Construction building intelligent deformation sensing method and related equipment

Abstract

The invention discloses a construction building intelligent deformation sensing method and related equipment, and the method comprises the steps: laying a multi-point sensing array on a construction building structural plane, carrying out the registration and correction of the multi-point sensing array according to a reference coordinate system and a calibration target array, and constructing an initial reference plane model; the construction building structural plane is measured through a multi-point sensing array, measured data are registered to an initial reference plane model through an iterative nearest point algorithm, deformation information of the construction building structural plane is calculated, a deformation risk level is determined according to the deformation information and a grading threshold judgment algorithm, and monitoring data are processed and visually displayed. And executing the hierarchical response strategy. According to the embodiment of the invention, the deformation information of the construction building can be obtained in real time through the multi-point sensing array, and graded early warning and response are carried out, so that the real-time performance, precision and intelligent level of deformation monitoring of the construction building are effectively improved. The method can be widely applied to the technical field of communication engineering building monitoring.

Description

Technical Field

[0001] This application relates to the field of communication engineering building monitoring technology, and in particular to a method and related equipment for intelligent deformation sensing of construction buildings. Background Technology

[0002] In telecommunications engineering, the construction of facilities such as base station towers and utility tunnels requires real-time monitoring of structural deformation to ensure safety and stability. Traditional monitoring methods are limited in deployment in complex and confined spaces and have certain limitations in meeting the needs of detecting the tilt of tall towers or the continuous deformation of underground utility tunnels.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose an intelligent deformation sensing method and related equipment for construction buildings. By using a multi-point sensing array to acquire deformation information of construction buildings in real time and perform hierarchical early warning and response, the real-time performance, accuracy and intelligence level of construction building deformation monitoring can be effectively improved.

[0005] To achieve the above objectives, one aspect of this application proposes an intelligent deformation sensing method for construction buildings, the method comprising: A multi-point sensing array is deployed on the surface of the building structure, and the multi-point sensing array is registered to the reference coordinate system. The calibration target array is measured by the multi-point sensing array to obtain first measurement data. The first measurement data is then corrected for errors based on the actual position of the calibration target array to obtain second measurement data. An initial reference surface model of the construction building structure surface is then constructed based on the second measurement data. The multi-point sensing array measures the surface of the construction building structure according to a preset period to obtain first real-time measurement data. The first real-time measurement data is then registered to the initial reference surface model using an iterative nearest point algorithm to obtain second real-time measurement data. The displacement data, velocity data, and acceleration data of the construction building structure surface are then determined based on the second real-time measurement data. The deformation risk level is determined based on the displacement data, the velocity data, the acceleration data, and a preset grading threshold judgment algorithm. The displacement data, velocity data, and acceleration data are synchronized, preprocessed, and visualized. A preset graded response strategy is executed according to the deformation risk level. The first measurement data and the first real-time measurement data both include point cloud data and image data.

[0006] In some embodiments, the step of deploying a multi-point sensing array on the surface of the construction structure and registering the multi-point sensing array to a reference coordinate system includes: Based on a preset spatial distribution density algorithm, several deformation-sensitive points on the construction building structure surface are selected as monitoring nodes, and sensing terminals are deployed on each monitoring node to obtain the multi-point sensing array. The position information of each sensing terminal in the multi-point sensing array is obtained by a three-dimensional measurement device, and a set of terminal installation coordinate points is constructed based on the position information. The reference coordinate system is constructed based on the preset reference points, and the reference point set is determined based on the reference coordinate system; Based on the spatial correspondence between the terminal installation coordinate point set and the reference point set, the terminal installation coordinate point set is registered to the reference coordinate system using a rigid transformation model. The sensing terminal includes a laser module and a vision module.

[0007] In some embodiments, the step of correcting the first measurement data for errors based on the actual positions of the calibrated target array to obtain the second measurement data includes: The theoretical ideal measurement value is determined based on the actual position of the calibration target array; Construct a measurement error objective function based on the theoretical ideal measurement value and the first measurement data; The measurement error objective function is solved by the least squares method to obtain the set of correction parameters that minimize the measurement error objective function; The first measurement data is corrected for errors based on the set of correction parameters to obtain the second measurement data.

[0008] In some embodiments, registering the first real-time measurement data to the initial reference surface model using an iterative nearest-point algorithm to obtain the second real-time measurement data includes: The original measurement data are determined based on the initial reference surface model; A registration objective function is constructed based on the first real-time measurement data and the original measurement data; The registration objective function is solved by the iterative nearest point algorithm to obtain the rotation matrix and translation vector that minimizes the registration objective function. The first real-time measurement data is registered using the rotation matrix and the translation vector to obtain the second real-time measurement data.

