A long distance linear infrastructure monitoring method and system
By establishing a monitoring network using total stations and combining measurement robots and consistency judgment with adjustment algorithms, the problem of insufficient benchmarks in the monitoring of long-distance linear structures is solved, achieving high-precision and high-reliability monitoring. It is suitable for monitoring the health status and deformation of long-distance linear structures such as tunnels, bridges, and pipelines.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEBEI CONSTR & INVESTIGATION RES INST
- Filing Date
- 2025-04-14
- Publication Date
- 2026-06-30
AI Technical Summary
In long-distance linear building monitoring in closed or semi-closed environments, insufficient stability of benchmark points leads to the accumulation of measurement errors. Coordinate systems between multiple monitoring stations are difficult to unify, there is a lack of effective benchmark transfer mechanisms, and traditional methods cannot detect and process abnormal data in a timely manner, affecting monitoring accuracy and data consistency.
A total station was used to set up monitoring sites and construct a monitoring network. A measuring robot was used to measure the edges and corners. The consistency of the reference transfer points was determined by the traversal method. The adjustment solution was calculated by combining the conditional adjustment method and the Hermite adjustment convergence algorithm to ensure the accuracy and consistency of the coordinate data.
It enables precise monitoring of the entire line in a unified coordinate system in closed or semi-closed environments, improving monitoring accuracy and data reliability, reducing subjective errors, enhancing the anti-interference capability of the monitoring network, reducing long-term monitoring costs, and providing reliable data support.
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Figure CN120293104B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building monitoring technology, and in particular to a long-distance linear building monitoring method and system. Background Technology
[0002] Long-distance linear building monitoring technology has wide applications in modern engineering inspection. Traditional methods for monitoring linear buildings mainly include manual inspection, GPS technology, InSAR technology, and fiber optic sensor technology. Manual inspection involves on-site inspections using equipment such as levels and theodolites, but it is inefficient and highly susceptible to subjective factors. GPS technology enables continuous dynamic monitoring, but signal strength is limited in closed or semi-closed environments. InSAR technology utilizes satellite radar imagery for large-scale monitoring, but its timeliness is poor and it is limited by weather conditions. Fiber optic sensor technology has high sensitivity and resistance to electromagnetic interference, but the system is complex and expensive. While each method has its advantages in practical applications, they all face a key challenge: obtaining a sufficient number of stable and reliable reference points in scenarios such as long-distance closed tunnels or long-distance semi-closed bridges.
[0003] Existing technologies generally suffer from significant shortcomings in the monitoring process: First, the stability of benchmark points cannot be guaranteed, leading to the accumulation of measurement errors. Second, it is difficult to unify the coordinate systems among multiple monitoring stations, resulting in data inconsistency. Third, the lack of an effective benchmark transfer mechanism causes the accuracy of long-distance monitoring to decrease rapidly with increasing distance. Furthermore, traditional methods lack effective evaluation and screening mechanisms for benchmark transfer point data quality, making it impossible to promptly detect and handle abnormal data. These shortcomings severely restrict the development and application of long-distance linear building monitoring technology. Summary of the Invention
[0004] This invention provides a method and system for monitoring long-distance linear buildings and structures, in order to overcome the shortcomings of existing technologies.
[0005] This invention provides a method for monitoring long-distance linear buildings and structures, comprising:
[0006] S1: Total stations are deployed at the survey points, and a monitoring network is built based on the survey points;
[0007] S2: The measurement robot measures the edges and corners of the survey points to obtain basic measurement data;
[0008] S3: By using the traversal method, the consistency of the reference transfer points in the survey points is determined, and optimized qualified measurement data is obtained;
[0009] S4: By combining the conditional adjustment method with the Hermite adjustment convergence algorithm, the qualified measurement data is adjusted and solved to obtain the coordinate data of the survey points, so as to complete the monitoring of long-distance linear structures.
[0010] According to the long-distance linear building monitoring method provided by the present invention, the survey points in step S1 include: reference points, reference transfer points, and monitoring points.
[0011] According to the long-distance linear building monitoring method provided by the present invention, step S1 further includes:
[0012] S11: Deploy multiple total station sites to obtain a measurement base station network, wherein the straight-line distance between adjacent total station sites is less than or equal to 100 meters;
[0013] S12: Obtain a coordinate reference system by setting up more than 3 or more reference points at the stable entrance and exit positions of a linear building structure;
[0014] S13: A coordinate transfer chain is obtained by setting up multiple reference transfer points between the stations of the total station;
[0015] S14: Deploy monitoring points to obtain a deformation monitoring network.
[0016] According to the long-distance linear building monitoring method provided by the present invention, the basic measurement data in step S2 includes: distance measurement value and angle measurement value.
[0017] According to the long-distance linear building monitoring method provided by the present invention, step S3 further includes:
[0018] S31: Calculate the coordinates of the reference transfer point by using the reference points on both sides of the building to obtain two sets of calculated coordinates;
[0019] S32: Obtain the coordinate difference by calculating the difference between the two sets of calculated coordinates;
[0020] S33: Compare the coordinate difference with the limit value to obtain a consistency determination result, wherein when the coordinate difference is greater than the limit value, the consistency determination result is inconsistent, and when the coordinate difference is less than the limit value, the consistency determination result is consistent.
[0021] S34: Remove the reference transfer points and their corresponding measurement data that are inconsistent in the consistency determination result to obtain qualified measurement data.
[0022] According to the long-distance linear building monitoring method provided by the present invention, the expression for the limit difference value in step S33 is:
[0023]
[0024] Where α is the accuracy of the reference point, n is the number of stations set up by the total station, and β is the measurement accuracy of a single station of the total station.
