assess indoor chemical pollution.

[0121] Select light environment as an environmental quality monitoring parameter. Measure indoor illuminance and analyze the distribution of vertical and horizontal illuminance, as well as the balance between natural and artificial light, to ensure sufficient and well-distributed indoor light, thereby reducing visual fatigue.

[0122] Thermal comfort is selected as an environmental quality monitoring parameter. This comprehensive consideration includes air temperature, globe temperature (which reflects the temperature actually felt by the human body), air velocity (which influences perceived coolness), and relative humidity. Scientific data is used to support the optimization and adjustment of the indoor thermal environment to meet the comfort needs of different seasons and individuals.

[0123] Then, the house is evaluated based on the house structure evaluation layer, indoor environment quality evaluation layer and house target detection information to obtain the house analysis and evaluation results.

[0124] The results of the wall crack analysis of the house are obtained through the house crack gap prediction model in the house structure evaluation layer; the horizontal deformation analysis results of the house structure are obtained based on the house horizontal deformation calculation model in the house structure evaluation layer; the relationship analysis results of the house tilt angle and uneven settlement are obtained using the house tilt degree and uneven settlement analysis model in the house structure evaluation layer; and the indoor environmental parameter analysis results of the house are obtained based on the indoor environmental quality evaluation layer. Finally, the house analysis and evaluation results are obtained by combining the wall crack analysis results, horizontal deformation analysis results, relationship analysis results and indoor environmental parameter analysis results.

[0125] In order to comprehensively and deeply evaluate the condition of a house, the house structure evaluation layer, indoor environment quality evaluation layer and house target detection information are integrated to form a comprehensive house analysis and evaluation system.

[0126] First, multiple models were used for analysis and calculation at the structural evaluation level. The House Crack Gap Prediction Model accurately identified and quantified wall cracks, providing a crucial reference for the structural safety of the house. Furthermore, the House Horizontal Deformation Calculation Model calculated the horizontal deformation of the house structure, further assessing its stability and durability. Furthermore, based on the House Tilt and Uneven Settlement Analysis Model, the inherent relationship between the house's tilt angle and uneven settlement was explored, providing a scientific basis for predicting and analyzing the overall deformation trend of the house.

[0127] Secondly, in terms of indoor environmental quality evaluation, the indoor environmental parameters of the house are comprehensively monitored and analyzed, including but not limited to the acoustic environment, indoor air quality, light environment, thermal comfort and other aspects. The monitoring results of the above parameters not only reflect the current environmental conditions of the house, but also provide information basis for subsequent environmental improvements.

[0128] Finally, a comprehensive analysis of the results from the housing structure evaluation layer and the indoor environmental quality evaluation layer was conducted. Combined with the results of the wall crack analysis, horizontal deformation analysis, relationship analysis, and indoor environmental parameter analysis, a comprehensive and objective housing analysis and evaluation result was obtained. This result not only provides a detailed report on the housing condition, but also provides a decision-making basis for subsequent housing evaluation, maintenance, and renovation.

[0129] In summary, by integrating the housing structure evaluation layer, the indoor environment quality evaluation layer, and the housing target detection information, a comprehensive and scientific housing analysis and evaluation system is constructed in the embodiment, which provides strong support for the accurate assessment of the housing condition.

[0130] Furthermore, the method for constructing the house structure and environment assessment system in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the house structure and environment assessment system can be adjusted according to the actual situation of the house structure and the requirements of the house health intelligent monitoring and assessment. This can ensure that the assessment method is more in line with the actual situation of the house, improve the accuracy and reliability of the assessment results, help improve the accuracy and reliability of the assessment results, and provide information support for the assessment, monitoring and maintenance of the house.

[0131] S4. Combine the above housing analysis and assessment results with the housing target detection information to conduct a comprehensive evaluation of the housing condition to achieve intelligent monitoring and accurate assessment of the housing health status. The specific implementation content is as follows: When comprehensively assessing the health status of a house, it is necessary not only to combine the results of house structural health monitoring (key indicators such as cracks, deformation, tilt) and indoor environmental quality monitoring (multiple dimensions such as sound, light, heat, and air quality), but also to comprehensively analyze the impact of natural conditions and human factors on the health and safety performance of the house.

