Structural object deformation monitoring method and device based on laser measurement device

Abstract

The invention relates to the technical field of laser measurement structure deformation, in particular to a structure deformation monitoring method and device based on laser measurement equipment. The method comprises the steps of obtaining a data deviation degree of a monitoring point in a target period; according to the difference between the correlation degree and the data deviation degree of distance data of each monitoring point and other monitoring points in the target period, obtaining a deformation ratio coefficient of each monitoring point in the target period; filtering the distance data of the monitoring point in the target period in combination with the data deviation degree and the deformation proportion coefficient to obtain a filtering comprehensive distance of the monitoring point in the target period, constructing a three-dimensional model of the structure by using the filtering comprehensive distance, and monitoring the deformation of the structure according to the difference between the three-dimensional model of the structure in the target period and the previous monitoring period. According to the method, the distance data is filtered in consideration of the noise influence degree on the distance data acquired by the laser measurement equipment, so that the accuracy of the three-dimensional model of the structure is improved, and the accuracy of monitoring the deformation of the structure is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of laser measurement of structural deformation, and in particular to a structural deformation monitoring method and equipment based on laser measurement equipment. Background Art

[0002] With the rapid development of infrastructure construction, structures such as bridges, tunnels, high-rise buildings and dams are prone to deformation or damage during use due to the influence of natural environment and human factors, threatening the safety and stability of the structures. Therefore, real-time and accurate monitoring of the deformation of these structures is of great significance for preventing disasters and ensuring public safety.

[0003] In current engineering practice, laser measurement equipment has shown significant advantages in high-precision, non-contact and long-distance measurement when analyzing structural deformation. Existing methods use laser measurement equipment to collect distance values ​​from the equipment to the structure, obtain three-dimensional point cloud data on the surface of the structure through these values, and then obtain a three-dimensional model of the structure, compare the three-dimensional model of the structure with its historical three-dimensional model, and analyze the deformation of the structure. If the laser measurement equipment is in a harsh environment with large noise interference, the distance values ​​collected by the laser measurement equipment may be affected by the environmental noise, resulting in errors in the constructed three-dimensional model of the structure, thereby reducing the accuracy of structural deformation monitoring. Summary of the invention

[0004] In order to solve the technical problem that the distance data collected by the laser measurement equipment is inaccurate due to the influence of environmental noise, and thus the deformation monitoring of the structure has errors, the purpose of the present invention is to provide a method and equipment for structure deformation monitoring based on laser measurement equipment, and the technical scheme adopted is as follows:

[0005] Obtain the distance data of each monitoring point of the structure at each moment in different monitoring cycles, and select any one monitoring cycle from the remaining monitoring cycles except the first monitoring cycle as the target cycle;

[0006] According to the difference between the distance data of each monitoring point at all times in the target period and the reference value, and the degree of change of the distance data at each moment and its adjacent moments, the data deviation of each monitoring point in the target period is obtained;

[0007] Obtain the deformation ratio of each monitoring point in the target period according to the correlation between the distance data of each monitoring point and the other monitoring points at all times in the target period and the difference between the data deviations;

[0008] According to the data offset and the deformation ratio coefficient, the distance data of each monitoring point at all times in the target period are filtered to obtain the filtered comprehensive distance of each monitoring point in the target period; the three-dimensional model of the structure in the target period is constructed by using the filtered comprehensive distance of all monitoring points of the structure in the target period; and the deformation of the structure is monitored according to the difference between the three-dimensional model of the structure in the target period and the monitoring period before it.

[0009] Furthermore, the method for obtaining the data deviation of each monitoring point in the target period includes:

[0010] For each monitoring point, the comprehensive distance benchmark difference of the monitoring point in the target period is obtained according to the average of the difference between the distance data of the monitoring point at all times in the target period and the benchmark value;

[0011] The average of the absolute values ​​of the slopes between the distance data of the monitoring point at each moment except the last moment in the target cycle and its adjacent next moment is taken as the comprehensive distance change degree of the monitoring point in the target cycle;

[0012] The data deviation of the monitoring point in the target period is obtained according to the comprehensive distance benchmark difference and the comprehensive distance variation.

[0013] Furthermore, the method for obtaining the deformation ratio coefficient of each monitoring point in the target period includes:

[0014] Arrange the distance data of each monitoring point at all times in the target period according to time to obtain a distance sequence of each monitoring point; select the first control point of each monitoring point; and take the average of the correlation coefficients between each monitoring point and the distance sequence of its first control point as the deformation influence degree of each monitoring point in the target period;

[0015] Selecting a second control point for each monitoring point, and obtaining the noise misleading degree of each monitoring point in the target period according to the correlation coefficient between the distance sequence of each monitoring point and its second control point, and the difference between the data deviations;

[0016] According to the deformation influence degree and the noise misleading degree, the deformation proportion coefficient of each monitoring point in the target period is obtained.

[0017] Furthermore, the method for obtaining the noise misleading degree of each monitoring point in the target period includes:

[0018] Obtaining a deviation concentration value of each monitoring point in the target period according to the data deviation of the second control point of each monitoring point in the target period; taking the absolute value of the difference between the data deviation of each monitoring point in the target period and the deviation concentration value as the deviation difference of each monitoring point in the target period;

[0019] The average value of the correlation coefficient between each monitoring point and the distance sequence of its second control point is used as the change correlation degree of each monitoring point in the target period;

[0020] The noise misleading degree of each monitoring point in the target period is obtained according to the deviation difference and the change correlation.

