Building exterior wall water seepage measurement method and system for building

Abstract

The invention belongs to the technical field of constructional engineering detection, and discloses a building exterior wall water seepage measurement method and system.The method comprises the steps that humidity sensor arrays are arranged on the surface and inside an exterior wall, multi-point humidity original data are collected, abnormity correction and noise reduction are conducted, and a complete preprocessing data set is obtained; inputting the data into a diffusion modeling module, carrying out time sequence analysis and spatial distribution estimation, generating a water seepage diffusion characteristic matrix and extracting key diffusion parameters, calculating a predicted value in a penetration depth estimation model based on a parameter set, constructing a water seepage distribution curve, calling a risk judgment module, and comparing a predicted result with a safety threshold. And outputting the risk level. The system comprises a data acquisition and preprocessing module, a diffusion modeling and feature extraction module and a penetration estimation and risk assessment module. According to the invention, dynamic monitoring and grading risk assessment of external wall water seepage can be realized, and a reliable basis is provided for building maintenance and safety management.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of building engineering detection, and particularly relates to a building outer wall water seepage measuring method and system. BACKGROUND

[0002] As an important protective layer of buildings against external environment, the durability of the outer wall of a building is directly related to the safety and service life of the building. However, under the action of environmental factors such as rain, humidity and temperature difference for a long time, the outer wall is prone to water seepage. The existing outer wall water seepage detection method mainly relies on manual inspection or single-point humidity sampling. Such methods have defects such as limited coverage, discontinuous data, and difficulty in reflecting the dynamic process of water seepage diffusion in real time. In addition, some methods can only give the local position of water seepage, and cannot quantify the penetration depth or form a risk classification result, resulting in lagging building maintenance strategy, increasing structural deterioration and safety hazards.

[0003] With the development of building intelligence and sensing technology, how to use the data of multi-point arranged sensors to realize the whole process monitoring, dynamic trend prediction and risk level evaluation based on quantitative indicators of outer wall water seepage through systematic modeling analysis method has become a technical problem urgently to be solved in the field of building structure safety. Therefore, a building outer wall water seepage measuring method and system which integrates data acquisition, diffusion modeling, depth prediction and risk evaluation is proposed, which has important engineering application value. SUMMARY

[0004] In order to achieve the above-mentioned purpose of the application, the application provides the following technical scheme: a building outer wall water seepage measuring method, comprising the following steps:

[0005] Step S1, arranging a humidity sensor array at preset monitoring points on the surface and internal structure of the building outer wall, collecting humidity original data of different spatial positions and different time instants in real time, and transmitting the data to a central processing unit; performing abnormal point detection, missing data correction and noise reduction on the original data in the central processing unit to obtain a complete and reliable preprocessed data set;

[0006] Step S2, inputting the preprocessed data set into a diffusion modeling module, using time series analysis and spatial distribution estimation method to fit and interpolate the dynamic process of the outer wall humidity change with time and space, generating a water seepage diffusion feature matrix, and extracting a key diffusion parameter set including water seepage rate, penetration lag time and spatial difference from the matrix;

[0007] Step S3, inputting the key diffusion parameter set into a penetration depth estimation model, calculating a predicted value of the outer wall penetration depth based on a nonlinear regression method, and constructing a water seepage distribution curve in the monitoring area according to the predicted result to reflect the water penetration at different positions and different times.

[0008] Step S4, based on the water seepage distribution curve, calling a dynamic risk judgment module, comparing the outer wall penetration depth prediction result with the building safety threshold, and combining the water seepage rate and the spatial difference, finally outputting the water seepage risk grade index, obtaining the water seepage detection and risk assessment result of the building outer wall.

[0009] Preferably, the step S1 further comprises:

[0010] A distributed humidity sensor array is arranged on the surface and internal structure layer of the building outer wall. The humidity original data set is formed by continuously collecting humidity original data at different positions and different times at each monitoring point. Since there are often sensor signal fluctuations, data missing, and abnormal values caused by sudden interference in the outer wall monitoring process, the humidity original data set needs to be systematically preprocessed in the central processing unit. The preprocessing process includes repairing missing data, removing abnormal data, and smoothing and reducing noise. Specifically, the neighborhood information of the collection point is corrected and compensated to ensure data integrity. The data points that do not conform to the overall change rule are removed by statistical discrimination of the mutation signal. The instability caused by transient interference is reduced by repeated sampling and smoothing algorithm. Finally, a preprocessed data set that can fully reflect the humidity change state of the building outer wall is obtained.

