Vertical deformation monitoring method and system based on three-dimensional scanning and differential pressure level gauge

Abstract

The invention relates to the technical field of constructional engineering and geological monitoring, and discloses a vertical deformation monitoring method and system based on three-dimensional scanning and a differential pressure level gauge. The method comprises the following steps: acquiring point cloud data of a building structure in real time by using a three-dimensional scanner, and acquiring vertical displacement data of the building structure in real time by using a differential pressure level gauge; acquiring vertical coordinates of the point cloud by adopting a three-point registration algorithm, and calculating a vertical coordinate difference value of the point cloud to obtain a first difference value; calculating a first vertical displacement data mean value of the current monitoring time period and a second vertical displacement data mean value of the previous monitoring time period, and calculating a difference value between the first vertical displacement data mean value and the second vertical displacement data mean value to obtain a second difference value; and according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, taking the first difference value and the second difference value as weights, and calculating the final vertical deformation of the building structure by adopting a weighted average method. By implementing the invention, the manual measurement cost is reduced, and the defect of a single data source is changed.

Description

Technical Field

[0001] The present invention relates to the technical fields of construction engineering and geological monitoring, and particularly to a vertical deformation monitoring method and system based on three-dimensional scanning and differential pressure level gauges. Background Art

[0002] In many fields such as construction engineering and geological monitoring, accurate monitoring of structural vertical deformation is crucial. It not only concerns the safety and stability of building structures but also provides key basis for engineering decisions. There are many problems with traditional vertical deformation monitoring. For example, high manual dependence: The traditional total station monitoring method completely relies on manual operation. Monitoring personnel need to set up equipment, conduct measurements, and record data on site. This makes the monitoring work highly affected by environmental factors. For example, in adverse weather conditions such as heavy rain and fog, it will seriously affect the observation line of sight, resulting in a decrease in measurement accuracy or even inability to conduct measurements; strong light irradiation may also interfere with instrument readings, increasing measurement errors. Low data accuracy: As a single measurement device, the total station is easily interfered by various error factors during the measurement process, such as the accuracy limitation of the instrument itself, atmospheric refraction effects, and human operation errors. Moreover, this method lacks a multi-source data verification mechanism and is difficult to effectively evaluate and correct the accuracy of measurement data, resulting in relatively low reliability of the final data. Summary of the Invention

[0003] In view of this, the present invention provides a vertical deformation monitoring method and system based on three-dimensional scanning and differential pressure level gauges to solve the problems of high manual dependence and low data accuracy in traditional vertical deformation monitoring.

[0004] In the first aspect, the present invention provides a vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauges, and the method includes: Using a three-dimensional scanner to collect point cloud data of a building structure in real time, and using a differential pressure level gauge to collect vertical displacement data of the building structure in real time; Preprocessing the point cloud data, using a three-point registration algorithm to obtain the vertical coordinates of the point cloud, and calculating the difference in vertical coordinates of the point cloud to obtain a first difference; Eliminating outliers in the vertical displacement data, calculating the mean value of the first vertical displacement data in the current monitoring period and the mean value of the second vertical displacement data in the previous monitoring period, and calculating the difference between the mean value of the first vertical displacement data and the mean value of the second vertical displacement data to obtain a second difference; Assigning weights to the first difference and the second difference according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, and calculating the final vertical deformation amount of the building structure by using the weighted average method.

[0005] A vertical deformation monitoring method based on 3D scanning and differential pressure level provided by the present invention integrates the point cloud data obtained by a 3D scanner and the pressure difference data collected by a differential pressure level, dynamically calibrates multi-source data through a specific algorithm, constructs an accurate deformation monitoring model, greatly changes the inconvenience of traditional monitoring methods, optimizes costs and construction periods, and reduces the manual measurement cost. Moreover, through the comparative analysis of the two types of data, the defect of a single data source in the traditional method is changed, and the true accuracy of the monitoring data is further ensured.

[0006] In an alternative embodiment, preprocessing the point cloud data includes: Using statistical filtering to remove the outliers in the point cloud data and retaining the point cloud with a density conforming to the structural characteristics; Using voxel grid filtering to reduce the point cloud density.

