CN-121168108-B - Door and window installation dynamic wind pressure detection method and system based on intelligent sensor

CN121168108BCN 121168108 BCN121168108 BCN 121168108BCN-121168108-B

Abstract

The invention provides a door and window installation dynamic wind pressure detection method and system based on an intelligent sensor, and relates to the technical field of door and window wind pressure detection, comprising the following steps of acquiring strain data of door and window glass based on a plurality of strain sensors embedded in the door and window glass, combining point cloud data of the door and window glass, and constructing a three-dimensional deformation model of the door and window glass by using a deformation mapping method; based on a three-dimensional deformation model of the door and window glass, a finite element inverse analysis method and an inversion compensation method are utilized to calculate dynamic wind pressure distribution characteristics of the surface of the door and window glass in combination with material parameters of the door and window glass, and according to the dynamic wind pressure distribution characteristics of the surface of the door and window glass, the installation performance of the door and window is estimated, so that wind pressure performance estimation and detection results are obtained. The invention is helpful to find the installation problem in time and take improvement measures, thereby improving the installation quality and safety of doors and windows.

Inventors

CHEN FUPENG
JIAN LING
YANG XIAOTONG
ZHOU JUN
GUO JING
DENG WENPING
YE YUAN
HUANG LICHUN

Assignees

成都家蜂窝家居有限公司

Dates

Publication Date: 20260512
Application Date: 20250818

Claims (8)

