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

Abstract

The present application relates to the field of construction engineering and geological monitoring technology, and discloses a vertical deformation monitoring method and system based on three-dimensional scanning and differential pressure level gauge. The method comprises the following steps: collecting point cloud data of a building structure in real time by using a three-dimensional scanner, and collecting vertical displacement data of the building structure in real time by using a differential pressure level gauge; obtaining the vertical coordinates of the point cloud by using a three-point registration algorithm, and calculating the vertical coordinate difference value of the point cloud to obtain a first difference value; calculating the average of the first vertical displacement data of the current monitoring period and the average of the second vertical displacement data of the previous monitoring period, and calculating the difference value between the average of the first vertical displacement data and the average of the second vertical displacement data to obtain a second difference value; and weighting the first difference value and the second difference value 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 of the building structure by using a weighted average method. By implementing the present application, the cost of manual measurement is reduced, and the defect of single data source is changed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction engineering and geological monitoring, and particularly relates to a vertical deformation monitoring method and system based on three-dimensional scanning and differential pressure level gauge. BACKGROUND

[0002] In many fields such as construction engineering and geological monitoring, it is crucial to accurately monitor the vertical deformation of structures. It not only concerns the safety and stability of building structures, but also provides key basis for engineering decision-making. Traditional vertical deformation monitoring has many problems, such as high dependence on manual operation: the traditional total station monitoring method completely relies on manual operation, and the monitoring personnel need to set up equipment, measure and record data on site. This makes the monitoring work greatly affected by environmental factors, for example, in adverse weather conditions such as heavy rain and fog, the observation line will be seriously affected, resulting in a decrease in measurement accuracy or even the inability to measure; strong light may also interfere with instrument readings, increasing measurement errors. Low data accuracy: as a single measurement device, the total station is easily disturbed by various error factors during measurement, such as the precision limit of the instrument itself, atmospheric refraction, human operation errors, etc. Moreover, this method lacks a multi-source data verification mechanism, making it difficult to effectively evaluate and correct the accuracy of measurement data, resulting in low reliability of the final data. SUMMARY

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

[0004] In a first aspect, the present application provides a vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauge, which comprises:

[0005] Real-time acquisition of point cloud data of the building structure by a three-dimensional scanner, and real-time acquisition of vertical displacement data of the building structure by a differential pressure level gauge;

[0006] Pretreatment of the point cloud data, acquisition of the vertical coordinates of the point cloud by a three-point registration algorithm, and calculation of the vertical coordinate difference value of the point cloud to obtain a first difference value;

[0007] Elimination of outliers in the vertical displacement data, calculation of the first vertical displacement data mean value of the current monitoring period and the second vertical displacement data mean value of the previous monitoring period, and calculation of the difference value between the first vertical displacement data mean value and the second vertical displacement data mean value to obtain a second difference value;

[0008] According to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, the first difference value and the second difference value are weighted, and the final vertical deformation of the building structure is calculated by a weighted average method.

[0009] The application provides a vertical deformation monitoring method based on three-dimensional scanning and a differential pressure level gauge, fuses point cloud data acquired by a three-dimensional scanner and pressure difference data collected by a differential pressure level gauge, dynamically calibrates multi-source data through a specific algorithm, constructs an accurate deformation monitoring model, greatly changes the inconvenience of a traditional monitoring method, optimizes cost and construction period, and reduces manual measurement cost. Through comparison and analysis of the two kinds of data, the defects of a single data source in the traditional method are changed, and the real accuracy of the monitoring data is further ensured.

[0010] In an optional embodiment, the point cloud data is preprocessed, including:

[0011] Outlier points in the point cloud data are removed by statistical filtering, and point clouds conforming to structural characteristics in density are reserved;

[0012] The point cloud density is reduced by using voxel grid filtering.

