Construction engineering multi-dimensional monitoring system based on panoramic imaging

Abstract

The invention discloses a building engineering multi-dimensional monitoring system based on panoramic imaging, and relates to the technical field of building engineering monitoring and sensing. Comprising a panoramic image acquisition module, an image splicing and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical association modeling module and a risk identification and intelligent response module, an image acquisition array formed by high-resolution cameras is installed at key positions of a building structure, images of all the cameras are obtained, and an original image sequence covering a monitoring area is output. According to the invention, through fusion of the panoramic image and the multi-source sensing data, visualization, intelligent identification and risk prediction of the structure state are realized, a closed-loop monitoring system is constructed, the problems of single traditional monitoring means, data splitting and the like are solved, and the safety monitoring intelligent level of constructional engineering is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction engineering monitoring and sensing, and particularly relates to a construction engineering multi-dimensional monitoring system based on panoramic imaging. BACKGROUND

[0002] The construction engineering multi-dimensional monitoring system based on panoramic imaging refers to a comprehensive monitoring technical scheme integrating 360-degree panoramic image acquisition, multi-sensor data synchronous acquisition and remote real-time transmission. The system constructs a panoramic imaging unit according to specific angles through multiple high-resolution cameras, combines multiple types of sensors such as acceleration, inclination, temperature and humidity, and realizes comprehensive perception of the structure state, construction progress and environmental factors of the building. The image and sensor data are deeply fused through time synchronization and space mapping mechanisms, and are transmitted in real time to a remote platform for visual presentation and abnormal linkage analysis with the aid of edge processing and 5G communication technology. The system has the characteristics of strong continuity, visual intuition and strong collaborative fusion capability, and is suitable for safety supervision and construction process management of large and complex construction projects.

[0003] The prior art has the following disadvantages: The existing building monitoring technology generally has the problems of single monitoring means, one-sided data and lagging response. Traditional schemes mostly rely on point sensors (such as strain gauges and displacement meters) to collect local physical quantities, and cannot provide state perception of the overall three-dimensional structure of the building. At the same time, manual inspection is low in efficiency and strong in subjectivity, and cannot meet the continuous dynamic monitoring needs of construction progress and structural abnormalities. In addition, the image information and sensor data in the existing monitoring system lack a unified processing mechanism, and cannot realize the time consistency and spatial correspondence of data fusion, which leads to the inability to accurately locate and trace the causes of structural deformation, cracks and other abnormal phenomena, and seriously restricts the improvement of intelligent monitoring and emergency response capability of construction projects.

[0004] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a construction engineering multi-dimensional monitoring system based on panoramic imaging, which realizes the visualization, intelligent identification and risk prediction of the structure state through the fusion of panoramic images and multi-source sensor data, constructs a closed-loop monitoring system, solves the problems of single traditional monitoring means and data fragmentation, and improves the intelligent level of safety monitoring of construction projects, so as to solve the problems in the background technology.

[0006] In order to achieve the above object, the present application provides the following technical scheme: the building engineering multi-dimensional monitoring system based on panoramic imaging, comprising a panoramic image acquisition module, an image stitching and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical correlation modeling module and a risk identification and intelligent response module: The panoramic image acquisition module installs an image acquisition array composed of high-resolution cameras at key positions of the building structure, acquires images of each camera, and outputs a raw image sequence covering the monitoring area; The image stitching and panoramic reconstruction module performs feature extraction and matching on the raw image sequence, constructs a homography matrix between images, and generates a high-fidelity panoramic image; The multi-source data synchronous acquisition module acquires data based on the high-fidelity panoramic image as a spatial positioning reference, and corresponds the acquired data to the image frame based on a unified timestamp to construct a multi-dimensional structure monitoring data set; The image-data fusion construction module determines the spatial projection position of each sensor in the high-fidelity panoramic image based on the multi-dimensional structure monitoring data set, establishes a binding relationship between image pixels and sensor sampling points, and performs Kalman filter noise reduction processing on various types of sensor data in the multi-dimensional structure monitoring data set to generate a fusion data structure; The image-physical correlation modeling module extracts the response relationship between the image feature change and the corresponding physical quantity change value based on the fusion data structure, constructs a multivariate mapping model between image information and structure state, and obtains an image-physical fusion analysis model; The risk identification and intelligent response module performs abnormality discrimination on the suspicious area in the high-fidelity panoramic image based on the image-physical fusion analysis model, outputs a risk label, and transmits the risk label to a remote monitoring platform, while driving a local feedback mechanism to update the state and control the response.

[0007] Preferably, the step of constructing the image acquisition array and outputting the raw image sequence covering the monitoring area comprises: A plurality of high-resolution cameras are arranged at a plurality of key positions of the building structure according to the structure layout and the angle of view requirement, and a wide-angle lens is used to realize field of view coverage; All cameras are connected to the image acquisition control unit through a high-speed signal channel, and a synchronous trigger circuit sends an acquisition instruction at a unified time point to realize synchronous acquisition of all camera images; The acquired image frames are numbered, time-stamped and labeled with an angle label, then archived uniformly, and a raw image sequence data set is formed locally; A self-checking mechanism is performed for each image acquisition cycle to complete automatic verification of field of view visibility, image brightness, synchronization state and data path, so as to guarantee the integrity, accuracy and space-time consistency of the image sequence.

[0008] Preferably, the step of constructing the high-fidelity panoramic image comprises: A scale-invariant feature transform algorithm is performed on each frame of original image to extract stable local feature points and generate feature descriptors; The feature descriptors between adjacent images are matched, and false matching point pairs are removed by geometric consistency verification; The homography matrix between the images is calculated based on the verified matching point pairs, and geometric transformation alignment is completed; The pixel fusion is performed on the overlapping area of the aligned images by using a distance weighted average strategy, and a high-fidelity panoramic image with spatial continuity and pixel consistency is generated.

[0009] Preferably, the step of constructing the multi-dimensional structure monitoring data set comprises: The spatial layout positions of the plurality of physical quantity sensors are determined according to the high-fidelity panoramic image, and image coordinate calibration is completed; The physical quantity data is collected by each sensor according to a unified sampling period, and time stamp and spatial position information are added; The sensor data and the corresponding image frame are time-synchronized according to the image acquisition time stamp; The physical quantity data is embedded into the image coordinate system based on the spatial mapping relationship, forming a multi-dimensional structure monitoring data set with time synchronization and spatial mapping characteristics.