[0132] Environmental factors: Comprehensively analyze the potential threats to the house structure from wind speed and direction, temperature and humidity fluctuations, rainfall, and potential earthquake activities at the house's location to further ensure the comprehensiveness of the assessment results.

[0133] Usage and maintenance history: Trace the house's past usage records, repair history and renovations to assess their impact on the current health of the house.

[0134] In terms of future development trends: predict new challenges that factors such as climate change and urbanization may bring to housing safety, and provide a forward-looking perspective for long-term maintenance planning.

[0135] Intelligent building health monitoring and assessment methods also include dynamic monitoring and simulation mechanisms. Establishing real-time or near-real-time monitoring and simulation mechanisms to continuously track building health can help identify and provide early warning of potential risks and health fluctuations.

[0136] The application of housing health monitoring and assessment results is not limited to ensuring the safety of current houses and the health of residents, but also provides a scientific basis for housing managers to formulate scientific and reasonable maintenance plans and emergency response plans, effectively extend the service life of the house, and help formulate or optimize housing safety management policies and improve the level of residential safety.

[0137] The above-mentioned intelligent housing health monitoring and assessment method has the ability of comprehensive analysis and dynamic evolution, and can conduct comprehensive and in-depth monitoring and assessment of houses, providing solid scientific support for the full life cycle management of houses, and continuously promoting the optimization and upgrading of the living environment.

[0138] See Figure 4 In an optional embodiment, to efficiently implement the intelligent housing health monitoring and assessment method provided by the present invention, the present invention further provides an intelligent housing health monitoring and assessment system, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, wherein the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the specific steps of the intelligent housing health monitoring and assessment method and related embodiments provided by the present invention. The intelligent housing health monitoring and assessment system of the present invention is structurally complete, objective, and stable.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A housing health intelligent monitoring and assessment method, characterized in that: The method comprises: Setting up a house structure monitoring subsystem according to the house structure condition, and monitoring the house in real time through the house structure monitoring subsystem to obtain initial house detection data; Constructing a housing monitoring data optimization mechanism to obtain three-dimensional detection information of the housing structure based on the initial detection data of the housing, and processing the three-dimensional detection information of the housing structure using the housing monitoring data optimization mechanism to obtain housing target detection information; Establishing a housing structure and environment assessment system, evaluating the housing by combining the housing structure and environment assessment system with the housing target detection information, and obtaining a housing analysis and assessment result; The housing condition is comprehensively evaluated in combination with the housing analysis and evaluation results and the housing target detection information to achieve intelligent monitoring and accurate evaluation of the housing health status.

2. The intelligent monitoring and evaluation method for house health according to claim 1, characterized in that: The housing structure monitoring subsystem is set up according to the housing structure condition, including: Configure sensors, layout sensors and design data transmission mode in the house structure monitoring subsystem according to the house structure conditions.

3. The intelligent monitoring and evaluation method for house health according to claim 1, characterized in that: The construction of the housing monitoring data optimization mechanism includes: Establish a housing information verification model and a housing 3D data adjustment model in the housing monitoring data optimization mechanism; A housing monitoring data optimization mechanism is constructed by combining the housing information verification model and the housing three-dimensional data adjustment model.

4. The intelligent monitoring and evaluation method for house health according to claim 3, characterized in that: The establishment of a housing information verification model and a housing three-dimensional data adjustment model in the housing monitoring data optimization mechanism includes: Establish a housing information inspection model based on 3D data features and housing spatial structure principles; The housing information verification model satisfies the following relationship: in, represents the average distance from the reference monitoring point to the neighboring monitoring points, An index that represents the influence of the geometric characteristics of the building surface, Indicates the number of neighboring monitoring points within the preset distance range of the reference monitoring point. Indicates the actual distance from the neighborhood monitoring point to the reference monitoring point, Indicates the total number of housing data monitoring points, Indicates the influence coefficient corresponding to the house surface color and material reflectivity; in, represents the standard deviation of the distance between the reference monitoring point and the mean, Indicates the total number of housing data monitoring points, Indicates the number of neighboring monitoring points within the preset distance range of the reference monitoring point. Indicates the actual distance from the neighborhood monitoring point to the reference monitoring point, Indicates the average distance from the reference monitoring point to the neighboring monitoring points.

