A vertical detection method and system for load-bearing walls of house building

Abstract

The application discloses a vertical detection method and system for a load-bearing wall of a house building, relates to the technical field of building structure safety monitoring, and collects construction data in real time according to a three-dimensional drawing of the building and a sensor group installed on the load-bearing wall, pre-processes to obtain a building information data group, and stores the building information data group in a time sequence storage library; load-bearing wall balance evaluation is carried out through calculation of a building multi-scale balance index DPH; when the load-bearing wall balance evaluation is found to be inclined, a load-bearing wall structure prediction model is constructed, the building multi-scale balance index DPH is inputted, a multi-scale balance prediction index YCH is calculated, the future time load-bearing wall structure safety is accurately predicted and evaluated again; the sampling frequency and data fusion weight of the system are automatically adjusted according to real-time monitoring results, and a broken line chart is used for display, real-time monitoring, accurate evaluation and feedback optimization are realized, and the safety and stability of the load-bearing wall structure of the house building are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building structure safety monitoring, in particular to a vertical detection method and system for a building load-bearing wall. BACKGROUND

[0002] With the continuous acceleration of urbanization, various high-rise and super high-rise buildings have increased rapidly, and building structure safety problems have gradually attracted the attention of all sectors of society. In recent years, due to the inclination of building load-bearing walls, uneven settlement of foundations, and external environmental factors, housing safety accidents have occurred frequently, not only threatening the safety of residents' lives and property, but also causing social public concern about the effectiveness of building safety and management system. Therefore, it is urgent to establish a more refined, high-precision, real-time and predictable building load-bearing wall vertical safety detection system to ensure the safety of urban living environment and reduce social problems and economic losses caused by structural safety hazards.

[0003] The building load-bearing wall, as an important load-bearing component in building structure, its verticality directly affects the overall stability and safety of the building. The traditional detection method generally uses conventional sensors and static measurement methods, which are not easy to obtain high-precision structural posture data and micro-change information of the foundation, making it difficult to detect early subtle hazards. Secondly, most of the existing technologies use single factor analysis, lack of multi-scale fusion evaluation of building structure stability influencing factors, and are not easy to fully reflect the real state of the building. In addition, the current detection system generally lacks accurate structure prediction model and dynamic feedback adjustment mechanism, which makes it difficult to make early risk prediction and real-time optimization feedback in actual application, seriously restricting the protection ability of the load-bearing wall structure safety. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a vertical detection method and system for a building load-bearing wall, which solves the problems in the background art.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme: a vertical detection system for a building load-bearing wall, comprising a building data acquisition module, a stability analysis module, a fusion analysis module, a structure prediction module and an optimization feedback module;

[0006] The building data acquisition module is used to collect the construction data of the building in real time according to the three-dimensional drawing of the building and the sensor group installed on the building load-bearing wall, to preprocess the building information data set and store it in the time sequence storage library;

[0007] The stability analysis module is used to calculate the vertical posture offset index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz according to the building information data set;

[0008] The fusion analysis module is configured to calculate the vertical attitude offset index jqx, the foundation fractal settlement index sbj, and the wind shear twist index fnz, obtain a building multi-scale balance index DPH, and perform a bearing wall balance evaluation with a preset first structure alert threshold A and a second structure alert threshold B.

[0009] The structure prediction module is configured to, when the bearing wall balance evaluation indicates that there is a tilt, construct a bearing wall structure prediction model, input the building multi-scale balance index DPH into the bearing wall structure prediction model, output a multi-scale balance prediction index YCH, and perform a predicted structure balance evaluation with the preset second structure alert threshold B.

[0010] The optimization feedback module is configured to automatically adjust the sampling frequency and the data fusion weight of the system, and record and display the building multi-scale balance index DPH change rate in the 30 days before the warning.

[0011] Preferably, the building data acquisition module includes a data acquisition unit, a preprocessing unit, and a data storage unit.

[0012] The data acquisition unit is configured to acquire the construction data of the house building in real time according to the three-dimensional drawing of the building and the sensor group installed on the bearing wall of the building.

[0013] The sensor group includes a quantum tunneling effect sensor, an ultra-resolution optical range finder, a foundation pressure sensor, a foundation resistivity tester, and a laser gyroscope.

[0014] Preferably, the preprocessing unit is configured to perform denoising, data correction, outlier detection, data time synchronization, dimensionless processing, and moment of inertia analysis processing on the acquired construction data to obtain a building information data set.

[0015] The moment of inertia analysis processing is configured to calculate the moment of inertia it of the bearing wall section according to the three-dimensional drawing of the building to obtain the bearing wall section width kd and the bearing wall section height gd, and specifically: and then calculate the polar moment of inertia jt of the bearing wall according to the obtained moment of inertia it of the bearing wall section, and specifically: , where it(x) and it(y) represent the lateral and longitudinal moments of inertia of the bearing wall, respectively.

[0016] The building information data set includes an offset data set, a foundation data set, and a material data set.

[0017] The offset data set includes a building top micro-displacement qm, a building vertical offset oh, and a building height H.

[0018] The foundation data set includes a foundation pressure gradient ps, a foundation resistivity rd, and a foundation diameter db.

[0019] The material data set comprises a load-bearing wall material elastic modulus ec, a load-bearing wall section moment of inertia it, a load-bearing wall polar moment of inertia jt, a wind shear force induced torsional angular velocity ws, a load-bearing wall section area ac and a load-bearing wall material shear modulus fc;

[0020] The data storage unit is used to build a time sequence storage library and store the obtained building information data set into the time sequence storage library in real time through a local area network.