[0021] Furthermore, the method for obtaining the filtered comprehensive distance of each monitoring point in the target period includes:

[0022] According to the data deviation and the deformation ratio of each monitoring point in the target period, the filtering weight of each monitoring point in the target period is obtained; based on the filtering weight, the distance data of each monitoring point at all times in the target period are filtered using a filtering algorithm to obtain the filtered distance data of each monitoring point at each time in the target period;

[0023] The mean of the filtered distance data of each monitoring point at all times in the target period is taken as the filtered comprehensive distance of each monitoring point in the target period.

[0024] Furthermore, the method for monitoring deformation of a structure according to the difference between the three-dimensional model of the structure in the target period and the previous monitoring period includes:

[0025] Matching the three-dimensional model of the structure of the target period and the previous monitoring period, and forming a matching point pair by combining each point in the three-dimensional model of the structure of the target period with its matching point in the three-dimensional model of the structure in the previous monitoring period of the target period;

[0026] The average value of the distance between two points in all matching point pairs between the target period and the three-dimensional model of the structure in the previous monitoring period is obtained as the deformation distance of the structure in the target period.

[0027] Furthermore, the reference value is the filtered comprehensive distance of each monitoring point in a previous monitoring cycle of the target cycle.

[0028] Furthermore, the method for obtaining the deviation concentration value of each monitoring point in the target cycle includes: taking the mean of the data deviation of the second control point of each monitoring point in the target cycle as the deviation concentration value of each monitoring point in the target cycle.

[0029] Furthermore, the correlation coefficient is a Pearson correlation coefficient.

[0030] A structure deformation monitoring device based on a laser measurement device, the device comprising: a data acquisition module, a deviation analysis module, a deformation analysis model and a deformation monitoring model;

[0031] The data acquisition module is used to obtain the distance data of each monitoring point of the structure at each time in different monitoring cycles, and select any one monitoring cycle from the remaining monitoring cycles except the first monitoring cycle as the target cycle;

[0032] The deviation analysis module is used to obtain the data deviation of each monitoring point in the target period according to the difference between the distance data of each monitoring point at all times in the target period and the reference value, and the degree of change of the distance data at each time and its adjacent time;

[0033] The deformation analysis model is used to obtain the deformation ratio of each monitoring point in the target period according to the correlation degree of the distance data between each monitoring point and the other monitoring points at all times in the target period, and the difference between the data deviations;

[0034] The deformation monitoring model is used to filter the distance data of each monitoring point at all times in a target period according to the data offset and the deformation ratio to obtain the filtered comprehensive distance of each monitoring point in the target period; construct a three-dimensional model of the structure in the target period by using the filtered comprehensive distance of all monitoring points of the structure in the target period; and monitor the deformation of the structure according to the difference between the three-dimensional model of the structure in the target period and the monitoring period before it.

[0035] The present invention has the following beneficial effects:

[0036] In the embodiment of the present invention, the reference value is not affected by noise, and the difference between the distance data of the monitoring point in the target period and the reference value shows the degree of deviation of the target period compared with the reference value. At the same time, the change of the distance data of the monitoring point in the target period is analyzed by the degree of change of the distance data at each moment and its adjacent moments, and the data deviation of the monitoring point in the target period is obtained by combining the above two factors; the deviation degree of the distance data of the monitoring point in the target period is caused by the combined effect of the deformation of the structure and the noise. When a specific monitoring area is deformed, the surrounding monitoring points in the monitoring area are affected by the deformation, resulting in the distance data of the corresponding monitoring point in the monitoring area and its surrounding monitoring points showing a similar change trend, but the noise such as light and smoke generated by vehicles passing through the tunnel It may cause similar changes to the distance data of the monitoring point and its surrounding monitoring points. Combined with the data deviation, further analysis is performed to improve the accuracy of the deformation ratio coefficient in presenting the proportion of tunnel deformation influence in the data deviation of the monitoring point in the target period; combined with the data deviation and the deformation ratio coefficient for analysis, the degree of deviation of the distance data of the monitoring point in the target period caused by the tunnel deformation is obtained, and then the degree of deviation of the distance data in the target period caused by the noise is obtained, and the distance data is filtered to adaptively adjust the smoothness of the distance data filtering of the monitoring point in the monitoring period, improve the accuracy of data denoising, make the three-dimensional model of the structure constructed based on the filtered comprehensive distance of the monitoring point in the target period more accurate, and improve the accuracy of structure deformation monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A flowchart of a method for monitoring deformation of a structure based on a laser measuring device provided by an embodiment of the present invention;

[0039] Figure 2 A cross-sectional schematic diagram of the position arrangement of a laser measurement device in a tunnel provided by one embodiment of the present invention;

[0040] Figure 3 A longitudinal cross-sectional schematic diagram of the position arrangement of a laser measurement device in a tunnel provided by an embodiment of the present invention;

[0041] Figure 4 A flowchart of a method for obtaining a deformation ratio coefficient of a monitoring point in a target period provided by an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of a computer device of a structure deformation monitoring device based on a laser measuring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the structural deformation monitoring method and device based on a laser measurement device proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0044] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0045] The following is a detailed description of a method and device for monitoring deformation of a structure based on a laser measuring device provided by the present invention in conjunction with the accompanying drawings.

