Digital construction method and device
By using a multi-view high-speed camera array and image processing technology, the problem of difficulty in detecting and locating early signs of local instability in the formwork support system has been solved, achieving full-view monitoring and precise positioning, thus improving construction safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-03-13
AI Technical Summary
In existing technologies, it is difficult to detect and accurately locate early signs of local instability at any location in the template support system, posing a safety hazard.
A multi-view high-speed industrial camera array is used to acquire continuous image sequences of the template support system. Image datasets in a unified coordinate system are generated through feature point matching and spatial registration. Pixel displacement tracking and frequency domain transformation are performed to generate a vibration spectrum distribution map. Vibration energy anomalies are detected and the three-dimensional spatial coordinates of the abnormal area are calculated by combining triangulation. Risk assessment indicators are generated and early warning signals are output.
It achieves full-view monitoring of the formwork support system, enabling timely detection of minute vibration changes and precise location of resonance precursors, significantly improving construction safety.
Smart Images
Figure CN121661111A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of concrete pouring construction monitoring technology, and more specifically, to the fields of construction safety monitoring, formwork support system vibration analysis, high-speed visual measurement and structural instability early warning technology, and specifically to a digital construction method and device. Background Technology
[0002] In concrete pouring construction, the formwork support system bears the static load of wet concrete and the dynamic impact load generated by vibration. The formwork support system consists of numerous uprights and crossbars, and its stability directly affects construction safety. During pouring, the concrete load continuously increases, while vibration generates dynamic excitation. When the excitation frequency approaches the natural frequency of a particular upright, local resonance occurs, and the amplitude amplifies sharply. This precursor to instability manifests as an increase in the amplitude of minute vibrations. Instability of the formwork support system often begins with the local buckling of any upright, and it is impossible to predict which upright will fail first. Therefore, comprehensive real-time monitoring of the support system is necessary.
[0003] Current technology primarily employs vibration monitoring by installing accelerometers at key locations, combined with manual inspections to observe for any significant deformation of the supports. Accelerometers measure vibration acceleration signals at specific monitoring points, and amplitude thresholds are set to determine the presence of abnormal vibrations. Manual inspections rely on construction management personnel conducting regular checks, visually observing for tilting of uprights, deformation of crossbars, and other similar phenomena.
[0004] However, existing technologies have the following drawbacks: First, the number of accelerometers is limited, covering only a small number of pre-selected key points. Since template support systems typically contain hundreds or even thousands of uprights, the discretely arranged sensors have numerous blind spots, making it impossible to monitor all members simultaneously. Second, early signs of instability manifest as an increase in the amplitude of minute vibrations, which are difficult to detect visually during manual inspections. Third, even if sensors detect abnormal vibrations, their fixed installation locations and limited number make it impossible to accurately pinpoint the exact location of instability, especially when multiple uprights vibrate simultaneously, making it difficult to distinguish which upright is at the critical point of instability.
[0005] Therefore, the main technical problem with the existing technology is that due to the limited number of acceleration sensors, there are blind spots in the monitoring, and manual inspection cannot detect minute vibration changes. As a result, it is difficult to detect and accurately locate the early signs of local instability at any position of the template support system, which poses a safety hazard. Summary of the Invention
[0006] This invention provides a digital construction method that solves the technical problem in related technologies that it is difficult to detect and accurately locate the early signs of local instability at any position in the formwork support system.
[0007] This invention discloses a digital construction method, comprising: Step 1: Obtain the continuous image sequence of the template support system and generate a multi-view synchronized image dataset; Step 2: Analyze the feature point matching relationship in the multi-view synchronized image dataset, calculate the spatial registration transformation matrix, and generate a sequence of registered images in a unified coordinate system; Step 3: Perform pixel displacement tracking on the registered image sequence, calculate the displacement vector sequence of each pixel, and generate full-view displacement field time series data; Step 4: Perform frequency domain transformation on the full-field displacement field time series data, extract the vibration dominant frequency and energy value at each pixel location, and generate a vibration spectrum distribution map; Step 5: Detect abnormally concentrated areas of vibration energy in the vibration spectrum distribution map, and calculate the three-dimensional spatial coordinates of the abnormal areas by combining multi-view triangulation to generate a set of suspected instability points; Step 6: Perform frequency matching degree calculation and energy change trend analysis on each point in the set of suspected instability points to generate risk assessment indicators; Step 7: Determine the resonance risk based on the frequency matching degree and energy growth slope, generate a support instability early warning signal and mark the coordinates of the instability location.