[0007] In an alternative embodiment, the vertical coordinates of the point cloud are obtained by using a three-point registration algorithm, including: Selecting three non-collinear fixed reference points on the building structure; Calculating the rotation matrix and translation vector of the current point cloud and the reference point cloud through singular value decomposition, and converting the point cloud from the scanner coordinate system to the global coordinate system; Calculating the error after registration and determining whether the error is greater than a first threshold; If the error is greater than the first threshold, finding the nearest neighbor point cloud of the current point cloud in the global point cloud and establishing a correspondence between the current point cloud and the nearest neighbor point cloud; Returning to the step of calculating the rotation matrix and translation vector of the current point cloud and the reference point cloud through singular value decomposition and converting the point cloud from the scanner coordinate system to the global coordinate system until the error is not greater than the first threshold; Extracting the vertical coordinates of the target monitoring points in the registered point cloud.

[0008] In an alternative embodiment, calculating the vertical coordinate difference of the point cloud to obtain a first difference, including: Scanning the target monitoring points multiple times and calculating the single-scan mean value of the target monitoring points; Using the time series difference method to calculate the difference between two adjacent single-scan mean values to obtain the first difference.

[0009] In an alternative embodiment, removing the outliers in the vertical displacement data includes: Calculating the difference between the vertical displacement data at the next monitoring moment and the vertical displacement data at the current monitoring moment; If the difference is greater than a second threshold, removing the vertical displacement data at the next monitoring moment; Construct a normal data sequence pattern and find the best matching path between the current time series and the normal data sequence pattern; Calculate the distance deviation between each vertical displacement data in the current time series and the corresponding matching data in the normal data sequence pattern; If the distance deviation is greater than the third threshold, remove the corresponding vertical displacement data in the current time series.

[0010] In an alternative embodiment, before assigning weights to the first difference and the second difference according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, the method further includes: Calculate the deviation between the first difference and the second difference, and determine whether the deviation is greater than the fourth threshold; If the deviation is not greater than the fourth threshold, then perform the steps of assigning weights to the first difference and the second difference according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, and calculating the final vertical deformation amount of the building structure by using the weighted average method; If the deviation is greater than the fourth threshold, trigger a re-inspection mechanism.

[0011] In an alternative embodiment, the method further includes: Calculate the standard deviation of the fused data according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data; Analyze the reliability of the final vertical deformation amount of the building structure according to the standard deviation of the fused data.

[0012] In a second aspect, the present invention provides a vertical deformation monitoring system based on 3D scanning and differential pressure level, including: a 3D scanner, a differential pressure level, and a computer device, wherein, The 3D scanner is connected to the computer device through a data transmission line. The 3D scanner is configured to collect point cloud data of a building structure in real time and send the point cloud data to the computer device; The differential pressure level is connected to the computer device through a data transmission line. The differential pressure level is configured to collect vertical displacement data of the building structure in real time and send the vertical displacement data to the computer device.

[0013] The computer device includes: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the vertical deformation monitoring method based on 3D scanning and differential pressure level in the first aspect or any corresponding embodiment thereof.

[0014] A vertical deformation monitoring system based on 3D scanning and differential pressure level provided by the present invention utilizes the high-precision characteristics of a 3D scanner and the real-time data acquisition characteristics of a differential pressure level, greatly changing the inconvenience of traditional monitoring methods, optimizing costs and construction periods, and reducing manual measurement costs. Moreover, through the comparative analysis of the two types of data, it changes the defect of the single data source in the traditional method, and further guarantees the true accuracy of the monitoring data.

[0015] In a third aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the vertical deformation monitoring method based on 3D scanning and differential pressure level in the first aspect or any corresponding implementation manner thereof.

[0016] In a fourth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the vertical deformation monitoring method based on 3D scanning and differential pressure level in the first aspect or any corresponding implementation manner thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation manners of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific implementation manners or the prior art. Obviously, the drawings in the following description are some implementation manners of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 is a schematic flowchart of the vertical deformation monitoring method based on 3D scanning and differential pressure level according to an embodiment of the present invention; Figure 2 is a schematic block diagram of the vertical deformation monitoring system based on 3D scanning and differential pressure level according to an embodiment of the present invention; Figure 3 is a schematic hardware structure diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0020] In many fields such as construction engineering and geological monitoring, it is crucial to accurately monitor the vertical deformation of structures. This not only concerns the safety and stability of building structures but also provides key evidence for engineering decision-making. Traditional vertical deformation monitoring mainly relies on total station for manual monitoring, and there is a relatively fixed process in practical applications.