[0009] In some embodiments, determining the displacement data, velocity data, and acceleration data of the construction structure surface based on the second real-time measurement data includes: The original measurement data are determined based on the initial reference surface model; A displacement vector sequence is determined based on the second real-time measurement data and the original measurement data; Determine the velocity vector sequence and acceleration scalar sequence based on the displacement vector sequence; The displacement vector sequence is used as the displacement data, the velocity vector sequence is used as the velocity data, and the acceleration scalar sequence is used as the acceleration data.

[0010] In some embodiments, determining the deformation risk level based on the displacement data, the velocity data, the acceleration data, and a preset grading threshold determination algorithm includes: A first deformation risk score is determined based on the displacement data, the velocity data, and the acceleration data; The first deformation risk score is smoothed using a spatial moving average algorithm to obtain the second deformation risk score. The second deformation risk score is mapped to the deformation risk level using the grading threshold determination algorithm.

[0011] In some embodiments, the step of synchronizing, preprocessing, and visualizing the displacement data, velocity data, acceleration data, and deformation risk level, and executing a preset graded response strategy according to the deformation risk level, includes: The displacement data, velocity data, acceleration data, and deformation risk level are synchronized to the remote management platform via a communication network. The displacement data, velocity data, acceleration data, and deformation risk level are filtered and time-series aligned. The three-dimensional engineering structure model of the construction building is rendered according to the deformation risk level, and a time series trend chart and regional heat map are generated based on the displacement data, velocity data, acceleration data and deformation risk level. The remote management platform displays the three-dimensional engineering structure model, the time series trend chart, and the regional heat map. The graded response strategy is executed according to the deformation risk level. A structural health report is generated based on the displacement data, the velocity data, the acceleration data, and the deformation risk level.

[0012] To achieve the above objectives, another aspect of this application proposes a construction intelligent deformation sensing device, the device comprising: A multi-point sensing array registration module is used to deploy a multi-point sensing array on the surface of the construction building structure and register the multi-point sensing array to a reference coordinate system. The measurement error correction module is used to measure the calibration target array through the multi-point sensing array to obtain first measurement data, correct the error of the first measurement data according to the actual position of the calibration target array to obtain second measurement data, and construct the initial reference surface model of the construction building structure surface according to the second measurement data. The deformation information extraction module is used to measure the surface of the construction building structure according to a preset period through the multi-point sensing array to obtain first real-time measurement data, and to register the first real-time measurement data to the initial reference surface model through an iterative nearest point algorithm to obtain second real-time measurement data. The displacement data, velocity data and acceleration data of the construction building structure are determined based on the second real-time measurement data. The graded risk assessment module is used to determine the deformation risk level based on the displacement data, the velocity data, the acceleration data, and a preset graded threshold assessment algorithm. The data processing and graded response module is used to synchronize, preprocess, and visualize the displacement data, velocity data, and acceleration data, and execute a preset graded response strategy according to the deformation risk level. The first measurement data and the first real-time measurement data both include point cloud data and image data.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the methods described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0015] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above. The embodiments of this application include at least the following beneficial effects: This application provides a method and related equipment for intelligent deformation sensing of construction buildings. This solution deploys a multi-point sensing array on the structural surface of the construction building, and uses a reference coordinate system and a calibration target array to register and correct the measurement data, unifying the measurement data of each point to the same coordinate system, effectively reducing measurement errors. Based on the corrected data, an initial reference surface model is constructed, which can provide an accurate reference for subsequent deformation calculation and improve monitoring accuracy. By acquiring real-time measurement data of the structural surface of the construction building and using an iterative nearest-point algorithm to register it to the initial reference surface model, high-precision registration of real-time measurement data and initial measurement data is achieved, ensuring the accuracy of deformation extraction. Based on the registered real-time measurement data, displacement, velocity, and acceleration information of the structural surface are extracted, which can effectively reflect the deformation characteristics of the construction building in the time dimension and provide multi-dimensional data support for subsequent risk level classification. Through hierarchical deformation risk level judgment and hierarchical response strategy, hierarchical early warning of the deformation state of the construction building is realized. Combined with data synchronization and visualization, the real-time performance, accuracy, and intelligence level of construction building deformation monitoring are effectively improved. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of an intelligent deformation sensing method for construction buildings provided in this application embodiment; Figure 2 This is a structural schematic diagram of an intelligent deformation sensing device for construction buildings provided in an embodiment of this application; Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0019] The concept of the present invention will now be explained in conjunction with the background art.

[0020] In telecommunications engineering, the construction of facilities such as base station towers and utility tunnels requires real-time monitoring of structural deformation to ensure safety and stability. Traditional monitoring methods are limited in deployment in complex and confined spaces and have certain limitations in meeting the needs of detecting the tilt of tall towers or the continuous deformation of underground utility tunnels.