[0008] Furthermore, in step 1, a high-speed industrial camera array is deployed at multiple fixed positions around the template support system to synchronously acquire a continuous image sequence with a frame rate of not less than 500fps; each camera is triggered to acquire images according to a unified clock signal, and the acquired images are organized according to timestamps and camera identifiers to generate a multi-view synchronous image dataset.
[0009] Furthermore, in step 2, feature point detection and matching are performed on the image sequences of each camera; using the same feature points in the overlapping fields of view, the spatial registration transformation matrix between each camera is calculated based on epipolar geometric constraints; and the images from each viewpoint are projected onto the same spatial coordinate system through the transformation matrix to generate a registered image sequence.
[0010] Furthermore, in step 3, in the registered unified coordinate system, optical flow pixel displacement tracking is performed on the continuous frame images; for each pixel in the image, its displacement vector between adjacent frames is calculated; the displacement vectors of multiple consecutive frames are organized in time order to generate displacement field temporal data covering the entire field of view.
[0011] Furthermore, in step 4, a fast Fourier transform is performed on the displacement vector sequence of each pixel to convert the time-domain displacement signal into a frequency-domain spectrum; the vibration dominant frequency and the energy value of the corresponding frequency band of the pixel position are extracted from the spectrum; the vibration dominant frequencies and energy values of all pixels are organized according to their spatial positions to generate a vibration spectrum distribution map covering the entire field of view.
[0012] Furthermore, in step 5, the vibration energy value of each pixel is calculated in the vibration spectrum distribution map; pixels with energy values exceeding the dynamic threshold are detected, and adjacent abnormal pixels are clustered to generate abnormal cluster regions; using the pixel coordinates of the same abnormal region in the multi-view image, the three-dimensional spatial coordinates corresponding to the abnormal region are calculated based on the triangulation principle and marked as a set of suspected instability points.
[0013] Furthermore, in step 6, for each point in the set of suspected instability points, the dominant vibration frequency is extracted and compared with the preset theoretical natural frequency of the template support pole to calculate the frequency matching degree; the vibration energy value sequence of the suspected instability point within a continuous time window is extracted, the slope of the energy value change with time is calculated, and it is determined whether there is a continuous growth trend.
[0014] Furthermore, in step 7, when the frequency matching degree of a suspected instability point exceeds a preset matching threshold and the energy growth slope exceeds a preset slope threshold, it is determined that the suspected instability point has a resonance risk; a support instability warning signal is generated, and the precise position coordinates of the pole with resonance risk are highlighted in the three-dimensional space view.
[0015] Furthermore, based on step 7, step 8 is also included: pushing the early warning signal and instability location information to the construction control system in real time, and outputting instructions to pause pouring or adjust the vibration position.
[0016] Furthermore, the formula for calculating the frequency matching degree is: Where is the frequency matching degree, is the dominant vibration frequency, and is the theoretical natural frequency.
[0017] Furthermore, the formula for calculating the slope of the energy change is: Where is the energy growth slope, is the i-th time point, is the energy value at the i-th time point, and are the mean values of time and energy respectively, and is the number of sampling points.
[0018] Furthermore, the formula for calculating the theoretical natural frequency is as follows: Where is the length of the pole, is the elastic modulus of the material, is the moment of inertia of the cross section, is the density of the material, is the cross-sectional area, and is the eigenvalue coefficient related to the end constraint conditions.