[0021] 1) Preparation before monitoring: Before starting the monitoring work, the staff need to clarify the monitoring object and content, and formulate corresponding monitoring accuracy requirements according to the engineering needs. Common accuracy requirements are millimeter level or sub-millimeter level. Subsequently, it is necessary to thoroughly understand the on-site environment, select a location far from the deformation area and with stable geology as the reference point (control point), and at the same time determine the monitoring point positions at the key parts of the monitoring object to ensure good visibility between each point. If the on-site visibility is poor, it is also necessary to add forced centering devices or prisms. The preparatory work also includes the preparation of equipment such as total station, supporting prisms, tripods, centering rods, etc. It is necessary to calibrate the total station to ensure the measurement accuracy, and at the same time check whether the battery power and storage space are sufficient. In addition, it is necessary to establish or follow the existing coordinate system to unify the coordinate systems of the reference point and the monitoring points; if an independent coordinate system needs to be established, the coordinates of the reference point are determined by resection or connection measurement with known points.

[0022] 2) Layout of reference points and monitoring points: The reference points should be selected at stable positions far from the deformation area, and the number should be no less than 3. These reference points form a reference network to provide a stable reference framework for subsequent measurements. When laying out the reference points, forced centering devices or concrete observation piers are often used to reduce the errors caused by human factors. Monitoring points are laid out at the key positions of the monitoring object, and prisms or reflectors are installed according to the actual situation to ensure good visibility between the monitoring points and the total station; for some special monitoring environments, 360° prisms are also used to ensure all-round monitoring effects.

[0023] 3) Initial data collection: Set up the total station on the reference point or known point, and strictly carry out centering and leveling operations, which are key steps to ensure measurement accuracy. Set meteorological parameters such as temperature and air pressure to correct the ranging error, because meteorological conditions will have a certain impact on the measurement results. Each monitoring point needs to be observed 2 to 3 times repeatedly, and the average value is taken as the measurement result to reduce accidental errors. During the observation process, the observation time, weather conditions, instrument height, prism height, observation values and other relevant remarks should be recorded in detail.

[0024] 4) Periodic monitoring: Carry out periodic monitoring according to the specific requirements of the project. In case of abnormal situations such as earthquakes and heavy rains, the observation frequency needs to be increased. Each time of observation, the same instrument, the same reference point should be used, and the measurement should be carried out along the same observation route, which can effectively reduce systematic errors. At the same time, keep the prism height and instrument parameter settings consistent to ensure the comparability of measurement data.

[0025] 5) Data processing and analysis: Export the observed data from the total station to the computer and process it using professional data processing software. Calculate the coordinate change amounts and displacement vectors of each monitoring point, and judge the deformation condition of the monitored object based on these data. Check whether the closing error and round-trip difference are within the allowable range. If they exceed the range, the data needs to be checked and processed. Eliminate gross error data. For abnormal data caused by measurement errors and other reasons, rework and supplementary measurement are necessary when required. Finally, draw displacement-time curves, contour maps, etc., visually analyze the deformation trend through the charts, and comprehensively judge whether the deformation is abnormal in combination with load, environmental factors, etc.

[0026] As can be seen from the above content, there are many problems in traditional vertical deformation monitoring. For example, high manual dependence: The traditional total station monitoring method completely relies on manual operation. The monitoring personnel need to set up equipment, conduct measurements, and record data on-site. This makes the monitoring work greatly affected by environmental factors. For example, in bad weather conditions such as heavy rain and fog, it will seriously affect the observation line of sight, resulting in a decrease in measurement accuracy or even inability to conduct measurements; strong light irradiation may also interfere with instrument readings and increase measurement errors. Low data accuracy: As a single measurement device, the total station is easily interfered by various error factors during the measurement process, such as the accuracy limitation of the instrument itself, the influence of atmospheric refraction, and human operation errors. Moreover, this method lacks a multi-source data verification mechanism and is difficult to effectively evaluate and correct the accuracy of measurement data, resulting in relatively low reliability of the final data.