[0011] Preferably, the step S2 further comprises:

[0012] The processed humidity data set is input into the diffusion modeling module. The time series modeling method is used to fit the humidity change trend over time, and the spatial interpolation method is used to estimate the humidity of the area without sensors, thereby generating a humidity distribution map covering the entire monitoring range. In this process, the diffusion modeling module can comprehensively consider the spatial position relationship and time evolution law to gradually form a diffusion characteristic matrix that can describe the water seepage process of the outer wall. Through the diffusion characteristic matrix, not only the overall humidity distribution of the outer wall can be obtained, but also representative diffusion characteristic parameters in the water seepage process can be extracted. The diffusion characteristic parameters include the speed of water seepage diffusion, the delay effect of humidity at different levels, and the difference degree between different regions. The extraction of the above diffusion characteristic parameters provides key indicators for subsequent penetration depth prediction and enhances the explanatory power and adaptability of the model to complex water seepage behavior.

[0013] Preferably, the step S3 further comprises:

[0014] The key diffusion parameters obtained in step S2 are input into a penetration depth estimation model, a multivariate prediction relationship is established by using a nonlinear regression method, and a prediction value of the penetration depth of the outer wall is obtained, in the prediction process, the model not only considers the influence of the water seepage speed and the lag time on the depth, but also combines the difference degree of the spatial distribution, so that the fitting degree of the result to the actual situation is ensured, then, based on the penetration depth data obtained by prediction, a complete water seepage distribution curve is constructed at different monitoring points of the outer wall, the water seepage distribution curve can directly reflect the water seepage expansion with time and position, and the dynamic process of water seepage in the surface layer and the interior of the wall is shown, and through the water seepage distribution curve, the propagation trend of water seepage in the outer wall can be clearly identified, thereby providing an intuitive reference basis for building maintenance and repair.

[0015] Preferably, the step S4 further comprises:

[0016] The generated water seepage distribution curve is compared with a building structure safety threshold database, the penetration depth, the diffusion speed and the spatial distribution difference are comprehensively analyzed by a dynamic risk judgment module, a building outer wall water seepage risk grade index is generated, the building outer wall water seepage risk grade index not only considers the comparison between the penetration depth and the safety limit, but also combines the change of the diffusion speed and the stability of the humidity difference between different regions, so that a comprehensive risk judgment result is obtained;

[0017] According to the evaluation result, the building outer wall monitoring area can be divided into three categories of low risk, medium risk and high risk, and the corresponding detection conclusion is output, the low risk area indicates that the water seepage has little influence on the building structure, and the monitoring can be continued, the medium risk area prompts that preventive measures such as reinforcement and coating repair need to be taken, and the high risk area needs to be intervened and maintained immediately to avoid further damage to the safety of the outer wall structure, the grading mechanism realizes the whole process closed loop from data modeling to risk evaluation, so that the outer wall water seepage detection not only stays in the numerical analysis level, but also can be implemented to the specific risk prevention and control and engineering decision.

[0018] The application also provides a building outer wall water seepage measuring system for buildings, comprising the following modules:

[0019] The data acquisition and preprocessing module is used for laying a humidity sensor array on the surface and internal structure layer of the building outer wall, acquiring three-dimensional coordinates and humidity original data at different time points, and forming a complete preprocessed data set through abnormal point detection, missing value correction and noise reduction;

[0020] The diffusion modeling and feature extraction module is used for carrying out time series analysis and spatial interpolation estimation based on the preprocessed data set, generating a humidity distribution matrix covering the whole area, and extracting the water seepage rate, the lag time and the spatial variance;

[0021] A permeation estimation and risk assessment module is configured to calculate a predicted value of the permeation depth of the building outer wall based on the water infiltration rate, the lag time, and the spatial variance, generate a water infiltration distribution curve, compare the predicted value with a building safety threshold database, and finally output a water infiltration risk level and a safety assessment conclusion of the outer wall.

[0022] The data acquisition and preprocessing module specifically includes the following units:

[0023] A distributed humidity sensing unit is configured to arrange monitoring points on the surface layer and the internal structure layer of the outer wall to realize real-time acquisition of three-dimensional spatial coordinates and humidity values at different times. The distributed humidity sensing unit supports multi-point parallel sampling and can maintain high sensitivity response when the environmental humidity fluctuates rapidly, thereby ensuring that effective monitoring signals can be obtained in the early stage of water infiltration. In addition, the unit has a self-checking function and can identify and report failed and abnormal reading sampling points to ensure the long-term stability of the monitoring network.