[0046] Embodiment 1:

[0047] The present invention proposes a method for monitoring the deformation of a structure based on a laser measuring device. Figure 1 , which shows a flowchart of a method for monitoring deformation of a structure based on a laser measuring device according to an embodiment of the present invention, the method comprising:

[0048] Step S1: Obtain the distance data of each monitoring point of the structure at each moment in different monitoring cycles, and select any one monitoring cycle from the remaining monitoring cycles except the first monitoring cycle as the target cycle.

[0049] Laser measuring equipment is arranged in the structure to be measured. This scheme uses the tunnel as an example to monitor deformation. The vast area in the tunnel may have an adverse effect on the measurement accuracy and stability of the laser measuring equipment. To meet this challenge, the embodiment of the present invention designs the layout position of the laser measuring equipment in the tunnel, specifically: the tunnel is divided into left and right sides based on the central axis of the tunnel, and a laser measuring device numbered 1 is arranged on the left tunnel wall, and a laser measuring device numbered 2 is arranged on the right tunnel wall, and each laser measuring device is installed in parallel; from the longitudinal section of the tunnel, it can be seen that the two types of laser measuring devices on the left and right walls are arranged in alternate rows, and the laser measuring devices in each row are located in the middle of the two adjacent laser measuring devices in the next row, and the distance between the laser measuring devices in the two adjacent rows is equal. The position distribution design of the above-mentioned laser measuring equipment not only significantly reduces the number of laser measuring devices used, but also effectively reduces the interference of external noise through the joint processing of related signals, thereby ensuring the accuracy and reliability of the monitoring results.

[0050] Figure 2 A cross-sectional schematic diagram of the position arrangement of a laser measurement device in a tunnel provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the tunnel is divided into left and right sides based on the central axis L of the tunnel. Points A1, A2 and A3 are left monitoring points, and laser measuring equipment numbered 1 is installed; points B1 and B2 are right monitoring points, and laser measuring equipment numbered 2 is installed; since all laser measuring equipment are installed in parallel, the laser measuring equipment at monitoring points A1, A2 and A3 monitors the tunnel deformation at positions a1, a2 and a3 in turn, and the laser measuring equipment at monitoring points B1 and B2 monitors the tunnel deformation at positions b1 and b2 in turn. Figure 3 A longitudinal cross-sectional diagram of the position arrangement of a laser measuring device in a tunnel provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, Figure 3 The circle containing 1 represents the laser measuring device numbered 1, and the circle containing 2 represents the laser measuring device numbered 2.

[0051] It should be noted that, in the embodiment of the present invention, the laser measurement device is a laser rangefinder; during the installation of the laser rangefinder, a level is introduced to ensure the accuracy of the data collection of the device; Figure 3 Each laser measuring device numbered 1 is at the same distance from the position of the adjacent laser measuring device numbered 2 in the upper left, upper right, lower left and lower right directions. The same is true for the laser measuring device numbered 2. The empirical value of this distance is 0.2 meters, and the implementer can set it according to the specific situation. In other embodiments, laser levels, laser height and angle measuring integrated machines, laser interferometers and other devices can also be used, and the position arrangement of laser measuring devices in the structure can also be other ways, which are not limited here.

[0052] Laser measurement equipment is used to collect distance data of all monitoring points in the tunnel at each moment in each monitoring cycle. The amount of data obtained from all-weather real-time monitoring is too large and requires a long time and resources to process. In order to ensure the reliability and real-time nature of the monitoring results and reduce the data processing burden, the distance data in each monitoring cycle is temporarily stored in the server for preprocessing. The preprocessed data of each monitoring cycle is uploaded for calculation to generate a new three-dimensional tunnel model and analyze the tunnel deformation error.

[0053] It should be noted that in the embodiment of the present invention, the data collection frequency of the laser detection equipment is once every three seconds, and the duration of the monitoring cycle is ten minutes. The data collection frequency and the duration of the monitoring cycle of the laser measurement equipment can be set according to the specific situation.

[0054] Normally, the distance data of all monitoring points at each moment are converted into three-dimensional coordinate points. These three-dimensional coordinate points represent the spatial positions of points at different positions in the internal structure of the tunnel. The three-dimensional model of the tunnel generated by these three-dimensional coordinate points is used to perform deformation analysis on the tunnel. Due to the influence of light and smoke in the tunnel, the distance data of each monitoring point will be interfered by noise. Therefore, before using the distance data to build a three-dimensional model, the distance data needs to be preprocessed to remove the noise. This solution mainly performs denoising on the distance data.

[0055] For the convenience of description, any one monitoring cycle is selected from the remaining monitoring cycles except the first monitoring cycle and recorded as the target cycle.

[0056] Step S2: According to the difference between the distance data of each monitoring point at all times in the target period and the reference value, and the degree of change of the distance data at each time and its adjacent time, the data deviation of each monitoring point in the target period is obtained.

[0057] The external light and smoke generated by the passage of vehicles in the tunnel will cause noise interference to the distance data collected by the monitoring point, causing the distance data to deviate. The deformation of the tunnel will also cause the distance data to deviate. The deviation degree of the distance data of the monitoring point is affected by both the deformation of the structure and the noise. In order to accurately evaluate the above influences, the deviation degree of the distance data of the monitoring point within the target period is analyzed.