[0019] Furthermore, the formula for calculating the dynamic threshold is: Where is the dynamic threshold, is the average energy of the entire field of view within the current time window, is the standard deviation, and is the sensitivity coefficient.
[0020] The present invention also discloses a digital construction device, comprising: High-speed industrial camera array, used to synchronously acquire continuous image sequences of a template support system; The image processing module is used to perform spatial registration, pixel displacement tracking, and frequency domain transformation to generate a vibration spectrum distribution map; Anomaly detection module is used to detect areas of abnormal vibration energy accumulation and calculate three-dimensional spatial coordinates; The risk assessment module is used to calculate frequency matching degree and energy change trend, and generate risk assessment indicators. The early warning output module is used to determine the risk of resonance and generate an early warning signal, which is then output to the construction control system.
[0021] This invention achieves full-field coverage acquisition of the template support system through a multi-view high-speed industrial camera array. Spatial registration is completed by matching feature points in the overlapping areas of the field of view, unifying the scattered image data into the same coordinate system. Through pixel-level optical flow displacement tracking and point-by-point Fourier transform, a vibration spectrum distribution map covering the entire monitoring area is generated, transforming discrete point monitoring into continuous surface monitoring.
[0022] First, by employing high-speed visual vibration spectrum imaging technology, the monitoring coverage is expanded from a limited number of sensor installation points to any location within the camera's field of view. This overcomes the monitoring blind spot problem caused by the limited number of acceleration sensors and enables simultaneous monitoring of all members of the template support system.
[0023] Secondly, since the frequency and energy characteristics of minute vibrations can be extracted through frequency domain analysis of pixel displacement, the problem that minute vibration changes cannot be detected by manual inspection is overcome, and abnormal vibrations can be detected in time during the instability precursor stage.
[0024] Third, by employing a combined analysis mechanism of vibration energy anomaly detection and frequency matching criteria, the dominant vibration frequency is compared with the theoretical natural frequency of the pole, and the energy growth trend is used for judgment. Therefore, it is possible to identify resonance precursors that are close to the natural frequency of the pole, effectively reducing the false alarm rate.
[0025] Fourth, because multi-view triangulation is used to achieve precise three-dimensional spatial positioning of abnormal areas, the precise location coordinates of poles with resonance risks can be highlighted in the three-dimensional spatial view, providing accurate information for on-site personnel to quickly locate and handle the situation.
[0026] In summary, this invention solves the technical problem that it is difficult to detect and accurately locate the early signs of local instability at any position in the formwork support system in the prior art. It achieves the technical effects of continuous monitoring of the entire field of view, high sensitivity detection of small vibrations, accurate identification of resonance precursors and precise location of instability, and significantly improves the safety monitoring level of concrete pouring construction. Attached Figure Description
[0027] Figure 1 This is a flowchart of the digital construction method of the present invention. Detailed Implementation
[0028] In concrete pouring construction, the formwork support system bears the static load of wet concrete and the dynamic impact load generated by vibration. The formwork support system consists of numerous uprights and horizontal bars, and its stability directly affects construction safety. Current practices involve installing accelerometers at key locations for vibration monitoring, combined with manual inspections to observe for any significant deformation of the supports.
[0029] However, the limited number of accelerometers can only cover a small number of pre-selected key points, and the instability of the formwork support system often begins with the local buckling of any single upright, making it impossible to predict which upright will fail first. During the pouring process, the concrete load continuously increases, and vibration generates dynamic excitation. When the excitation frequency approaches the natural frequency of a certain upright, local resonance occurs, and the amplitude amplifies sharply. This precursor to instability manifests as an increase in the amplitude of minute vibrations, which is difficult to detect during manual inspection. Furthermore, the discretely arranged sensors have monitoring blind spots and cannot simultaneously monitor all members, leading to the omission of instability precursors.
[0030] Therefore, the existing technology has the following technical problems: due to the limited number of acceleration sensors, there are monitoring blind spots, and manual inspection cannot detect minute vibration changes, making it difficult to detect and accurately locate the early signs of local instability at any position of the template support system in a timely manner.