[0013] In an optional embodiment, a three-point registration algorithm is used to acquire the vertical coordinates of the point cloud, including:

[0014] Three non-collinear fixed reference points are selected on the building structure;

[0015] A rotation matrix and a translation vector of the current point cloud and the reference point cloud are calculated through singular value decomposition, and the point cloud is converted from the scanner coordinate system to the global coordinate system;

[0016] The error after registration is calculated, and it is judged whether the error is greater than a first threshold value;

[0017] If the error is greater than the first threshold value, the nearest neighbor point cloud of the current point cloud is found in the global point cloud, and a corresponding relationship between the current point cloud and the nearest neighbor point cloud is established;

[0018] Return to the step of calculating the rotation matrix and the 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 value;

[0019] The vertical coordinates of the target monitoring point are extracted from the registered point cloud.

[0020] In an optional embodiment, the vertical coordinate difference value of the point cloud is calculated to obtain a first difference value, including:

[0021] The target monitoring point is scanned multiple times, and the single-scan mean value of the target monitoring point is calculated;

[0022] The difference value between the single-scan mean values of the adjacent two times is calculated by using a time series difference value method, and the first difference value is obtained.

[0023] In an optional embodiment, the outliers in the vertical displacement data are removed, comprising:

[0024] calculating the difference between the vertical displacement data at the next monitoring time and the vertical displacement data at the current monitoring time;

[0025] if the difference is greater than the second threshold value, the vertical displacement data at the next monitoring time is removed;

[0026] constructing a normal data sequence pattern, and finding the best matching path between the current time sequence and the normal data sequence pattern;

[0027] calculating the distance deviation of each vertical displacement data in the current time sequence and the corresponding matching data in the normal data sequence pattern;

[0028] if the distance deviation is greater than the third threshold value, the corresponding vertical displacement data in the current time sequence is removed.

[0029] In an optional embodiment, before the first difference value and the second difference value are weighted according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, the method further comprises:

[0030] calculating the deviation between the first difference value and the second difference value, and determining whether the deviation is greater than the fourth threshold value;

[0031] if the deviation is not greater than the fourth threshold value, then the step of calculating the final vertical deformation of the building structure by weighted average method according to the first difference value and the second difference value weighted according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data is performed;

[0032] if the deviation is greater than the fourth threshold value, a re-examination mechanism is triggered.

[0033] In an optional embodiment, the method further comprises:

[0034] 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;

[0035] analyzing the reliability of the final vertical deformation of the building structure according to the standard deviation of the fusion data.

[0036] In a second aspect, the present application provides a vertical deformation monitoring system based on three-dimensional scanning and differential pressure type level gauge, comprising: a three-dimensional scanner, a differential pressure type level gauge and a computer device, wherein,

[0037] The three-dimensional scanner is connected with the computer device through a data transmission line, and the three-dimensional scanner is used for collecting point cloud data of the building structure in real time and sending the point cloud data to the computer device.

[0038] The differential pressure level gauge is connected with the computer device through a data transmission line, and the differential pressure level gauge is used for collecting vertical displacement data of the building structure in real time and sending the vertical displacement data to the computer device.

[0039] The computer device comprises a memory and a processor, which are communicatively connected with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauge of the first aspect or any of the corresponding embodiments.

[0040] The vertical deformation monitoring system based on three-dimensional scanning and differential pressure level gauge provided by the application greatly changes the inconvenience of the traditional monitoring method, optimizes the cost and construction period, and reduces the manual measurement cost by using the high-precision characteristics of the three-dimensional scanner and the real-time data acquisition characteristics of the differential pressure level gauge.

[0041] In a third aspect, the application provides a computer readable storage medium having computer instructions stored thereon, and the computer instructions are used to make a computer execute the vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauge of the first aspect or any of the corresponding embodiments.

[0042] In a fourth aspect, the application provides a computer program product comprising computer instructions, and the computer instructions are used to make a computer execute the vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauge of the first aspect or any of the corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0044] Figure 1 It is a flowchart of the vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauge according to the embodiments of the application.