5. The intelligent monitoring and evaluation method for house health according to claim 3, characterized in that: The establishment of a housing information verification model and a housing three-dimensional data adjustment model in the housing monitoring data optimization mechanism includes: Establish a three-dimensional data adjustment model of the house based on the structural characteristics of the house; The three-dimensional data adjustment model of the house satisfies the following relationship: in, Represents the three-dimensional data result of the house after registration, Represents the rotation coefficient of the three-dimensional data of the house, Represents the difference value of the three-dimensional data of the house, Indicates the translation factor of the 3D data of the house.

6. The intelligent monitoring and evaluation method for house health according to claim 3, characterized in that: The processing of the three-dimensional detection information of the house structure by using the house monitoring data optimization mechanism to obtain house target detection information includes: The three-dimensional detection information of the house structure is inspected by the house information inspection model in the house monitoring data optimization mechanism to obtain the three-dimensional detection information of the house structure after inspection optimization; The house three-dimensional data adjustment model in the house monitoring data optimization mechanism is used to adjust the three-dimensional detection information of the house structure after inspection and optimization to obtain house target detection information.

7. The intelligent monitoring and evaluation method for house health according to claim 1, characterized in that: The establishment of the housing structure and environment assessment system, the evaluation of the housing by combining the housing structure and environment assessment system and the housing target detection information, and the acquisition of the housing analysis and assessment results include: A housing structure evaluation layer and an indoor environment quality evaluation layer are set up in a housing structure and environment evaluation system based on the housing target detection information; The house is evaluated based on the house structure evaluation layer, the indoor environment quality evaluation layer and the house target detection information to obtain a house analysis and evaluation result.

8. The intelligent monitoring and evaluation method for house health according to claim 7, characterized in that: The housing structure evaluation layer and the indoor environment quality evaluation layer are set in the housing structure and environment evaluation system based on the housing target detection information, including: In the housing structure evaluation layer, a housing crack gap prediction model, a housing horizontal deformation calculation model, and a housing tilt degree and uneven settlement analysis model are set; The house crack gap prediction model satisfies the following relationship: in, Indicates the distance between adjacent cracks on the wall of a house. represents the constraint coefficient corresponding to the thickness of the building wall, represents the constraint coefficient corresponding to the end of the building wall, Indicates the restriction coefficient of foundation on cracks in house walls. Indicates the tensile stress value on the wall of the building. Indicates the ultimate tensile strain value of the house concrete; The building horizontal deformation calculation model satisfies the following relationship: in, Indicates the horizontal deformation value of the overall structure of the house. represents the horizontal distance of the overall structure of the house at time i, Indicates the initial horizontal distance of the overall structure of the house, Represents the error coefficient in the process of obtaining the overall structure of the house; The building tilt degree and the uneven settlement analysis model satisfy the following relationship: in, Represents the dependent variable of the building tilt and uneven settlement analysis model, Indicates the degree of building inclination and the slope of the uneven settlement analysis model. Indicates the independent variable of the building tilt and uneven settlement analysis model, The coefficient representing the degree of building tilt and the differential settlement analysis model.

9. The intelligent monitoring and evaluation method for house health according to claim 8, characterized in that: The establishment of the housing structure and environment assessment system, the evaluation of the housing by combining the housing structure and environment assessment system and the housing target detection information, and the acquisition of the housing analysis and assessment results include: The wall crack analysis results of the house are obtained through the house crack gap prediction model in the house structure evaluation layer; According to the house horizontal deformation calculation model in the house structure evaluation layer, the horizontal deformation analysis results of the house structure are obtained; The relationship between the building tilt angle and uneven settlement is analyzed using the building tilt degree and uneven settlement analysis model in the building structure evaluation layer. Obtaining indoor environmental parameter analysis results of the house based on the indoor environmental quality evaluation layer; A house analysis and evaluation result is obtained based on the wall crack analysis result, the horizontal deformation analysis result, the relationship analysis result and the indoor environmental parameter analysis result.

10. A house health intelligent monitoring and evaluation system, characterized in that: The system includes a processor, an input device, an output device and a memory, which are interconnected. The memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute the intelligent house health monitoring and assessment method according to any one of claims 1 to 9.

Images