[0010] Preferably, the step of generating the fusion data structure comprises: The pixel position of the sensor in the high-fidelity panoramic image is calculated by a perspective transformation algorithm according to the camera parameters and the three-dimensional coordinates of the sensor; Each frame of image and the sensor data in the corresponding time period are one-to-one bound according to the time stamp, forming a time-space binding structure; Kalman filtering is performed on the bound sensor data to eliminate noise and maintain the continuity of the physical quantity; Each frame of image and its bound filtered sensor data are uniformly packaged as a structured data unit to form a fusion data structure.

[0011] Preferably, the construction of the image-physical fusion analysis model comprises the following steps: The image feature variation and the corresponding physical quantity variation in the fusion data structure are extracted as a joint feature set; The relationship between the image features and the structure state is statistically constructed according to the training samples to construct a prior probability model; After inputting the new image features, the probability distribution of each structure state is calculated to obtain the likelihood probability; The posterior probability of each structure state is calculated through Bayesian inference, and an image-physical fusion analysis model with predictive ability is output.

[0012] Preferably, the step of outputting a risk label and performing response control comprises: Each frame of image in the high-fidelity panoramic image is divided into regions and image feature change indicators are extracted to identify suspicious regions; The identified suspicious regions are input into the image-physical fusion analysis model in synchronization with the corresponding time and space binding sensor data to determine the structure state; Based on the structure state determination result, a risk label is generated, including abnormal position, abnormal type, occurrence time and confidence level; The risk label is transmitted to the remote monitoring platform and drives the local feedback mechanism to complete the closed-loop intelligent monitoring process from perception collection to response control.

[0013] In the above technical solution, the technical effects and advantages provided by the present application are: The present application covers the entire building area through high-fidelity panoramic images, realizing the intuitive visualization of the structure appearance state; combined with synchronous data collection and spatial mapping of multiple sensors, a deep fusion system of image and physical data is constructed; the Bayesian inference model is used to realize the correlation modeling between image features and structure states, giving the system predictive ability and abnormal recognition ability; finally, through risk label generation and linkage feedback, the whole process of closed-loop monitoring from perception collection, state recognition, risk warning to response control is realized. This technical solution effectively solves the problems of single monitoring method, one-sided data, response lag, information fragmentation and other problems in traditional monitoring, has the remarkable beneficial effects of continuous dynamic monitoring, abnormal visual identification, risk quantitative evaluation and response linkage control, and is especially suitable for intelligent safety management of large and complex construction projects, sensitive structures or high-risk scenes. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0015] Figure 1 The module schematic diagram of the building engineering multi-dimensional monitoring system based on panoramic imaging of the present application. DETAILED DESCRIPTION

[0016] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations, however, can be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the inventive aspects of the example implementations to those skilled in the art.

[0017] The present application provides a multi-dimensional monitoring system for construction engineering based on panoramic imaging as shown in the figure, comprising: Figure 1 The panoramic image acquisition module is installed at key positions of the building structure, and an image acquisition array composed of a plurality of high-resolution cameras is arranged at the key positions according to a preset angle. The image acquisition array is based on a synchronous control circuit to acquire images of each camera at a unified time point, and output a raw image sequence covering the monitoring area. To realize high-precision and full-coverage image information acquisition of the key area of the building structure, the image acquisition array is constructed, and the raw image sequence covering the monitoring area is acquired. The specific process includes the following steps: According to the structural layout, component distribution characteristics and key areas of the construction engineering to be monitored, a plurality of key installation positions are selected, and on-site space surveying and visual coverage analysis are performed. According to the geometric shape of the building and the observation requirements, the space projection modeling technology is used to plan the camera view angle arrangement scheme. At least two high-resolution cameras with complementary view angles are arranged at each key position. The cameras are industrial-grade imaging devices with wide dynamic range, low noise and high sensitivity image sensors, and wide-angle or fisheye lenses are used to expand the coverage range. Each camera is fixed on the main structure of the building (such as column, beam end, wall surface, etc.) through a high-strength support, and the orientation of each camera is accurately calibrated according to the set panoramic stitching geometric rules to ensure that the shooting fields of view have a moderate overlapping area, thereby providing sufficient feature redundancy and spatial continuity support for subsequent image stitching and panoramic reconstruction. All cameras are connected to the image acquisition control unit through high-frequency low-delay digital signal connection lines. The control unit internally integrates a set of precise circuits for synchronous control of multiple cameras. The core is a synchronous trigger circuit network with nanosecond-level response capability. The synchronous circuit sends synchronous trigger instructions to all cameras through a central clock chip to ensure that image exposure and data acquisition are completed at a unified time point (i.e. in the same frame acquisition period), thereby ensuring the absolute consistency of each image frame in time and avoiding image misalignment or dynamic blur problems caused by time offset. In this process, the acquisition control process also includes unified configuration of image acquisition parameters such as exposure time, gain value, image format, resolution, etc., thereby realizing parameter standardization of the acquisition process and global synchronization of the sampling time.

[0018]

[0019] ​In each image acquisition cycle, each camera completes image sensing according to the synchronization instruction and returns the raw image data to the image processing node through the high-speed transmission channel. The image is in the format of raw data without compression and processing, retaining complete gray scale, color and boundary detail information, so as to maximize the retention of spatial details and structural texture features of the monitoring target. All images are uniformly archived and cached according to camera number, acquisition timestamp and perspective label, and written into the local storage device to form a complete raw image sequence data set, which is used for subsequent image stitching, image feature extraction and fusion analysis with sensor data. In order to ensure data integrity and transmission stability, error check codes are also added to each image frame to support data integrity verification and automatic retransmission mechanism, ensuring the reliability of data transmission in the complex electromagnetic environment of the construction site.