[0021] Preferably, the stability analysis module comprises a vertical offset analysis unit, a foundation fractal analysis unit and a torsion analysis unit.

[0022] The vertical offset analysis unit is used to obtain a vertical attitude offset index jqx through summarized calculation according to the obtained offset data set.

[0023] The vertical attitude offset index jqx is obtained through the following formula:

[0024] ;

[0025] In the formula, arctan represents an inverse tangent function, cos represents a cosine function, represents a minimum value for preventing the denominator from being zero, a represents a tilt correction factor, and is corrected through historical data;

[0026] The foundation fractal analysis unit is used to obtain a foundation fractal settlement index sbj through summarized calculation according to the obtained foundation data set.

[0027] The foundation fractal settlement index sbj is obtained through the following formula:

[0028] ;

[0029] In the formula, ln represents a logarithmic function, e represents an exponential function, b represents a foundation stability correction factor, t represents a time variable, and dt represents a time differential.

[0030] The torsion analysis unit is used to obtain a wind shear torsion index fnz through summarized calculation according to the obtained material data set.

[0031] The wind shear torsion index fnz is obtained through the following formula:

[0032] ;

[0033] In the formula, ws(h) represents a wind shear force distribution at different heights h, dh represents a height differential, sin represents a sine function, and a represents a wind load correction factor.

[0034] Preferably, the fusion analysis module comprises a load-bearing wall balance analysis unit and a load-bearing wall structure evaluation unit;

[0035] The load-bearing wall balance analysis unit is configured to perform a summary calculation based on the acquired vertical attitude deviation index jqx, the foundation fractal settlement index sbj, and the wind shear torsion index fnz, to obtain a building multi-scale balance index DPH.

[0036] ;

[0037] In the formula, tanh represents the hyperbolic sine function, and c represents a correction parameter of the building spatiotemporal multi-scale balance index, which is set by experimental data.

[0038] Preferably, the load-bearing wall structure evaluation unit is configured to calculate all historical building multi-scale balance indexes DPH based on all historical building information data groups in the time-series storage library, and calculate the mean value and the standard deviation of the historical building multi-scale balance indexes DPH by using a statistical method, and set a first structure alert threshold A and a second structure alert threshold B based on the mean value and the standard deviation , specifically as , wherein k represents an alert coefficient, and the building multi-scale balance index DPH is used to perform load-bearing wall balance evaluation, and the specific evaluation scheme is as follows.

[0039] When the building multi-scale balance index DPH is less than the first structure alert threshold A, the load-bearing wall does not exist in a tilt, and the structure is stable.

[0040] When the first structure alert threshold A is less than or equal to the building multi-scale balance index DPH and less than the second structure alert threshold B, the load-bearing wall exists in a tilt, and a tilt structure prediction is performed.

[0041] When the building multi-scale balance index DPH is greater than or equal to the second structure alert threshold B, the load-bearing wall tilt reaches a critical point of instability, and there is a risk of collapse, in which case risk warning information is generated, and relevant agencies are immediately notified to intervene in repair and demolition through a local area network.

[0042] Preferably, the structure prediction module comprises a model construction unit and a prediction evaluation unit.

[0043] The model construction unit is configured to perform a tilt structure prediction when the load-bearing wall balance evaluation indicates that the load-bearing wall exists in a tilt.

[0044] The inclined structure prediction is used to construct a bearing wall structure prediction model according to a nonlinear chaotic dynamics model and a fractal geometry model, and all bearing wall inclined and non-inclined building information data sets in the historical time sequence repository are labeled and input into the bearing wall structure prediction model for segmentation training and verification, the bearing wall structure prediction model is optimized, and then the real-time acquired building multi-scale balance index DPH is input into the bearing wall structure prediction model, and a multi-scale balance prediction index YCH is output according to the bearing wall structure prediction model, as follows:

[0045] ;

[0046] In the formula, DPH(t) represents the building space-time multi-scale balance index at time t, e represents an exponential function, represents a nonlinear adjustment coefficient of the building multi-scale balance index DPH, sin represents a sine function, represents a circular constant, and the value is two decimal places, tanh represents a hyperbolic sine function, log represents a logarithmic function, and w represents a perturbation correction coefficient.

[0047] Preferably, the prediction evaluation unit is used to perform structure balance evaluation according to the acquired multi-scale balance prediction index YCH and a preset second structure alert threshold B, and the specific evaluation scheme is as follows:

[0048] When the multi-scale balance prediction index YCH is less than the preset second structure alert threshold B, it indicates that the bearing wall structure at future time t is not damaged, and the building structure is safe and has no influence;

[0049] When the multi-scale balance prediction index YCH is greater than or equal to the preset second structure alert threshold B, it indicates that the bearing wall structure at future time t is damaged, and building risk warning information is generated at this time, and relevant agencies are notified to intervene in repair through a local area network.

[0050] Preferably, the optimization feedback module comprises an adaptive adjustment unit and a feedback optimization unit.

[0051] The adaptive adjustment unit is used to automatically adjust the sampling frequency and data fusion weight of the detection system according to the structure characteristics, environmental influence factors and measurement accuracy requirements of different building bearing walls.

