CN118408489B - Intelligent tunnel deformation monitoring method and system - Google Patents
Intelligent tunnel deformation monitoring method and system Download PDFInfo
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Abstract
The application relates to the technical field of tunnel measurement, in particular to an intelligent tunnel deformation monitoring method and system, wherein the method comprises the following steps: acquiring point cloud data of tunnels at each historical moment and current moment and standard point cloud data of the tunnels; acquiring point cloud data of each section, and taking the position corresponding to each section as a tunnel deformation monitoring point; for each tunnel deformation monitoring point, determining deformation differences of the cross section point cloud data of the tunnel deformation monitoring points at each historical moment based on differences among local distribution conditions of the cross section point cloud data of the tunnel deformation monitoring points at different historical moments; determining deformation monitoring weights and section shape variables of deformation monitoring points of each tunnel; and determining the comprehensive deformation amount of the tunnel by combining the section shape variables and the deformation monitoring weights of all the tunnel deformation monitoring points at the current moment, and judging the deformation condition of the tunnel. The application aims to improve the precision and efficiency of intelligent monitoring of tunnel deformation.
Description
Technical Field
The application relates to the technical field of tunnel measurement, in particular to an intelligent tunnel deformation monitoring method and system.
Background
The tunnel deformation monitoring is a basic guarantee of the safety and reliability of the tunnel structure, and potential structural problems can be found in time by monitoring the deformation of the tunnel, so that preventive measures are taken, and serious safety accidents such as tunnel collapse and the like are avoided. For the tunnel which is put into operation, deformation monitoring can ensure the continuous safety of the tunnel, and the safety of passing vehicles and passengers is ensured. In the tunnel construction process, deformation monitoring is helpful for evaluating the effectiveness of the construction method and process, and the construction scheme is timely adjusted to ensure the construction quality. The periodic deformation monitoring can provide important data for the health condition of the tunnel structure, and help engineers evaluate the durability and reliability of the structure; meanwhile, maintenance and repair work of the tunnel can be guided, and the maintenance priority and repair scope can be determined, so that resource allocation is optimized.
The traditional tunnel deformation measurement mode mainly comprises the mode of measuring by using a total station and a level, measuring by using an optical fiber sensing technology and measuring by using a three-dimensional laser scanning technology to monitor the deformation condition of a tunnel, wherein the three-dimensional laser scanning technology has higher precision and efficiency compared with other measurement technologies, but in general, the tunnel is subjected to deformation monitoring by matching errors between a three-dimensional model reconstructed according to tunnel point cloud data and an original tunnel model, and the mode is influenced by the matching errors of the point cloud data and the data quantity of the point cloud, so that the precision and the efficiency of tunnel deformation monitoring are lower.
Disclosure of Invention
In view of the above, it is necessary to provide a method and a system for intelligently monitoring tunnel deformation, which improve the accuracy and efficiency of intelligently monitoring tunnel deformation compared with the traditional method and system for intelligently monitoring tunnel deformation:
In a first aspect, an embodiment of the present application provides a method for intelligently monitoring tunnel deformation, where the method includes the following steps:
Acquiring point cloud data of tunnels at each historical moment and current moment and standard point cloud data of the tunnels; taking point cloud data with the same x coordinate as the point cloud data of each section every preset value, and taking the position corresponding to each section as a tunnel deformation monitoring point;
For each tunnel deformation monitoring point, determining deformation differences of the cross section point cloud data of the tunnel deformation monitoring points at each historical moment based on differences among local distribution conditions of the cross section point cloud data of the tunnel deformation monitoring points at different historical moments;
Determining deformation monitoring weights of all tunnel deformation monitoring points based on the similarity of the corresponding deformation differences of different tunnel deformation monitoring points at all historical moments;
Determining the section shape variable of each tunnel deformation monitoring point based on the distance between the standard point cloud data of the tunnel and the section point cloud data of the tunnel at the current moment;
And determining the comprehensive deformation amount of the tunnel by combining the section shape variables and the deformation monitoring weights of all the tunnel deformation monitoring points at the current moment, and judging the deformation condition of the tunnel.