1. The door and window installation dynamic wind pressure detection method based on the intelligent sensor is characterized by comprising the following steps of: s1, acquiring strain data of door and window glass based on a plurality of strain sensors embedded in the door and window glass, and constructing a three-dimensional deformation model of the door and window glass by utilizing a deformation mapping method in combination with point cloud data of the door and window glass; S2, determining a time interval of time sequence dispersion based on sampling frequency of a strain sensor, extracting a three-dimensional deformation model corresponding to each moment according to the time interval of time sequence dispersion, establishing a space matching relation between all model nodes and an initial three-dimensional model, calculating three-dimensional displacement of the three-dimensional deformation model nodes at each moment relative to the initial three-dimensional model nodes based on the space matching relation, supplementing global displacement field information of the surface of the door and window glass at each moment through interpolation, establishing a mechanical mapping relation of deformation and wind pressure by combining a finite element inverse analysis method based on the displacement field information of the surface of the door and window glass with the material parameters of the door and window glass, and preliminarily solving preliminary dynamic wind pressure distribution of the door and window glass; And S3, evaluating the installation performance of the door and window according to the dynamic wind pressure distribution characteristics of the surface of the door and window glass, and obtaining a wind pressure performance evaluation detection result.
2. The method for detecting the dynamic wind pressure of door and window installation based on the intelligent sensor according to claim 1, wherein the method for constructing the three-dimensional deformation model of the door and window glass by using the deformation mapping method based on the plurality of strain sensors embedded in the door and window glass, collecting the strain data of the door and window glass and combining the point cloud data of the door and window glass comprises the following steps: S11, acquiring strain data of door and window glass under the action of wind pressure by using a strain sensor, and preprocessing the corresponding data by using a rough difference removal method to obtain standard strain data; S12, combining preset space coordinates of the strain sensor, and converting standard strain data into a three-dimensional displacement field based on generalized Hooke' S law and door and window glass material parameters and by matching with a structural deformation theory; S13, collecting initial point cloud data of the door and window glass under the windless condition, fitting the initial point cloud data by using a poisson reconstruction algorithm, and constructing an initial three-dimensional geometric model of the door and window glass; And S14, mapping the three-dimensional displacement field into an initial three-dimensional model, and performing deformation adjustment on the initial three-dimensional model to obtain a three-dimensional deformation model under the action of wind pressure.
3. The method for detecting the dynamic wind pressure of door and window installation based on the intelligent sensor according to claim 2, wherein the step of acquiring the strain data of the door and window glass under the action of wind pressure by using the strain sensor, and preprocessing the corresponding data by using a rough difference removal method to obtain the standard strain data comprises the following steps: s111, acquiring a monitoring data stream of each strain sensor, and calculating a first derivative sequence of each monitoring data stream; S112, calculating an abnormal threshold value of each first derivative sequence by using a wavelet threshold method, and eliminating rough difference points larger than the abnormal threshold value in the first derivative sequence; S113, carrying out wavelet decomposition on the monitoring data stream with the data points removed, extracting a low-frequency trend item, and identifying and removing a secondary rough difference point by utilizing a Laida criterion to obtain a removed data stream; and S114, interpolating and filling the data points removed from the removed data stream by using a linear interpolation method to obtain standard strain data.
4. The method for detecting the dynamic wind pressure of door and window installation based on the intelligent sensor according to claim 2, wherein the step of converting standard strain data into a three-dimensional displacement field based on generalized hooke's law and door and window glass material parameters and by matching with a structural deformation theory comprises the following steps: S121, determining the strain direction of the door and window glass based on standard strain data, and constructing a mapping relation between the space coordinate and the strain direction according to preset space coordinates of a strain sensor to form space strain distribution data; s122, acquiring the elastic modulus and Poisson 'S ratio of the door and window glass material, and converting the spatial strain distribution data into stress tensor distribution data by using a generalized Hooke' S law; S123, combining structural boundary conditions of door and window glass and a deformation theory, establishing a control equation between stress and displacement, solving the control equation by a numerical method, inverting to obtain three-dimensional displacement vectors of all space points, and forming a three-dimensional displacement field.
5. The method for detecting the dynamic wind pressure of door and window installation based on the intelligent sensor according to claim 2, wherein the mapping of the three-dimensional displacement field into the initial three-dimensional model, and the deformation adjustment of the initial three-dimensional model, the obtaining of the three-dimensional deformation model under the action of wind pressure, comprises the following steps: S141, constructing a corresponding relation with a point cloud node in an initial three-dimensional geometric model based on coordinate information of each space point in the three-dimensional displacement field, and forming a matching data set of the model node and the three-dimensional displacement; s142, based on a matching data set of the model node and the three-dimensional displacement, superposing the three-dimensional displacement vector to a corresponding point cloud node to realize the spatial position update of the model node coordinate and form a deformed node coordinate set; S143, carrying out model reconstruction on the deformed node coordinate set, generating an initial three-dimensional deformation geometric model under the action of wind pressure, and carrying out smooth adjustment on the initial three-dimensional deformation geometric model to obtain a final three-dimensional deformation geometric model.
6. The method for detecting the dynamic wind pressure of door and window installation based on the intelligent sensor according to claim 5, wherein the method for establishing a mechanical mapping relation of deformation and wind pressure by combining the displacement field information of the surface of the door and window glass with the material parameters of the door and window glass and utilizing a finite element inverse analysis method and preliminarily solving the preliminary dynamic wind pressure distribution of the door and window glass comprises the following steps: S221, constructing a finite element inverse analysis model of the door and window glass based on the door and window glass material parameters; S222, inputting displacement field information of the surface of the door and window glass as a known boundary into a finite element inverse analysis model, and determining a mechanical mapping relation between door and window deformation and wind pressure through a structural mechanical equilibrium equation; s223, solving the mapping relation by utilizing finite element inverse analysis, and calculating a wind pressure load value to form preliminary wind pressure space distribution; S224, combining time sequence displacement field information, and integrating wind pressure space distribution at each moment to obtain dynamic wind pressure distribution of the surface of the door and window glass along with time change.
7. The method for detecting the dynamic wind pressure of door and window installation based on the intelligent sensor according to claim 6, wherein the steps of correcting the preliminary dynamic wind pressure distribution by an inversion compensation method, extracting the wind pressure peak value, the distribution area and the time variation characteristic according to the correction result, and forming the dynamic wind pressure distribution characteristic of the surface of the door and window glass comprise the following steps: s231, performing error analysis and local fitting correction based on the preliminary dynamic wind pressure distribution result to obtain a correction result; s232, extracting a wind pressure peak value according to the corrected preliminary dynamic wind pressure distribution result, and extracting a distribution area and time variation characteristics through spatial clustering and time sequence statistics; S233, integrating the wind pressure peak value, the distribution area and the time variation characteristic to obtain the dynamic wind pressure distribution characteristic of the surface of the window glass.
8. A door and window installation dynamic wind pressure detection system based on an intelligent sensor for realizing the door and window installation dynamic wind pressure detection method based on the intelligent sensor as claimed in any one of claims 1 to 7, characterized in that the system comprises: The model construction module is used for acquiring the strain data of the door and window glass based on a plurality of strain sensors embedded in the door and window glass, and constructing a three-dimensional deformation model of the door and window glass by utilizing a deformation mapping method in combination with the point cloud data of the door and window glass; The characteristic calculation module is used for calculating dynamic wind pressure distribution characteristics of the surface of the door and window glass by utilizing a finite element inverse analysis method and an inversion compensation method based on a three-dimensional deformation model of the door and window glass and combining material parameters of the door and window glass; and the wind pressure performance evaluation module is used for evaluating the installation performance of the door and window according to the dynamic wind pressure distribution characteristics of the surface of the door and window glass to obtain a wind pressure performance evaluation detection result.