[0021] At the same time, with the accelerated construction of 5G networks and the expansion of urban integrated pipe corridors, higher requirements are placed on the accuracy of full-domain dynamic perception of structural deformation during construction and the reliability of real-time early warning. It is necessary to develop multi-source perception fusion technology that can adapt to distributed construction scenarios. Furthermore, the intelligent upgrading of communication facility operation and maintenance urgently requires the construction of a closed-loop link from deformation data to safety management and control, so as to improve the proactive protection capability from single-point alarm to trend prediction.

[0022] In view of this, the main objective of this application is to propose an intelligent deformation sensing method and related equipment for construction buildings, which can acquire deformation information of construction buildings in real time through a multi-point sensing array and perform hierarchical early warning and response, effectively improving the real-time performance, accuracy and intelligence level of construction building deformation monitoring.

[0023] This application provides a method for intelligent deformation sensing of construction buildings, relating to the field of information technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the intelligent deformation sensing method for construction buildings, but is not limited to the above forms.

[0024] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0025] Figure 1 This is an optional flowchart of a construction intelligent deformation sensing method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0026] S101. Deploy a multi-point sensing array on the surface of the building structure and register the multi-point sensing array to the reference coordinate system. In some embodiments, a multi-point sensing array is deployed on the surface of the construction structure, and the multi-point sensing array is registered to a reference coordinate system, including: S1011. Select several deformation-sensitive points on the construction building structure surface as monitoring nodes according to the preset spatial distribution density algorithm, and deploy sensing terminals on each monitoring node to obtain a multi-point sensing array. S1012. Obtain the position information of each sensing terminal in the multi-point sensing array through a three-dimensional measurement device, and construct a set of terminal installation coordinate points based on the position information; S1013. Construct a reference coordinate system based on the preset reference points, and determine the reference point set based on the reference coordinate system; S1014. Based on the spatial correspondence between the terminal installation coordinate point set and the reference point set, the terminal installation coordinate point set is registered to the reference coordinate system using a rigid transformation model. The sensing terminal includes a laser module and a vision module.

[0027] Specifically, in this embodiment, a multi-point sensing array needs to be deployed on the construction building to spatially cover the key surfaces of the construction structure and establish a unified high-precision reference coordinate system to provide a reliable spatial reference basis for subsequent deformation monitoring.

[0028] For example, in step S1011, this embodiment selects several deformation-sensitive points as monitoring nodes based on the stress characteristics and possible deformation patterns of the engineering structure, combined with the actual environmental conditions of the construction site, to form a multi-point sensing array. The deployment principle is to ensure that the monitoring points are evenly distributed on the key structural surfaces, satisfying continuous spatial coverage, while also taking into account the stability of equipment installation and ease of maintenance. Specifically, the spatial distribution density of the deployment points is described by the following formula:

[0029]

[0030] in, This represents the deployment density of sensing terminals at spatial coordinates (x, y, z); This represents the ideal number of sensing points per unit volume, determined based on engineering safety specifications and monitoring accuracy requirements. Indicates a function that indicates the critical region of the structure; In step S1012, after each sensing terminal is installed, this embodiment uses high-precision three-dimensional measuring equipment (such as a total station or laser scanner) to obtain its actual installation coordinate point set. : ; Where N represents the total number of sensing terminals deployed; This represents the installation coordinates of the i-th sensing terminal; In step S1013, this embodiment uses the stable point or preset benchmark point of the construction structure as a reference to construct a three-dimensional benchmark coordinate system P0. The benchmark coordinate system is defined as a Cartesian coordinate system, with the origin set at the selected stable marker point. The coordinate axis directions are determined according to the engineering drawings or the actual directions on site. The benchmark point set is defined as follows: ; In step S1014, this embodiment matches the coordinates of the sensing terminal point {P}. i The spatial correspondence between} and the reference point set B is initially registered using a rigid transformation model: ; in, Represents the three-dimensional spatial coordinates of the j-th reference point; The coordinates of the i-th sensing terminal are mapped to the reference coordinate system P0 (the transformed result), used to unify the spatial reference; t represents the translation vector, which represents the translation of the origin between the coordinate systems, used to convert the measured coordinates into the reference coordinates; R represents the rotation matrix, which is the spatial rotation part in the rigid transformation, and is a 3×3 orthogonal matrix; M represents the number of reference points. S102. Measure the calibration target array using a multi-point sensing array to obtain first measurement data. Correct the first measurement data for errors based on the actual position of the calibration target array to obtain second measurement data. Construct an initial reference surface model of the construction building structure based on the second measurement data. In some embodiments, error correction is performed on the first measurement data based on the actual position of the calibrated target array to obtain the second measurement data, including: S1021. Determine the theoretical ideal measurement value based on the actual position of the calibrated target array; S1022. Construct a measurement error objective function based on the theoretical ideal measurement value and the first measurement data; S1023. Solve the objective function of measurement error using the least squares method to obtain the set of correction parameters that minimize the objective function of measurement error; S1024. Perform error correction on the first measurement data according to the correction parameter set to obtain the second measurement data.