In one embodiment, the deformation difference determining process is as follows:
For cross section point cloud data of each tunnel deformation monitoring point at each historical moment, determining an effective feature sequence of the cross section point cloud data based on the distribution condition of the cross section point cloud data;
Taking the point with the largest Z coordinate in the section point cloud data as a characteristic point;
calculating the difference of the number of points at the left side and the right side of the characteristic point in the cross-section point cloud data, and recording the difference as the difference of the number;
calculating the distance between the tunnel deformation monitoring points at each historical moment and the effective characteristic sequences of the cross-section point cloud data at other historical moments, and recording the distances as comparison distances;
And combining the cross section point cloud data of the tunnel deformation monitoring points at each historical moment to correspond to all the comparison distances and the quantity differences, and obtaining the deformation differences.
In one embodiment, the determining process of the valid feature sequence is:
taking a characteristic point as a center in the cross-section point cloud data, forming an effective characteristic pair by a first point on the left side of the characteristic point and a first point on the right side of the characteristic point, forming an effective characteristic pair by a second point on the left side and a second point on the right side, and acquiring other effective characteristic pairs by adopting the same method;
and calculating the distance between two points in each effective feature pair, and forming an effective feature sequence from top to bottom according to the effective feature pairs by all the distances.
In one embodiment, the deformation difference is a sum of the fusion result of all control distances and the number difference.
In one embodiment, the determining of the deformation monitoring weight is:
for each tunnel deformation monitoring point, forming an effective comparison characteristic sequence according to time sequence by the deformation differences of the tunnel deformation monitoring points at all historic moments;
Calculating the average value of the distances between each tunnel deformation monitoring point and the effective comparison characteristic sequences of all other tunnel deformation monitoring points;
calculating the cumulative sum of the average values of all tunnel deformation monitoring points;
And combining the average value and the accumulated sum of each tunnel deformation monitoring point to obtain the deformation monitoring weight.
In one embodiment, the deformation monitoring weight is a ratio of the average value to the cumulative sum.
In one embodiment, the determining process of the section deformation is as follows:
For each tunnel deformation monitoring point at the current moment, mapping cross-section point cloud data and standard cross-section point cloud data of the tunnel deformation monitoring point into the same two-dimensional rectangular coordinate system, taking circle fitting results of the cross-section point cloud data and the standard cross-section point cloud data in the two-dimensional rectangular coordinate system formed by the Y-Z axes as a first monitoring circle and a second monitoring circle respectively, merging circle centers of the first monitoring circle and the second monitoring circle into a point, taking a vertical downward direction as a 0-degree direction, starting from the merged circle centers, respectively taking a ray at each preset angle, intersecting each ray with the first monitoring circle and the second monitoring circle respectively, calculating the distance between two intersection points corresponding to each ray, and taking the mean value of the distances corresponding to all the rays as the cross-section deformation quantity of the tunnel deformation monitoring point.
In one embodiment, the determining process of the integrated deformation is as follows:
And calculating the product of the section shape variable of each tunnel deformation monitoring point and the deformation monitoring weight, recording the product as a weighted product, carrying out forward combination on the weighted products of all tunnel deformation monitoring points, and taking the forward combination result as the comprehensive deformation variable of the tunnel.
In one embodiment, the process of determining the deformation condition of the tunnel is:
When the comprehensive deformation amount of the tunnel is larger than a preset comprehensive deformation amount threshold, judging that the tunnel is deformed, otherwise, judging that the tunnel is not deformed.