[0045] Figure 2is a principle block diagram of a vertical deformation monitoring system based on three-dimensional scanning and differential pressure level gauge according to an embodiment of the present application;

[0046] Figure 3 is a hardware structure schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some, but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0048] In many fields such as building engineering and geological monitoring, accurate monitoring of the vertical deformation of a structure is very important. It not only relates to the safety and stability of the building structure, but also provides a key basis for engineering decision-making. The traditional vertical deformation monitoring mainly relies on manual monitoring by a total station. This method has a relatively fixed process in actual application.

[0049] 1) Preparation before monitoring: Before carrying out the monitoring work, the staff needs to determine the monitoring object and the monitoring content, and formulate the corresponding monitoring accuracy requirements according to the engineering requirements. Common accuracy requirements are millimeter level or sub-millimeter level. Then, the on-site environment needs to be understood in depth, and a position far away from the deformation area and with stable geology is selected as the reference point (control point). At the same time, the monitoring point position is determined at the key part of the monitoring object, so as to ensure that the points have good visibility. If the on-site visibility is poor, a forced centering device or a prism needs to be added. The preparation work also includes the preparation of the total station, the supporting prism, the tripod, the centering rod and other equipment. The total station needs to be calibrated to ensure the measurement accuracy, and the battery capacity and storage space need to be checked to ensure that they are sufficient. In addition, a coordinate system needs to be established or the existing coordinate system needs to be used to unify the coordinate system of the reference point and the monitoring point. If an independent coordinate system needs to be established, the reference point coordinates are determined by the resection or known point joint measurement method.

[0050] 2) Layout of reference points and monitoring points: The reference points should be selected in stable positions far away 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 measurement. When laying out the reference points, a forced centering device or a concrete observation pier is often used to reduce the error caused by human factors. The monitoring points are laid out at the key positions of the monitoring object. According to the actual situation, a prism or a reflector is installed to ensure that the monitoring point can have good visibility with the total station. For some special monitoring environments, a 360° prism is used to ensure the all-around monitoring effect.

[0051] 3) Initial data collection: Set up the total station on the reference point or known point, strictly centering and leveling operation, which is the key step to ensure the accuracy of measurement. Set the weather parameters such as temperature, air pressure, etc. for correction of ranging error, because the weather conditions will have a certain impact on the measurement results. Each monitoring point needs to be observed 2 to 3 times, and the average value is taken as the measurement result, so as to reduce accidental error. During the observation process, the observation time, weather conditions, instrument height, prism height, observation value and other related note information should be recorded in detail.

[0052] 4) Periodic monitoring: According to the specific requirements of the project, periodic monitoring is carried out, and when encountering abnormal situations such as earthquakes, heavy rains, etc., the observation frequency needs to be encrypted. Each observation should use the same instrument, the same reference point, and follow the same observation route, which can effectively reduce the system error. At the same time, keep the prism height and instrument parameter settings consistent to ensure the comparability of measurement data.

[0053] 5) Data processing and analysis: Export the observation data from the total station to the computer and process it using professional data processing software. Calculate the coordinate change and displacement vector of each monitoring point to determine the deformation of the monitoring object. Check if the closure error and the measuring error are within the allowed range, if not, the data needs to be checked and processed. Eliminate gross error data, and for abnormal data caused by measurement errors, etc., if necessary, rework and supplement the measurement. Finally, draw displacement-time curve, contour map, etc. to analyze the deformation trend intuitively, and judge whether the deformation is abnormal by combining with load, environmental factors, etc.

[0054] From the above content, it can be seen that the traditional vertical deformation monitoring has many problems, such as high dependence on manual operation: the traditional total station monitoring method completely depends on manual operation, and the monitoring personnel need to set up equipment, measure 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, heavy fog, etc., the observation line of sight will be seriously affected, leading to a decrease in measurement accuracy or even inability to measure; strong light may also interfere with instrument readings, increasing measurement error. Low data accuracy: the total station as a single measurement device is easily disturbed by various error factors during measurement, such as instrument precision limitations, atmospheric refraction effects, human operation errors, etc. Moreover, this method lacks a multi-source data verification mechanism, making it difficult to effectively evaluate and correct the accuracy of measurement data, resulting in low reliability of the final data.