[0052] The feedback optimization unit is used to record the building multi-scale balance index DPH change rate of the current time and the 30 days before the warning when the system detects that the building multi-scale balance index DPH exceeds the preset safety threshold, and uses a broken line chart to display the building multi-scale balance index DPH fluctuation in the past 30 days, and then compares the change trend of the building multi-scale balance index DPH before and after reinforcement to evaluate the effectiveness of the reinforcement measures.

[0053] A method for vertical detection of load-bearing walls in building construction includes the following steps:

[0054] S1. Based on the 3D architectural drawings and the sensor group installed on the load-bearing walls of the building, the construction data of the building is collected in real time, preprocessed to obtain the building information data group, and stored in the time series storage repository.

[0055] S2. Calculate the vertical attitude offset index jqx, foundation fractal settlement index sbj, and wind shear torsion index fnz based on the building information data set;

[0056] S3. The vertical attitude offset index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz are correlated and calculated to obtain the building multi-scale balance index DPH, and the load-bearing wall balance is evaluated with the preset first structural alert threshold A and the second structural alert threshold B.

[0057] S4. When the load-bearing wall balance assessment indicates that there is tilt, construct a load-bearing wall structure prediction model, input the building multi-scale balance index DPH into the load-bearing wall structure prediction model, output the multi-scale balance prediction index YCH, and then perform a predicted structural balance assessment with the preset second structural alert threshold B.

[0058] S5. Automatically adjust the system's sampling frequency and data fusion weights, record and display the building multi-scale equilibrium index (DPH) change rate for the 30 days prior to the warning.

[0059] This invention provides a method and system for vertical detection of load-bearing walls in building construction. It offers the following advantages:

[0060] (1) The system's building data acquisition module acquires high-precision and multi-dimensional building data in real time, dynamically perceives and preprocesses the structural status of the building's load-bearing walls, and obtains building information data sets. Utilizing novel high-precision sensing devices such as quantum tunneling effect sensors, super-resolution optical rangefinders, and foundation pressure sensors, it collects parameters including micro-displacement of the building roof, vertical offset, foundation pressure gradient and resistivity, and torsional angular velocity caused by wind shear force. These diverse parameters enable the system to accurately identify subtle abnormal changes in the building structure, achieving refined storage and efficient management of building structural data, laying a solid data foundation for subsequent accurate analysis and prediction.

[0061] (2) The system stability analysis module calculates the innovative vertical posture offset index jqx, the foundation fractal settlement index sbj and the wind shear twist index fnz based on the building information data set, and performs quantitative analysis on the unique indexes, accurately identifies the subtle changes of the building structure stability, and is especially suitable for structure safety evaluation under complex environment and extreme conditions; the fusion analysis module further comprehensively operates the three indexes to obtain the building multi-scale balance index DPH, and performs bearing wall balance evaluation with the preset first structure alert threshold A and the second structure alert threshold B, and through complex multi-dimensional correlation analysis, the safety state of the bearing wall structure is more comprehensively evaluated. Especially with the help of the preset structure alert threshold determination mechanism, the stability grade of the bearing wall structure is clearly divided, the potential structure risk is found in time and accurately, the reliability of the safety warning is greatly improved, and the risk of building structure inclination instability or even collapse is prevented.

[0062] (3) The system structure prediction module and the optimization feedback module further deepen the function of the system, the bearing wall structure prediction model is established by introducing the nonlinear chaotic dynamics and the fractal geometry model, the future stability state of the bearing wall structure is predicted, and the multi-scale balance prediction index YCH is used to develop active prevention measures in time. At the same time, the detection frequency and the data fusion mode are adaptively adjusted, and the optimization effect is monitored and fed back in real time. Through the long-term trend analysis and visual comparison of the building multi-scale balance index DPH, the system not only realizes the active intervention and efficient feedback of the building structure safety, but also can scientifically evaluate the effectiveness of the reinforcement and repair measures, and ensures the long-term safe operation of the building. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 A flowchart of a vertical detection system for a bearing wall of a house building according to the present application;

[0064] Figure 2 A flowchart of a vertical detection method for a bearing wall of a house building according to the present application;

[0065] Figure 3 A vertical detection system for a bearing wall of a house building according to the present application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0067] Embodiment 1

[0068] Please refer toFigure 1 The application provides a vertical detection system for a load-bearing wall of a house building, and achieves the above object through the following technical scheme: comprising a building data acquisition module, a stability analysis module, a fusion analysis module, a structure prediction module and an optimization feedback module;

[0069] The building data acquisition module is used for collecting the construction data of the house building in real time according to a three-dimensional drawing of the building and a sensor group installed on the load-bearing wall of the building, performing preprocessing to obtain a building information data group, and storing the building information data group to a time sequence storage library;

[0070] The stability analysis module is used for calculating a vertical posture deviation index jqx, a foundation fractal settlement index sbj and a wind shear torsion index fnz according to the building information data group;

[0071] The fusion analysis module is used for associatively calculating the vertical posture deviation index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz, obtaining a building multiscale balance index DPH, and performing load-bearing wall balance evaluation with a preset first structure alert threshold A and a second structure alert threshold B;

[0072] The structure prediction module is used for constructing a load-bearing wall structure prediction model when the load-bearing wall balance evaluation is that there is an inclination, inputting the building multiscale balance index DPH into the load-bearing wall structure prediction model, outputting a multiscale balance prediction index YCH, and performing prediction structure balance evaluation with the preset second structure alert threshold B;

[0073] The optimization feedback module is used for automatically adjusting the sampling frequency and the data fusion weight of the system, recording and showing the building multiscale balance index DPH change rate in the 30 days before the early warning.