In a second aspect, an embodiment of the present application further provides a tunnel deformation intelligent monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The application has at least the following beneficial effects:
According to the method, the problem that in the tunnel deformation monitoring process, deformation monitoring results are obtained through matching errors between a three-dimensional model reconstructed according to tunnel point cloud data and an original tunnel model, and the method is seriously influenced by the matching errors of the point cloud data and the quantity of the point cloud data is considered, so that the point cloud data of multiple sections of the tunnel are analyzed;
Analyzing the distribution condition of the point cloud data at the same position of the tunnel at different historical moments, determining deformation differences, further determining deformation monitoring weights based on the similarity of the deformation differences corresponding to the different positions, and giving higher weights to positions which can reflect the whole deformation problem of the tunnel;
The deformation amount of each position is determined based on the distance between the standard point cloud data at the same position and the tunnel point cloud data at the current moment, the deformation amounts of all positions are fused with the corresponding weights to obtain the deformation amount of the tunnel, the influence of a matching error can be reduced, the calculated amount is reduced, and the precision and the efficiency of intelligent monitoring of the deformation of the tunnel are improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for intelligently monitoring deformation of a tunnel according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a point correspondence;
FIG. 3 is a schematic illustration of determining a flow path difference of deformation;
FIG. 4 is a schematic diagram of determining a flow path of deformation monitoring weights;
FIG. 5 is a schematic diagram of a process for obtaining the integrated deformation;
FIG. 6 is a graph comparing the deformation measurement results of the method of the present application and the three-dimensional laser scanning technique;
FIG. 7 is a graph showing the comparison of the processing time of deformation measurement by the method of the present application and the three-dimensional laser scanning technique.
Detailed Description
In describing embodiments of the present application, words such as "exemplary," "or," "such as," and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," "such as," and the like are intended to present related concepts in a concrete fashion.
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 application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It is to be understood that, unless otherwise indicated, a "/" means or.
It should be further noted that the terms "first" and "second" are used herein to distinguish similar objects from each other and are not used to describe a particular order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart, including one or more steps for implementing the method, may be performed in an order that the steps may be interchanged with one another, and some steps may be deleted without departing from the scope of the claims.
The application provides a tunnel deformation intelligent monitoring method and a system specific scheme by combining the drawings.
Referring to fig. 1, a flowchart of steps of a method for intelligently monitoring deformation of a tunnel according to an embodiment of the application is shown, the method includes the following steps:
step 1, acquiring point cloud data of tunnels at each historical moment and current moment and standard point cloud data of the tunnels; and taking point cloud data with the same x coordinate as the point cloud data of each section every preset value, and taking the position corresponding to each section as a tunnel deformation monitoring point.
And acquiring the point cloud data of the tunnel at each historical moment and current moment by using a three-dimensional laser scanner.
It is to be understood that: when the point cloud data is acquired, the time interval between the adjacent moments is a preset time length, in one embodiment of the application, the value of the preset time length is 1 day, the value of the preset time length is preset manually, an implementer can set the value by himself, and the application is not limited in particular.
The method for acquiring the standard point cloud data of the tunnel in the tunnel deformation monitoring system comprises the step of acquiring the standard point cloud data of the tunnel in the same mode as the tunnel point cloud data at each historical moment.
Because the tunnel monitoring environment is complex, the influence of the reflection intensity of objects on the surface of the tunnel is easy to occur in the acquisition process, so that noise interference existing in the point cloud data is large, and the acquired point cloud data is subjected to denoising.
In one embodiment of the present application, a gaussian filtering method is used to perform denoising processing on the point cloud data, and as other embodiments, based on the fact that denoising processing on the point cloud data can be performed, an operator may use other existing technologies to perform denoising processing on the point cloud data, for example, a mean filtering method, a median filtering method, etc., which is not limited in particular by the present application.
And taking point cloud data with the same x coordinate as the point cloud data of each section every preset value, and taking the position corresponding to each section as a tunnel deformation monitoring point.
In one embodiment of the present application, the preset value is 0.1 meter, and the preset value is manually preset, so that the operator can set the preset value by himself, and the present application is not limited in particular.
And 2, for each tunnel deformation monitoring point, determining deformation differences of the cross section point cloud data of the tunnel deformation monitoring points at each historical moment based on differences among local distribution conditions of the cross section point cloud data of the tunnel deformation monitoring points at different historical moments.