Description

Door and window installation dynamic wind pressure detection method and system based on intelligent sensor Technical Field The invention relates to the technical field of door and window wind pressure detection, in particular to a door and window installation dynamic wind pressure detection method and system based on an intelligent sensor. Background Glass door and window systems are important channels for interaction between home environment and natural environment, and are also one of the core elements of building design. With the continuous development of building design concepts, the door and window area of a single communication area (same opening) shows an increasing trend. This variation has several effects, among which the increase in the weight of the whole door and window is the most remarkable. The self weight increases to make the mechanical environment borne by the door and window more complex when the door and window is acted by external forces such as wind pressure. Wind pressure is one of key factors influencing the performance of doors and windows, and the dynamic change of the wind pressure brings higher requirements to the installation quality of the doors and windows. In the door and window installation process, if the installation is improper, if the connection is not firm, the sealing is not tight and the like, abnormal deformation of the door and window can be caused under the action of wind pressure. The abnormal deformation not only can influence the attractiveness and the service life of the door and window, but also can reduce the wind pressure resistance of the door and window, thereby threatening the overall safety of a building. For example, in strong wind weather, a door or window that is not firmly installed may shake, deform or even fall off, causing damage to indoor personnel and property. However, at present, the defects of door and window installation can cause the wind pressure to be concentrated in a local area, and the traditional detection method is mostly dependent on manual visual inspection or single-point pressure test, lacks quantitative analysis on the relevance of the global wind pressure distribution and the installation defects, and is difficult to find potential installation problems. For the problems in the related art, no effective solution has been proposed at present. Disclosure of Invention In view of the above, the present invention provides a method and a system for detecting dynamic wind pressure of door and window installation based on an intelligent sensor, so as to solve the above-mentioned problems. In order to solve the problems, the invention adopts the following specific technical scheme: According to an aspect of the invention, there is provided a door and window installation dynamic wind pressure detection method based on an intelligent sensor, comprising the following steps: s1, acquiring strain data of door and window glass based on a plurality of strain sensors embedded in the door and window glass, and constructing a three-dimensional deformation model of the door and window glass by utilizing a deformation mapping method in combination with point cloud data of the door and window glass; s2, calculating dynamic wind pressure distribution characteristics of the surface of the door and window glass by using a finite element inverse analysis method and an inversion compensation method based on a three-dimensional deformation model of the door and window glass and combining material parameters of the door and window glass; And S3, evaluating the installation performance of the door and window according to the dynamic wind pressure distribution characteristics of the surface of the door and window glass, and obtaining a wind pressure performance evaluation detection result. Preferably, the method for constructing a three-dimensional deformation model of the door and window glass by using a deformation mapping method based on a plurality of strain sensors embedded in the door and window glass, collecting strain data of the door and window glass and combining point cloud data of the door and window glass comprises the following steps: S11, acquiring strain data of door and window glass under the action of wind pressure by using a strain sensor, and preprocessing the corresponding data by using a rough difference removal method to obtain standard strain data; S12, combining preset space coordinates of the strain sensor, and converting standard strain data into a three-dimensional displacement field based on generalized Hooke' S law and door and window glass material parameters and by matching with a structural deformation theory; S13, collecting initial point cloud data of the door and window glass under the windless condition, fitting the initial point cloud data by using a poisson reconstruction algorithm, and constructing an initial three-dimensional geometric model of the door and window glass; And S14, mapping the three-dimensional displace