[0074] In this embodiment, the building data acquisition module realizes real-time, accurate and efficient collection of building bearing wall state information. By combining building three-dimensional drawings with advanced sensors, real-time building construction data is obtained, and after preprocessing, building information data sets are obtained and stored in a time series database. This method significantly improves the data richness, accuracy and real-time performance of building bearing wall safety monitoring. Compared with traditional single or periodic manual detection methods, it effectively reduces information lag and errors, ensuring the timeliness and comprehensiveness of the data. The stability analysis module and the fusion analysis module calculate the vertical attitude deviation index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz innovatively, and form the building multi-scale balance index DPH by fusion, which significantly improves the comprehensiveness and accuracy of structural safety evaluation. Compared with traditional technology that can only analyze settlement or deviation, this system first realizes the evaluation of building structure stability from multiple dimensions and multiple factors, avoiding the limitations and misjudgment risks brought by single-factor detection. Through the bearing wall balance evaluation with the preset first structure alert threshold A and the second structure alert threshold B, the system timely identifies the structural inclination or unstable tendency of the bearing wall, significantly enhancing the timeliness and reliability of safety risk warning. The cooperation of the structure prediction module and the optimization feedback module further improves the system's active intervention and self-optimization capabilities. When structural inclination risks are found, the system uses nonlinear chaotic dynamics and fractal geometry theory to construct a structure prediction model, outputting a multi-scale balance prediction index YCH to accurately predict the future state of the bearing wall structure and take appropriate measures as soon as possible; at the same time, by automatically adjusting the sampling frequency and data fusion weight of the system, dynamic optimization is realized. Compared with traditional passive warning or even ineffective repair methods, the active and forward-looking prediction and feedback capabilities of this system significantly improve the control efficiency of building structure safety, realizing a significant shift from post-disposal to active prevention, and ensuring the long-term structural safety of buildings.

[0075] Embodiment 2

[0076] This embodiment is an explanation and description in Embodiment 1, please refer to Figure 1 and Figure 3 , in particular: the building data acquisition module includes a data acquisition unit, a preprocessing unit and a data storage unit;

[0077] The data acquisition unit is used to collect the construction data of the building in real time according to the building three-dimensional drawings and the sensor group installed on the building bearing wall;

[0078] The sensor group includes a quantum tunneling effect sensor, an ultra-resolution optical range finder, a foundation pressure sensor, a foundation resistivity tester and a laser gyroscope;

[0079] The quantum tunneling effect sensor is used to obtain the building top micro-displacement qm;

[0080] The super-resolution optical range finder is used to acquire the building vertical offset oh;

[0081] The foundation pressure sensor is used to acquire the foundation pressure gradient ps;

[0082] The foundation resistivity tester is used to acquire the foundation resistivity rd;

[0083] The laser gyroscope is used to acquire the torsional angular velocity ws caused by wind shear force;

[0084] The building material elastic modulus ec and the building material shear modulus fc are directly acquired through building material properties;

[0085] The building height L, the bearing wall cross-sectional area ac, the foundation diameter db, the bearing wall cross-sectional width kd and the bearing wall cross-sectional height gd are directly acquired through three-dimensional drawings.

[0086] The preprocessing unit is used to denoise, data correction, outlier detection, data time synchronization, dimensionless processing and moment of inertia analysis processing on the collected construction data, to acquire the building information data set;

[0087] The moment of inertia analysis processing is used to calculate the bearing wall cross-sectional moment of inertia it according to the bearing wall cross-sectional width kd and the bearing wall cross-sectional height gd acquired from the three-dimensional drawing of the building, specifically: And then calculate the bearing wall polar moment of inertia jt according to the acquired bearing wall cross-sectional moment of inertia it, specifically: , wherein it(x) and it(y) represent the bearing wall transverse cross-sectional moment of inertia and longitudinal cross-sectional moment of inertia, respectively;

[0088] The building information data set includes offset data set, foundation data set and material data set;

[0089] The offset data set includes building top micro-displacement qm, building vertical offset oh and building height H;

[0090] The foundation data set includes foundation pressure gradient ps, foundation resistivity rd and foundation diameter db;

[0091] The material data set includes bearing wall material elastic modulus ec, bearing wall cross-sectional moment of inertia it, bearing wall polar moment of inertia jt, torsional angular velocity ws caused by wind shear force, bearing wall cross-sectional area ac and bearing wall material shear modulus fc;

[0092] The data storage unit is used to construct a time series storage library, and store the acquired building information data set in real time to the time series storage library through a local area network.

[0093] In this embodiment, the building data acquisition module significantly improves the accuracy, timeliness and effectiveness of the bearing wall monitoring data by fusing the data acquisition unit, the preprocessing unit and the data storage unit. In particular, combined with high-precision sensing equipment such as quantum tunneling effect sensors, super-resolution optical range finders, foundation pressure sensors, foundation resistivity testers and laser gyroscopes, the microstate construction data including building top micro-displacement, vertical offset, foundation pressure gradient, foundation resistivity and torsional angular velocity caused by wind shear force can be accurately captured. In addition, through the preprocessing unit, the data is denoised, corrected, abnormality detected, time synchronized, dimensionless and inertia moment analyzed, forming a comprehensive building information data set of offset, foundation and material data, which is then stored in real time to the time series database through the local area network, so that subsequent analysis has the advantages of high efficiency, accuracy and instant response. This detailed and multi-dimensional data processing and storage mechanism fully solves the problems of data isolation, insufficient accuracy and information lag in traditional detection technology, greatly improving the comprehensive performance and reliability of bearing wall safety monitoring.