In general, deformation monitoring is performed on a tunnel through a matching error between a three-dimensional model reconstructed according to tunnel point cloud data and a tunnel original model, however, the method is influenced by the matching error of the point cloud data and the quantity of the point cloud data, so that the accuracy and the efficiency of tunnel deformation monitoring are lower.
Firstly, analyzing the relative characteristic difference of tunnel deformation monitoring points at the same position on time sequence, wherein as the cross section change characteristics at different positions are different when the tunnel is locally deformed along with the time, the relative analysis is performed on the cross section point cloud data of the tunnel deformation monitoring points at the same position at different moments, and the specific analysis process is as follows:
And regarding cross-section point cloud data of each tunnel deformation monitoring point at each historical moment, taking the point with the largest Z coordinate in the cross-section point cloud data as a characteristic point, taking the characteristic point as a center in the cross-section point cloud data, forming an effective characteristic pair by a first point on the left side of the characteristic point and a first point on the right side, forming an effective characteristic pair by a second point on the left side and a second point on the right side, acquiring the rest effective characteristic pairs by adopting the same method, calculating the distance between two points in each effective characteristic pair, and forming an effective characteristic sequence by all distances according to the sequence of the effective characteristic pairs from top to bottom. The point correspondence is schematically shown in fig. 2, where 1 is a feature point and 2 and 3 represent a set of valid feature pairs.
In one embodiment of the present application, the distance between two points is a euclidean distance, and as other embodiments, on the basis of the measurable distance between two points, an implementer may use other existing technologies to obtain the distance between two points, for example, a manhattan distance, a minkowski distance, and the like, and the present application is not limited in particular.
Calculating the difference of the number of points at the left side and the right side of the characteristic point in the cross-section point cloud data, and recording the difference as the difference of the number;
calculating the distance between the tunnel deformation monitoring points at each historical moment and the effective characteristic sequences of the cross-section point cloud data at other historical moments, and recording the distances as comparison distances;
And taking the sum of the fusion results of the cross section point cloud data of the tunnel deformation monitoring points at each historical moment corresponding to all the comparison distances and the quantity difference as the deformation difference of the cross section point cloud data of the tunnel deformation monitoring points at each historical moment.
In one embodiment of the present application, the distance between the effective feature sequences is SAX (Symbolic Aggregate Approximation), and as other embodiments, the practitioner may use other existing technologies to obtain the distance between the sequences, such as DTW distance, manhattan distance, etc., on the basis that the distance between the sequences can be measured, and the present application is not limited in particular.
It is to be understood that: fusion refers to combining multiple independent variables together in a manner of enhancing the overall effect, such as addition relation, multiplication relation and the like, and an implementer can define the fusion according to actual situations.
In one embodiment of the application, the deformation difference of the cross section point cloud data of the tunnel deformation monitoring points at each historical moment is as follows: the cross section point cloud data of the tunnel deformation monitoring points at each historical moment corresponds to the sum value of the average value of all comparison distances and the quantity difference.
The calculation formula of the deformation difference of the cross section point cloud data of the tunnel deformation monitoring point at the f-th historical moment is as follows:
in the method, in the process of the invention, The deformation difference of the cross section point cloud data of the tunnel deformation monitoring point at the f-th historical moment is represented,The difference of the number of the cross section point cloud data of the tunnel deformation monitoring points at the f-th historical moment is represented, N represents the number of the historical moments,And (5) representing the comparison distance between the effective characteristic sequences of the cross-section point cloud data of the tunnel deformation monitoring points at the f-th historical moment and the i-th historical moment.
In another embodiment of the present application, the deformation difference of the cross-section point cloud data of the tunnel deformation monitoring point at each historical moment is: the cross section point cloud data of the tunnel deformation monitoring points at each historical moment corresponds to the sum value of the accumulated value and the quantity difference of all the comparison distances.