[0020] In order to further improve the monitoring efficiency and the convenience of on-site operation, remote configuration and self-checking mechanism of the image acquisition unit are also deployed during implementation. Before the start of each monitoring cycle, the camera field of view visibility check, image brightness uniformity evaluation and data path connectivity verification will be automatically completed. If abnormalities such as camera offset, picture obstruction or synchronization failure are found, fault alarm will be issued through the local feedback signal, and the current acquisition task will be suspended to prevent false data from entering the subsequent processing flow. At the same time, it has self-learning scheduling ability, adjusts the sampling frequency according to the historical image frame difference analysis, automatically reduces the sampling rate when there is no obvious change in the monitoring area, thereby effectively saving data bandwidth and storage resources, improving the continuous operation ability and on-site deployment adaptability. Through the above complete image acquisition array layout and control process, the raw image sequence can ensure time uniformity, spatial continuity and content integrity, providing accurate and reliable visual input source for subsequent visual monitoring and multi-dimensional data fusion of building structures.

[0021] The main role of this step is to provide a high-quality image-based data source for building engineering monitoring with time synchronization, spatial coverage, and visual continuity, thereby supporting subsequent image stitching, structural anomaly identification, and spatial fusion with sensor data. By deploying multiple high-resolution cameras at key positions of the building structure and forming a fixed arrangement according to the preset angle parameters, comprehensive and dead-angle-free visual coverage of complex structural areas can be achieved. This multi-view acquisition method can significantly enhance the redundancy and detail retention capability of image data compared to traditional single-point or small-angle monitoring methods, and is particularly suitable for typical scenarios such as building obstructions, multiple layers, complex facades, etc. At the same time, by configuring a synchronous control circuit to uniformly control all cameras, the cameras can capture images at the same time point, ensuring strict time consistency of all image frames. This is crucial for capturing small dynamic changes during construction, implementing multi-view image fusion, and constructing high-fidelity panoramic images that truly reflect the building's state. In addition, the output raw image sequence can serve as the main visual input for subsequent image feature extraction, anomaly region identification, visual and sensor information matching, and other analysis tasks, becoming the core foundation data of the entire monitoring process. Therefore, this step is not only the starting point of the data acquisition chain, but also an important guarantee link for the accuracy, stability, and timeliness of the entire building multi-dimensional monitoring system.

[0022] The image stitching and panoramic reconstruction module performs feature point extraction and matching operations on the raw image sequence, constructs the homography matrix between images for geometric alignment, uses a distance-weighted average strategy for pixel fusion of aligned images, and generates a high-fidelity panoramic image with spatial continuity and pixel consistency. The specific steps of accurately stitching multiple raw image sequences of different angles into a panoramic image with high fidelity and spatial continuity are as follows: Feature point extraction is performed on each raw image. This process is based on the scale-invariant feature transform algorithm, which performs multi-scale analysis on the raw image by constructing a Gaussian pyramid, extracts stable local feature points in the image, and calculates their descriptor information for subsequent matching. The extracted feature points have scale invariance, rotation invariance, and strong light robustness, ensuring that the image can still identify corresponding image structural features such as building edges, contours, corner points, and texture features under different angles and different brightness. This stage also performs grayscale equalization preprocessing on the image to improve the stability and effectiveness of feature point extraction.

[0023] The Scale-Invariant Feature Transform (SIFT) is a classical image feature extraction method, whose core purpose is to extract local feature points from images that remain stable under scale transformation, rotation change, and partial illumination change, for image registration, stitching, and target recognition. In this step, the Scale-Invariant Feature Transform algorithm is used to accurately extract matching structural key points from multiple building images at different angles, different fields of view, and possible brightness differences, so that the subsequent image geometric alignment process has high robustness and matching accuracy. The specific implementation steps are as follows: A scale space is constructed for the original image, i.e., a series of blurred images are obtained by performing multi-scale blur processing on the image using a Gaussian blur function, forming a pyramid structure, and calculating the difference image (DoG) between each scale layer to enhance edge and corner features.

[0024] Local extreme points are searched in all scale layers, i.e., the pixel points with maximum or minimum gray value in the spatial and scale neighborhood are found, which are potential stable feature points.

[0025] Each extreme point is precisely positioned and screened for key point stability, and edge unstable points and low contrast points are removed, while the main direction information of the key point is calculated to give it direction invariance.

[0026] A descriptor is constructed around each key point, i.e., the gradient direction and amplitude information within its neighborhood range are collected and normalized encoded in a rotation-invariant manner to form a 128-dimensional feature vector. This descriptor can be used for feature matching between subsequent images and serves as the basis for solving the geometric relationship of images.

[0027] Through the above process, stable and repeatable key feature points can be extracted from complex, multi-scale, and multi-angle building images, thereby laying a solid foundation for high-precision panoramic stitching and spatial consistency fusion.

[0028] After completing the feature point extraction of all image frames, perform accurate matching operation on the feature points between adjacent image frames. The specific method is to calculate the Euclidean distance between all feature point descriptors in each image, and select the smallest number of pairs as the preliminary matching point pairs. To eliminate false matching pairs in the matching process, a geometric consistency verification method based on the principle of random sample consensus is used to further filter matching point pairs that do not meet the geometric constraint condition. Through this process, the point pairs used for subsequent alignment calculation have high matching accuracy and reasonable distribution, avoiding image misplacement or overlapping distortion problems caused by false matching.

[0029] According to the verified feature matching point pairs, the homography matrix between images is calculated. The homography matrix represents the geometric mapping relationship from one image plane to another, including rotation, translation, scaling and projection transformation information between images. By minimizing the reprojection error of all matching point pairs, the best fitting homography parameters are obtained. The current image is geometrically transformed using the homography matrix, and all images are transformed into the same reference image coordinate system. This process is the most critical step in panoramic stitching, directly determining the final image alignment accuracy and visual continuity. To avoid edge ghosting and image layering problems, an image boundary smoothing method is also introduced to feather the edge areas of the transformed image, enhancing the naturalness of the transition.