[0024] A data correction unit is configured to perform preprocessing operations on the humidity raw data collected by the distributed humidity sensing unit, including missing value completion, abnormal value elimination, and noise reduction. The missing value completion is achieved through a weighted estimation method based on neighborhood information to maintain data continuity when some sensors temporarily fail. The abnormal value elimination identifies extreme values that deviate from the distribution rule through statistical methods to avoid their interference with the overall trend. The noise reduction is performed through multiple repeated sampling and smoothing algorithms to eliminate invalid fluctuation signals introduced by electromagnetic interference and environmental transient fluctuations.

[0025] A central processing unit is configured to uniformly gather and standardize the corrected humidity data and form a complete and computable preprocessed data set. The unit supports batch uploading and real-time synchronization of data to ensure that the remote server and the local host can obtain consistent data input under different working conditions. Through standardized processing, data of different sensor models and different sampling frequencies can be integrated under a unified coordinate and time reference to provide reliable input for subsequent modeling and analysis.

[0026] The diffusion modeling and feature extraction module specifically includes the following units:

[0027] A time series modeling unit is configured to fit and predict the preprocessed humidity data in the time dimension to form a trend curve of humidity change over time. The unit can capture short-term fluctuation patterns and long-term diffusion trends to reveal the evolution process of water infiltration at different stages. Through time series modeling, the future direction of humidity change can be inferred, and the possible peak time of water infiltration can be identified to provide pre-warning basis for subsequent risk assessment.

[0028] A spatial interpolation unit is configured to estimate humidity values in areas where sensors are not arranged, so as to generate a continuous humidity distribution map covering the entire monitoring area, the unit combines the difference between adjacent sensor points and the spatial position relationship to calculate the humidity of the unsampled points, ensuring that the entire range of the external wall is covered, the result of spatial interpolation not only reflects the distribution of humidity on the surface layer, but also reveals the spatial gradient of the penetration depth in the vertical direction, ensuring the three-dimensional integrity of the modeling result,

[0029] A feature parameter extraction unit is configured to extract key features describing the water seepage behavior from the generated humidity distribution matrix, including diffusion speed, time delay and spatial difference, the diffusion speed reflects the conduction efficiency of water seepage in the external wall material, the time delay reflects the time difference of water seepage reaching saturation state from the surface layer to different depths, and the spatial difference is used to measure the uniformity of water seepage at different monitoring positions.

[0030] The penetration estimation and risk assessment module specifically includes the following units:

[0031] A penetration depth prediction unit is configured to calculate the penetration depth prediction value of the external wall at different positions and time points based on the key feature parameters output by the diffusion modeling module, the unit can still give stable and reasonable prediction results in the presence of environmental noise and material heterogeneity through multi-dimensional parameter fitting, and the depth value predicted is constructed as a continuous curve in the entire monitoring area, which intuitively shows the distribution trend of water seepage along the space and the evolution law with time;

[0032] A safety threshold comparison unit is configured to compare the predicted penetration depth with the allowable value in the building safety specification, and form a comprehensive risk index in combination with the diffusion speed and the spatial difference, the unit can not only determine whether the water seepage exceeds the safety standard, but also evaluate the local risk it may cause, and through comprehensive comparison, it can provide graded early warning for building operation and maintenance personnel;

[0033] A risk output unit is configured to divide the external wall monitoring area into low-risk, medium-risk and high-risk levels according to the comprehensive risk index, and generate the corresponding risk assessment results, the low-risk area indicates that the water seepage sign is slight and has no obvious threat to the structure in the short term, the medium-risk area indicates that potential water seepage risk has occurred in the local area and needs to be arranged for key inspection, and the high-risk area means that the water seepage trend is significant and causes substantial impact on the durability and safety of the building, so repair and reinforcement measures need to be taken, the risk output unit also supports generating intuitive graphical reports and text reports, so that decision makers can quickly master the overall situation and develop corresponding disposal measures.

[0034] Compared with the prior art, the beneficial effects of the present application are:

[0035] The application can realize comprehensive monitoring of the water seepage state of the building outer wall through dynamic collection and preprocessing of multi-source humidity data of the building outer wall, effectively avoid the problems of limited monitoring range and result lag of traditional detection methods, and improve the accuracy and real-time performance of the detection results.

[0036] The application can quantitatively analyze the water seepage process by establishing a water seepage diffusion model and performing deep prediction, output the water seepage depth and diffusion range, thereby providing a clear technical basis for outer wall maintenance and reinforcement, and significantly improving the scientificity and accuracy of water seepage detection.