[0031] Specifically, this embodiment utilizes a standardized calibration target array combined with the least squares method to optimize and correct multi-terminal measurement errors, and constructs an initial reference surface model.

[0032] For example, in order to eliminate measurement errors and minor deviations in equipment installation, this embodiment deploys a standardized array of calibration target points on the structural surface. The actual positions of the target points are denoted as follows: ; Simultaneously, each sensing terminal performs laser ranging and visual imaging measurements on these target points, obtaining measurement values ​​(i.e., the first measurement data): the distance value measured by the i-th terminal on the k-th target point through the laser ranging module. The two-dimensional projected coordinates of the k-th target point extracted by the i-th terminal through the visual imaging module. ; In steps S1021-S1024, this embodiment uses the least squares method to optimize and correct the installation deviation and system error of the laser and vision sensors based on the multi-view target point measurement data. The objective function for measurement error is defined as: ; Where S represents the set of standardized calibration target points deployed on the structural surface for multi-terminal identification and ranging; Represents the three-dimensional spatial coordinates of the k-th target point; Represents i terminals to target point S k The actual measured value; Indicates the i-th terminal to the target point S k The theoretical ideal measurement value; E represents the overall measurement error function, which is used to accumulate and quantify the measurement errors of all terminals to all target points; By solving Obtain the set of correction parameters for each terminal, including the rotation correction matrix R. i Translation vector t i Other system parameters θ i (e.g., laser direction deviation, visual lens distortion parameters), to complete error correction; After calibration, the coordinates and measurement data of all sensing terminals are uniformly mapped to the reference coordinate system P0, and an initial reference surface model M0 in standard space is constructed for subsequent deformation offset calculation. The formula for constructing the reference surface is: ; Wherein, M0 represents the uniformly constructed structural initial state reference surface model, that is, the standard geometric state of the monitored structural surface before deformation; This represents the coordinates of the kth calibration target point after registration and correction in the reference coordinate system (i.e., the second measurement data).

[0033] S103. The construction building structure surface is measured by a multi-point sensing array according to a preset period to obtain the first real-time measurement data. The first real-time measurement data is registered to the initial reference surface model by an iterative nearest point algorithm to obtain the second real-time measurement data. The displacement data, velocity data and acceleration data of the construction building structure surface are determined based on the second real-time measurement data. In some embodiments, the first real-time measurement data is registered to the initial reference surface model using an iterative nearest-point algorithm to obtain the second real-time measurement data, including: S1031. Determine the original measurement data based on the initial datum surface model; S1032. Construct a registration objective function based on the first real-time measurement data and the original measurement data; S1033. Solve the registration objective function by using the iterative nearest point algorithm to obtain the rotation matrix and translation vector that minimizes the registration objective function. S1034. Register the first real-time measurement data according to the rotation matrix and translation vector to obtain the second real-time measurement data.

[0034] In some embodiments, determining displacement data, velocity data, and acceleration data of the construction structure surface based on second real-time measurement data includes: S1035. Determine the original measurement data based on the initial reference surface model; S1036. Determine the displacement vector sequence based on the second real-time measurement data and the original measurement data; S1037. Determine the velocity vector sequence and acceleration scalar sequence based on the displacement vector sequence; S1038. Use the displacement vector sequence as displacement data, the velocity vector sequence as velocity data, and the acceleration scalar sequence as acceleration data.