[0055] According to the embodiment of the present application, a vertical deformation monitoring method based on three-dimensional scanning and differential pressure level is provided. It should be noted that the steps shown in the flowchart of the 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 an order different from here.

[0056] In this embodiment, a vertical deformation monitoring method based on three-dimensional scanning and differential pressure level is provided, which can be used in the above mobile terminal, such as mobile phone, tablet computer and the like, Figure 1 The flowchart of the vertical deformation monitoring method based on three-dimensional scanning and differential pressure level according to the embodiment of the present application is shown as Figure 1 The flowchart includes the following steps:

[0057] Step S1, using three-dimensional scanner to collect point cloud data of building structure in real time, using differential pressure level to collect vertical displacement data of building structure in real time.

[0058] Specifically, the three-dimensional scanner used in the present application uses laser scanning technology, which can quickly obtain point cloud data with millimeter level precision. Its working principle is to calculate the distance between the scanner and each point on the surface of the object by emitting laser beam and measuring the time of laser reflection, so as to construct the three-dimensional point cloud model of the object. In vertical deformation monitoring, by comparing and analyzing the point cloud data of building structure at different times, the geometric deformation information of the structure can be accurately captured. In the future, higher precision laser scanning technology or improved optical system of existing equipment can be introduced to further improve the precision of point cloud data.

[0059] The differential pressure level used in the present application is based on the principle of communicating vessel and the pressure difference of liquid to collect vertical displacement data in real time. In the instrument, by measuring the pressure difference of liquid at different positions, the vertical displacement amount is obtained through conversion. The accuracy of the level can reach 0.25mm, which can provide high-precision data support for vertical deformation monitoring. It can be considered to optimize the pressure sensor inside the instrument to improve its sensitivity and stability to obtain more accurate pressure difference data.

[0060] Step S2, preprocessing the point cloud data, using three-point registration algorithm to obtain the vertical coordinates of the point cloud, and calculating the vertical coordinate difference value of the point cloud to obtain the first difference value.

[0061] Specifically, the preprocessing of point cloud data includes the following steps:

[0062] Step S201, using statistical filtering to remove outliers in point cloud data, and retaining point cloud with density consistent with structure characteristics.

[0063] Step S202, using voxel grid filtering to reduce point cloud density.

[0064] In the embodiment of the present application, statistical outlier removal is used to remove outliers and retain point clouds conforming to structural features. The calculation process is as follows: the mean and standard deviation of the distance between each point and its k neighbor points are calculated , and points with a distance exceeding are removed. Wherein, and k may be set as empirical values, for example, n = 50, k = 2. The point cloud density is reduced by using voxel grid filtering, thereby improving the registration efficiency. The voxel size is set to 0.05 m. n

[0065] Further, in an optional embodiment, a three-point registration algorithm is used to obtain the vertical coordinates of the point cloud, including the following steps:

[0066] Step S203: selecting three non-collinear fixed reference points on the building structure.

[0067] Step S204: calculating the rotation matrix and translation vector of the current point cloud and the reference point cloud by singular value decomposition, and converting the point cloud from the scanner coordinate system to the global coordinate system.

[0068] Step S205: calculating the error after registration, and determining whether the error is greater than a first threshold value.

[0069] Step S206: if the error is greater than the first threshold value, 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.

[0070] Step S207: returning to the step of calculating the rotation matrix and translation vector of the current point cloud and the reference point cloud by 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 value.

[0071] Step S208: extracting the vertical coordinates of the target monitoring point in the registered point cloud.