[0037] The application converts the detection results into low-risk, medium-risk and high-risk levels based on a water seepage risk level determination mechanism, can provide intuitive decision-making reference for building managers, promote the rational allocation of maintenance resources, and further improve the intelligentization and efficiency of building outer wall waterproof management. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A method step flowchart is provided for the present application;

[0039] Figure 2 A system module schematic diagram is provided for the present application. DETAILED DESCRIPTION

[0040] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0041] REFERENCE Figure 1 The embodiment of the present application provides a building outer wall water seepage measurement method for buildings, comprising the following steps:

[0042] Step one, humidity sensor array is laid on the surface and internal preset monitoring points of the building outer wall, the humidity original data of different spatial coordinates and different time points of the outer wall are collected in real time , and the original data is transmitted to a central processing unit for abnormal point detection, data loss correction and noise reduction to generate a cleaned preprocessing data set .

[0043] In step one, the measuring points are laid on the surface and internal structure layer of the outer wall by distributed capacitance humidity sensors, and the three-dimensional coordinates and different time points humidity data under the following formula, forming a humidity raw data set , the humidity raw data set There are missing values, outliers and noise signals in the collection process, so data preprocessing is needed in the central processing unit, which includes:

[0044] The missing values are corrected by the neighborhood weighting method, using the following interpolation formula:

[0045] ;

[0046] where, is the corrected humidity value, is the weight coefficient calculated according to the Euclidean distance, is the humidity value of the adjacent point;

[0047] The transient noise is reduced by repeated sampling and mean smoothing method;

[0048] The outliers are removed by boxplot statistical method;

[0049] Finally, the complete and accurate preprocessed data set .

[0050] Step two, input the preprocessed data set into the diffusion modeling module, use time series analysis and spatial distribution estimation method to fit and interpolate the dynamic process of external wall humidity change with time and space, get the water infiltration diffusion characteristic matrix , and extract the key diffusion parameter variable set including water infiltration rate, lag time and spatial variance from it ;

[0051] In step two, the preprocessed data set obtained in the previous step is input into the diffusion modeling module, and the autoregressive moving average model is used to fit the humidity change trend, to get the prediction curve of humidity change with time;

[0052] At the same time, the spatial interpolation method is used to estimate the humidity value of the monitoring points without sensors, forming the humidity distribution covering the entire monitoring area, generating the water infiltration diffusion characteristic matrix , wherein the formula of the spatial interpolation method is as follows:

[0053] ;

[0054] where, is the estimated humidity value, is the weight coefficient, which is calculated by the covariance function and satisfies , the corrected humidity value obtained in step S1;

[0055] the water seepage diffusion feature matrix :

[0056] ;

[0057] wherein, the water seepage diffusion feature matrix, , the water seepage position coordinates, the depth, the time point of water seepage, the preprocessed data set, , the weight coefficient, satisfying ;

[0058] based on the water seepage diffusion feature matrix , a set of key diffusion parameter variables in the water seepage diffusion process of the external wall is extracted , wherein, the water seepage rate is obtained by comparing the difference between the continuous time points, the lag time is obtained by analyzing the time difference of the humidity peak value at different depths , the spatial variance is obtained by the humidity distribution difference degree of different at the same time point.

[0059] Step three, inputting the set of key diffusion parameter variables into the permeation depth estimation model, the external wall permeation depth prediction value is calculated through nonlinear regression , and based on the prediction value, the water seepage distribution curve is constructed in the external wall monitoring area to reflect the water permeation situation of different positions at different times;

[0060] In step three, the set of key diffusion parameter variables is input into the permeation depth estimation model, and the permeation depth estimation model is a multivariate prediction model based on nonlinear regression, which is used to calculate the external wall permeation depth prediction value , and the specific formula is as follows:

[0061] ;

[0062] wherein, , , the fitting parameters are derived from historical building water seepage experiment samples training;

[0063] Based on the predicted depth of the outer wall penetration, the continuous depth value is calculated at different monitoring points, and the water penetration distribution curve is generated by curve fitting , to reflect the expansion trend of the overall water penetration in the outer wall range.