[0030] After completing the geometric alignment of all images, pixel-level image fusion is performed to generate the final high-fidelity panoramic image. To improve the fusion effect and image quality, a distance-weighted average strategy based on pixel position is used to fuse the overlapping areas of the images. Specifically, for each pixel in the overlapping area of the image, the fusion weight is calculated according to its distance to the image center. The center area pixels are given higher weights, and the edge area pixels are given lower weights. The corresponding pixels in all participating images are weighted and averaged according to the weights to generate the pixel value of the fused image. This strategy not only effectively alleviates the seams and ghosting caused by exposure differences or alignment errors at the image edges, but also enhances the overall color and brightness consistency of the image. The final panoramic image has strong spatial continuity, natural pixel transition, and sufficient detail preservation, and can fully present the appearance of the building structure, providing a high-quality image foundation for subsequent multi-source data fusion and anomaly identification analysis.

[0031] The purpose of this step is to spatially unify and pixel-fuse multiple raw image sequences taken from different angles to generate a panoramic image with spatial continuity, visual consistency, and high fidelity, providing an accurate and complete image basis for the visualization of building structure status and subsequent multi-dimensional data fusion. In the construction site, images captured by multiple high-resolution cameras have characteristics such as angle differences, overlapping areas, and spatial deformations. If used directly, it will result in misalignment, distortion, or visual discontinuity between images, which cannot truly reflect the overall state of the building structure. Therefore, this step identifies key point pairs with high stability and repeatability between images through feature point extraction and matching operations, calculates the homography matrix between images, which describes the geometric transformation matrix of the spatial mapping relationship between images, to achieve precise alignment of different images in the same coordinate system. After alignment, there are often problems such as light differences, edge blurring, or texture inconsistencies in the overlapping areas. To eliminate these stitching traces, this step further uses a distance-weighted average strategy to fuse the overlapping areas of the aligned images, making the fused image transition naturally in pixel values, smooth boundaries, and maximize the preservation of detail information and visual continuity. The final high-fidelity panoramic image not only covers the key structural areas of the building but also has the characteristics of spatial information integrity and image detail richness, which can be used as the main spatial carrier in the monitoring platform, providing highly accurate visual input for subsequent processes such as structural anomaly identification, sensor spatial positioning, and data fusion modeling. In summary, this step is the core and key step for multi-view image stitching, high-quality image construction, and overall visual presentation of building structures, directly affecting the data quality and analysis effect of the entire monitoring system.

[0032] The multi-source data synchronous acquisition module takes the high-fidelity panoramic image as a spatial positioning reference, starts the data acquisition process of multiple source sensors including vibration acceleration sensors, tilt angle sensors, and temperature and humidity sensors, and matches the collected sensor data at each time with the corresponding image frame based on a unified timestamp, constructing a multi-dimensional structure monitoring data set with time synchronization and spatial mapping characteristics. The specific steps to achieve synchronous perception and visual expression of building structure multi-dimensional state are as follows: The high-fidelity panoramic image generated in the previous stage is used as a spatial reference. Based on the clear architectural structure features in the image, the deployment positions of multiple types of physical quantity sensors are planned and deployed. The deployment needs to be combined with the mechanical properties of the building and the safety control requirements of the structure to select the points, ensuring that the sensors can cover the key stressed members, typical deformation areas, and sensitive parts with significant temperature and humidity fluctuations. The sensors used include vibration acceleration sensors, tilt angle sensors, and temperature and humidity sensors, which are used to obtain the dynamic response of building members under different working conditions, the overall attitude change, and the influence of environmental coupling factors on the structure performance. Each sensor needs to be spatially calibrated in the panoramic image before installation to ensure that it has a unique and locatable mapping position in the image coordinate system, so that subsequent image-data fusion spatial binding can be achieved.

[0033] The data collection process of multi-source sensors is started, and various sensors work according to a unified sampling period through digital sampling circuits. To ensure the consistency of the sampling time, a time synchronization chip with integrated high-precision crystal oscillator elements is used as the time control core to stamp a standard time stamp for each data sampling event. In this process, each sensor not only records its current physical quantity reading, but also attaches sampling time information and pre-set spatial position information, forming a structured sampling data entry. All sensor data collection nodes upload data in real time to the on-site data processing node through high-speed wired or low-power wireless communication, and cache locally to deal with the risk of data delay caused by communication abnormalities.

[0034] In the collection period of the high-fidelity panoramic image, the time mapping relationship between the image frame and the sensor data is established. The image collection period is set as the main time axis reference, and the closest sensor sampling records within the time window are selected around the collection time stamp of each image frame, and a one-to-one or many-to-one time binding mechanism is established. This mechanism ensures that each frame of image not only has spatial visual information, but also associates with the building structure physical state data that matches the time most closely, thereby realizing the time synchronization of image information and sensor information. After time synchronization, the spatial coordinates of each sensor are embedded in the image coordinate system according to their mapping results in the panoramic image during deployment, establishing the spatial projection relationship between image pixels and physical quantity data points.

[0035] After the completion of the dual synchronization of time and space binding, a set of data structures containing image frames, time stamps, physical quantity readings, and their image mapping positions is formed, which constitutes a building engineering multi-dimensional structure monitoring data collection. This data collection has three core features: first, time uniformity, all data are arranged in order around the main time axis; second, spatial correspondence, physical quantity data can be directly located and displayed in the image; third, diversity of sources, different types of sensors can reflect the structure, posture and environmental state at the same time. This data collection can not only be used for real-time display of building state, but also provides basic data input for subsequent image-physical fusion modeling and intelligent recognition analysis. Through the whole process of multi-source acquisition-synchronization matching-space mapping-structure construction, the deep collaboration of sensor physical data and image visual information is realized, which provides key support for the intelligent transformation of building engineering monitoring from two-dimensional image expression to spatio-temporal unification and multi-dimensional perception.