[0058] In the embodiment of the present invention, the reference value is the filtered comprehensive distance of each monitoring point in the previous monitoring cycle of the target cycle. The filtered comprehensive distance is the result obtained after the distance data in the monitoring cycle is processed by noise and is not affected by noise. The difference between the distance data of the monitoring point in the target cycle and the reference value shows the degree of deviation of the distance data in the target cycle compared with the distance data in the previous monitoring cycle; at the same time, the change of the distance data of the monitoring point in the target cycle is analyzed. If the degree of change of the distance data at each moment and its adjacent moments is greater, the deviation of the distance data at that moment is greater, and thus the overall deviation of the distance data of the monitoring point in the target cycle is greater. Therefore, the data deviation of the monitoring point in the target cycle is obtained by combining the above two factors.

[0059] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the data deviation includes: for each monitoring point, according to the average of the absolute values ​​of the differences between the distance data of the monitoring point at all moments in the target period and the reference value, obtaining the comprehensive distance reference difference of the monitoring point in the target period; taking the average of the absolute values ​​of the slopes between the distance data of the monitoring point at each moment except the last moment in the target period and its adjacent next moment as the comprehensive distance variation of the monitoring point in the target period; according to the comprehensive distance reference difference and the comprehensive distance variation, obtaining the data deviation of the monitoring point in the target period.

[0060] It should be noted that if the comprehensive distance benchmark difference is larger, the target cycle is greater than the deviation of the distance data in the previous monitoring cycle, and the data deviation of the monitoring point in the target cycle is greater. The absolute value of the slope between the distance data of the monitoring point at each moment in the target cycle and its adjacent next moment is equivalent to the ratio of the difference between the distance data of each moment and its adjacent next moment and the duration between two adjacent moments; if the absolute value of the slope between the distance data of the monitoring point at each moment in the target cycle and its adjacent next moment is larger, it means that the local deviation of the distance data at each moment is greater, and the local deviation of all moments in the target cycle is comprehensively considered to obtain the comprehensive distance variation; when the comprehensive distance variation is greater, the overall deviation of the distance data at all moments in the target cycle is greater, and the data deviation is greater. Therefore, the comprehensive distance benchmark difference and the comprehensive distance variation are both positively correlated with the data deviation.

[0061] In an embodiment of the present invention, the product of the comprehensive distance reference difference and the comprehensive distance variation degree can be normalized to obtain the data deviation of the monitoring point in the target period; when the data deviation degree is larger, the greater the deviation degree of the distance data of the monitoring point in the target period, the deviation result may be caused by tunnel deformation or noise.

[0062] In a specific implementation of the embodiment of the present invention, for each monitoring point, the data deviation of the monitoring point in the target period is expressed by the formula:

[0063]

[0064] Where WE is the data deviation of the monitoring point in the target period; N is the total number of monitoring points in the target period; w n is the distance data of the monitoring point at the nth moment in the target cycle; w_z is the reference value; is the comprehensive distance benchmark difference of the monitoring point in the target period; t n It is the slope between the distance data of the monitoring point at the nth moment and the next adjacent moment in the target cycle; is the comprehensive distance change of the monitoring point in the target period; || is the absolute value function; Norm is the normalization function.

[0065] Step S3: Obtain the deformation ratio of each monitoring point in the target period according to the correlation between the distance data of each monitoring point and the other monitoring points at all times in the target period and the difference between the data deviations.

[0066] The degree of deviation of the distance data of the monitoring point within the target period is caused by the combined effect of the deformation of the structure and the noise. This scheme mainly performs denoising on the distance data of the monitoring point within the target period. Since the noise such as light and smoke generated by vehicles passing through the tunnel is uncertain and cannot be measured specifically, the deviation of the distance data caused by the deformation of the tunnel can be measured. The deviation effect of noise on the distance data can be converted into the contribution of the tunnel deformation to the data deviation, and the deformation ratio coefficient can be obtained, which is convenient for denoising the distance data.

[0067] When a specific monitoring area is deformed, it not only causes deviations in the monitoring results of the monitoring area, but also has a certain ripple effect on the surrounding monitoring points. This influence effect gradually weakens with the increase of the distance from the deformation center, so that the distance data of the corresponding monitoring point in the monitoring area and the surrounding monitoring points show similar change trends, that is, the correlation between the distance data of the corresponding monitoring point in the monitoring area and its periodic monitoring points is relatively large; the noise such as light and smoke generated by passing vehicles in the tunnel may cause similar changes in the distance data of the monitoring point and its surrounding monitoring points, so that the correlation between the distance data of the monitoring point and its surrounding monitoring points within the target period is relatively large. At the same time, the difference in data deviation between each monitoring point and the other monitoring points within the target period is analyzed to improve the accuracy of deformation ratio coefficient analysis.

[0068] See also Figure 4 , which shows a flowchart of the steps of a method for obtaining a deformation ratio coefficient of a monitoring point in a target period provided by an embodiment of the present invention, the method comprising:

[0069] Step S310: Arrange the distance data of each monitoring point at all times within the target period according to time to obtain a distance sequence of each monitoring point; select the first control point of each monitoring point; and take the mean of the correlation coefficients between each monitoring point and the distance sequence of its first control point as the deformation influence of each monitoring point in the target period.