The calculation formula of the deformation difference of the cross section point cloud data of the tunnel deformation monitoring point at the f-th historical moment is as follows:
in the method, in the process of the invention, The deformation difference of the cross section point cloud data of the tunnel deformation monitoring point at the f-th historical moment is represented,The difference of the number of the cross section point cloud data of the tunnel deformation monitoring points at the f-th historical moment is represented, N represents the number of the historical moments,And (5) representing the comparison distance between the effective characteristic sequences of the cross-section point cloud data of the tunnel deformation monitoring points at the f-th historical moment and the i-th historical moment.
It should be noted that: the larger the number difference, the larger the center shift due to tunnel deformation may be; the larger the comparison distance is, the larger the difference of the shapes of the tunnel sections at different moments is; the larger the deformation difference is, the larger the distribution situation difference of the cross-section point cloud data representing the same position of the tunnel is, and the greater the probability of deformation of the tunnel is. The flow chart of the deformation difference determination is shown in fig. 3.
And step 3, determining deformation monitoring weights of the deformation monitoring points of the tunnels based on the similarity of the corresponding deformation differences of the deformation monitoring points of the different tunnels at all historical moments.
Under the normal condition, the phenomenon of recession, inclination and offset of a local area occurs when the tunnel is deformed, so that regional characteristic differences occur in cross section point cloud data at different positions, mainly the distribution situation differences caused by the phenomenon of recession, inclination and offset, therefore, the deformation monitoring weight of each tunnel deformation monitoring point is determined based on the deformation differences corresponding to all tunnel deformation monitoring points, and the specific process is as follows:
for each tunnel deformation monitoring point, forming an effective comparison characteristic sequence according to time sequence by the deformation differences of the tunnel deformation monitoring points at all historic moments;
Calculating the average value of the distances between each tunnel deformation monitoring point and the effective comparison characteristic sequences of all other tunnel deformation monitoring points;
In one embodiment of the present application, the distance between the effective contrast feature sequences is a framingly distance, and as other embodiments, the practitioner may use other existing techniques to obtain the distance between the sequences, such as DTW distance, manhattan distance, etc., on the basis that the distance between the sequences can be measured, and the present application is not limited in particular.
And calculating the cumulative sum of the average values of all the tunnel deformation monitoring points, and taking the ratio of the average value of each tunnel deformation monitoring point to the cumulative sum as the deformation monitoring weight of each tunnel deformation monitoring point.
The calculation formula of the deformation monitoring weight of the e-th tunnel deformation monitoring point is as follows:
in the method, in the process of the invention, The deformation monitoring weight of the e-th tunnel deformation monitoring point is represented,Represents the average value of the distances between the e-th tunnel deformation monitoring point and the effective comparison characteristic sequences of all other tunnel deformation monitoring points, K represents the number of tunnel deformation monitoring points,And representing the average value of the distances between the j-th tunnel deformation monitoring point and the effective comparison characteristic sequences of all other tunnel deformation monitoring points.
It should be noted that: the larger the comparison distance is, the larger the tunnel deformation relative difference between the tunnel deformation monitoring points and other tunnel deformation monitoring points in the same tunnel deformation monitoring time period is, the more the deformation problem of the whole tunnel can be reflected, and the higher the weight is given; the larger the deformation monitoring weight value is, the higher the confidence degree of the tunnel deformation quantity at the tunnel deformation monitoring point is reflected by the point cloud data difference. The flow chart of the deformation monitoring weight determination is shown in fig. 4.
And 4, determining the section shape variable of each tunnel deformation monitoring point based on the distance between the standard point cloud data of the tunnel and the section point cloud data of the tunnel at the current moment.
For each tunnel deformation monitoring point at the current moment, mapping the cross-section point cloud data and the standard cross-section point cloud data of the tunnel deformation monitoring point into the same two-dimensional rectangular coordinate system, and taking the circle fitting result of the cross-section point cloud data and the standard cross-section point cloud data in the two-dimensional rectangular coordinate system formed by the Y-Z axis as a first monitoring circle and a second monitoring circle respectively;
In one embodiment of the application, the circle centers of the first monitoring circle and the second monitoring circle are combined to form a point, the direction of the vertical downward direction is taken as 0 degree, a ray is made every 1 degree between 60 degrees and 300 degrees from the combined circle centers, each ray respectively intersects with the first monitoring circle and the second monitoring circle at a point, the distance between the two intersection points corresponding to each ray is calculated, and the average value of the distances corresponding to all the rays is taken as the section deformation quantity of the tunnel deformation monitoring point.