[0064] Step four, based on the water penetration distribution curve , call the dynamic risk judgment module, compare the outer wall penetration depth prediction value with the building structure safety threshold, and output the water penetration risk level index combined with the water penetration rate and spatial variance , to get the final water penetration detection and risk assessment result of the building outer wall;

[0065] In step four, the water penetration distribution curve is compared with the building safety threshold database, and the water penetration risk level index is generated by combining the outer wall penetration depth prediction value , the water penetration rate and the spatial variance through the dynamic risk judgment algorithm , the dynamic risk judgment algorithm adopts a weighted threshold function model, and the calculation formula is as follows:

[0066] ;

[0067] Among them, is the maximum penetration depth threshold allowed, which is derived from the building safety specification, is the critical water penetration rate threshold, which is derived from the experimental standard result, is the spatial reference variance, which is derived from the historical monitoring data, , , is the weight coefficient, and satisfies ;

[0068] Finally, the water penetration risk level index , when , the outer wall area is divided into low risk, when , the outer wall area is divided into medium risk, when , the outer wall area is divided into high risk, and the final water penetration detection and risk assessment result is output.

[0069] Referring to Figure 2 , the embodiment of the application provides a building outer wall water penetration measuring system for building, comprising the following modules:

[0070] 6. A building outer wall water penetration measuring system for building, characterized in that it comprises the following modules:

[0071] A data acquisition and preprocessing module is configured to lay humidity sensor arrays on the surface of the building outer wall and the internal structural layer, collect three-dimensional coordinate and humidity raw data at different time points, and form a complete preprocessed data set through abnormal point detection, missing value correction, and noise reduction.

[0072] In the data acquisition and preprocessing module, the data acquisition and preprocessing module realizes high-precision data acquisition and transmission through the distributed humidity sensor units pre-laid on the surface of the building outer wall and the internal structural layer. The sensor units are distributed in a grid manner at key positions of the outer wall, including water-permeable parts of the outer facade, window frame periphery, expansion joints, and thermal insulation layer interfaces, and sensors are embedded in the wall body at different depths to synchronously obtain humidity changes at different times in a three-dimensional coordinate system. Due to the influence of environmental factors such as rainfall, temperature, and wind direction, the humidity value may fluctuate sharply, so the sensor needs to have high sensitivity and fast response characteristics to ensure that it can capture early water seepage signals. In addition, the sensor unit is also equipped with self-detection and fault-tolerant functions, which can automatically identify abnormal states of the sensor during the acquisition process, such as sampling values exceeding the reasonable physical range or long-term non-response, thereby avoiding interference of false data on the overall trend judgment.

[0073] In the data transmission and aggregation process, the collected humidity raw data first enters the data correction unit, which is responsible for multi-level preprocessing of the raw data. For missing data caused by temporary failure of the sensor, the correction unit performs weighted estimation completion based on the information of the adjacent area of the sensor points, thereby maintaining the continuity and integrity of the data. For abnormal values affected by environmental disturbances, the correction unit identifies and removes them through statistical methods to ensure the reasonableness of the data set. For high-frequency noise or sudden fluctuations in the signal, the correction unit uses a combination of repeated sampling and smoothing processing to reduce noise and remove the influence of invalid fluctuations. Finally, the corrected data is uniformly aggregated to the central processing unit, where standardized processing and storage are completed to ensure that data collected by different types of sensors can be integrated under a unified time and space reference, thereby forming a complete preprocessed data set that can be used for subsequent analysis.

[0074] A diffusion modeling and feature extraction module is configured to perform time series analysis and spatial interpolation estimation based on the preprocessed data set, generate a humidity distribution matrix covering the entire area, and extract water seepage rate, lag time, and spatial variance.

[0075] In the diffusion modeling and feature extraction module, the diffusion modeling and feature extraction module is mainly used for dynamic modeling and spatial distribution analysis of the preprocessed humidity data. First, the time series modeling unit fits and predicts the humidity data collected at different positions in the time dimension. This unit uses multi-segment fitting to model short-term humidity fluctuations and long-term trends separately, which can accurately depict the evolution process of water seepage at different stages. Through this processing, not only the overall change of humidity value over time can be revealed, but also the future development direction of humidity can be predicted, and the possible peak time of water seepage can be identified to provide early warning for building maintenance.

[0076] Secondly, the spatial interpolation unit estimates the humidity of the area on the wall body where no sensor is set, generating a three-dimensional humidity distribution matrix covering the entire monitoring area. This unit estimates the humidity value of the unsampled points by combining the numerical difference of adjacent sensor points and their spatial position relationship, thereby realizing the spatial continuity of data. When the results are output, spatial interpolation can not only show the humidity distribution on the surface of the external wall, but also reveal the change gradient of the penetration depth in the vertical direction, and fully reflect the diffusion path and range of water seepage in space.