[0027] According to an embodiment of the present invention, an embodiment of a vertical deformation monitoring method based on 3D scanning and differential pressure level is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0028] In this embodiment, a vertical deformation monitoring method based on 3D scanning and differential pressure level is provided, which can be used for the above-mentioned mobile terminals, such as mobile phones, tablets, etc. Figure 1 is a flowchart of the vertical deformation monitoring method based on 3D scanning and differential pressure level according to an embodiment of the present invention, as Figure 1 shown, and this process includes the following steps: Step S1, use a 3D scanner to collect point cloud data of the building structure in real time, and use a differential pressure level to collect vertical displacement data of the building structure in real time.

[0029] Specifically, the 3D scanner used in this application utilizes laser scanning technology and can quickly obtain point cloud data with millimeter-level accuracy. Its working principle is to emit laser beams and measure the time it takes for the laser to reflect back, calculate the distance between the scanner and each point on the object surface, and thus construct a 3D point cloud model of the object. In vertical deformation monitoring, by comparing and analyzing the point cloud data of the building structure at different times, the geometric deformation information of the structure can be accurately captured. In the future, it is possible to explore the introduction of higher-precision laser scanning technology or improve the optical system of the existing equipment to further improve the accuracy of point cloud data.

[0030] The differential-level instrument used in this application is based on the principle of communicating vessels and liquid pressure difference to collect vertical displacement data in real time. Inside the instrument, by measuring the pressure difference of the liquid at different positions and through conversion, the vertical displacement amount is obtained. The accuracy of this level instrument can reach 0.25 mm, which can provide high-precision data support for vertical deformation monitoring. It is possible to consider optimizing the pressure sensor inside the instrument to improve its sensitivity and stability in order to obtain more accurate pressure difference data.

[0031] Step S2: Preprocess the point cloud data, use the three-point registration algorithm to obtain the vertical coordinates of the point cloud, and calculate the difference in the vertical coordinates of the point cloud to obtain the first difference.

[0032] Specifically, the preprocessing of the point cloud data includes the following steps: Step S201: Use statistical filtering to remove the outlier points in the point cloud data and retain the point cloud with a density conforming to the structural characteristics.

[0033] Step S202: Use voxel grid filtering to reduce the point cloud density.

[0034] In the embodiment of the present invention, statistical filtering (Statistical Outlier Removal) is used to remove the outlier points and retain the point cloud with a density conforming to the structural characteristics. The calculation process is as follows: Calculate the distance mean k between each point and its neighboring points and the standard deviation , and remove the points with a distance exceeding k . Among them, n and k can be set as empirical values. For example, n = 50,

[0035] = 2. By using voxel grid filtering (Voxel Grid) to reduce the point cloud density, the registration efficiency is further improved. Among them, the voxel size is set to 0.05 m. Step S203: Select three non-collinear fixed reference points on the building structure.

[0036] Step S204: Calculate the rotation matrix and translation vector between the current point cloud and the reference point cloud through singular value decomposition, and transform the point cloud from the scanner coordinate system to the global coordinate system.

[0037] Step S205: Calculate the error after registration and determine whether the error is greater than the first threshold.

[0038] Step S206: If the error is greater than the first threshold, find the nearest neighbor point cloud of the current point cloud in the global point cloud and establish the corresponding relationship between the current point cloud and the nearest neighbor point cloud.

[0039] Step S207: Return to the step of calculating the rotation matrix and translation vector between the current point cloud and the reference point cloud through singular value decomposition, and transform the point cloud from the scanner coordinate system to the global coordinate system until the error is not greater than the first threshold.

[0040] Step S208: Extract the vertical coordinates of the target monitoring points from the registered point cloud.