[0031] According to an embodiment of this method, the method is applied to a concrete pouring construction scenario. The computer device executing this method is communicatively connected to a high-speed industrial camera array deployed around the formwork support system. The high-speed industrial camera array includes multiple fixed-position industrial cameras, each with a field of view covering different areas of the support system, and adjacent cameras having overlapping fields of view. The method includes the following steps: Step 1: Obtain the continuous image sequence of the template support system and generate a multi-view synchronized image dataset.
[0032] A high-speed industrial camera array, deployed at multiple fixed locations around the template support system, synchronously acquires a continuous image sequence with a frame rate of no less than 500fps. Each camera is triggered to acquire images by a unified clock signal, and the acquired images are organized according to timestamps and camera identifiers to generate a multi-view synchronous image dataset.
[0033] It should be noted that the aforementioned frame rate of at least 500fps refers to the camera's acquisition frame rate meeting the requirements for capturing the vibration characteristics of the template support rods. The natural frequencies of the template support rods are typically in the range of 10Hz to 100Hz. According to the Nyquist sampling theorem, the sampling frequency must be greater than twice the highest frequency of the signal. A frame rate of 500fps can effectively capture vibration signals within 250Hz, satisfying the requirement for complete acquisition of the rod's vibration characteristics.
[0034] In this embodiment, at the construction site of the concrete pouring for the basement roof slab of a commercial complex, the formwork support system uses φ48×3.5mm steel pipe uprights, with an upright height of 4.2m and an upright spacing of 0.9m×0.9m, supporting an area of approximately 850 square meters. Four high-speed industrial cameras are deployed around the support system at the construction site. The camera configuration and acquisition parameters are shown in the table below.
[0035] Table 1 Configuration parameters of high-speed industrial cameras The acquisition time was set to 1.5 seconds, with each camera simultaneously acquiring 1500 frames of images. These images were then organized by timestamp and camera identifier to generate a multi-view synchronized image dataset containing 6000 frames. The overlap of the fields of view between adjacent cameras was approximately 4 meters wide, covering about 45 poles.
[0036] Step 2: Analyze the feature point matching relationship in the multi-view synchronized image dataset, calculate the spatial registration transformation matrix, and generate a registered image sequence in a unified coordinate system.
[0037] Feature point detection and matching are performed on the image sequences from each camera. Using corresponding feature points in the overlapping fields of view, the spatial registration transformation matrix between cameras is calculated based on epipolar geometry constraints. The images from each viewpoint are then projected onto the same spatial coordinate system using the transformation matrix to generate a registered image sequence.
[0038] The aforementioned feature point detection and matching employs the ORB feature matching algorithm. The input to the ORB feature matching algorithm is the sequence of grayscale images acquired by each camera, and the output is the set of coordinates of the detected feature points in each image and the feature point matching pairs between images from different viewpoints.
[0039] It should be noted that the spatial registration transformation matrix mentioned above refers to the matrix describing the geometric transformation relationship between different camera coordinate systems. For any two cameras, their spatial registration transformation matrix includes rotation and translation components, enabling points in the camera coordinate system to be transformed to the camera coordinate system. Step 3: Perform pixel displacement tracking on the registered image sequence, calculate the displacement vector sequence of each pixel, and generate full-view displacement field time series data.
[0040] In the registered unified coordinate system, optical flow pixel displacement tracking is performed on consecutive frames of images. For each pixel in the image, its displacement vector between adjacent frames is calculated. The displacement vectors of multiple consecutive frames are organized in temporal order to generate temporal displacement field data covering the entire field of view.
[0041] In this embodiment, to improve the accuracy and robustness of displacement tracking, the pyramid optical flow method is used for pixel displacement calculation. The pyramid optical flow method decomposes the image at multiple scales, first estimating large displacements at low-resolution layers, and then refining the calculation layer by layer to high-resolution layers, thereby enabling the simultaneous handling of tracking requirements for both large and small displacements.