[0094] Embodiment 3

[0095] This embodiment is an explanation and description in embodiment 2, please refer to Figure 1 and Figure 3 , specifically: the stability analysis module includes a vertical offset analysis unit, a foundation fractal analysis unit and a torsion analysis unit;

[0096] The vertical offset analysis unit is used to calculate the vertical attitude offset index jqx according to the obtained offset data set;

[0097] The vertical attitude offset index jqx is calculated by the following formula:

[0098] ;

[0099] In the formula, arctan represents the inverse tangent function, cos represents the cosine function, represents the minimum value to prevent the denominator from being zero, and a represents the inclination correction factor, which is corrected by historical data;

[0100] The foundation fractal analysis unit is used to calculate the foundation fractal settlement index sbj according to the obtained foundation data set;

[0101] The foundation fractal settlement index sbj is calculated by the following formula:

[0102] ;

[0103] In the formula, ln represents the logarithmic function, e represents the exponential function, b represents the foundation stability correction factor, and t represents the time variable and dt represents the time integral.

[0104] The torsion analysis unit is used to perform summary calculation according to the obtained material data set, and obtain a wind shear torsion index fnz;

[0105] The wind shear torsion index fnz is obtained by the following formula:

[0106]

[0107] In the formula, ws(h) represents the wind shear force distribution at different heights h, dh represents the height differential element, sin represents the sine function, and a represents the wind load correction factor.

[0108] In the embodiment, the stability analysis module realizes fine and comprehensive evaluation of the state of the building structure through collaborative calculation of the vertical posture offset analysis unit, the foundation fractal analysis unit and the torsion analysis unit. The vertical posture offset index jqx innovatively introduces the inclination correction factor a, and accurately corrects the error deviation commonly seen in previous inclination evaluation. The foundation fractal settlement index sbj uses a combination of fractals and calculus to comprehensively reflect the dynamic evolution characteristics of the foundation in the time scale, effectively avoiding the one-sidedness of traditional static analysis methods. The wind shear torsion index fnz accurately captures the structural torsion changes at the micro scale by using the wind load correction factor a in view of the different distribution of wind loads at different heights, and makes up for the defects of traditional methods that ignore the influence of local perturbations. This multi-dimensional fusion calculation method significantly improves the sensitivity, accuracy and early warning capability of the detection system to structural risks, overcomes the shortcomings of traditional single-method evaluation, and ensures that the bearing wall structure safety evaluation is more accurate, more scientific and more reliable.

[0109] Embodiment 4

[0110] This embodiment is an explanation and description in Embodiment 3. Please refer to Figure 1 and Figure 3 Specifically, the fusion analysis module includes a bearing wall balance analysis unit and a bearing wall structure evaluation unit.

[0111] The bearing wall balance analysis unit is used to perform summary calculation according to the obtained vertical posture offset index jqx, foundation fractal settlement index sbj and wind shear torsion index fnz, and obtain a building multi-scale balance index DPH.

[0112]

[0113] In the formula, tanh represents the hyperbolic sine function, and c represents the correction parameter of the building space-time multi-scale balance index, which is used to correct the influence of the vertical posture offset index on the overall stability of the tower body, and its specific value is set by experimental data.

[0114] ​​The load-bearing wall structure evaluation unit is used for calculating a historical all-building multi-scale balance index DPH according to the building information data sets in the time sequence storage library, and calculating the mean value and the standard deviation of the historical all-building multi-scale balance index DPH by using a statistical method and the standard deviation Based on the mean value and the standard deviation , a preset first structure alert threshold A and a second structure alert threshold B are calculated, specifically , , wherein k represents an alert coefficient, and the obtained building multi-scale balance index DPH is used for load-bearing wall balance evaluation, and the specific evaluation scheme is as follows:

[0115] When the building multi-scale balance index DPH is less than the first structure alert threshold A, the load-bearing wall does not exist tilting, the structure is stable, and normal monitoring is maintained.

[0116] When the first structure alert threshold A is less than or equal to the building multi-scale balance index DPH and less than the second structure alert threshold B, the load-bearing wall exists tilting, and a tilting structure prediction is performed.

[0117] When the building multi-scale balance index DPH is greater than or equal to the second structure alert threshold B, the load-bearing wall tilting reaches a critical point of instability, and there is a risk of collapse, at this time, risk warning information is generated, and relevant agencies are immediately notified through a local area network to intervene in repair and demolition.

[0118] In this embodiment, the fusion analysis module sets a load-bearing wall balance analysis unit and a structure evaluation unit, innovatively uses a vertical posture deviation index jqx, a foundation fractal settlement index sbj, and a wind shear torsion index fnz to construct a building multi-scale balance index DPH. This unique multi-scale comprehensive index calculation can accurately reflect the overall stability of the load-bearing wall structure of the building under the action of different factors, and realize risk grading according to the self-adaptive setting of the alert threshold. At the same time, by setting the first structure alert threshold A and the second structure alert threshold B, three-level real-time monitoring and early warning of the load-bearing wall state from normal, tilting to critical instability are realized, which greatly improves the structure risk identification and response speed, avoids potential serious structure accidents, and provides key support for long-term safe operation of the building. The module has stronger risk predictability and accuracy, can quickly identify and warn in the early stage of load-bearing wall structure instability, thereby winning valuable time for subsequent active intervention repair or demolition, significantly reducing the risk of building structure collapse accidents, and improving the intelligence and initiative of overall building safety management.