[0070] In step S1 of this scheme, the tunnel is divided into left and right sides based on the central axis of the tunnel. Ideally, if the tunnel is a uniform and symmetrical structure, and the deformation is local and symmetrical, usually one side of the tunnel will not be deformed significantly due to the deformation of the other side of the tunnel, while other positions on the side where the deformation position is located will be deformed significantly. Therefore, in the process of analyzing the deformation of the monitoring point, the remaining monitoring points are selected from the monitoring points on the same side as each monitoring point as the first control point of each monitoring point.

[0071] If the deformation of the tunnel has a greater impact on the data deviation of a certain monitoring point in the tunnel, the surrounding monitoring points of the monitoring point, i.e., the first control point, will also be affected by the same deformation signal, so that the distance data of the monitoring point and its first control point show similar change trends within the target period. In the embodiment of the present invention, the correlation coefficient between the distance sequence of each monitoring point and its first control point is used to measure the correlation degree between the change trend of the distance data of each monitoring point and its first control point within the target period. If the correlation coefficient between the distance sequence of each monitoring point and its first control point is larger, and the change trend of the distance data of each monitoring point and its first control point within the target period is more similar, then the proportion of the influence of the tunnel deformation in the data deviation of the monitoring point in the target period is higher, and the deformation influence is greater.

[0072] The method for obtaining the first reference point of the monitoring point is: mapping the monitoring points in the tunnel to the same plane to obtain the equipment location map, and Figure 3 Approximately, Figure 3 It is regarded as an equipment location map; within the preset first range of each monitoring point on the equipment location image, other monitoring points with the same number as each monitoring point are used as the first reference points of each monitoring point. The same number means that each monitoring point and its first reference point are located on the same side of the tunnel.

[0073] It should be noted that the preset first range of the monitoring point is a circular area with the monitoring point as the center and the preset first distance as the radius. In the embodiment of the present invention, the preset first distance takes an empirical value of 1 meter, and the implementer can set it according to the specific situation; the correlation coefficient can be a Pearson correlation coefficient, or a Spearman correlation coefficient, a Kendall rank correlation coefficient, or a typical correlation coefficient. Among them, the Pearson correlation coefficient is a well-known technology and will not be repeated here.

[0074] Step S320: Select the second control point of each monitoring point, and obtain the noise misleading degree of each monitoring point in the target period according to the correlation coefficient between the distance sequence of each monitoring point and its second control point, and the difference between the data deviations.

[0075] In the process of in-depth analysis of the influence of tunnel deformation on data deviation, it is necessary to fully consider the noise factors such as light and smoke generated by vehicles passing through the tunnel. These noise factors may cause the data of the monitoring point and the surrounding monitoring points to show similar changes. In order to accurately evaluate the degree of misleading noise on data deviation, the data of the monitoring points on the left and right sides of the tunnel are analyzed in this embodiment. The specific method is as follows:

[0076] Within the preset second range of each monitoring point on the equipment location image, a monitoring point with a different number from each monitoring point is used as the second reference point of each monitoring point, and each monitoring point and its second reference point are located on the left and right sides of the tunnel respectively. It should be noted that the preset second range of the monitoring point is a circular area with the monitoring point as the center and the preset second distance as the radius. In the embodiment of the present invention, the preset second distance takes an empirical value of 0.5 meters, and the implementer can set it according to the specific situation.

[0077] The noise factor will interfere with the distance data collected by the monitoring points in the tunnel at the same time, making the distance data of the monitoring points in the tunnel present a similar change trend within the target period, making the correlation coefficient between the distance series of the monitoring point and its second control point larger, and the influence of the noise factor on the data of the monitoring point and its second control point is close, and the data deviation between the monitoring point and its second control point is closer; however, since one side of the tunnel will not be deformed significantly due to the deformation of the other side of the tunnel, the deformation of the tunnel will cause the data change of the monitoring point and its second control point to be different, and the correlation coefficient between the distance series of the monitoring point and its second control point is small and the difference in data deviation is large. Therefore, when the data change trend of the monitoring point and its second control point is more consistent within the target period and the data deviation is closer, the distance data of the monitoring point within the target period is more affected by the noise, and the misleading degree of the noise is higher.

[0078] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the noise misleading degree includes: obtaining the deviation concentration value of each monitoring point in the target period based on the data deviation of the second control point of each monitoring point in the target period; taking the absolute value of the difference between the data deviation of each monitoring point in the target period and the deviation concentration value as the deviation difference of each monitoring point in the target period; taking the mean of the correlation coefficient between each monitoring point and its second control point as the change correlation of each monitoring point in the target period; and obtaining the noise misleading degree of each monitoring point in the target period based on the deviation difference and the change correlation.

[0079] The deviation concentration value presents the concentration level of the data deviations of all the second control points of the monitoring point in the target period. In the embodiment of the present invention, the mean of the data deviations of the second control point of each monitoring point in the target period is used as the deviation concentration value of each monitoring point in the target period; statistical indicators such as the mean, mode and median reflect the concentration level of a group of data. In other embodiments of the present invention, the mean can also be replaced by the median or mode.