[0042] The aforementioned pyramid optical flow method takes registered consecutive grayscale image pairs as input and outputs a two-dimensional displacement vector of each pixel between adjacent frames.
[0043] Step 4: Perform frequency domain transformation on the full-view displacement field time series data, extract the vibration dominant frequency and energy value at each pixel position, and generate a vibration spectrum distribution map.
[0044] Perform a Fast Fourier Transform on the displacement vector sequence of each pixel to convert the time-domain displacement signal into a frequency-domain spectrum. Extract the dominant vibration frequency and the energy value of the corresponding frequency band from the spectrum. Organize the dominant vibration frequencies and energy values of all pixels according to their spatial locations to generate a vibration spectrum distribution map covering the entire field of view.
[0045] It should be noted that the aforementioned dominant vibration frequency refers to the frequency component with the highest energy in the spectrum, and the aforementioned energy value refers to the integral of the spectral energy within a set bandwidth near the dominant frequency. For the displacement timing of a pixel, its spectrum is: Furthermore, the displacement time sequence is a discrete sampling sequence in actual calculation, where , , ..., are the displacement values at the 0th, 1st, ..., N-1th sampling times, respectively, and is the number of sampling points. The spectrum is calculated using discrete Fourier transform.
[0046] The dominant vibration frequency and energy value are as follows: Furthermore, the frequency search range is determined based on the vibration characteristics of the template support pole, wherein the value is 5Hz to eliminate low-frequency drift interference, and the value is half of the sampling frequency, i.e., the Nyquist frequency.
[0047] Where is the set half-width of the frequency band.
[0048] Furthermore, the value of the half-width of the frequency band is related to the spectral resolution. If the acquisition time is , then the spectral resolution is , and the value is an integer multiple of the spectral resolution, with a typical value range of to .
[0049] A frequency domain transformation was performed on the acquired 1.5-second image data, with a spectral resolution of Hz and a half-width of 2Hz. Within the CAM-02 field of view, pixels located at the pole positions (e.g., row 1024, column 768) were selected for FFT calculation. The displacement time-series signal was then transformed in the frequency domain to obtain its spectral curve. The spectral analysis results of some pixels extracted from the full field-of-view displacement field are shown in the table below.
[0050] Table 2. Vibration spectrum analysis results of typical pixels Step 5: Detect abnormally concentrated areas of vibration energy in the vibration spectrum distribution map, and calculate the three-dimensional spatial coordinates of the abnormal areas by combining multi-view triangulation to generate a set of suspected instability points.
[0051] In the vibration spectrum distribution map, the vibration energy value of each pixel is calculated. Pixels with energy values exceeding the dynamic threshold are detected, and adjacent abnormal pixels are clustered to generate abnormal cluster regions. Using the pixel coordinates of the same abnormal region in multi-view images, the three-dimensional spatial coordinates corresponding to the abnormal region are calculated based on the principle of triangulation and marked as a set of suspected instability points.
[0052] Furthermore, the specific calculation method of the triangulation principle is as follows: Let the pixel coordinates of the same abnormal region in the camera and the camera image be and , respectively. Based on the camera intrinsic parameter matrix, and the spatial registration transformation matrix between the cameras, two line-of-sight equations are constructed. The intersection or nearest point of the two lines of sight is solved as the three-dimensional spatial coordinates of the abnormal region, where represents the transpose.
[0053] The aforementioned clustering employs the DBSCAN density clustering algorithm. The input to the DBSCAN density clustering algorithm is a set of two-dimensional image coordinates of anomalous pixels, and the output is a set of anomalous cluster regions, where each cluster region contains a group of spatially adjacent anomalous pixels.
[0054] Furthermore, the key parameters of the DBSCAN density clustering algorithm include the neighborhood radius and the minimum number of points. The neighborhood radius is determined based on the image resolution and the projection width of the pole in the image, and its value is 1 to 3 times the projection width of the pole in pixels. The minimum number of points is determined based on the minimum effective area of the clustered region, and its value ranges from 3 to 10.