[0119] Embodiment 5

[0120] This embodiment is an explanation and description in embodiment 4, please refer to Figure 1 andFigure 3 Specifically, the structure prediction module comprises a model construction unit and a prediction evaluation unit.

[0121] The model construction unit is configured to perform inclined structure prediction when the bearing wall balance evaluation indicates that the bearing wall is inclined.

[0122] The inclined structure prediction is configured to construct a bearing wall structure prediction model based on a nonlinear chaotic dynamics model and a fractal geometry model, input all bearing wall inclined and non-inclined building information data sets in the historical time series repository into the bearing wall structure prediction model for segmentation training and verification after labeling, optimize the bearing wall structure prediction model, input the real-time acquired building multiscale balance index DPH into the bearing wall structure prediction model, output the multiscale balance prediction index YCH based on the bearing wall structure prediction model, and the specific process is as follows.

[0123] ;

[0124] In the formula, DPH(t) represents the building spatiotemporal multiscale balance index at time t, e represents an exponential function, represents a nonlinear adjustment coefficient of the building multiscale balance index DPH, which is calibrated based on historical data, sin represents a sine function, represents a circular constant, taking two decimal places, tanh represents a hyperbolic sine function, log represents a logarithmic function, and w represents a perturbation correction coefficient for compensating for deviations caused by uncertain factors.

[0125] The prediction evaluation unit is configured to perform prediction structure balance evaluation based on the acquired multiscale balance prediction index YCH and a preset second structure alert threshold B, and the specific evaluation scheme is as follows.

[0126] When the multiscale balance prediction index YCH is less than the preset second structure alert threshold B, it indicates that the bearing wall structure at time t in the future is not damaged, and the building structure is safe and has no impact.

[0127] When the multiscale balance prediction index YCH is greater than or equal to the preset second structure alert threshold B, it indicates that the bearing wall structure at time t in the future is damaged, and building risk warning information is generated at this time, and relevant agencies are notified to intervene in repair through a local area network.

[0128] In this embodiment, the structure prediction module is constructed by fusing a nonlinear chaotic dynamic model and a fractal geometric model to predict the structure of a load-bearing wall. For the first time, the stability of the load-bearing wall in the inclined state is accurately predicted. The annotation and model segmentation training optimization of all building information data sets of the load-bearing wall in the inclined and non-inclined state in the time sequence storage library are used to establish a load-bearing wall structure prediction model with high accuracy and robustness. By inputting the building multi-scale balance index DPH in real time, the multi-scale balance prediction index YCH is accurately output. Not only can the potential damage of the load-bearing wall structure in the future be accurately predicted, but also the safety critical state can be identified in time and the warning information can be automatically generated. Compared with the traditional passive detection method, the module significantly enhances the advance and reliability of prediction and warning, changes the building safety management from passive response to active prevention, timely informs the relevant agencies to intervene in repair, and effectively ensures the long-term safe operation and structural stability of the building.

[0129] Embodiment 6

[0130] This embodiment is an explanation and description in embodiment 1. Please refer to Figure 1 and Figure 3 , specifically: the optimization feedback module comprises an adaptive adjustment unit and a feedback optimization unit;

[0131] The adaptive adjustment unit is used to automatically adjust the sampling frequency and data fusion weight of the detection system according to the structural characteristics of different building load-bearing walls, environmental influence factors and measurement accuracy requirements;

[0132] The feedback optimization unit is used to record the building multi-scale balance index DPH change rate at the current time and 30 days before the warning when the system detects that the building multi-scale balance index DPH exceeds the preset safety threshold, and use the line chart to show the building multi-scale balance index DPH fluctuation in the past 30 days, and then compare the change trend of the building multi-scale balance index DPH before and after reinforcement and repair to evaluate the effectiveness of the reinforcement measures.

[0133] In this embodiment, the optimization feedback module effectively improves the precision and dynamic adjustment capability of the safety monitoring of the load-bearing wall structure through the organic cooperation of its adaptive adjustment unit and feedback optimization unit. The adaptive adjustment unit can automatically adjust the sampling frequency and data fusion weight of the detection system according to different building structure characteristics, external environmental factors, and specific requirements for measurement accuracy, significantly improving the adaptability and accuracy of the monitoring system; the feedback optimization unit records and visualizes the detailed change trend of the multi-scale balance index DPH in the 30 days before and after the early warning, facilitating intuitive and rapid identification of the fluctuation of the structure state. In addition, combined with the comparison of the data trend changes before and after the reinforcement and repair, this module also has a unique advantage in evaluating the effectiveness of the reinforcement measures, helping users quickly verify and adjust the structure optimization scheme, thereby comprehensively enhancing the active safety control capability and decision-making efficiency of the building structure.