The calculation formula of the section shape variable of the e-th tunnel deformation monitoring point is as follows:
in the method, in the process of the invention, The deformation of the section of the e tunnel deformation monitoring point is represented, theta is represented as the angle of the ray, theta is an integer,The distance between two intersections corresponding to the rays taken at the angle θ is indicated.
It is to be understood that: 60 degrees, 300 degrees and 1 degree are just one embodiment of the present application, and the practitioner can set the value according to the actual situation.
In one embodiment of the present application, the distance between two intersection points is a euclidean distance, and as other embodiments, on the basis of the measurable distance between two intersection points, an implementer may use other existing technologies to obtain the distance between two intersection points, for example, a manhattan distance, a minkowski distance, and the like, and the present application is not limited in particular.
And 5, determining the comprehensive deformation amount of the tunnel by combining the section shape variables and the deformation monitoring weights of all the tunnel deformation monitoring points at the current moment, and judging the deformation condition of the tunnel.
The determination process of the comprehensive deformation of the tunnel comprises the following steps:
And calculating the product of the section shape variable of each tunnel deformation monitoring point and the deformation monitoring weight, recording the product as a weighted product, carrying out forward combination on the weighted products of all tunnel deformation monitoring points, and taking the forward combination result as the comprehensive deformation variable of the tunnel.
It is to be understood that: forward combining refers to combining multiple independent variables together in a manner that enhances the overall effect, such as additive relationships, multiplicative relationships, etc., and may be self-limiting by the practitioner according to the circumstances.
In one embodiment of the application, the sum of the weighted products of all tunnel deformation monitoring points is taken as the comprehensive deformation quantity of the tunnel.
The calculation formula of the comprehensive deformation of the tunnel is as follows:
Wherein A represents the comprehensive deformation quantity of the tunnel, K represents the number of deformation monitoring points of the tunnel, The section shape variable representing the e-th tunnel deformation monitoring point,And (5) representing the deformation monitoring weight of the e tunnel deformation monitoring point.
In another embodiment of the present application, the product of the weighted products of all tunnel deformation monitoring points is taken as the comprehensive deformation amount of the tunnel.
The calculation formula of the comprehensive deformation of the tunnel is as follows:
Wherein A represents the comprehensive deformation quantity of the tunnel, K represents the number of deformation monitoring points of the tunnel, The section shape variable representing the e-th tunnel deformation monitoring point,And (5) representing the deformation monitoring weight of the e tunnel deformation monitoring point. A schematic diagram of the process of obtaining the integrated deformation is shown in fig. 5.
When the comprehensive deformation amount of the tunnel is larger than a preset comprehensive deformation amount threshold value, the tunnel is judged to be deformed and needs to be repaired in time, otherwise, the tunnel is judged not to be deformed.
The comparison graph of the deformation measurement result of the method and the three-dimensional laser scanning technology is shown in fig. 6, wherein the abscissa in the graph represents the tunnel number, the ordinate represents the difference between the deformation measurement result and the actual result, the unit is mm, the dotted line represents the difference curve between the deformation measurement result and the actual result of the three-dimensional laser scanning technology, and the solid line represents the difference curve between the deformation measurement result and the actual result of the method;
as can be seen from the figure, the deformation measurement error of the method is obviously smaller than that of the three-dimensional laser scanning technology, and the method improves the accuracy of intelligent monitoring of tunnel deformation.