[0041] In the embodiment of the present invention, three non - collinear fixed reference points are selected on the building structure , , . The selection requirements are as follows: The fixed reference points are generally reference target points. Then, calculate the rotation matrix R and translation vector T between the current point cloud and the reference point cloud through singular value decomposition (SVD), and transform the point cloud from the scanner coordinate system to the global coordinate system. The specific process is as follows: Calculate the centroid: Centroid of the scanner coordinate system: , where , n is the total number of points in the point cloud data, is the coordinate of the i th point in the scanner coordinate system.

[0042] Centroid of the global coordinate system: , where is the coordinate of the i th point in the global coordinate system.

[0043] Coordinates after removing the mean: ,

[0044] Construct the covariance matrix: Calculate the covariance matrix of the point cloud after removing the mean:

[0045] Perform SVD decomposition on the covariance matrix: Perform singular value decomposition on H to obtain:

[0046] Solve the rotation matrix R:

[0047] Solve for the translation vector T: .

[0048] The error after registration can be measured by the root mean square error: .

[0049] If the RMSE exceeds the first threshold (e.g., 1mm), it is necessary to check whether the reference points are selected appropriately or to increase the number of reference points. If the initial registration accuracy is insufficient, the iterative closest point (ICP) algorithm can be used for further optimization: for the current point cloud P, find the nearest neighbor point in the global point cloud Q and establish a corresponding relationship; repeat the above SVD registration process until the RMSE converges or the maximum number of iterations is reached. Then, for the target monitoring point, extract its coordinate value in the registered point cloud. , where i=1,2,…,5 is the five samples of a single scan.

[0050] In the embodiments of the present invention, the global point cloud refers to the reference point cloud data, typically containing known precise coordinates (e.g., a pre-built 3D model or an environment point cloud in a fixed coordinate system). The current point cloud refers to the point cloud data that requires registration (e.g., a point cloud acquired in real time by a 3D scanner). The target monitoring point refers to the specific point whose vertical coordinates need to be extracted, typically a key location in the global point cloud (e.g., a building top or a monitoring landmark).

[0051] Furthermore, calculating the vertical coordinate difference of the point cloud to obtain a first difference includes the following steps: Step S209 , scanning the target monitoring point multiple times, and calculating the average value of a single scan of the target monitoring point.

[0052] In step S210 , a time series difference method is used to calculate the difference between the average values of two adjacent single scans to obtain a first difference.

[0053] In the embodiment of the present invention, the single scan mean value calculation formula is: The calculation process of the timing difference is as follows: Assume that the mean value of the previous scan is , the current difference is: .

[0054] Step S3: remove abnormal values from the vertical displacement data, calculate the mean of the first vertical displacement data in the current monitoring period and the mean of the second vertical displacement data in the previous monitoring period, and calculate the difference between the mean of the first vertical displacement data and the mean of the second vertical displacement data to obtain a second difference.

[0055] Specifically, removing outliers from the vertical displacement data includes the following steps: Step S301, calculate the difference between the vertical displacement data at the next monitoring moment and the vertical displacement data at the current monitoring moment.

[0056] Step S302, if the difference is greater than the second threshold, remove the vertical displacement data at the next monitoring moment.

[0057] Step S303, construct a normal data sequence pattern and find the best matching path between the current time series and the normal data sequence pattern.

[0058] Step S304, calculate the distance deviation between each vertical displacement data in the current time series and the corresponding matching data in the normal data sequence pattern.

[0059] Step S305, if the distance deviation is greater than the third threshold, remove the corresponding vertical displacement data in the current time series.

[0060] In the embodiment of the present invention, data range screening: for data collected in a time series , calculate the difference between data at adjacent moments . When (the second threshold), it indicates that this data point has a significant jump relative to the data at adjacent moments. Such a jump does not conform to the variation law of normal measurement data and is likely caused by external interference (such as momentary vibration, electromagnetic interference, etc.). Mark the corresponding as an outlier and remove it.

[0061] Dynamic time warping filtering: First, a relatively stable and undisturbed data segment needs to be selected from historical data as a reference sequence , and a normal data sequence pattern is constructed. This data should be able to represent the data variation characteristics during the normal operation of the instrument. For example, data collected during several consecutive days of stable operation of the instrument can be selected, and through statistical analysis (such as calculating the mean, standard deviation, etc.) to determine the fluctuation range and trend of normal data.