[0072] In the embodiment of the present application, 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 the rotation matrix R and the translation vector T of the current point cloud and the reference point cloud are calculated by singular value decomposition (SVD), and the point cloud is converted from the scanner coordinate system to the global coordinate system. The specific process is as follows:

[0073] ​Compute the centroid of the scanner coordinate system:

[0074] Compute the 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. i

[0075] Compute the centroid of the global coordinate system: where, is the coordinate of the i-th point in the global coordinate system. i

[0076] Compute the de-meaned coordinates: ,

[0077] Construct the covariance matrix:

[0078] Compute the de-meaned point cloud covariance matrix:

[0079] Perform SVD decomposition on the covariance matrix:

[0080] Perform SVD decomposition on H to obtain:

[0081] Solve for the rotation matrix R:

[0082]

[0083] Solve for the translation vector T:

[0084] .

[0085] The error after registration can be measured by the root mean square error: .

[0086] If the RMSE exceeds a first threshold (e.g., 1 mm), check whether the reference points are reasonably selected or increase the number of reference points. If the initial registration accuracy is insufficient, use the iterative closest point (ICP) algorithm for further optimization: find the closest neighbor points in the global point cloud Q for the current point cloud P, establish a correspondence 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 values in the registered point cloud where i = 1, 2, …, 5 is the five sampling times of a single scan.

[0087] ​​In the embodiments of the present application, the global point cloud refers to point cloud data as a reference, usually including known accurate coordinates (such as a pre-constructed three-dimensional model, an environment point cloud under a fixed coordinate system). The current point cloud refers to point cloud data that needs to be registered (such as point cloud collected by a three-dimensional scanner in real time). The target monitoring point refers to a specific point that needs to extract a vertical coordinate, usually a key position in the global point cloud (such as the top of a building, a monitoring marker point).

[0088] Further, the vertical coordinate difference of the point cloud is calculated to obtain a first difference value, including the following steps:

[0089] Step S209, the target monitoring point is scanned multiple times, and a single-scan mean value of the target monitoring point is calculated.

[0090] Step S210, a time sequence difference method is used to calculate a difference value between the single-scan mean values of two adjacent times, to obtain the first difference value.

[0091] In the embodiments of the present application, the single-scan mean value calculation formula is: The time sequence difference calculation process is as follows: the mean value of the previous scan is , and the current difference value is: .

[0092] Step S3, the abnormal value in the vertical displacement data is removed, a first vertical displacement data mean value of a current monitoring period and a second vertical displacement data mean value of a previous monitoring period are calculated, and a difference value between the first vertical displacement data mean value and the second vertical displacement data mean value is calculated to obtain a second difference value.

[0093] Specifically, the abnormal value in the vertical displacement data is removed, including the following steps:

[0094] Step S301, a difference value between the vertical displacement data of a next monitoring time and the vertical displacement data of a current monitoring time is calculated.

[0095] Step S302, if the difference value is greater than a second threshold value, the vertical displacement data of the next monitoring time is removed.

[0096] Step S303, a normal data sequence pattern is constructed, and a best matching path between the current time sequence and the normal data sequence pattern is found.

[0097] Step S304, a distance deviation of each vertical displacement data in the current time sequence and a corresponding matching data in the normal data sequence pattern is calculated.

[0098] Step S305, if the distance deviation is greater than a third threshold value, the corresponding vertical displacement data in the current time sequence is removed.

[0099] In the embodiments of the present application, data range screening: for data collected in time sequence Calculate the difference between data at adjacent time points. .when When the threshold (second threshold) is reached, it indicates a significant jump in the data point compared to adjacent time points. This jump does not conform to the normal variation pattern of measurement data and is likely caused by external interference (such as instantaneous vibrations, electromagnetic interference, etc.). Mark as an outlier and remove.

[0100] Dynamic time warping filtering: First, a relatively stable and unaffected data sequence needs to be selected from historical data as a reference sequence. Construct a normal data sequence pattern. This data should represent the data variation characteristics when the instrument is working normally. For example, data collected during several consecutive days of stable operation can be selected, and statistical analysis (such as calculating the mean and standard deviation) can be used to determine the fluctuation range and trend of normal data.