[0077] Finally, the feature parameter extraction unit extracts key feature indicators from the humidity distribution matrix, including water seepage diffusion speed, time delay and spatial difference degree. Among them, the diffusion speed is used to measure the conduction efficiency of water seepage in the external wall material, the time delay reflects the time difference required for water seepage to transfer from the surface layer to different depths, and the spatial difference degree is used to evaluate the uniformity of water seepage in different areas. Through the extraction and quantification of these feature parameters, the dynamic process and potential risks of external wall water seepage can be more accurately reflected, and necessary input data can be provided for penetration depth prediction and risk level assessment.

[0078] The penetration estimation and risk assessment module is used to calculate the penetration depth prediction value of the building external wall based on the water seepage rate, lag time and spatial variance, generate a water seepage distribution curve, and compare the prediction results with the building safety threshold database to finally output the water seepage risk level and external wall safety evaluation conclusion.

[0079] In the penetration estimation and risk assessment module, first, the penetration depth prediction unit calculates the penetration depth prediction value of different positions and different time points based on the diffusion speed, time delay and spatial difference degree output by the diffusion modeling and feature extraction module. This prediction not only considers the influence of environmental noise, but also takes into account the heterogeneity of wall materials, so it can generate stable and reliable prediction results and intuitively display the evolution law of water seepage over time through a spatial continuous curve.

[0080] Subsequently, the safety threshold comparison unit compares the predicted penetration depth value with the standard threshold value in the building safety specification, and combines the diffusion speed and the spatial difference to form a comprehensive risk index, which is used to measure whether the external wall water seepage has exceeded the allowed range and the potential risk degree. Through this comparison, the system can quantitatively determine the water seepage hidden danger and avoid the subjective judgment of relying on experience.

[0081] Finally, the risk output unit divides the monitoring area into low-risk, medium-risk and high-risk levels according to the results of the comprehensive risk index, and outputs the corresponding risk assessment conclusion. The low-risk area indicates that the water seepage sign is slight and has limited impact on the wall safety. The medium-risk area indicates that there may be potential water seepage hidden danger in the local area and needs to be arranged for regular re-inspection. The high-risk area indicates that the water seepage trend is significant and poses a threat to the durability of the building structure, so immediate repair measures need to be taken. The risk output unit can not only generate a graded conclusion, but also support the output of visual three-dimensional graphics and text reports to help building managers quickly grasp the overall risk situation and develop corresponding maintenance strategies.

[0082] It should be noted that the embodiments in the present application and the features and technical solutions in the embodiments can be combined with each other without conflict.

[0083] Obviously, the above-described embodiments are only some of the embodiments of the present application, not all the embodiments. The preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be realized in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some technical features. Any equivalent structure made by using the content of the present application specification and drawings, directly or indirectly applied to other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A method of measuring water penetration of an architectural exterior wall of a building, characterized by, The method comprises the following steps: Step S1, arranging a humidity sensor array at preset monitoring points on the surface of the building outer wall and the internal structure, collecting real-time humidity original data at different spatial positions and different time points, and transmitting the data to a central processing unit; In the central processing unit, the original data is subjected to outlier detection, missing data correction and noise reduction to obtain a complete and reliable preprocessed data set; Step S2, inputting the preprocessed data set into a diffusion modeling module, using time series analysis and spatial distribution estimation methods to fit and interpolate the dynamic process of the outer wall humidity change with time and space, generating a water seepage diffusion feature matrix, and extracting a key diffusion parameter set including water seepage rate, seepage lag time and spatial difference from the matrix; Step S3, inputting the key diffusion parameter set into a seepage depth estimation model, calculating the predicted value of the outer wall seepage depth based on a nonlinear regression method, and constructing a water seepage distribution curve in the monitoring area according to the prediction result to reflect the water seepage situation at different positions and different times; Step S4, based on the water seepage distribution curve, calling a dynamic risk judgment module, comparing the outer wall seepage depth prediction result with the building safety threshold, and combining the water seepage rate and spatial difference, finally outputting a water seepage risk grade index to obtain the water seepage detection and risk assessment result of the building outer wall.