[0062] For the time series data to be processed currently , use the DTW algorithm to calculate its similarity with the reference sequence R. The core of the DTW algorithm is to find the best matching path between two sequences through dynamic programming. Specifically, it calculates a distance matrix D, where represents and between the distances (usually using the Euclidean distance . Then, starting from , through the recurrence formula (the boundary conditions are ) Find the minimum cumulative distance path from to . This path represents the best match between the two sequences.

[0063] According to the found best match path, calculate the distance deviation of each data point from the corresponding matching point in the reference sequence. If the distance deviation of a certain data point from the corresponding matching point exceeds a pre-set third threshold (for example, the deviation is greater than 3 times the standard deviation of the normal data fluctuation range), then this data point is considered to deviate from the main trend and is an outlier, and it is excluded).

[0064] Furthermore, after data range screening and dynamic time warping (DTW) filtering, the effective data within the monitoring time period is obtained . Here, m is the number of effective data, which is less than or equal to the number of original collected data because outliers have been excluded. Calculate the data mean: . Let the mean of the previous time period be , which is the average value obtained within the previous monitoring time period by the same method of calculating the mean of effective data. The current difference is used to measure the change in vertical displacement between two time periods, and the calculation formula is: .

[0065] Step S4, calculate the deviation between the first difference and the second difference, and determine whether the deviation is greater than the fourth threshold.

[0066] Step S5, if the deviation is not greater than the fourth threshold, then execute Step S7.

[0067] Step S6, if the deviation is greater than the fourth threshold, then trigger the re-inspection mechanism.

[0068] Specifically, first, calculate the deviation between the vertical coordinate difference obtained by the 3D scanner and the vertical displacement difference obtained by the level, . This deviation value reflects the degree of difference between the results obtained by two different measurement methods. When the deviation is less than or equal to the set fourth threshold (such as 0.5 mm), it is considered that the results obtained from the two data sources of the 3D scanner and the level are within the acceptable error range, indicating that the consistency of the two data is better and the credibility of the final result is relatively higher. When the deviation is greater than When the fourth threshold is reached, it indicates that the results of the two data sources vary significantly, and there may be measurement errors or other problems. The reliability of the results is low, and a re-inspection mechanism needs to be triggered. Specifically, it includes: checking whether the three-dimensional scanning reference points are displaced, calibrating the pressure sensor of the level, and re-collecting two sets of data and calculating.

[0069] Step S7: Assign weights to the standard deviation of the point cloud data and the first difference and second difference of the vertical displacement data, and use the weighted average method to calculate the final vertical deformation of the building structure.

[0070] Specifically, in order to obtain a more accurate final vertical deformation, the weighted average method is used. The formula is , and the standard deviation reflects the degree of dispersion of the data. The smaller the degree of dispersion (the smaller the standard deviation), the higher the accuracy of the data source, and the greater the weight it occupies in the weighted average. After data consistency verification and processing, the is the final vertical deformation. It combines the data information of the three-dimensional scanner and the level, and under the condition of data consistency, a relatively more accurate result is obtained through the weighted average method, which is used to evaluate the vertical deformation of the structure. Among them, is the standard deviation of the vertical displacement data, is the standard deviation of the point cloud data.

[0071] A vertical deformation monitoring method based on three-dimensional scanning and differential pressure level provided by the present invention integrates the point cloud data obtained by the three-dimensional scanner and the pressure difference data collected by the differential pressure level, dynamically calibrates the multi-source data through a specific algorithm, constructs an accurate deformation monitoring model, greatly changes the inconvenience of the traditional monitoring method, optimizes the cost and construction period, and reduces the manual measurement cost. And through the comparative analysis of the two data, it changes the defect of the single data source of the traditional method, and further guarantees the true accuracy of the monitoring data.

[0072] In an alternative embodiment, the vertical deformation monitoring based on three-dimensional scanning and differential pressure level further includes the following steps: Step S8: Calculate the standard deviation of the fused data according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data.

[0073] Step S9: Analyze the reliability of the final vertical deformation of the building structure according to the standard deviation of the fused data.