[0036] The role of this step is to realize the accurate alignment of image information and multi-type sensor data in the time dimension and spatial coordinates, and to construct a structure monitoring data collection with high spatio-temporal consistency and multi-dimensional data fusion capability, providing basic data support for subsequent building structure state analysis, anomaly diagnosis and image-physical fusion modeling. In traditional building monitoring methods, image information and sensor data are often collected separately and independently, which leads to the inability to establish a one-to-one correspondence in actual analysis, especially in the face of structural abnormal changes such as crack expansion, settlement deviation, etc., it is difficult to directly locate the problem area and its corresponding physical change trend from the image. This step uses high-fidelity panoramic images as a unified spatial reference, uses multi-angle spliced images as monitoring coordinate carriers, and physically lays out and spatially calibrates various sensors, so that each sensor position has a clear coordinate projection in the image. At the same time, the consistency of image and sensor sampling time is controlled through the unified timestamp mechanism, ensuring that each image can correspond to the physical quantity data in a specific time window, achieving time synchronization. The multi-dimensional structure monitoring data collection established finally not only contains image content, but also carries numerical information reflecting structure state and environmental conditions such as acceleration, inclination angle, temperature and humidity, and has image visual position identification, which can be used for subsequent image-data collaborative analysis, spatial heat map drawing, fault area backtracking and trend prediction analysis. This step breaks the traditional barrier of "image cannot be quantified, data cannot be visualized", and makes the monitoring system from "multi-source separation" to "fusion association", which is an important foundation for intelligent building monitoring and fine structure evaluation.

[0037] An image-data fusion construction module, on the basis of the multi-dimensional structure monitoring data set, determines the spatial projection position of each sensor in the high-fidelity panoramic image coordinate system, establishes the spatial binding relationship between the image pixels and the sensor sampling points, and performs Kalman filter noise reduction processing on each type of sensing data in the multi-dimensional structure monitoring data set, to generate a fusion data structure of the fusion image information and the physical quantity data; The specific steps of realizing the deep fusion analysis of image visual information and building structure physical quantity data are as follows: On the basis of the constructed multi-dimensional structure monitoring data set, the installation positions of each type of physical quantity sensor are reconstructed in spatial coordinates. By combining the field layout record and the building plan, the actual position coordinates of each sensor in three-dimensional space are determined, and these spatial coordinates are mapped to the two-dimensional pixel coordinate system of the high-fidelity panoramic image through a perspective transformation algorithm. In this mapping process, the known camera internal and external parameters are used to perform coordinate transformation and projection conversion on the spatial coordinates, so that each sensor can be accurately calibrated within a specific pixel area in the image, thereby realizing the visualization of the structure physical data in the image dimension, and providing basic positioning support for subsequent image and sensor collaborative recognition and analysis.

[0038] The perspective transformation algorithm is a method for mapping the actual coordinate position in three-dimensional space to the two-dimensional image plane. Its essential role is to simulate the image projection changes caused by the viewing angle, distance and perspective relationship when the human eye or camera observes an object in the real world. In the present application, this algorithm is used to accurately convert the layout position of each sensor in the actual three-dimensional space of the building structure to the pixel position in the high-fidelity panoramic image, thereby realizing the spatial correspondence between the sensor and the image, so that the specific area monitored by the sensor can be intuitively located in the image during subsequent analysis.

[0039] The specific process includes the following steps: The physical installation position of each sensor in the building structure, i.e., its coordinate position in three-dimensional space, is determined, which is usually obtained through construction drawings or laser measurement.

[0040] The spatial viewing angle model of the camera is established by combining the camera parameters used to generate the high-fidelity panoramic image, including the focal length, lens orientation, imaging scale of the camera, and its actual installation position and shooting angle in the field. This model is equivalent to describing how the camera "sees" each point on the building from a certain position and at a certain angle.

[0041] With this perspective model, the three-dimensional coordinates of the sensors are converted to simulate where they should appear in the image under the camera perspective, thereby calculating the two-dimensional pixel position of the sensor on the image. Finally, considering that the high-fidelity panoramic image is spliced from multiple perspectives, it is necessary to reposition this pixel position to the unified coordinate system of the panoramic image to ensure the accuracy and uniqueness of the point in the final image.

[0042] Through the above steps, the perspective transformation algorithm realizes the accurate conversion of the spatial position of the sensor to the image plane position, so that the image not only has visual information, but also has physical data positioning capability, thereby providing a spatial correlation basis for image and sensor data fusion processing, visualization analysis and anomaly identification. This way effectively overcomes the problem of image and data separation in traditional building monitoring, and is an important foundation for intelligent building state monitoring.

[0043] After the sensor completes the image space positioning, the binding relationship between the image pixel and the corresponding sensor sampling point is established. The binding process not only includes the static mapping of the position, but also introduces a timestamp matching mechanism to ensure that each binding relationship meets the requirements of spatial consistency and temporal consistency. The specific method is as follows: in each frame of the panoramic image, according to its acquisition time, select the sensor sampling record closest to its timestamp, and associate its data value with the positioned sensor pixel position in the frame image one by one to form a time-space binding structure. In the image sequence, this binding structure is dynamically updated with time, ensuring that the entire image sequence carries complete and accurate physical quantity information on the time axis, thereby establishing a time-space coordinated image-sensor dual-channel data expression basis.

[0044] To improve the accuracy and continuity of structural physical quantity data, Kalman filtering is performed on all sensor data in the multi-dimensional structural monitoring data set. The Kalman filtering algorithm is based on state estimation theory and combines the current sampling value with the estimated state at the previous time to dynamically fuse and predict, effectively suppressing random noise and environmental interference while preserving data trend changes. This processing is particularly suitable for vibration acceleration, inclination angle and other time series characteristic sensor signals, which can smooth high-frequency fluctuations and retain key response information. Each type of sensor data needs to be normalized and scaled before filtering to ensure that different physical quantities remain consistent and comparable in numerical representation during fusion, avoiding calculation bias due to inconsistent dimensions.

[0045] The main purpose of performing Kalman filtering on all sensor data in the multi-dimensional structural monitoring data set is to remove the random interference, noise fluctuations and transmission errors commonly found in building site monitoring without losing key dynamic information, making the sensor data more stable, reliable and continuous. The specific processing steps are as follows: Initialize the state estimation value and estimation error of each type of sensor, including its initial observation value and possible range of variation.

[0046] After each sensor completes data collection, the system predicts the theoretical value of the current state based on the estimated state at the last time, which is a reasonable guess of the "current true value".

[0047] Compare the actual observation value collected this time with the predicted value, and according to the gap between the two, combined with the previously set observation error and process error weight, calculate an optimal weighted average value as the corrected output value, so as to get the updated state estimation.