[0035] Specifically, after completing the deployment of the multi-point sensing array and the calibration of the reference surface, this embodiment enters the core stage of deformation monitoring, namely the continuous acquisition of structural surface morphology and estimation of spatial displacement. Through dual-mode collaborative sensing of laser ranging and visual recognition, point cloud information of the structural surface is acquired in real time. Based on the initial reference model, spatial matching and error models are used to accurately estimate the deformation displacement value, ultimately achieving continuous identification and quantitative analysis of structural morphological changes. For example, the specific implementation process is as follows: After the sensing terminal enters the monitoring state, it uses a set sampling period T. s Periodic data collection is performed. Each terminal obtains distance information (point cloud data) of key points on the structural surface through the laser module and acquires image features or structural textures (image data) from the corresponding viewpoint through the vision module. Record the time label for each round of sampling And establish the set of observation points at that moment: ; Among them, t n This represents the sampling time point of the nth round, i.e., the current time; n is a natural number, i.e., the sampling sequence number; This indicates that the i-th terminal is at time t. n The collected set of structural surface points (i.e., the first real-time measurement data); K i This represents the number of valid measurement points for the i-th terminal in this round of sampling, i.e., the number of spatial points contained in each frame of the point cloud; This indicates that terminal i is at time t. n The three-dimensional coordinates of the k-th point collected; In steps S1031-S1034, in order to compare the structural morphological changes at different times, this embodiment registers the current frame point cloud data to the unified coordinate system where the initial reference surface model M0 is located. An improved ICP (Iterative Closest Point) algorithm is used to solve the registration objective function, with the goal of finding the rigid transformation (R... n ,T n This makes the current point cloud Align the points with the reference plane as closely as possible, and output the set of points of the current structure plane after registration. (i.e., the second real-time measurement data), specifically, the rotation matrix and translation vector at the minimum value of the registration objective function are solved using the following formula: ; Among them, R n The rotation matrix is ​​a 3×3 orthogonal matrix, T. nRepresents the translation vector; This represents the k-th measurement point after merging (from multiple terminals, i.e., the first real-time measurement data), that is, the k-th structural point at time t. n The original measured coordinates; This represents the reference point (i.e., the original measurement data) corresponding to the k-th point in the reference plane; K represents the total number of all measurement points at the current moment, which is the sum of the number of points collected by each terminal. This represents the coordinates of the k-th point after registration, that is, the coordinates of the data collected at the current time after transformation to the reference coordinate system, characterizing the structure point at time t. n Location; In steps S1035-S1038, this embodiment is based on the k-th structural point at the reference time (t=0). (i.e., the original measurement data) and the coordinates of the k-th point after current acquisition and registration. (i.e., the second real-time measurement data), the displacement vector is defined as: ; That is, the offset of the k-th point in three-dimensional space at the current time; The overall structural deformation field modeling results are as follows: Characterizing the structural surface at time t n Continuous displacement distribution under; in, This represents the position coordinates of the k-th structural point at the reference time (initial time); This indicates that the k-th point is at time t. n The three-dimensional displacement vector characterizes the amount of structural deformation; This represents the displacement in the X direction. This represents the displacement in the Y direction. This represents the displacement in the Z direction. This represents the three-dimensional displacement field dataset of the structural surface at the current moment. It is a set of displacement vectors of all sampling points and is the direct output of deformation estimation. To achieve dynamic identification of structural evolution, a sequence of deformation variables is constructed within a continuous time window: ; Speed ​​estimate for each point: ; And calculate the maximum displacement and instantaneous acceleration: ; in, This indicates that the k-th point represents the entire time interval from t1 to t2. N The continuous displacement history sequence (i.e., displacement vector sequence) during the period. This indicates that the k-th point at time t n The velocity vector (i.e., the velocity vector sequence) is the difference between two adjacent displacements divided by the sampling period; This indicates that the k-th point at time t n The acceleration scalar (i.e., the sequence of acceleration scalars).

[0036] S104. Determine the deformation risk level based on displacement data, velocity data, acceleration data, and a preset grading threshold judgment algorithm; In some embodiments, the deformation risk level is determined based on displacement data, velocity data, acceleration data, and a preset grading threshold determination algorithm, including: S1041. Determine the first deformation risk score based on displacement data, velocity data, and acceleration data; S1042. The first deformation risk score is smoothed by the spatial moving average algorithm to obtain the second deformation risk score; S1043. The second deformation risk score is mapped to the deformation risk level through a graded threshold determination algorithm.

[0037] Specifically, in this embodiment, a multi-dimensional fusion indicator system and judgment model are constructed to achieve quantitative assessment of structural health status and risk level determination. The specific implementation process is as follows: In step S1041, the displacement data of monitoring point k is acquired. Speed ​​data and acceleration data Define a structural point deformation risk index, and use displacement, velocity, and acceleration information for quantitative scoring: ; The risk scores of neighboring points are smoothed using the spatial moving average method: ; in, Indicates that point k at time... The deformation risk index (i.e., the first deformation risk score) integrates the total risk level after considering displacement, velocity, and acceleration; α, β, and γ represent the fusion weighting coefficients, which control the proportion of displacement, velocity, and acceleration in the risk index, respectively; Nk represents the spatial neighborhood set of point k, defined as all neighboring measurement points whose Euclidean distance is less than a certain threshold r; |Nk| represents the number of points in the neighborhood (neighborhood size). The smoothed risk value (i.e., the second deformation risk score) of point k at the current time is the average of the risk values ​​of all points in its neighborhood; For example, this embodiment designs a multi-level risk threshold system through a hierarchical threshold determination algorithm, which will... The segmentation is mapped to risk levels, using the following rules:

[0038] in, δ1, δ2, and δ3 represent the risk level label of point k at time tn; δ1, δ2, and δ3 represent the risk level judgment thresholds, which convert continuous risk scores into graded labels. The response strategy corresponding to this multi-level risk threshold system can be designed as follows: when the system identifies that any point reaches level 3 (high risk), or multiple points are continuously at level 2 (medium risk) for more than a certain set threshold, a structural deformation warning will be automatically triggered and pushed to the monitoring terminal or the construction personnel's mobile device through the platform.

[0039] S105. Perform data synchronization, preprocessing, and visualization of displacement, velocity, and acceleration data, and execute a preset graded response strategy according to the deformation risk level. The first measurement data and the first real-time measurement data both include point cloud data and image data.

[0040] In some embodiments, displacement data, velocity data, acceleration data, and deformation risk level are synchronized, preprocessed, and visualized. A preset graded response strategy is executed based on the deformation risk level, including: S1051. The displacement data, velocity data, acceleration data, and deformation risk level are synchronized to the remote management platform through the communication network; S1052. Perform data filtering and time-series alignment on displacement data, velocity data, acceleration data, and deformation risk level; S1053. Render the three-dimensional engineering structure model of the building under construction according to the deformation risk level, and generate a time series trend chart and regional heat map based on displacement data, velocity data, acceleration data and deformation risk level. S1054. Display the three-dimensional engineering structure model, time series trend chart, and regional heat map through a remote management platform; S1055. Implement a graded response strategy based on the deformation risk level; S1056. Generate a structural health report based on displacement data, velocity data, acceleration data, and deformation risk level.

[0041] Specifically, in this embodiment, through data synchronization, visualization, and remote scheduling, real-time perception, multi-dimensional display, and intelligent linkage response of structural deformation information are achieved, forming a closed-loop management mechanism of monitoring-early warning-response, as follows: In S1051-S1052, this embodiment unifies the deformation, velocity, acceleration, and risk level data of the aforementioned monitoring points to the edge processing node. Considering the complex and ever-changing on-site environment, this part needs to have the following characteristics: It supports hierarchical synchronization of on-site data, and low-power wireless or fiber optic communication is used between monitoring nodes and relay nodes to ensure real-time performance. The relay equipment performs preliminary data screening, error detection, and time sequence alignment to improve data quality; The processed data is sent to a remote platform to achieve unified storage and query scheduling. If the edge nodes have sufficient computing power, they can directly complete risk identification and classification, and can run independently and cache data when the network is down. In S1053-S1054, after receiving the fusion analysis results, this embodiment will construct a multi-level, multi-view visualization interface based on the actual layout of the structures and geographical information, including but not limited to: Overlay of 3D structural models: Binds 3D engineering structural models to sensing array points, dynamically displays the risk level of different points through color changes, and supports interactive operations such as rotation and scaling; Time series trend charts: Each monitoring point can generate independent deformation-time curves, velocity-time curves, risk level trend charts, etc., and supports viewing historical and current status; Regional heatmaps and trend animations: Map heat to local areas, identify anomalous clusters, and support animation playback to observe the deformation evolution process; Intelligent filtering and alarm list: It can filter monitoring points of a specified level and list all monitoring points currently in a warning state and their recent evolution in a table format; In S1055, this embodiment, based on the aforementioned risk assessment results and visualization, supports setting different response mechanisms according to risk levels, thereby achieving intelligent and differentiated remote linkage response: Low risk (Level 1): Automatically recorded and added to the observation list, which is reviewed by the system at regular intervals without human intervention; Medium risk (Level 2): ​​The platform pushes graphic and text alarm information to the designated responsible person (such as the supervisor or technical person in charge) and suggests arranging manual review; High risk (Level 3): Immediately trigger the emergency notification mechanism via SMS, voice, or WeChat / APP, and notify all on-duty management personnel through a strong background notification window; Simultaneously, the video surveillance system automatically focuses on the relevant area and records images of the scene; In S1056, this embodiment automatically summarizes the analysis results, risk events and response records for each monitoring cycle, and generates structural deformation analysis reports by day, week or month, including but not limited to: structural health trend analysis, statistics of high-risk areas and time periods, evaluation of response efficiency and processing results, and analysis of data integrity and perception coverage.