[0134] Embodiment 7

[0135] Please refer to Figure 2 A vertical detection method for a load-bearing wall of a building, comprising the following steps:

[0136] S1, collecting building construction data of the building in real time according to a three-dimensional drawing of the building and a sensor group installed on the load-bearing wall of the building, preprocessing to obtain a building information data group, and storing the building information data group in a time sequence storage;

[0137] S2, calculating a vertical posture deviation index jqx, a foundation fractal settlement index sbj, and a wind shear torsion index fnz according to the building information data group;

[0138] S3, associatively calculating the vertical posture deviation index jqx, the foundation fractal settlement index sbj, and the wind shear torsion index fnz to obtain a building multi-scale balance index DPH, and performing load-bearing wall balance evaluation on the building multi-scale balance index DPH and a preset first structure alert threshold A and a second structure alert threshold B;

[0139] S4, when the load-bearing wall balance evaluation indicates that there is a tilt, constructing a load-bearing wall structure prediction model, inputting the building multi-scale balance index DPH into the load-bearing wall structure prediction model, outputting a multi-scale balance prediction index YCH, and performing prediction structure balance evaluation on the multi-scale balance prediction index YCH and the preset second structure alert threshold B;

[0140] S5, automatically adjusting the sampling frequency and data fusion weight of the system, recording and displaying the change rate of the building multi-scale balance index DPH in the 30 days before the early warning.

[0141] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vertical detection system for load bearing walls in house construction, characterised in that: The building data acquisition module, the stability analysis module, the fusion analysis module, the structure prediction module and the optimization feedback module are comprised. The building data acquisition module is used for collecting the construction data of the building in real time according to the three-dimensional drawing of the building and the sensor group installed on the load-bearing wall of the building, preprocessing to obtain the building information data group, and storing to the time sequence storage library. The building data acquisition module comprises a data acquisition unit, a preprocessing unit and a data storage unit. The data acquisition unit is used for collecting the construction data of the building in real time according to the three-dimensional drawing of the building and the sensor group installed on the load-bearing wall of the building. The sensor group comprises a quantum tunneling effect sensor, an ultra-resolution optical range finder, a foundation pressure sensor, a foundation resistivity tester and a laser gyroscope. The stability analysis module is used for calculating the vertical attitude deviation index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz according to the building information data group. The fusion analysis module is used for correlating and calculating the vertical attitude deviation index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz, obtaining the building multi-scale balance index DPH, and performing load-bearing wall balance evaluation with the preset first structure alert threshold A and the second structure alert threshold B. The structure prediction module is used for constructing a load-bearing wall structure prediction model when the load-bearing wall balance evaluation shows that there is a tilt, inputting the building multi-scale balance index DPH into the load-bearing wall structure prediction model, outputting a multi-scale balance prediction index YCH, and performing prediction structure balance evaluation with the preset second structure alert threshold B. The optimization feedback module is used for automatically adjusting the sampling frequency and data fusion weight of the system, recording and displaying the building multi-scale balance index DPH change rate in the 30 days before the warning.

2. A vertical detection system for load bearing walls of building construction according to claim 1, characterized in that: The preprocessing unit is used for denoising, data correction, outlier detection, data time synchronization, dimensionless processing and moment of inertia analysis processing on the collected construction data, to obtain the building information data group. The inertia moment analysis processing is used to calculate the inertia moment it of the load-bearing wall section according to the width kd and the height gd of the load-bearing wall section in the three-dimensional drawing of the building, and specifically is: Then, the polar inertia moment jt of the load-bearing wall is calculated according to the obtained inertia moment it of the load-bearing wall section, and specifically is: In the formula, it(x) and it(y) represent the transverse and longitudinal inertia moments of the load-bearing wall section, respectively. The building information data group comprises an offset data group, a foundation data group and a material data group. The offset data group comprises a building top micro-displacement qm, a building vertical offset oh and a building height H. The foundation data group comprises a foundation pressure gradient ps, a foundation resistivity rd and a foundation diameter db. The material data group comprises a load-bearing wall material elastic modulus ec, a load-bearing wall section moment of inertia it, a load-bearing wall polar moment of inertia jt, a wind shear force induced torsional angular velocity ws, a load-bearing wall section area ac and a load-bearing wall material shear modulus fc. The data storage unit is used for constructing a time sequence storage library, and storing the obtained building information data group to the time sequence storage library in real time through a local area network.

3. A vertical detection system for load bearing walls of building construction according to claim 2, characterized in that: The stability analysis module comprises a vertical offset analysis unit, a foundation fractal analysis unit and a torsion analysis unit. The vertical offset analysis unit is used for calculating the vertical attitude deviation index jqx according to the obtained offset data group. The vertical attitude deviation index jqx is calculated by the following formula: ； where arctan represents an inverse tangent function, cos represents a cosine function, denotes a minimum value to prevent the denominator from being zero, and a denotes a tilt correction factor, which is corrected by historical data. The foundation fractal analysis unit is used for calculating the foundation fractal settlement index sbj according to the obtained foundation data group. The foundation fractal settlement index sbj is calculated by the following formula: ； In the formula, ln represents a logarithmic function, e represents an exponential function, b represents a foundation stability correction factor, t represents a time variable, and dt represents a time differential. The torsion analysis unit is configured to perform a summary calculation based on the obtained material data set to obtain a wind shear torsion index fnz. The wind shear torsion index fnz is calculated by the following formula: ； In the formula, ws(h) represents a wind shear force distribution at different heights h, dh represents a height differential, sin represents a sine function, and a represents a wind load correction factor.