[0048] This step will be repeated continuously with each new data collection, so that the data of each sensor will go through the dynamic update cycle of "prediction-observation-correction", thereby effectively suppressing abnormal data jumps caused by sudden impact, equipment instability, electromagnetic interference or short-term misalignment. In the context of building structure monitoring, Kalman filtering is particularly suitable for signal smoothing of continuous dynamic physical quantities such as vibration, inclination and displacement, ensuring that the system makes accurate judgments based on reliable and continuous input data during data fusion and subsequent analysis. Through this process, the quality of various physical data in the entire multi-dimensional structure monitoring data set is significantly improved, enhancing stability, reliability and sensitivity to abnormal changes.

[0049] After completing the image coordinate binding and sensor data filtering, each frame of image and its bound noise-reduced sensor data are packaged into a structured data unit, called fusion data structure. This structure includes image frame number, image content, image projection position of each sensor, corresponding timestamp, filtered sensor data value and its category identifier. All fusion data structures are stored in chronological order to form a fusion data sequence, which can be used as the basis for subsequent image-physical fusion modeling, structure response prediction and risk judgment tasks. This structure not only provides physical quantity support for building structure state in the spatial image layer, but also improves data quality and robustness through filtering, significantly enhancing the accuracy, interpretability and visual expression ability of the monitoring results.

[0050] The role of this step is to build a precise mapping relationship between image information and structural physical quantity data, and to improve the reliability and continuity of sensing data, thereby forming a unified data infrastructure that can be used for multi-modal fusion analysis, which is the core link to realize image-physical collaborative perception and intelligent monitoring. In the process of construction engineering monitoring, image information provides intuitive spatial perception ability, while sensors provide quantitative data of the state of the building structure, such as vibration response, inclination change, or environmental temperature and humidity, etc. However, both are naturally in different data systems, image is two-dimensional pixel information, and sensor data is time series physical readings, lacking a common reference framework, making it difficult to realize data fusion and joint analysis. This step first projects the spatial coordinates of the existing multi-dimensional structural monitoring data set to the pixel coordinate system of the high-fidelity panoramic image based on the actual installation location of the sensor, establishes the spatial positioning relationship between the image and the sensor, and makes each sensor have a clear image annotation position; secondly, based on the time synchronization mechanism, each frame of image is bound with its corresponding sensor data at the same time, ensuring the spatio-temporal unity of the information. After completing the bidirectional binding of space and time, in order to further improve the data quality, Kalman filter algorithm is used to denoise the various physical quantity data, effectively removing abnormal values and signal jumps caused by on-site interference, transmission fluctuations or equipment errors, and retaining the true response trend of the structure. The final fusion data structure contains not only the spatial visual information of each region in the image, but also the time series of physical quantities at the corresponding position, realizing the deep fusion and linkage expression of image and data, and providing a unified, accurate and stable original input for subsequent image-assisted analysis, abnormal linkage discrimination and image-data modeling.

[0051] The image-physical correlation modeling module, based on the fusion data structure, performs joint feature modeling process, extracts the response relationship between image feature change and corresponding physical quantity change value, uses Bayesian inference strategy to build a multivariate mapping model between image information and structural state, and obtains an image-physical fusion analysis model with prediction ability; The specific steps to realize predictive identification of the state of the building structure and linkage modeling of multi-source data are as follows: Based on the fusion data structure generated in the previous stage, the synchronous change trajectories of image information and physical quantity data on the time axis are extracted. Specifically, the image segments of the representative key monitoring areas are selected, and the image feature value changes such as texture gradient change, edge contour shift, brightness imbalance degree, and crack width expansion trend are extracted. At the same time, within the time window corresponding to the image frame, the physical quantity change values of multiple types of sensors bound with their spatial positions are obtained, such as acceleration response amplitude change, inclination angle change rate, temperature or humidity mutation degree, etc. These image feature changes and physical quantity changes constitute a set of one-to-one time series samples, which are the core data basis for joint modeling.

[0052] For the aforementioned feature data pairs, a quantitative response relationship between image changes and physical response values ​​is established. During processing, all data samples need to be normalized to eliminate the impact of dimensional differences on the modeling results. Subsequently, through statistical analysis and preliminary correlation modeling, highly correlated feature combinations are selected to serve as the input variable set for subsequent inference models. For example, if a synchronous relationship is found between the decreasing trend of crack edge sharpness and the increasing peak acceleration frequency in a certain area, these two can be used as feature pairs for input modeling. Dynamic time warping techniques can also be introduced at this stage to align data sources with different response speeds, improving the model's uniformity and generalization ability.

[0053] Based on the feature dataset, a multivariate mapping model between image information and structural states is constructed using Bayesian inference. Bayesian inference, grounded in the relationship between prior probabilities and observed data, calculates the posterior probability of a certain structural state occurring under given image changes, thus forming a probabilistic model with conditional judgment capabilities. During model building, model parameters are continuously refined using training data samples, ensuring stable judgment results across multiple scenes, components, and working conditions. The model output not only includes the probability of a certain state occurring but also quantifies its confidence level, improving the interpretability of the recognition results. For example, when a region of an image exhibits nonlinear deformation and texture changes, and the tilt angle at the corresponding location intensifies, the model can output a predicted label of "structural deformation has occurred in this region," with a prediction confidence level exceeding 90%.

[0054] Bayesian inference is a statistical reasoning method based on probability theory. Its core idea is to combine existing prior knowledge (prior probability) with current observation data (likelihood probability) to dynamically update the judgment result for unknown states (posterior probability), thereby achieving prediction and decision-making for complex states. In this invention, the role of Bayesian inference is to model the correlation between image feature changes and structural physical responses, establishing a multivariate mapping model that can infer the current or future state of a building structure based on the changing trends of image features, giving it the ability to predict structural states and quantify uncertainties. The specific implementation steps are as follows: The image feature changes (such as texture perturbation, edge shift, brightness anomaly, etc.) and their corresponding structural physical response values ​​(such as vibration enhancement, tilt change, temperature and humidity fluctuation) are extracted from the fused data structure and used as a joint input feature set. The correspondence between image changes and structural states in historical samples was statistically analyzed, and prior probability models of various structural states were constructed. In practical applications, after inputting new image feature data, the probability distribution of its occurrence under different structural states is calculated, i.e., the likelihood probability. The prior probability is combined with the likelihood probability by the Bayesian formula to output the posterior probability of each structural state, realizing quantitative inference from image features to structural states.