[0042] It can be recognized that this embodiment establishes a high-precision reference coordinate system by deploying a laser and vision fusion sensing terminal array on the key surfaces of the building structure and using calibration target points to correct multi-source data errors; it acquires three-dimensional point clouds and images of the structural surface in real time, registers them to the reference model using an improved ICP algorithm to calculate displacement vectors, and integrates displacement, velocity, and acceleration to construct a deformation risk index and provide graded early warning; it combines edge computing to achieve synchronous data processing, dynamically displays the risk status through a three-dimensional visualization platform, and triggers a graded response mechanism. This invention forms a closed-loop management system of monitoring, early warning, and disposal, effectively improving the real-time performance, accuracy, and intelligence level of deformation monitoring in construction buildings.

[0043] Please see Figure 2 This application also provides an intelligent deformation sensing device for construction buildings, which can implement the above-mentioned method. The device includes: The multi-point sensing array registration module is used to deploy a multi-point sensing array on the construction building structure surface and register the multi-point sensing array to the reference coordinate system. The measurement error correction module is used to measure the calibration target array through a multi-point sensing array to obtain the first measurement data, correct the error of the first measurement data according to the actual position of the calibration target array to obtain the second measurement data, and construct the initial reference surface model of the construction building structure based on the second measurement data. The deformation information extraction module is used to measure the surface of the construction building structure according to a preset period through a multi-point sensing array to obtain the first real-time measurement data. The first real-time measurement data is then registered to the initial reference surface model through an iterative nearest point algorithm to obtain the second real-time measurement data. The displacement data, velocity data, and acceleration data of the construction building structure are determined based on the second real-time measurement data. The risk assessment module is used to determine the deformation risk level based on displacement data, velocity data, acceleration data, and a preset risk assessment algorithm with a threshold. The data processing and graded response module is used to synchronize, preprocess, and visualize displacement, velocity, and acceleration data, and execute preset graded response strategies according to the deformation risk level. The first measurement data and the first real-time measurement data both include point cloud data and image data.

[0044] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0045] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0046] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0047] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0048] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0049] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0050] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0051] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0052] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0053] This application provides an intelligent deformation sensing method and related equipment for construction buildings. The method involves deploying a multi-point sensing array on the structural surface of the construction building, registering and correcting the measurement data using a reference coordinate system and a calibration target array, unifying the measurement data of each point into the same coordinate system, effectively reducing measurement errors. An initial reference surface model is constructed based on the corrected data, providing an accurate reference for subsequent deformation calculations and improving monitoring accuracy. By acquiring real-time measurement data of the structural surface of the construction building and registering it to the initial reference surface model using an iterative nearest-point algorithm, high-precision registration between real-time and initial measurement data is achieved, ensuring the accuracy of deformation extraction. Based on the registered real-time measurement data, displacement, velocity, and acceleration information of the structural surface are extracted, effectively reflecting the deformation characteristics of the construction building in the time dimension, providing multi-dimensional data support for subsequent risk level classification. Through graded deformation risk level judgment and graded response strategies, graded early warning of the deformation state of the construction building is achieved. Combined with data synchronization and visualization, the real-time performance, accuracy, and intelligence level of construction building deformation monitoring are effectively improved.

[0054] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0055] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0056] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0058] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0061] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0062] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0063] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for intelligent deformation sensing in construction buildings, characterized in that, include: A multi-point sensing array is deployed on the surface of the building structure, and the multi-point sensing array is registered to the reference coordinate system. The calibration target array is measured by the multi-point sensing array to obtain first measurement data. The first measurement data is then corrected for errors based on the actual position of the calibration target array to obtain second measurement data. An initial reference surface model of the construction building structure surface is then constructed based on the second measurement data. The multi-point sensing array measures the surface of the construction building structure according to a preset period to obtain first real-time measurement data. The first real-time measurement data is then registered to the initial reference surface model using an iterative nearest point algorithm to obtain second real-time measurement data. The displacement data, velocity data, and acceleration data of the construction building structure surface are then determined based on the second real-time measurement data. The deformation risk level is determined based on the displacement data, the velocity data, the acceleration data, and a preset grading threshold judgment algorithm. The displacement data, velocity data, and acceleration data are synchronized, preprocessed, and visualized. A preset graded response strategy is executed according to the deformation risk level. The first measurement data and the first real-time measurement data both include point cloud data and image data.

2. The intelligent deformation sensing method for construction buildings according to claim 1, characterized in that, The step of deploying a multi-point sensing array on the construction structure surface and registering the multi-point sensing array to a reference coordinate system includes: Based on a preset spatial distribution density algorithm, several deformation-sensitive points on the construction building structure surface are selected as monitoring nodes, and sensing terminals are deployed on each monitoring node to obtain the multi-point sensing array. The position information of each sensing terminal in the multi-point sensing array is obtained by a three-dimensional measurement device, and a set of terminal installation coordinate points is constructed based on the position information. The reference coordinate system is constructed based on the preset reference points, and the reference point set is determined based on the reference coordinate system; Based on the spatial correspondence between the terminal installation coordinate point set and the reference point set, the terminal installation coordinate point set is registered to the reference coordinate system using a rigid transformation model. The sensing terminal includes a laser module and a vision module.