The comparison of the processing time of the deformation quantity measurement of the method and the three-dimensional laser scanning technology is shown in fig. 7, wherein the abscissa in the diagram represents the tunnel number, the ordinate represents the processing time of the deformation quantity measurement, the unit is days, the black columns represent the processing time of the deformation quantity measurement of the method, and the zebra columns represent the processing time of the deformation quantity measurement of the three-dimensional laser scanning technology;
As can be seen from the figure, the method only carries out analysis and measurement according to the point cloud data of the tunnel section, so that the processing time of the point cloud data is reduced, the processing time of deformation measurement of the method is obviously less than that of deformation measurement of a three-dimensional laser scanning technology, and the method improves the intelligent monitoring efficiency of tunnel deformation.
In one embodiment of the present application, the preset value of the integrated deformation amount threshold is 5, the value of the integrated deformation amount threshold is preset manually, and the operator can set the value according to the actual situation, and the present application is not particularly limited.
Based on the same inventive concept as the method, the embodiment of the application also provides a tunnel deformation intelligent monitoring system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the tunnel deformation intelligent monitoring methods when executing the computer program.
In summary, in the application, the problem that the deformation monitoring result is obtained by the matching error between the three-dimensional model reconstructed according to the point cloud data of the tunnel and the original model of the tunnel in the tunnel deformation monitoring process is seriously influenced by the matching error of the point cloud data and the quantity of the point cloud data is considered, and the point cloud data of a plurality of sections of the tunnel is analyzed;
Further, analyzing distribution conditions of point cloud data at the same position of the tunnel at different historical moments, determining deformation differences, further determining deformation monitoring weights based on similarity of the deformation differences corresponding to the different positions, and giving higher weights to positions capable of reflecting the whole deformation problem of the tunnel;
Further, the deformation amount of each position is determined based on the distance between the standard point cloud data at the same position and the tunnel point cloud data at the current moment, the deformation amounts of all positions are fused with the corresponding weights to obtain the deformation amount of the tunnel, the influence of the matching error can be reduced, the calculated amount is reduced, and the precision and the efficiency of intelligent monitoring of the deformation of the tunnel are improved.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the essential characteristics thereof. The above-described embodiments of the application should therefore be regarded as illustrative in all respects and not restrictive.
Claims (10)
1. The intelligent tunnel deformation monitoring method is characterized by comprising the following steps of:
Acquiring point cloud data of tunnels at each historical moment and current moment and standard point cloud data of the tunnels; taking point cloud data with the same x coordinate as the point cloud data of each section every preset value, and taking the position corresponding to each section as a tunnel deformation monitoring point;
The point with the largest Z coordinate in the point cloud data of each section of each tunnel deformation monitoring point is marked as a characteristic point, the distances of points with the same distance from left to right of the characteristic point are calculated in the point cloud data of the section, and the distances form an effective characteristic sequence; calculating distances of effective feature sequences of tunnel deformation monitoring points at different historical moments, recording the distances as comparison distances, acquiring deformation differences of cross-section point cloud data according to the comparison distances and the quantity differences of points at the left side and the right side of the feature points, and determining the deformation differences of the cross-section point cloud data of the tunnel deformation monitoring points at each historical moment for each tunnel deformation monitoring point based on the differences among local distribution conditions of the cross-section point cloud data of the tunnel deformation monitoring points at the different historical moments;
Forming an effective comparison characteristic sequence according to all historic deformation differences of the tunnel deformation monitoring points; calculating the distances of effective comparison feature sequences of different tunnel deformation monitoring points, and acquiring deformation monitoring weights according to the accumulated sum of the distance average value and all the distance average values of each tunnel deformation monitoring point; determining deformation monitoring weights of all tunnel deformation monitoring points based on the similarity of the corresponding deformation differences of different tunnel deformation monitoring points at all historical moments;
Determining the section shape variable of each tunnel deformation monitoring point based on the distance between the standard point cloud data of the tunnel and the section point cloud data of the tunnel at the current moment;
And determining the comprehensive deformation amount of the tunnel by combining the section shape variables and the deformation monitoring weights of all the tunnel deformation monitoring points at the current moment, and judging the deformation condition of the tunnel.