[0055] In this embodiment, to reduce false detections, a dynamic threshold is used to determine energy anomaly regions. The dynamic threshold is calculated based on the mean and standard deviation of the energy across the entire field of view within the current time window. Wherein, is a configurable sensitivity coefficient, typically ranging from 2 to 4.
[0056] Furthermore, the length of the current time window is determined based on the frequency characteristics of the vibration signal, and is set to 10 to 50 times the vibration period to be detected, in order to ensure the frequency resolution of the spectrum analysis. The typical value range is 0.5 seconds to 2 seconds.
[0057] Based on the above spectral analysis results, the mean energy (mm²·Hz) and standard deviation (mm²·Hz) of the entire field of view within the current time window are calculated. With a sensitivity coefficient of 3, the dynamic threshold is: Pixels with energy values exceeding the threshold were marked as outliers. DBSCAN clustering (pixels, ) was performed on the outlier pixels, identifying two anomalous clustering regions. Triangulation was performed on the outlier regions using images from both CAM-02 and CAM-03 perspectives. The pixel coordinates of a certain outlier region in CAM-02 are given, and the corresponding pixel coordinates in CAM-03 are given. The three-dimensional spatial coordinates and the set of suspected unstable points obtained through triangulation are shown in the table below.
[0058] Table 3 Three-dimensional spatial coordinates of suspected instability points Step 6: Perform frequency matching degree calculation and energy change trend analysis on each point in the set of suspected instability points to generate risk assessment indicators.
[0059] For each point in the set of suspected instability points, its dominant vibration frequency is extracted and compared with the pre-set theoretical natural frequency of the template support column to calculate the frequency matching degree. Furthermore, the frequency matching degree is constrained to be 0 when the calculation result is less than 0, which indicates a perfect match.
[0060] Simultaneously, the vibration energy value sequence of suspected instability points within a continuous time window is extracted, where , , ..., n are the vibration energy values at time points 1, 2, ..., n, respectively. The slope of the energy value change with time is calculated to determine whether it shows a continuous increasing trend. Where is the time index, is the i-th time point, and and are the mean values of time and energy, respectively.
[0061] Furthermore, the relationship between the number of sampling points and the time window length of the energy value sequence is as follows: where is the sampling interval of the energy value, and the value is an integer multiple of the spectral analysis time window; the time window length is determined according to the typical development time of the instability precursor, and the value ranges from 5 seconds to 30 seconds.
[0062] It should be noted that the above-mentioned theoretical natural frequency refers to the first-order bending natural frequency calculated in advance based on the material properties (elastic modulus, density) and geometric parameters (length, moment of inertia of section, end constraints) of the template support upright.
[0063] Furthermore, the formula for calculating the theoretical natural frequency is as follows: Where is the length of the pole, is the elastic modulus of the material, is the moment of inertia of the section, is the density of the material, is the cross-sectional area, and is the eigenvalue coefficient related to the end constraint conditions, for both ends of the constraint being hinged, and for one end being fixed and the other end being free.
[0064] The material parameters for the φ48×3.5mm steel pipe uprights are: elastic modulus Pa, density kg / m³; geometric parameters are: upright length m, outer diameter mm, wall thickness mm, cross-sectional area m², and moment of inertia m. 4 Calculate the theoretical natural frequency based on the hinged constraint at both ends: Calculate the frequency matching degree for the dominant vibration frequency (Hz) of the suspected instability point SP-01: Within a continuous 15-second monitoring time window (sampling interval of 1.5 seconds, for a total of 10 sampling points), the energy value sequence of SP-01 was extracted and the energy growth slope was calculated.
[0065] Table 4 Energy Value Time Series of Suspected Instability Point SP-01 The energy growth slope (mm²·Hz / s) was calculated using linear regression. The risk assessment indicators for the two suspected instability points are summarized in the table below.