[0080] When the difference between the data deviation of the monitoring point in the target period and the deviation concentration value, i.e. the deviation difference, is smaller, it means that the data deviation of the monitoring point and its second control point in the target period is closer, and the possibility that the distance data of the monitoring point in the target period is affected by noise and causes data deviation is greater, and the noise misleading degree is greater; when the change correlation is greater, the more similar the change trend of the distance data between the monitoring point and its second control point in the target period is, the greater the possibility that the monitoring point is affected by noise, and the greater the noise misleading degree. Therefore, the deviation difference is negatively correlated with the noise misleading degree, and the change correlation is positively correlated with the noise misleading degree; in an embodiment of the present invention, the product of the result of negative correlation mapping of the deviation difference of each monitoring point in the target period and the change correlation is normalized to obtain the noise misleading degree of each monitoring point in the target period.

[0081] In the embodiment of the present invention, the correlation between the deviation difference and the change correlation and the noise misleading degree can also be constructed through other basic mathematical operations, which are not limited or elaborated here; the Norm function is used for normalization processing. In the embodiment of the present invention, other normalization methods can also be selected, such as function conversion, which are not limited here.

[0082] In a specific implementation of the embodiment of the present invention, the noise misleading degree of the monitoring point in the target period is expressed by the formula:

[0083]

[0084] Where UI is the noise misleading degree of each monitoring point in the target period; ΔWE is the deviation difference of each monitoring point in the target period; P is the total number of the second control points of each monitoring point; MH is the distance sequence of each monitoring point; MH p is the distance sequence of the pth second control point of each monitoring point; ρ(MH,MH p ) is the correlation coefficient between the distance sequence of each monitoring point and its p-th second control point; is the change correlation of each monitoring point in the target period; Norm is the normalization function; ∈ is a preset positive number, with an empirical value of 1, which is used to prevent the denominator from being zero and causing the fraction to be meaningless. In the embodiment of the present invention, the reciprocal of the deviation difference ΔWE is taken to achieve negative correlation mapping of the deviation difference. Other embodiments can also use an exponential function with a natural constant as the base for negative correlation mapping.

[0085] Step S330: Obtain the deformation ratio of each monitoring point in the target period according to the deformation influence and the noise misleading degree.

[0086] If the deformation influence is greater and the noise misleading is smaller, the proportion of tunnel deformation influence in the data deviation of the monitoring point in the target period is higher and more accurate, and the deformation proportion coefficient is larger, then the deformation influence and the deformation proportion coefficient are positively correlated, and the noise misleading and the deformation proportion coefficient are negatively correlated. In an embodiment of the present invention, the difference between the constant 1 and the noise misleading of each monitoring point in the target period is calculated, and the product of the difference and the deformation influence is normalized to obtain the deformation proportion coefficient of each monitoring point in the target period. In an embodiment of the present invention, the correlation between the deformation influence and the noise misleading and the deformation proportion coefficient can also be constructed through other basic mathematical operations, which will not be limited or elaborated here.

[0087] Step S4: According to the data offset and deformation ratio, the distance data of each monitoring point at all times in the target period are filtered to obtain the filtered comprehensive distance of each monitoring point in the target period; the three-dimensional model of the structure in the target period is constructed using the filtered comprehensive distance of all monitoring points of the structure in the target period; the deformation of the structure is monitored according to the difference between the three-dimensional model of the structure in the target period and the previous monitoring period.

[0088] The deformation proportion coefficient presents the proportion of the influence of tunnel deformation in the data deviation of the monitoring point in the target period. Combined with the data deviation, the deviation degree of the distance data of the monitoring point in the target period caused by the tunnel deformation is analyzed, and then the deviation degree of the distance data in the target period caused by the noise is obtained. In order to remove the influence of noise on the distance data, the filtering weight is set based on the deviation degree of the distance data in the target period caused by the noise, so as to adaptively adjust the smoothness of the distance data filtering of the monitoring point in the monitoring period and improve the accuracy of data denoising.

[0089] According to the data deviation and deformation ratio of each monitoring point in the target period, the filtering weight of each monitoring point in the target period is obtained. In a specific implementation of the embodiment of the present invention, the filtering weight is expressed by the formula:

[0090] σ=WE×(1-PJ)

[0091] In the formula, σ is the filtering weight of each monitoring point in the target period; WE is the data deviation of each monitoring point in the target period; PJ is the deformation ratio coefficient of each monitoring point in the target period. It should be noted that 1-PJ presents the noise influence ratio in the data deviation of the monitoring point in the target period. When 1-PJ is larger, the distance data of the monitoring point in the target period is more affected by noise. In order to effectively suppress noise, the filtering weight required in the filtering process of the distance data in the target period should be larger.

[0092] Based on the filtering weight, the distance data of each monitoring point at all times in the target cycle are filtered using a filtering algorithm to obtain the filtered distance data of each monitoring point at each time in the target cycle; the mean of the filtered distance data of each monitoring point at all times in the target cycle is taken as the filtered comprehensive distance of each monitoring point in the target cycle.

[0093] In the embodiment of the present invention, a Gaussian filtering algorithm is selected to filter the distance data of the monitoring point within the target period. The specific method is: weighting the standard deviation of the standard Gaussian kernel according to the filtering weight of each monitoring point in the target period, obtaining the corrected standard deviation of the filtering kernel of the distance data of each monitoring point at all times within the target period, and using the Gaussian filtering algorithm to filter all the distance data of each monitoring point within the target period to obtain the filtered distance data of each monitoring point at each moment within the target period.