[0055] Through this method, not only can the structure be identified whether it is abnormal, but also the judgment result with confidence such as "the probability of the region occurring tilt abnormality is 85%" can be output, so that the monitoring system has reasoning ability, prediction ability and risk level quantification ability, which is a key statistical means to realize image-physical fusion intelligent identification.

[0056] After the model training is completed and stable convergence, it is embedded into the subsequent image analysis process to form an image-physical fusion analysis model with real-time prediction ability. The model can continuously receive input content of the fusion data structure, calculate the feature change amount in real time at each image update, and generate the judgment result and trend prediction of the structural state by linking the corresponding physical quantity. For example, the structural stress abnormality level of a certain region is output in real time, and the abnormal state or fault evolution trend about to occur is warned. The introduction of the model not only breaks through the traditional limitation of isolated visual image information, but also transforms it into an active participating element of structural state reasoning, significantly improving the intelligent level, response speed and warning ability of building engineering monitoring.

[0057] The purpose of this step is to realize the ability to predict the state of the building structure from the changes in the visual image by constructing a mathematical mapping model between image features and physical responses, so as to make the monitoring system have the intelligent analysis function of upgrading from "describing state" to "predicting state", which is the core link of realizing image-physical deep fusion and structure health assessment. In traditional building monitoring, image information is often used to assist observation, while physical sensing data is used for independent alarm, and there is a lack of quantitative connection between the two, which leads to the fact that image information cannot be used for active reasoning and sensing data lacks intuitive explanation. Based on the time synchronization and spatial correspondence data basis established by the fusion data structure, first, the image feature change (such as crack expansion, edge displacement, texture disturbance, etc.) and the physical response data (such as vibration intensity, inclination angle change, temperature fluctuation, etc.) at the same space-time position are extracted, and a multivariate response relationship between the two is constructed. Through statistical analysis and joint feature modeling, the sensitivity and regularity of image changes to structure response are determined, and then the Bayesian inference strategy is introduced to dynamically calculate the posterior probability of the structure being in different health states based on prior knowledge and current observation, thereby outputting the structure prediction results with confidence. For example, if the boundary blur in a certain area of the image is significantly increased, the small vibration data collected by the sensor in that area can be combined to judge the possibility of crack development in that area through the Bayesian model, and the corresponding probability value is output. The image-physical fusion analysis model formed finally can not only realize the visual interpretation of the structure state, but also has the prediction ability driven by data, which provides decision support for construction safety warning, structure health assessment and risk classification management, and is the key supporting step for the intelligent and active transformation of the building engineering monitoring system.

[0058] The risk identification and intelligent response module, based on the image-physical fusion analysis model, performs joint analysis on the suspicious areas appearing in the high-fidelity panoramic image, combines the change trend of the sensing data at the corresponding spatial position, performs abnormal state joint discrimination, outputs the risk label containing the abnormal position, abnormal type and confidence level, and transmits the risk label to the remote monitoring platform, while driving the local feedback mechanism to perform state updating and response control, realizing the whole-process intelligent monitoring from sensing collection to closed-loop response; The specific steps to realize real-time identification, position labeling and feedback linkage control of abnormal state of building structure are as follows: Based on the trained image-physical fusion analysis model, real-time analysis of high-fidelity panoramic image sequences is continuously performed. Within the image update cycle, each frame of panoramic image is processed in a fine-grained partitioning manner, and regional evaluation is performed according to image change feature indicators such as edge shape change, crack expansion speed, brightness offset trend, etc. to identify suspicious areas in the image that may have abnormalities. At the same time, the sensor data at the time point corresponding to the image frame that has a spatial binding relationship with the image region is retrieved from the fusion data structure, and the dynamic change trend within its time window is extracted synchronously, such as vibration peak mutation, inclination angle rapid drift or abnormal humidity fluctuation. Taking the image region and its physical data as a group of inputs, the image-physical fusion analysis model is input into the comprehensive judgment to form a structural state prediction output for each image region.

[0059] According to the judgment result of the image-physical fusion analysis model, the joint feature discrimination and risk label generation operation is performed on the region predicted as "potential abnormality". This process not only outputs the exact pixel position of the abnormal event in the image, but also generates structured risk label information combining the structural state type (such as crack risk, foundation settlement, structural inclination, etc.) and the corresponding confidence probability value output by the model. The content of the risk label includes but is not limited to: image coordinates of the abnormal position, corresponding physical data node number, abnormal state type description, occurrence timestamp and model evaluation confidence level score. All label information is packaged as a data packet in a standardized format, providing a standardized data interface for subsequent remote transmission and local response, and having a data redundancy check mechanism to ensure accurate recording and unique identification of abnormal events.

[0060] Based on the generation of risk labels, the information is transmitted in real time to the remote monitoring platform through a high-speed communication channel. After the platform receives the risk label, it automatically calls the visualization presentation logic to highlight the abnormal area position in the panoramic image interface, superimpose the abnormal type and confidence level information, and label the related sensor data fluctuation trend curve in a layer manner, providing a visual, real-time, panoramic building structure state abnormal warning interface for remote monitoring personnel. The platform supports multi-terminal viewing, historical trajectory backtracking, state change trend analysis and other operations, forming a closed-loop remote decision support mechanism to improve the warning ability and response efficiency of key risk areas.

[0061] While the remote platform completes the abnormality discrimination, the local part synchronously receives the risk label information, and according to the abnormality level and position characteristics, triggers the corresponding on-site feedback control logic. For example, if the abnormality confidence level of a certain area is higher than the preset threshold, the relevant components can be automatically driven to improve the sampling frequency, accelerate the image acquisition frame rate, or issue a sound and light warning to remind the on-site personnel; if the abnormality affects the key structure node, the construction management platform can also be linked to suspend the related work or retrieve the historical data for model backtracking analysis. Through the above process, a complete closed loop of “image recognition-sensor linkage-intelligent discrimination-label generation-remote push-local response” is formed, realizing the intelligent monitoring capability of the whole process of building structure from perception collection, state recognition to linkage response control, effectively improving the safety management level of building engineering and the self-adaptive response capability of the monitoring system.