[0101] For the time series data that needs to be processed The DTW algorithm is used to calculate the similarity between the sequence and the reference sequence R. The core of the DTW algorithm is to find the optimal matching path between the two sequences using dynamic programming. Specifically, it calculates a... The distance matrix D, where express and The distance between them (usually using Euclidean distance) Then, from Initially, using a recursive formula (Boundary conditions are) Find from arrive The path with the minimum cumulative distance. This path represents the best match between the two sequences.

[0102] Calculate the value of each data point based on the best matching path found. Matching points in the reference sequence The distance deviation. If a certain data point Matching points If the distance deviation 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 the data point is considered invalid. It deviated from the main trend and was an outlier, so it was removed.

[0103] Furthermore, after data range filtering and Dynamic Time Warping (DTW) filtering, valid data within the monitoring period were obtained. Here, 'm' represents the number of valid data points, which is less than or equal to the number of original data points because outliers have been removed. Calculate the data mean: Let the mean of the previous time period be , which is obtained by the same method of calculating the mean of the effective data in the previous monitoring time period. The current difference is used to measure the change of vertical displacement between two time periods, and the calculation formula is: .

[0104] Step S4, calculate the deviation between the first difference and the second difference, and determine whether the deviation is greater than the fourth threshold value.

[0105] Step S5, if the deviation is not greater than the fourth threshold value, then execute step S7.

[0106] Step S6, if the deviation is greater than the fourth threshold value, then trigger the rechecking mechanism.

[0107] Specifically, first, calculate the deviation between the vertical coordinate difference value obtained by the three-dimensional scanner and the vertical displacement difference value obtained by the level. , This deviation value reflects the difference degree of the results obtained by the two different measurement methods. When the deviation is less than or equal to the set fourth threshold value (such as 0.5mm), it is considered that the results obtained by the three-dimensional scanner and the level are within the acceptable error range, which means that the better the consistency of the two data, the higher the reliability of the final result. When the deviation is greater than the fourth threshold value, it means that the results of the two data sources are quite different, which may exist measurement error or other problems, and the result is less reliable, so the rechecking mechanism needs to be triggered. Specifically, it includes checking whether the three-dimensional scanning reference point is shifted, calibrating the level pressure sensor, and re-collecting two sets of data and calculating.

[0108] Step S7, according to the standard deviation of the point cloud data, the standard deviation of the vertical displacement data, the first difference and the second difference are weighted, and the weighted average method is used to calculate the final vertical deformation of the building structure.

[0109] Specifically, in order to obtain more accurate final vertical deformation, the weighted average method is adopted. The formula is , and the standard deviation reflects the dispersion degree of the data. The smaller the dispersion degree (the smaller the standard deviation), the higher the accuracy of the data source, and the greater the weight in the weighted average. The obtained after data consistency verification and processing is the final vertical deformation, which integrates the data information of the three-dimensional scanner and the level. In the case of consistent data, a relatively more accurate result is obtained through weighted average, which is used to evaluate the vertical deformation of the structure. Among them, a standard deviation of the point cloud data, a standard deviation of the point cloud data.

[0110] The application provides a vertical deformation monitoring method based on three-dimensional scanning and a pressure difference type level gauge, fuses point cloud data acquired by a three-dimensional scanner and pressure difference data collected by the pressure difference type level gauge, dynamically calibrates multi-source data through a specific algorithm, constructs an accurate deformation monitoring model, greatly changes the inconvenience of a traditional monitoring method, optimizes cost and a construction period, and reduces manual measurement cost.

[0111] In an alternative embodiment, the vertical deformation monitoring based on three-dimensional scanning and the pressure difference type level gauge further comprises the following steps:

[0112] In step S8, a standard deviation of fused data is calculated according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data.

[0113] In step S9, the reliability of the final vertical deformation of the building structure is analyzed according to the standard deviation of the fused data.

[0114] Specifically, the standard deviation of the fused data is calculated according to the following formula: Wherein, The standard deviation of the fused data integrates the standard deviation information of the two data sources and indicates the dispersion degree or uncertainty of the final result. The smaller the standard deviation of the fused data is, the more stable and reliable the final result is; The larger the standard deviation of the fused data is, the greater the uncertainty of the result is, and more caution is needed when the final vertical deformation is used for analysis and decision-making.