[0094] It should be noted that the standard deviation controls the width of the Gaussian kernel, and the width of the Gaussian kernel is adaptively adjusted by the filter weight of the monitoring point in the target period, thereby adaptively adjusting the smoothness of the distance data filtering. Among them, the Gaussian filtering algorithm is a well-known technology for those skilled in the art and will not be described in detail here.

[0095] Based on the location information of all monitoring points in the tunnel, the filtered comprehensive distance of each monitoring point in the target period is converted into three-dimensional coordinate points, i.e. point cloud data. Point cloud processing software such as MeshLab and CloudCompare are used to process and reconstruct the point cloud to generate a three-dimensional model of the structure in the target period. The three-dimensional model of the structure in the previous monitoring period of the target period is obtained. By comparing the difference between the three-dimensional model of the structure in the target period and the previous monitoring period, the deformation of the structure in the target period compared with the previous monitoring period of the target period is analyzed. The specific analysis methods include:

[0096] Match the three-dimensional model of the structure in the target period with that in the previous monitoring period, and form a matching point pair with each point in the three-dimensional model of the structure in the target period and its matching point in the three-dimensional model of the structure in the previous monitoring period; obtain the average of the distances between two points in all matching point pairs between the three-dimensional models of the structure in the target period and the previous monitoring period as the deformation distance of the structure in the target period; the larger the deformation distance, the greater the deformation degree of the structure in the target period compared with the previous monitoring period.

[0097] It should be noted that the method for constructing the three-dimensional model of the structure in each monitoring period is the same as the method for constructing the three-dimensional model of the structure in the target period, which is equivalent to replacing the target period with each monitoring period. In the embodiment of the present invention, the ICP algorithm is used to match the point cloud data in the two three-dimensional models, and the Fast Persistent Feature Histograms (FPFH) algorithm can also be used for point cloud matching; wherein the ICP algorithm is a well-known technology and will not be described in detail here.

[0098] So far, the present invention is completed.

[0099] In summary, in an embodiment of the present invention, the data deviation of the monitoring point in the target period is obtained; the difference in the correlation degree of the distance data of each monitoring point and the data deviation between the other monitoring points in the target period is used to obtain the deformation ratio of each monitoring point in the target period; the distance data of the monitoring point in the target period is filtered in combination with the data deviation to obtain the filtered comprehensive distance of the monitoring point in the target period, which is used to construct a three-dimensional model of the structure, and the deformation of the structure is monitored based on the difference between the three-dimensional model of the structure in the target period and the monitoring period before it. The present invention filters the distance data taking into account the degree of noise influence on the distance data collected by the laser measurement equipment, thereby improving the accuracy of the three-dimensional model of the structure and increasing the accuracy of monitoring the deformation of the structure.

[0100] Embodiment 2:

[0101] Figure 5 A computer device schematic diagram of a structure deformation monitoring device based on a laser measurement device provided by an embodiment of the present invention. Figure 5 As shown, based on the same concept as the above-mentioned embodiment 1, this embodiment further proposes a structure deformation monitoring device based on a laser measurement device, the device comprising: a data acquisition module, a deviation analysis module, a deformation analysis model and a deformation monitoring model, including:

[0102] The data acquisition module is used to obtain the distance data of each monitoring point of the structure at each moment in different monitoring cycles, and select any one monitoring cycle from the remaining monitoring cycles except the first monitoring cycle as the target cycle.

[0103] The deviation analysis module is used to obtain the data deviation of each monitoring point in the target cycle based on the difference between the distance data of each monitoring point at all times in the target cycle and the reference value, as well as the degree of change of the distance data at each moment and its adjacent moments.

[0104] The deformation analysis model is used to obtain the deformation ratio of each monitoring point in the target period according to the correlation degree of the distance data between each monitoring point and the other monitoring points at all times in the target period, and the difference between the data deviations.

[0105] The deformation monitoring model is used to filter the distance data of each monitoring point at all times in a target period according to the data offset and the deformation ratio to obtain the filtered comprehensive distance of each monitoring point in the target period; construct a three-dimensional model of the structure in the target period by using the filtered comprehensive distance of all monitoring points of the structure in the target period; and monitor the deformation of the structure according to the difference between the three-dimensional model of the structure in the target period and the monitoring period before it.

[0106] It should be understood that the device provided in this embodiment is used to execute the above-mentioned structure deformation monitoring method based on laser measurement equipment, and thus can achieve the same effect as the above-mentioned implementation method.

[0107] Embodiment 3:

[0108] This embodiment further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above-mentioned method for monitoring deformation of a structure based on a laser measurement device are implemented.

[0109] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring deformation of a structure based on a laser measuring device, characterized in that: The method includes: Obtain the distance data of each monitoring point of the structure at each moment in different monitoring cycles, and select any one monitoring cycle from the remaining monitoring cycles except the first monitoring cycle as the target cycle; According to the difference between the distance data of each monitoring point at all times in the target period and the reference value, and the degree of change of the distance data at each moment and its adjacent moments, the data deviation of each monitoring point in the target period is obtained; Obtain the deformation ratio of each monitoring point in the target period according to the correlation between the distance data of each monitoring point and the other monitoring points at all times in the target period and the difference between the data deviations; According to the data offset and the deformation ratio coefficient, the distance data of each monitoring point at all times in the target period are filtered to obtain the filtered comprehensive distance of each monitoring point in the target period; the three-dimensional model of the structure in the target period is constructed by using the filtered comprehensive distance of all monitoring points of the structure in the target period; and the deformation of the structure is monitored according to the difference between the three-dimensional model of the structure in the target period and the monitoring period before it.