4. A vertical detection system for load bearing walls of building construction according to claim 3, characterized in that: The fusion analysis module includes a load-bearing wall balance analysis unit and a load-bearing wall structure evaluation unit. The load-bearing wall balance analysis unit is configured to perform a summary calculation based on the obtained vertical attitude deviation index jqx, the foundation fractal settlement index sbj, and the wind shear torsion index fnz to obtain a building multi-scale balance index DPH. ； In the formula, tanh represents a hyperbolic sine function, and c represents a correction parameter of the building space-time multi-scale balance index, which is set by experimental data.

5. A vertical detection system for load bearing walls of building construction according to claim 4, characterized in that: The load-bearing wall structure evaluation unit is used to statistically calculate all historical building information data sets to calculate a historical all-building multi-scale balance index DPH, and then calculate the mean value and standard deviation of the historical all-building multi-scale balance index DPH by using a statistical method and standard deviation , and based on the mean value and standard deviation , a preset first structure alert threshold A and a second structure alert threshold B are obtained, specifically , , wherein k represents an alert coefficient, and the obtained building multi-scale balance index DPH is used for load-bearing wall balance evaluation, and the specific evaluation scheme is as follows; When the building multi-scale balance index DPH is less than a first structure alert threshold A, the load-bearing wall is not tilted, and the structure is stable. When the first structure alert threshold A is less than or equal to the building multi-scale balance index DPH and less than a second structure alert threshold B, the load-bearing wall is tilted, and a tilted structure prediction is performed. When the building multi-scale balance index DPH is greater than or equal to the second structure alert threshold B, the load-bearing wall tilts to a critical point of instability, and there is a risk of collapse, risk warning information is generated, and relevant agencies are notified through a local area network to intervene in repair and demolition.

6. A vertical detection system for load bearing walls of building construction according to claim 5, characterized in that: The structure prediction module includes a model construction unit and a prediction evaluation unit. The model construction unit is configured to perform a tilted structure prediction when the load-bearing wall balance evaluation indicates that the load-bearing wall is tilted. The tilted structure prediction is configured to construct a load-bearing wall structure prediction model based on a nonlinear chaotic dynamics model and a fractal geometry model, label all building information data sets of the load-bearing wall in the historical time series repository, input the labeled building information data sets into the load-bearing wall structure prediction model for segmentation training and verification, optimize the load-bearing wall structure prediction model, input the real-time building multi-scale balance index DPH into the load-bearing wall structure prediction model, and output a multi-scale balance prediction index YCH based on the load-bearing wall structure prediction model, as follows: ； In the formula, DPH(t) represents the building space-time multi-scale balance index at time t, e represents the exponential function, represents the nonlinear adjustment coefficient of the building multi-scale balance index DPH, sin represents the sine function, represents the circular constant, the value is two decimal places, tanh represents the hyperbolic sine function, log represents the logarithmic function, and w represents the perturbation correction coefficient.

7. A vertical detection system for load bearing walls of building construction according to claim 6, characterized in that: The prediction evaluation unit is configured to perform a prediction structure balance evaluation based on the obtained multi-scale balance prediction index YCH and a preset second structure alert threshold B, and the specific evaluation scheme is as follows: When the multi-scale balance prediction index YCH is less than the preset second structure alert threshold B, it indicates that the load-bearing wall structure is not damaged at a future time t, and the building structure is safe and has no impact. When the multi-scale balance prediction index YCH is greater than or equal to the preset second structure alert threshold B, it indicates that the load-bearing wall structure is damaged at a future time t, and building risk warning information is generated and relevant agencies are notified through a local area network to intervene in repair.

8. The vertical detection system for load bearing walls of building construction according to claim 1, wherein: The optimization feedback module includes an adaptive adjustment unit and a feedback optimization unit. The adaptive adjustment unit is used to automatically adjust the sampling frequency and data fusion weight of the detection system according to the structural characteristics of different building bearing walls, environmental influence factors and measurement accuracy requirements. The feedback optimization unit is used to record the current time and the building multi-scale balance index DPH change rate in the 30 days before the warning when the system detects that the building multi-scale balance index DPH exceeds the preset safety threshold, and use the broken line chart to show the building multi-scale balance index DPH fluctuation in the past 30 days, and then compare the change trend of the building multi-scale balance index DPH before and after reinforcement and repair to evaluate the effectiveness of the reinforcement measures.

9. A vertical detection method for building load-bearing wall, applied to the vertical detection system for building load-bearing wall according to any one of claims 1-8, characterized in that: The method comprises the following steps: S1, according to the building three-dimensional drawing and the sensor group installed on the building bearing wall, real-time collection of building construction data of the house building, preprocessing to obtain building information data set, and storage to time sequence storage library; S2, calculating the vertical posture deviation index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz according to the building information data set; S3, associating and calculating the vertical posture deviation index jqx, the foundation fractal settlement index sbj and the wind shear torsion index fnz to obtain the building multi-scale balance index DPH, and performing bearing wall balance evaluation with the preset first structure alert threshold A and the second structure alert threshold B; S4, when the bearing wall balance evaluation is inclined, constructing a bearing wall structure prediction model, inputting the building multi-scale balance index DPH into the bearing wall structure prediction model, outputting the multi-scale balance prediction index YCH, and performing prediction structure balance evaluation with the preset second structure alert threshold B; S5, automatically adjusting the sampling frequency and data fusion weight of the system, recording and showing the building multi-scale balance index DPH change rate in the 30 days before the warning.

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