[0115] The application provides a vertical deformation monitoring system based on three-dimensional scanning and a pressure difference type level gauge, as shown in the accompanying drawings, comprising a three-dimensional scanner, a pressure difference type level gauge and a computer device. Figure 2

[0116] The three-dimensional scanner is connected with the computer device through a data transmission line, and is used for collecting point cloud data of the building structure in real time and sending the point cloud data to the computer device. The pressure difference type level gauge is connected with the computer device through a data transmission line, and is used for collecting vertical displacement data of the building structure in real time and sending the vertical displacement data to the computer device.

[0117] ​Specifically, at the data acquisition layer, the application uses a three-dimensional scanner to quickly obtain point cloud data with millimeter-level precision using laser scanning technology, and sends the point cloud data to a computer device. At the same time, a differential pressure level gauge is used to collect vertical displacement data in real time, and the vertical displacement data is sent to the computer device. The device parameters of the three-dimensional scanner are as follows: distance accuracy: 1.2 mm + 10 ppm; point position accuracy within the range of the measuring distance: 5 mm - 80 m; 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 gauge are as follows: 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.

[0118] At the data transmission layer, the application supports two data transmission methods: RS485 bus or mobile Internet of Things (4G / 5G). The RS485 bus has the advantages of strong anti-interference ability and long transmission distance, and is suitable for short-distance monitoring scenarios with relatively low real-time requirements; the mobile Internet of Things (4G / 5G) has the characteristics of high speed and low delay, and can realize real-time uploading of data, meeting the needs of real-time monitoring and early warning. To enhance system stability, a backup transmission link can be added, such as optical fiber transmission, which automatically switches when wireless transmission fails, ensuring stable data uploading. A data verification and retransmission mechanism is established, which can request the sender to retransmit when the receiver finds data errors or missing, ensuring data integrity.

[0119] At the data processing layer, the computer device combines the point cloud data obtained by the three-dimensional scanner and the pressure difference data collected by the differential pressure level gauge. Through the algorithm in the above embodiment, the multi-source data is dynamically calibrated to construct an accurate deformation monitoring model. According to the model analysis results, a deformation trend analysis report is generated to provide intuitive and accurate monitoring information for monitoring personnel. The specific process is described in the above embodiment and will not be repeated here.

[0120] The vertical deformation monitoring system based on three-dimensional scanning and differential pressure level gauge provided by the application uses the high-precision characteristics of the three-dimensional scanner and the real-time data acquisition characteristics of the differential pressure level gauge, greatly changing the inconvenience of traditional monitoring methods, optimizing the cost and construction period, and reducing the cost of manual measurement. And through the comparison and analysis of the two kinds of data, the defects of the traditional method of single data source are changed, and the real accuracy of the monitoring data is further guaranteed. The monitoring data of the system can be processed through an online platform in real time and efficiently.

[0121] Please refer to Figure 3 , Figure 3This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 3 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 3 Take a processor 10 as an example.

[0122] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0123] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0124] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0125] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0126] The computer device also comprises a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0127] The embodiments of the present application further provide a computer readable storage medium, the method according to the embodiments of the present application can be implemented in hardware, firmware, or be implemented as computer code recorded in a storage medium, or be implemented through network downloading and originally stored in a remote storage medium or a non-transitory machine readable storage medium and to be stored in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general computer, a special processor or programmable or special hardware. Wherein, 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 disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, the processor, the microprocessor controller or the programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor or the hardware, the method shown in the above embodiments is implemented.