2. A method for monitoring deformation of a structure based on a laser measuring device according to claim 1, characterized in that: The method for obtaining the data deviation of each monitoring point in the target period includes: For each monitoring point, the comprehensive distance benchmark difference of the monitoring point in the target period is obtained according to the average of the difference between the distance data of the monitoring point at all times in the target period and the benchmark value; The average of the absolute values ​​of the slopes between the distance data of the monitoring point at each moment except the last moment in the target cycle and its adjacent next moment is taken as the comprehensive distance change degree of the monitoring point in the target cycle; The data deviation of the monitoring point in the target period is obtained according to the comprehensive distance benchmark difference and the comprehensive distance variation.

3. The method for monitoring deformation of a structure based on a laser measuring device according to claim 1, characterized in that: The method for obtaining the deformation ratio coefficient of each monitoring point in the target period includes: Arrange the distance data of each monitoring point at all times in the target period according to time to obtain a distance sequence of each monitoring point; select the first control point of each monitoring point; and take the average of the correlation coefficients between each monitoring point and the distance sequence of its first control point as the deformation influence degree of each monitoring point in the target period; Selecting a second control point for each monitoring point, and obtaining the noise misleading degree of each monitoring point in the target period according to the correlation coefficient between the distance sequence of each monitoring point and its second control point, and the difference between the data deviations; According to the deformation influence degree and the noise misleading degree, the deformation proportion coefficient of each monitoring point in the target period is obtained.

4. The method for monitoring deformation of a structure based on a laser measuring device according to claim 3, characterized in that: The method for obtaining the noise misleading degree of each monitoring point in the target period includes: Obtaining a deviation concentration value of each monitoring point in the target period according to the data deviation of the second control point of each monitoring point in the target period; taking the absolute value of the difference between the data deviation of each monitoring point in the target period and the deviation concentration value as the deviation difference of each monitoring point in the target period; The average value of the correlation coefficient between each monitoring point and the distance sequence of its second control point is used as the change correlation degree of each monitoring point in the target period; The noise misleading degree of each monitoring point in the target period is obtained according to the deviation difference and the change correlation.

5. The method for monitoring deformation of a structure based on a laser measuring device according to claim 1, characterized in that: The method for obtaining the filtered comprehensive distance of each monitoring point in the target period includes: According to the data deviation and the deformation ratio of each monitoring point in the target period, the filtering weight of each monitoring point in the target period is obtained; based on the filtering weight, the distance data of each monitoring point at all times in the target period are filtered using a filtering algorithm to obtain the filtered distance data of each monitoring point at each time in the target period; The mean of the filtered distance data of each monitoring point at all times in the target period is taken as the filtered comprehensive distance of each monitoring point in the target period.

6. The method for monitoring deformation of a structure based on a laser measuring device according to claim 1, characterized in that: The method for monitoring deformation of a structure according to the difference between the three-dimensional model of the structure in the target period and the previous monitoring period comprises: Matching the three-dimensional model of the structure of the target period and the previous monitoring period, and forming a matching point pair by combining each point in the three-dimensional model of the structure of the target period with its matching point in the three-dimensional model of the structure in the previous monitoring period of the target period; The average value of the distance between two points in all matching point pairs between the target period and the three-dimensional model of the structure in the previous monitoring period is obtained as the deformation distance of the structure in the target period.

7. The method for monitoring deformation of a structure based on a laser measuring device according to claim 1, characterized in that: The reference value is the filtered comprehensive distance of each monitoring point in the previous monitoring cycle of the target cycle.

8. The method for monitoring deformation of a structure based on a laser measuring device according to claim 4, characterized in that: The method for obtaining the deviation concentration value of each monitoring point in the target cycle includes: taking the mean value of the data deviation of the second control point of each monitoring point in the target cycle as the deviation concentration value of each monitoring point in the target cycle.

9. The method for monitoring deformation of a structure based on a laser measuring device according to claim 3, characterized in that: The correlation coefficient is the Pearson correlation coefficient.

10. A structure deformation monitoring device based on a laser measuring device, characterized in that: The device comprises: a data acquisition module, a deviation analysis module, a deformation analysis model and a deformation monitoring model; The data acquisition module is used to obtain the distance data of each monitoring point of the structure at each time in different monitoring cycles, and select any one monitoring cycle from the remaining monitoring cycles except the first monitoring cycle as the target cycle; The deviation analysis module is used to obtain the data deviation of each monitoring point in the target period according to the difference between the distance data of each monitoring point at all times in the target period and the reference value, and the degree of change of the distance data at each time and its adjacent time; The deformation analysis model is used to obtain the deformation ratio of each monitoring point in the target period according to the correlation degree of the distance data between each monitoring point and the other monitoring points at all times in the target period, and the difference between the data deviations; The deformation monitoring model is used to filter the distance data of each monitoring point at all times in a target period according to the data offset and the deformation ratio to obtain the filtered comprehensive distance of each monitoring point in the target period; construct a three-dimensional model of the structure in the target period by using the filtered comprehensive distance of all monitoring points of the structure in the target period; and monitor the deformation of the structure according to the difference between the three-dimensional model of the structure in the target period and the monitoring period before it.

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