2. The method for measuring water penetration of an exterior wall of a building according to claim 1, wherein The step S1 further comprises: A distributed humidity sensor array is arranged on the surface of the building outer wall and the internal structure layer, and humidity original data at different positions and different time points is continuously collected by each monitoring point to form a humidity original data set. Since there are often sensor signal fluctuations, data missing, and abnormal values caused by sudden interference in the outer wall monitoring process, the humidity original data set needs to be systematically preprocessed in the central processing unit. The preprocessing process includes repairing missing data, removing abnormal data, and smoothing and reducing noise. Specifically, the neighborhood information of the collection point is corrected and compensated to ensure the integrity of the data. The data points that do not conform to the overall change rule are removed by statistical discrimination of the mutation signal. The instability caused by transient interference is reduced by repeated sampling and smoothing algorithm. Finally, a preprocessed data set that can fully reflect the humidity change state of the building outer wall is obtained.

3. The method for measuring water penetration of an exterior wall of a building according to claim 1, wherein The step S2 further comprises: The processed humidity dataset is input into the diffusion modeling module, which uses time series modeling methods to fit the trend of humidity change over time, and combines spatial interpolation methods to estimate the humidity of areas without sensors, thereby generating a humidity distribution map covering the entire monitoring range. In this process, the diffusion modeling module can consider the spatial position relationship and time evolution law, and gradually form a diffusion characteristic matrix that can describe the water seepage process of the external wall. Through the diffusion characteristic matrix, not only the overall humidity distribution of the external wall can be obtained, but also representative diffusion characteristic parameters in the water seepage process can be extracted, including the speed of water seepage diffusion, the delay effect of humidity at different levels, and the difference degree between different regions. The extraction of the above diffusion characteristic parameters provides key indicators for subsequent penetration depth prediction, and also enhances the explanatory power and adaptability of the model to complex water seepage behavior.

4. The method for measuring water penetration of an exterior wall of a building according to claim 1, wherein The step S3 further comprises: The key diffusion parameters obtained in step S2 are input into the penetration depth estimation model, and a multivariate prediction relationship is established using nonlinear regression methods to obtain the predicted value of the penetration depth of the external wall. In this prediction process, the model not only considers the influence of water seepage speed and lag time on depth, but also considers the difference degree of spatial distribution to ensure the fitting degree of the result to the actual situation. Subsequently, based on the predicted penetration depth data, a complete water seepage distribution curve is constructed at different monitoring points of the external wall. The water seepage distribution curve can directly reflect the water seepage expansion with time and position, and show the dynamic process of water seepage in the surface and internal layers of the wall. Through the water seepage distribution curve, the propagation trend of water seepage in the external wall can be clearly identified, providing an intuitive reference for building maintenance and repair.

5. The method for measuring water penetration of an exterior wall of a building according to claim 1, wherein The step S4 further comprises: The generated water seepage distribution curve is compared with the building structure safety threshold database, and the penetration depth, diffusion speed and spatial distribution difference are comprehensively analyzed by the dynamic risk judgment module to generate a building external wall water seepage risk level index. The building external wall water seepage risk level index not only considers the comparison between the penetration depth and the safety limit, but also integrates the change of the diffusion speed and the stability of the humidity difference between different regions, thereby obtaining a comprehensive risk judgment result. According to the evaluation result, the building external wall monitoring area can be divided into low risk, medium risk and high risk three categories, and the corresponding detection conclusion is output. The low risk area indicates that the water seepage has little effect on the building structure, and can continue to be monitored. The medium risk area suggests that preventive measures such as reinforcement and coating repair should be taken. The high risk area needs immediate intervention and maintenance to avoid further damage to the safety of the external wall structure. This grading mechanism realizes the whole process closed loop from data modeling to risk assessment, so that the external wall water seepage detection not only stays at the numerical analysis level, but also can be implemented in specific risk prevention and engineering decision-making.

6. A building building exterior wall water permeation measuring system characterized by, The following modules are included: The data acquisition and preprocessing module is used for laying a humidity sensor array on the surface of the building outer wall and the internal structure layer, collecting three-dimensional coordinates and humidity original data at different time points, and forming a complete preprocessed data set through abnormal point detection, missing value correction and noise reduction; The diffusion modeling and feature extraction module is used for performing time series analysis and spatial interpolation estimation based on the preprocessed data set, generating a humidity distribution matrix covering the whole area, and extracting the water seepage rate, lag time and spatial variance; The permeation estimation and risk assessment module is used for calculating the permeation depth prediction value of the building outer wall based on the water seepage rate, lag time and spatial variance, generating a water seepage distribution curve, and comparing the prediction result with a building safety threshold database, and finally outputting a water seepage risk grade and an outer wall safety evaluation conclusion.