[0128] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A vertical deformation monitoring method based on three-dimensional scanning and differential pressure level gauge, characterized in that, The method comprises: Real-time acquisition of point cloud data of the building structure by a three-dimensional scanner, and real-time acquisition of vertical displacement data of the building structure by a differential pressure level gauge; Pretreatment of the point cloud data, acquisition of vertical coordinates of the point cloud by a three-point registration algorithm, and calculation of a vertical coordinate difference value of the point cloud to obtain a first difference value; Elimination of outliers in the vertical displacement data, calculation of a first vertical displacement data mean value of a current monitoring period and a second vertical displacement data mean value of a previous monitoring period, and calculation of 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; Weighting of the first difference value and the second difference value according to a standard deviation of the point cloud data and a standard deviation of the vertical displacement data, and calculation of a final vertical deformation amount of the building structure by a weighted average method; Acquisition of vertical coordinates of the point cloud by a three-point registration algorithm, comprising: Selection of three non-collinear fixed reference points on the building structure; Calculation of a rotation matrix and a translation vector of the current point cloud and the reference point cloud by singular value decomposition to convert the point cloud from a scanner coordinate system to a global coordinate system; Calculation of an error after registration, and determination of whether the error is greater than a first threshold value; If the error is greater than the first threshold value, 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 the translation vector of the current point cloud and the reference point cloud by singular value decomposition to convert the point cloud from the scanner coordinate system to the global coordinate system until the error is not greater than the first threshold value; Extraction of vertical coordinates of a target monitoring point in the registered point cloud; Elimination of outliers in the vertical displacement data, comprising: Calculation of a difference value between the vertical displacement data at a next monitoring time and the vertical displacement data at a current monitoring time; If the difference value is greater than a second threshold value, eliminating the vertical displacement data at the next monitoring time; Building a normal data sequence pattern, and finding a best matching path between the current time sequence and the normal data sequence pattern; Calculation of a distance deviation between each vertical displacement data in the current time sequence and the corresponding matching data in the normal data sequence pattern; If the distance deviation is greater than a third threshold value, eliminating the corresponding vertical displacement data in the current time sequence; Before weighting the first difference value and the second difference value according to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, the method further comprises: Calculation of a deviation between the first difference value and the second difference value, and determination of whether the deviation is greater than a fourth threshold value; If the deviation is not greater than the fourth threshold value, performing the step of weighting the first difference value and the second difference value 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 the weighted average method; If the deviation is greater than the fourth threshold value, triggering a re-examination mechanism.

2. The method for monitoring vertical deformation based on three-dimensional scanning and differential pressure type level gauge according to claim 1, characterized in that, The pretreatment of the point cloud data comprises: Elimination of outliers in the point cloud data by statistical filtering, and reservation of point clouds with density conforming to structural characteristics; Reduction of point cloud density by using voxel grid filtering. 3.The method of claim 1, wherein, The vertical coordinate difference value of the point cloud is calculated to obtain a first difference value, including: The target monitoring point is scanned multiple times to calculate a single scan mean value of the target monitoring point; The time sequence difference method is used to calculate the difference value of the single scan mean values of the adjacent two times to obtain the first difference value.

4. The method for monitoring vertical deformation based on three-dimensional scanning and differential pressure type level gauge according to claim 1, characterized in that, The method further includes: According to the standard deviation of the point cloud data and the standard deviation of the vertical displacement data, the standard deviation of the fusion data is calculated; According to the standard deviation of the fusion data, the reliability of the final vertical deformation of the building structure is analyzed.

5. A vertical deformation monitoring system based on three-dimensional scanning and differential pressure level gauge, characterized in that, It includes: A three-dimensional scanner, a differential pressure level gauge and a computer device, wherein, The three-dimensional scanner is connected with the computer device through a data transmission line, and the three-dimensional 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 gauge is connected with the computer device through a data transmission line, and the differential pressure level gauge is used to collect 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, which are communicatively connected with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the vertical deformation monitoring method based on the three-dimensional scanning and the differential pressure level gauge according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for making the computer execute the vertical deformation monitoring method based on the three-dimensional scanning and the differential pressure level gauge according to any one of claims 1 to 4.

7. A computer program product, characterised in that, It includes computer instructions for making the computer execute the vertical deformation monitoring method based on the three-dimensional scanning and the differential pressure level gauge according to any one of claims 1 to 4.

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