[0074] Specifically, the standard deviation calculation formula of the fused data is as follows: . Among them, combines the standard deviation information of the two data sources, which represents the degree of dispersion or uncertainty of the final result. The smaller it is, the more stable and reliable the final result is; The larger it is, the greater the uncertainty of the result, and more caution is needed when using the final vertical deformation for analysis and decision-making.

[0075] The present invention provides a vertical deformation monitoring system based on 3D scanning and differential pressure level, as Figure 2 shown, including: a 3D scanner, a differential pressure level, and a computer device.

[0076] Among them, the 3D scanner is connected to the computer device through a data transmission line. The 3D scanner is used to collect point cloud data of the building structure in real time and send the point cloud data to the computer device. The differential pressure level is connected to the computer device through a data transmission line. The differential pressure level is used to collect vertical displacement data of the building structure in real time and send the vertical displacement data to the computer device.

[0077] Specifically, at the data acquisition layer, this application uses a 3D scanner to utilize laser scanning technology to quickly obtain point cloud data with millimeter-level accuracy and send the point cloud data to the computer device. At the same time, a differential pressure level is used to collect vertical displacement data in real time and send the vertical displacement data to the computer device. Among them, the device parameters of the 3D scanner are as follows: distance accuracy: 1.2 mm + 10 ppm; point position accuracy within the measuring range: 5mm - 80m; scanning rate: 1,000,000 points / second; scanning range and reflectivity: up to 80 m; 18% reflectivity (minimum distance 0.4 m); operating temperature: -20°C ~ +50°C. The device parameters of the differential pressure level are as follows: measuring range: 2m / 5m / 10m; accuracy: 0.25mm; transmission method: RS485; temperature -20°~85°C; resolution: 0.1mm; data transmission method: mobile Internet of Things or RS485; protection: IP67.

[0078] At the data transmission layer, this application supports two data transmission methods: RS485 bus or mobile Internet of Things (4G / 5G). The RS485 bus has advantages such as strong anti-interference ability and long transmission distance, and is suitable for monitoring scenarios with short distances and relatively low requirements for real-time performance; the mobile Internet of Things (4G / 5G) has the characteristics of high speed and low latency, and can realize real-time data upload to meet the needs of real-time monitoring and early warning. To enhance the system stability, a backup transmission link can be added, such as using fiber optic transmission as a supplement at the same time, and automatically switching when the wireless transmission fails to ensure stable data upload. Establish a data verification and retransmission mechanism. When the receiving end finds data errors or omissions, it can timely request the sending end to retransmit to ensure data integrity.

[0079] At the data processing layer, the computer device integrates the point cloud data obtained by the 3D scanner and the pressure difference data collected by the differential leveling instrument. The multi-source data is dynamically calibrated through the algorithms in the above embodiments to construct an accurate deformation monitoring model. A deformation trend analysis report is generated based on the model analysis results to provide intuitive and accurate monitoring information for the monitoring personnel. For the specific process, please refer to the above embodiments and will not be elaborated here.

[0080] A vertical deformation monitoring system based on 3D scanning and differential leveling instrument provided by the present invention utilizes the high-precision characteristics of the 3D scanner and the real-time data acquisition characteristics of the differential leveling instrument, greatly changing the inconvenience of traditional monitoring methods, optimizing costs and construction periods, and reducing manual measurement costs. Moreover, through the comparative analysis of the two types of data, it changes the defect of the single data source in the traditional method and further ensures the true accuracy of the monitoring data. The monitoring data of this system can be processed through an online platform, which is real-time and efficient.

[0081] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As Figure 3 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common main board or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 3 Here, one processor 10 is taken as an example.

[0082] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0083] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0084] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0085] The memory 20 may include a volatile memory, for example, a random access memory; the memory may also include a non-volatile memory, for example, a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memories.