3. The intelligent deformation sensing method for construction buildings according to claim 1, characterized in that, The step of correcting the first measurement data based on the actual position of the calibrated target array to obtain the second measurement data includes: The theoretical ideal measurement value is determined based on the actual position of the calibration target array; Construct a measurement error objective function based on the theoretical ideal measurement value and the first measurement data; The measurement error objective function is solved by the least squares method to obtain the set of correction parameters that minimize the measurement error objective function; The first measurement data is corrected for errors based on the set of correction parameters to obtain the second measurement data.

4. The intelligent deformation sensing method for construction buildings according to claim 1, characterized in that, The step of registering the first real-time measurement data to the initial reference surface model using the iterative nearest point algorithm to obtain the second real-time measurement data includes: The original measurement data are determined based on the initial reference surface model; A registration objective function is constructed based on the first real-time measurement data and the original measurement data; The registration objective function is solved by the iterative nearest point algorithm to obtain the rotation matrix and translation vector that minimizes the registration objective function. The first real-time measurement data is registered using the rotation matrix and the translation vector to obtain the second real-time measurement data.

5. The intelligent deformation sensing method for construction buildings according to claim 1, characterized in that, The step of determining the displacement data, velocity data, and acceleration data of the construction structure surface based on the second real-time measurement data includes: The original measurement data are determined based on the initial reference surface model; A displacement vector sequence is determined based on the second real-time measurement data and the original measurement data; Determine the velocity vector sequence and acceleration scalar sequence based on the displacement vector sequence; The displacement vector sequence is used as the displacement data, the velocity vector sequence is used as the velocity data, and the acceleration scalar sequence is used as the acceleration data.

6. The intelligent deformation sensing method for construction buildings according to claim 1, characterized in that, The step of determining the deformation risk level based on the displacement data, the velocity data, the acceleration data, and a preset grading threshold judgment algorithm includes: A first deformation risk score is determined based on the displacement data, the velocity data, and the acceleration data; The first deformation risk score is smoothed using a spatial moving average algorithm to obtain the second deformation risk score. The second deformation risk score is mapped to the deformation risk level using the grading threshold determination algorithm.

7. The intelligent deformation sensing method for construction buildings according to claim 1, characterized in that, The process of synchronizing, preprocessing, and visualizing the displacement data, velocity data, acceleration data, and deformation risk level, and executing a preset graded response strategy based on the deformation risk level, includes: The displacement data, velocity data, acceleration data, and deformation risk level are synchronized to the remote management platform via a communication network. The displacement data, velocity data, acceleration data, and deformation risk level are filtered and time-series aligned. The three-dimensional engineering structure model of the construction building is rendered according to the deformation risk level, and a time series trend chart and regional heat map are generated based on the displacement data, velocity data, acceleration data and deformation risk level. The remote management platform displays the three-dimensional engineering structure model, the time series trend chart, and the regional heat map. The graded response strategy is executed according to the deformation risk level. A structural health report is generated based on the displacement data, the velocity data, the acceleration data, and the deformation risk level.

8. A construction building intelligent deformation sensing device, characterized in that, The device includes: A multi-point sensing array registration module is used to deploy a multi-point sensing array on the surface of the construction building structure and register the multi-point sensing array to a reference coordinate system. The measurement error correction module is used to measure the calibration target array through the multi-point sensing array to obtain first measurement data, correct the error of the first measurement data according to the actual position of the calibration target array to obtain second measurement data, and construct the initial reference surface model of the construction building structure surface according to the second measurement data. The deformation information extraction module is used to measure the surface of the construction building structure according to a preset period through the multi-point sensing array to obtain first real-time measurement data, and to register the first real-time measurement data to the initial reference surface model through an iterative nearest point algorithm to obtain second real-time measurement data. The displacement data, velocity data and acceleration data of the construction building structure are determined based on the second real-time measurement data. The graded risk assessment module is used to determine the deformation risk level based on the displacement data, the velocity data, the acceleration data, and a preset graded threshold assessment algorithm. The data processing and graded response module is used to synchronize, preprocess, and visualize the displacement data, velocity data, and acceleration data, and execute a preset graded response strategy according to the deformation risk level. The first measurement data and the first real-time measurement data both include point cloud data and image data.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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