[0066] Table 5 Risk Assessment Indicators for Suspicious Instability Points Step 7: Determine the resonance risk based on the frequency matching degree and energy growth slope, generate a support instability early warning signal and mark the coordinates of the instability location.
[0067] When the frequency matching degree of a suspected instability point exceeds a preset matching threshold (e.g., 0.9) and the energy growth slope exceeds a preset slope threshold, the suspected instability point is determined to have a resonance risk. A support instability early warning signal is generated, and the precise location coordinates of the pole with resonance risk are highlighted in the 3D spatial view.
[0068] Furthermore, the preset slope threshold is a positive value, and its value is determined based on the statistical characteristics of the energy value sequence. Let the standard deviation of the energy value sequence be and the time window length be , then , where is a configurable sensitivity coefficient with a value range of 0.5 to 2.
[0069] Set the frequency matching threshold. Calculate the slope threshold based on the standard deviation of the energy value sequence (mm²·Hz), the time window length (s), and the sensitivity coefficient. Resonance risk was assessed for two suspected instability points: SP-01 met both the frequency matching and energy growth slope conditions, indicating a resonance risk; SP-02 met the frequency matching but not the energy growth slope condition, so no warning was triggered, but it was marked as a continuously monitored object. The system generated a level-two warning signal, highlighting pole A-15 (coordinate m) in red in the 3D spatial view.
[0070] Table 6 Resonance Risk Assessment Results In addition to step 7, the following steps are also included: Step 8: Push the early warning signal and instability location information to the construction control system in real time, and output the pouring pause or vibration position adjustment command.
[0071] The generated early warning signals and instability location information are pushed to the pouring control console and the on-site safety broadcast system in real time. Based on the early warning level and instability location, corresponding control commands are automatically generated, including pouring pause commands or vibration position adjustment commands, and output to the construction control system for execution.
[0072] Technical effects of this embodiment: This implementation method achieves full-field coverage acquisition of the template support system using a multi-view high-speed industrial camera array. Spatial registration is completed by matching feature points in the overlapping areas of the field of view, unifying the scattered image data into the same coordinate system. Through pixel-level optical flow displacement tracking and point-by-point Fourier transform, a vibration spectrum distribution map covering the entire monitoring area is generated, transforming discrete point monitoring into continuous surface monitoring.
[0073] Because it employs high-speed visual vibration spectrum imaging technology, the monitoring coverage is expanded from limited sensor installation points to any location within the camera's field of view, thus overcoming the monitoring blind spot problem caused by the limited number of accelerometers. Since frequency and energy characteristics of minute vibrations can be extracted through frequency domain analysis of pixel displacement, the problem of not being able to detect minute vibration changes during manual inspections is overcome. Because it uses a joint analysis mechanism of vibration energy anomaly detection and frequency matching criteria, it can identify resonance precursors close to the natural frequency of the support pole. Therefore, this method solves the technical problem of the difficulty in timely detection and accurate location of local instability precursors at any position in the template support system.
Claims
1. A digital construction method, characterized in that, Includes the following steps: A high-speed industrial camera array deployed around the template support system is used to synchronously acquire continuous image sequences. The fields of view of each camera cover different areas of the support system and overlap. Feature point detection and matching are performed on image sequences from each camera. The spatial registration transformation matrix between cameras is calculated using the same feature points in the overlapping fields of view, and the images from each viewpoint are unified to the same spatial coordinate system. Perform pixel displacement tracking on the registered consecutive frame images and calculate the displacement vector sequence of each pixel in the time series. Perform frequency domain transformation on the displacement vector sequence of each pixel, extract the vibration dominant frequency and the energy value of the corresponding frequency band at that pixel location, and generate a vibration spectrum distribution map covering the entire field of view; In the vibration spectrum distribution map, abnormal clusters of vibration energy values exceeding a preset multiple of the average value of the surrounding area are detected. The three-dimensional spatial coordinates corresponding to the abnormal areas are calculated by combining multi-view triangulation, and a set of suspected instability points is generated. For each point in the set of suspected instability points, the frequency matching degree is calculated by comparing its dominant vibration frequency with the theoretical natural frequency of the template support pole, and the slope of the vibration energy value of the point over time is monitored. When the frequency matching degree of a certain point exceeds the threshold and the slope of the energy change is positive, it is determined that there is a risk of resonance at that point, a support instability warning signal is generated, and the three-dimensional spatial coordinates of the pole are marked.