[0062] The role of this step is to apply the image-physical fusion analysis model constructed in the previous step to the actual monitoring process, realize real-time identification, accurate positioning, risk quantification and intelligent response control of the potential abnormal state of the building structure, and build a whole-process intelligent monitoring mechanism from data perception to closed-loop decision-making, which is the key step for the invention to realize the “from monitoring to action” transformation. In traditional building monitoring, image information and sensor data are often analyzed independently, lacking a joint judgment mechanism, resulting in problems such as delay in abnormality identification, inaccurate positioning, and lag in response. This step first performs frame-by-frame analysis on the local change area in the high-fidelity panoramic image based on the fusion model, identifies suspicious features such as crack expansion, structure deformation, and brightness disturbance in the image in real time, and synchronously retrieves sensor data (such as acceleration, inclination angle, temperature and humidity changes, etc.) spatially bound to these image areas for time series trend analysis. Through model reasoning and calculation, the probability of the current structure state can be determined, and a clear risk label is generated. The label not only contains the image coordinates and type discrimination results of the abnormal area, but also marks the confidence level score of the judgment, thereby realizing the quantitative expression of the building safety state. Subsequently, the risk label is pushed to the remote monitoring platform in real time to realize remote visual presentation and auxiliary decision support, and at the same time, the response strategy is automatically triggered locally, such as adjusting the image sampling frequency, encrypting the monitoring area, or starting the alarm mechanism. Through this process, a complete closed loop from image perception, data correlation, intelligent reasoning, risk identification to response feedback is completed, significantly improving the automation, precision and forward-looking of building engineering monitoring.

[0063] The building engineering multi-dimensional monitoring system based on panoramic imaging can significantly improve the comprehensiveness, accuracy and intelligent level of building structure monitoring, and realize the transformation from passive inspection to active early warning. The system covers the entire building area through high-fidelity panoramic images, realizes the intuitive visualization of the structure appearance state, combines the synchronous data acquisition and spatial mapping of multiple sensors to build a deep fusion system of images and physical data, uses the Bayesian inference model to realize the correlation modeling between image features and structure state, and gives the system prediction and anomaly identification capabilities. Finally, through risk label generation and linkage feedback, the whole process of closed-loop monitoring from perception collection, state identification, risk warning to response control is realized. The technical scheme effectively solves the problems of single monitoring method, one-sided data, response lag and information fragmentation in traditional monitoring, has significant beneficial effects such as continuous dynamic monitoring, abnormal visual identification, risk quantitative evaluation and response linkage control, and is especially suitable for intelligent safety management of large and complex building engineering, sensitive structure or high-risk scene.

[0064] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the nearest real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0065] The above only describes some exemplary embodiments of the present application by way of illustration, and it is self-evident that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present application. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present application.

[0066] It should be noted that in this paper, if there are relationship terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0067] It should be understood that in various embodiments of the present application, the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0068] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0070] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0071] In addition, the functional units in each embodiment of the present application can be integrated into one processing unit, or each unit can be physically present alone, or two or more units can be integrated into one unit.

[0072] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0073] The above only describes some exemplary embodiments of the present application by way of illustration, and it is needless to say that those skilled in the art can modify the described embodiments in various ways without deviating from the spirit and scope of the present application. Therefore, the above figures and descriptions are illustrative in nature and should not be understood as limiting the scope of protection of the claims of the present application.

Claims

1. A multi-dimensional monitoring system for construction engineering based on panoramic imaging, characterized in that, The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module.

2. The panoramic imaging based construction engineering multi-dimension monitoring system according to claim 1, wherein, The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module.

3. The panoramic imaging based construction monitoring system of claim 1, wherein, The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module.

4. The panoramic imaging based construction monitoring system of claim 1, wherein, The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image-data fusion construction module, the image-physical correlation modeling module, and the risk identification and intelligent response module. 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The panoramic image acquisition module, the image stitching and panoramic reconstruction module, the multi-source data synchronous acquisition module, the image Determine the spatial layout positions of multiple physical quantity sensors according to the high-fidelity panoramic image and complete image coordinate calibration; Each sensor collects physical quantity data according to a unified sampling period and adds time stamp and spatial position information; According to the image acquisition time stamp, establish a time synchronization relationship between the sensor data and the corresponding image frame; Based on the spatial mapping relationship, embed the physical quantity data in the image coordinate system to form a multi-dimensional structural monitoring data set with time synchronization and spatial mapping characteristics.

5. The panoramic imaging based construction monitoring system of claim 1, wherein, The steps of generating the fusion data structure include: According to the camera parameters and sensor three-dimensional coordinates, calculate the pixel position of the sensor in the high-fidelity panoramic image through the perspective transformation algorithm; According to the time stamp, establish a one-to-one binding relationship between each image and the sensor data in the corresponding time period to form a time-space binding structure; Perform Kalman filtering processing on the bound sensor data of various types to eliminate noise and maintain the continuity of physical quantities; Encapsulate each image and its bound filtered sensor data as a structured data unit to form a fusion data structure.

6. The panoramic imaging based construction monitoring system of claim 1, wherein, The construction of the image-physical fusion analysis model includes the following steps: Extract the image feature change and the corresponding physical quantity change value in the fusion data structure as a joint feature set; According to the relationship between the image features and the structure state, construct a prior probability model based on training samples; After inputting the new image features, calculate the probability distribution of each structure state to obtain the likelihood probability; Calculate the posterior probability of each structure state through Bayesian inference and output the image-physical fusion analysis model with predictive ability.

7. The panoramic imaging based construction monitoring system of claim 1, wherein, The steps of outputting the risk label and executing the response control include: Perform region division on each frame of image in the high-fidelity panoramic image and extract the image feature change index to identify suspicious areas; Input the identified suspicious areas and the corresponding time and space bound sensor data into the image-physical fusion analysis model to determine the structure state; Based on the structure state determination result, generate a risk label, which includes abnormal position, abnormal type, occurrence time and confidence level; Transfer the risk label to the remote monitoring platform and drive the local feedback mechanism to complete the closed-loop intelligent monitoring process from perception collection to response control.

Images