2. The intelligent monitoring method for tunnel deformation according to claim 1, wherein the determining process of the deformation difference is as follows:
For cross section point cloud data of each tunnel deformation monitoring point at each historical moment, determining an effective feature sequence of the cross section point cloud data based on the distribution condition of the cross section point cloud data;
Taking the point with the largest Z coordinate in the section point cloud data as a characteristic point;
calculating the difference of the number of points at the left side and the right side of the characteristic point in the cross-section point cloud data, and recording the difference as the difference of the number;
calculating the distance between the tunnel deformation monitoring points at each historical moment and the effective characteristic sequences of the cross-section point cloud data at other historical moments, and recording the distances as comparison distances;
And combining the cross section point cloud data of the tunnel deformation monitoring points at each historical moment to correspond to all the comparison distances and the quantity differences, and obtaining the deformation differences.
3. The intelligent monitoring method for tunnel deformation according to claim 2, wherein the determining process of the effective feature sequence is as follows:
taking a characteristic point as a center in the cross-section point cloud data, forming an effective characteristic pair by a first point on the left side of the characteristic point and a first point on the right side of the characteristic point, forming an effective characteristic pair by a second point on the left side and a second point on the right side, and acquiring other effective characteristic pairs by adopting the same method;
and calculating the distance between two points in each effective feature pair, and forming an effective feature sequence from top to bottom according to the effective feature pairs by all the distances.
4. The intelligent monitoring method for tunnel deformation according to claim 2, wherein the deformation difference is a sum of the fusion result of all the comparison distances and the quantity difference.
5. The intelligent monitoring method for tunnel deformation according to claim 1, wherein the determining process of the deformation monitoring weight is as follows:
for each tunnel deformation monitoring point, forming an effective comparison characteristic sequence according to time sequence by the deformation differences of the tunnel deformation monitoring points at all historic moments;
Calculating the average value of the distances between each tunnel deformation monitoring point and the effective comparison characteristic sequences of all other tunnel deformation monitoring points;
calculating the cumulative sum of the average values of all tunnel deformation monitoring points;
And combining the average value and the accumulated sum of each tunnel deformation monitoring point to obtain the deformation monitoring weight.
6. The intelligent monitoring method for tunnel deformation according to claim 5, wherein the deformation monitoring weight is a ratio of the average value to the cumulative sum.
7. The intelligent monitoring method for tunnel deformation according to claim 1, wherein the determining process of the section deformation is as follows:
For each tunnel deformation monitoring point at the current moment, mapping cross-section point cloud data and standard cross-section point cloud data of the tunnel deformation monitoring point into the same two-dimensional rectangular coordinate system, taking circle fitting results of the cross-section point cloud data and the standard cross-section point cloud data in the two-dimensional rectangular coordinate system formed by the Y-Z axes as a first monitoring circle and a second monitoring circle respectively, merging circle centers of the first monitoring circle and the second monitoring circle into a point, taking a vertical downward direction as a 0-degree direction, starting from the merged circle centers, respectively taking a ray at each preset angle, intersecting each ray with the first monitoring circle and the second monitoring circle respectively, calculating the distance between two intersection points corresponding to each ray, and taking the mean value of the distances corresponding to all the rays as the cross-section deformation quantity of the tunnel deformation monitoring point.
8. The intelligent tunnel deformation monitoring method according to claim 1, wherein the determining process of the comprehensive deformation is as follows:
And calculating the product of the section shape variable of each tunnel deformation monitoring point and the deformation monitoring weight, recording the product as a weighted product, carrying out forward combination on the weighted products of all tunnel deformation monitoring points, and taking the forward combination result as the comprehensive deformation variable of the tunnel.
9. The intelligent monitoring method for tunnel deformation according to claim 1, wherein the process of judging the deformation condition of the tunnel is as follows:
When the comprehensive deformation amount of the tunnel is larger than a preset comprehensive deformation amount threshold, judging that the tunnel is deformed, otherwise, judging that the tunnel is not deformed.
10. A tunnel deformation intelligent monitoring system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-9 when executing the computer program.
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