[0086] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0087] The embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0088] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A vertical deformation monitoring method based on 3D scanning and differential pressure level, characterized in that, The method includes: Using a 3D scanner to collect point cloud data of the building structure in real time, and using a differential leveling instrument to collect vertical displacement data of the building structure in real time; Preprocessing the point cloud data, using a three-point registration algorithm to obtain the vertical coordinates of the point cloud, and calculating the difference in the vertical coordinates of the point cloud to obtain a first difference; Removing outliers from the vertical displacement data, calculating the mean value of the first vertical displacement data in the current monitoring period and the mean value of the second vertical displacement data in the previous monitoring period, and calculating the difference between the mean value of the first vertical displacement data and the mean value of the second vertical displacement data to obtain a second difference; Assigning weights to the first difference and the second difference according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, and using the weighted average method to calculate the final vertical deformation of the building structure.

2. The vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to claim 1, characterized in that, Preprocessing the point cloud data includes: Using statistical filtering to remove outliers from the point cloud data and retaining the point cloud with density conforming to the structural characteristics; Using voxel grid filtering to reduce the point cloud density.

3. The vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to claim 1, characterized in that Using a three-point registration algorithm to obtain the vertical coordinates of the point cloud includes: Selecting three non-collinear fixed reference points on the building structure; Calculating the rotation matrix and translation vector of the current point cloud and the reference point cloud through singular value decomposition, and converting the point cloud from the scanner coordinate system to the global coordinate system; Calculating the error after registration and determining whether the error is greater than a first threshold; If the error is greater than the first threshold, finding the nearest neighbor point cloud of the current point cloud in the global point cloud and establishing a correspondence between the current point cloud and the nearest neighbor point cloud; Returning to the step of calculating the rotation matrix and translation vector of the current point cloud and the reference point cloud through singular value decomposition and converting the point cloud from the scanner coordinate system to the global coordinate system until the error is not greater than the first threshold; Extracting the vertical coordinates of the target monitoring points in the registered point cloud.

4. The vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to claim 3, characterized in that, Calculating the difference in the vertical coordinates of the point cloud to obtain a first difference includes: Scanning the target monitoring points multiple times and calculating the mean value of a single scan of the target monitoring points; Using the time series difference method to calculate the difference between the mean values of two adjacent single scans to obtain a first difference.

5. The vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to claim 1, characterized in that Removing outliers from the vertical displacement data includes: Calculating the difference between the vertical displacement data at the next monitoring moment and the vertical displacement data at the current monitoring moment; If the difference is greater than a second threshold, removing the vertical displacement data at the next monitoring moment; Constructing a normal data sequence pattern and finding the best matching path between the current time series and the normal data sequence pattern; Calculating the distance deviation between each vertical displacement data in the current time series and the corresponding matching data in the normal data sequence pattern; If the distance deviation is greater than a third threshold, removing the corresponding vertical displacement data in the current time series.

6. The vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to claim 1, characterized in that, Before assigning weights to the first difference and the second difference according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, the method further includes: Calculating the deviation between the first difference and the second difference and determining whether the deviation is greater than a fourth threshold; If the deviation is not greater than the fourth threshold, perform the step of assigning weights to the first difference and the second difference according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, and calculating the final vertical deformation amount of the building structure by using the weighted average method; If the deviation is greater than the fourth threshold, trigger the re-inspection mechanism.

7. The vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to claim 1, characterized in that, The method further includes: Calculating the standard deviation of the fusion data according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data; Analyzing the reliability of the final vertical deformation amount of the building structure according to the standard deviation of the fusion data.

8. A vertical deformation monitoring system based on 3D scanning and differential pressure level, characterized in that, Including: A 3D scanner, a differential pressure level, and a computer device, wherein, The 3D scanner is connected to the computer device through a data transmission line, and the 3D scanner is used to collect the point cloud data of the building structure in real time and send the point cloud data to the computer device; The differential pressure level is connected to the computer device through a data transmission line, and the differential pressure level is used to collect the vertical displacement data of the building structure in real time and send the vertical displacement data to the computer device; The computer device includes: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the vertical deformation monitoring method based on 3D scanning and differential pressure level according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the vertical deformation monitoring method based on 3D scanning and differential pressure level according to any one of claims 1 to 7.

10. A computer program product, characterized in that, Including computer instructions, the computer instructions are used to cause a computer to execute the vertical deformation monitoring method based on 3D scanning and differential pressure level according to any one of claims 1 to 7.

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