2. The method according to claim 1, characterized in that, The high-speed industrial camera array has a frame rate of no less than 500fps. Each camera is triggered to acquire data by a unified clock signal. The acquired images are organized by timestamp and camera identifier to generate a multi-view synchronous image dataset.
3. The method according to claim 1, characterized in that, The spatial registration transformation matrix includes rotation and translation components. For any two cameras i and j, the points in the coordinate system of camera i are rotated through the rotation component in the registration transformation matrix, and then the translation component is superimposed to obtain the coordinates of the corresponding points in the coordinate system of camera j.
4. The method according to claim 1, characterized in that, The pixel displacement tracking method uses the pyramid optical flow method, which decomposes the image into multiple scales. First, it estimates large displacements at low-resolution layers, and then refines the image layer by layer to high-resolution layers to obtain pixel displacement vectors.
5. The method according to claim 1, characterized in that, The frequency matching degree is calculated as follows: subtract the absolute value of the difference between the vibration dominant frequency and the theoretical natural frequency from the value 1, and divide the result by the theoretical natural frequency to obtain the frequency matching degree index. The closer the index value is to 1, the better the vibration dominant frequency matches the theoretical natural frequency.
6. The method according to claim 1, characterized in that, The slope of energy change is obtained by linear regression calculation on the vibration energy value sequence within a continuous time window. Specifically, the product of the difference between each time point and the time mean and the difference between each energy value and the energy mean is calculated and divided by the sum of the squares of the differences between each time point and the time mean to obtain the slope value of energy change with time.
7. The method according to claim 1, characterized in that, The abnormal clustering areas whose detected vibration energy values exceed a preset multiple of the average value of the surrounding area are determined using a dynamic threshold. The dynamic threshold is calculated based on the average energy value and standard deviation of the entire field of view within the current time window, specifically the average energy value of the entire field of view plus the product of the sensitivity coefficient and the standard deviation.
8. The method according to claim 1, characterized in that, The dominant vibration frequency is the frequency component with the highest energy in the spectrum of the displacement vector sequence after frequency domain transformation, and the energy value of the corresponding frequency band is the integral value of the spectral energy within a set bandwidth near the dominant vibration frequency.
9. The method according to any one of claims 1 to 8, characterized in that, It also includes the following steps: The support instability early warning signal and the three-dimensional spatial coordinates of the instability location are pushed to the pouring control console and the on-site safety broadcasting system in real time. The pouring pause command or vibration position adjustment command is output according to the warning level and the instability location.
10. A digital construction device for performing the method according to any one of claims 1 to 9, characterized in that, include: The image acquisition module is used to synchronously acquire continuous image sequences through a high-speed industrial camera array deployed around the template support system; The spatial registration module is used to perform feature point detection and matching on image sequences from each camera, calculate the spatial registration transformation matrix, and unify images from different viewpoints to the same spatial coordinate system. The displacement tracking module is used to perform pixel displacement tracking on the registered consecutive frame images and calculate the displacement vector sequence of each pixel. The spectrum analysis module is used to perform frequency domain transformation on the displacement vector sequence of each pixel to generate a vibration spectrum distribution map covering the entire field of view; The anomaly detection module is used to detect areas of abnormal vibration energy accumulation in the vibration spectrum distribution map, and to calculate the three-dimensional spatial coordinates of the abnormal area by combining multi-view triangulation to generate a set of suspected instability points. The risk assessment module is used to calculate the frequency matching degree and energy change slope of each point in the set of suspected instability points, determine the resonance risk, and generate a support instability early warning signal.