7. A building exterior wall water leakage measuring system according to claim 6, wherein The data acquisition and preprocessing module specifically includes the following units: The distributed humidity sensing unit is used for laying monitoring points on the surface layer and the internal structure layer of the outer wall, realizing real-time collection of three-dimensional spatial coordinates and humidity values at different times, and supporting multi-point parallel sampling. The distributed humidity sensing unit can maintain high sensitivity response when the environmental humidity fluctuates rapidly, thereby ensuring that effective monitoring signals can be obtained in the early stage of water seepage. Meanwhile, the unit has a self-checking function, which can identify and report failed and abnormal reading sampling points, thereby ensuring the long-term stability of the monitoring network. The data correction unit is used for pre-processing the humidity original data collected by the distributed humidity sensing unit, including missing value completion, abnormal value elimination and noise reduction. The missing value completion is realized by a weighted estimation method based on neighborhood information, which can maintain data continuity when some sensors temporarily fail. The abnormal value elimination identifies extreme values deviating from the distribution rule through statistical methods, thereby avoiding interference with the overall trend. The noise reduction is performed through multiple repeated sampling and smoothing algorithm to eliminate invalid fluctuation signals introduced by electromagnetic interference and environmental transient fluctuations. The central processing unit is used for uniformly gathering and standardizing the corrected humidity data, forming a complete preprocessed data set that can be used for calculation. The unit supports batch uploading and real-time synchronization of data, ensuring that the remote server and local host can obtain consistent data input under different working conditions. Through standardized processing, data of different sensor models and different sampling frequencies can be integrated under a unified coordinate and time reference, providing reliable input for subsequent modeling and analysis.

8. The building exterior wall water leakage measuring system according to claim 6, wherein The diffusion modeling and feature extraction module specifically includes the following units: The time series modeling unit is used for fitting and predicting the preprocessed humidity data in the time dimension, forming a trend curve of humidity change over time. The unit can capture short-term fluctuation patterns and long-term diffusion trends, thereby revealing the evolution process of water seepage at different stages. Through time series modeling, the future direction of humidity change can be inferred, and the possible water seepage peak time can be identified, thereby providing pre-warning basis for subsequent risk assessment. The space interpolation unit is configured to estimate humidity values in areas where sensors are not arranged, so as to generate a continuous humidity distribution map covering the entire monitoring area. The unit combines the differences in values and spatial positions of adjacent sensing points to calculate the humidity of unsampled points, so as to ensure that the entire range of the external wall is covered. The result of the space interpolation not only reflects the distribution of humidity on the surface, but also reveals the spatial gradient of the penetration depth in the vertical direction, thereby ensuring the three-dimensional integrity of the modeling result. The feature parameter extraction unit is configured to extract key features describing the water seepage behavior from the generated humidity distribution matrix, including diffusion speed, time delay and spatial difference. The diffusion speed reflects the conduction efficiency of water seepage in the external wall material, the time delay reflects the time difference of water seepage reaching saturation state from the surface to different depths, and the spatial difference is used to measure the uniformity of water seepage at different monitoring positions.

9. The building exterior wall water leakage measuring system according to claim 6, wherein The penetration estimation and risk assessment module specifically includes the following units: The penetration depth prediction unit is configured to calculate the penetration depth prediction value of the external wall at different positions and time points based on the key feature parameters output by the diffusion modeling module. The unit can still give stable and reasonable prediction results in the presence of environmental noise and material heterogeneity through multi-dimensional parameter fitting. The predicted depth values are constructed into continuous curves in the entire monitoring area, which intuitively show the distribution trend of water seepage along the space and the evolution law over time. The safety threshold comparison unit is configured to compare the predicted penetration depth with the allowable value in the building safety specification, and form a comprehensive risk index by combining the diffusion speed and spatial difference. The unit can not only determine whether the water seepage exceeds the safety standard, but also assess the local risks that may be caused by the water seepage. Through comprehensive comparison, the unit can provide graded early warning for building operation and maintenance personnel. The risk output unit is configured to divide the external wall monitoring area into low-risk, medium-risk and high-risk levels according to the comprehensive risk index, and generate corresponding risk assessment results. The low-risk area indicates that the water seepage is slight and does not pose a significant threat to the structure in the short term. The medium-risk area indicates that potential water seepage risks have occurred in the local area, which needs to be arranged for key inspection. The high-risk area means that the water seepage trend is significant, which causes substantial impact on the durability and safety of the building, and needs to be repaired and reinforced. The risk output unit also supports generating intuitive graphical reports and text reports, so that decision makers can quickly grasp the overall situation and develop corresponding disposal measures.

Images