[0025] According to the long-distance linear building monitoring method provided by the present invention, step S4 further includes:
[0026] S41: Construct an observation equation that includes direction and distance values, the observation equation being used to characterize the relationship between the observed values and the unknown parameters;
[0027] S42: By introducing conditional equations for linear relationships, the constraints that the observed values should satisfy are obtained;
[0028] S43: Combine the observation equation and the condition equation to obtain the error equation;
[0029] S44: Construct the normal equations and solve the normal equations to obtain the correction vector of the unknown parameters;
[0030] S45: Update the unknown parameters according to the correction vector to obtain the adjusted values of the observations;
[0031] S46: Based on the adjustment values, calculate the unknown parameters using the observation equation to obtain the coordinate data of the survey points.
[0032] According to the long-distance linear building monitoring method provided by the present invention, the expression of the observation equation in step S41 is:
[0033] l i +v i =a i1 x1+a i2 x2 + ... + a it x t ;
[0034] Among them, l i For the i-th observation, v i Let a be the correction number for the i-th observation. it (t = 1, 2, ..., n) are the coefficients of the equation, x t (t = 1, 2, ..., n) are unknown parameters;
[0035] The expression for the condition equation in step S42 is:
[0036] f k (l i ,x j ) = 0;
[0037] Among them, f k For the k-th condition, xj This is the j-th unknown parameter;
[0038] The expression for the error equation in step S43 is:
[0039] v i =a i1 x1+a i2 x2 + ... + a it x t ―l i ;
[0040] The expression for the normal equation in step S44 is:
[0041] A T PAΔX+A T PL=0;
[0042] Where A is the coefficient matrix, P is the weight matrix, ΔX is the correction vector of the unknown parameters, and ; is the observation vector;
[0043] The expression for the correction vector in step S44 is:
[0044] ΔX=(A T PA) ―1 A T PL;
[0045] In step S45, the expression for updating the unknown parameters is:
[0046] X new =X old +ΔX;
[0047] Among them, X new X is the updated estimate of the unknown parameters. old These are the estimated values of the unknown parameters before the update.
[0048] The present invention also provides a long-distance linear building monitoring system, comprising:
[0049] The monitoring equipment is used to perform edge and corner measurements on the survey points based on the constructed monitoring network to obtain basic measurement data;
[0050] A data processing unit is used to process the basic measurement data obtained by the monitoring equipment. The data processing unit specifically includes:
[0051] The determination subunit is used to determine the consistency of the reference transfer points in the survey points by traversal method, so as to obtain optimized qualified measurement data.
[0052] The solution subunit is used to perform adjustment calculations on the qualified measurement data using the conditional adjustment method combined with the Hermès adjustment convergence algorithm to obtain the coordinate data of the survey points.
[0053] According to the present invention, a long-distance linear building monitoring system is provided, wherein the monitoring equipment specifically includes:
[0054] The total station and the surveying robot used to control the total station for monitoring are both deployed at the total station's stations.
[0055] A circular prism, wherein the circular prism is positioned at a reference point;
[0056] A 360° small prism, wherein the 360° small prism is positioned at the reference transfer point;
[0057] A single-sided right-angled small prism is placed at the monitoring point.
[0058] This invention provides a method and system for monitoring long-distance linear structures. By deploying a monitoring system, it solves the key problem of insufficient benchmark points in closed or semi-closed environments, achieving accurate monitoring of a unified coordinate system across the entire line. The invention overcomes the technical bottleneck of rapid accuracy decay during long-distance transmission using benchmark transfer points, maintaining a high level of monitoring accuracy. Furthermore, the consistency judgment mechanism provides a scientific quantitative standard for benchmark transfer point quality assessment, effectively identifying and eliminating abnormal data, significantly improving the reliability of coordinate transmission. The application of conditional adjustment with unknowns combined with the Helmholtz adjustment convergence algorithm achieves overall adjustment and optimization of the observation network, greatly improving the accuracy of the final coordinate solution.
[0059] The automated monitoring mode of this invention significantly reduces human intervention, minimizes subjective errors, improves monitoring efficiency and data consistency, and further enhances the geometric strength and anti-interference capabilities of the monitoring network, making monitoring results more accurate and reliable. Subsequent mathematical models and iterative calculation mechanisms ensure the rigor of the solution process and the optimality of the results. This invention not only achieves a technological breakthrough but also demonstrates value in practical applications. Its one-time deployment and long-term use characteristics greatly reduce long-term monitoring costs, requiring only routine maintenance to continuously acquire high-quality monitoring data. In summary, this invention effectively solves key technical challenges in the field of long-distance linear building monitoring, providing reliable data support for safety assessments and management decisions in related projects, and has broad application prospects and significant socio-economic benefits. Attached Figure Description
[0060] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0061] Figure 1 This is a schematic diagram of a long-distance linear building monitoring method provided by the present invention;
[0062] Figure 2 A schematic diagram of the monitoring network provided by the present invention;
[0063] Figure 3 This is a schematic diagram of a long-distance linear building monitoring system provided by the present invention.
[0064] Figure descriptions: 100, monitoring equipment; 200, data processing unit; 210, judgment subunit; 220, calculation subunit. Detailed Implementation
[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0066] The embodiments of the present invention are described below with reference to the figures.
[0067] like Figure 1 As shown, the present invention provides a method for monitoring long-distance linear buildings and structures, comprising:
[0068] S1: Total stations are deployed at the survey points, and a monitoring network is built based on the survey points.
[0069] The survey points in step S1 include: benchmark points, benchmark transfer points, and monitoring points.
[0070] Step S1 further includes:
[0071] S11: Deploy multiple total station sites to obtain a measurement base station network, wherein the straight-line distance between adjacent total station sites is less than or equal to 100 meters.
[0072] Specifically, multiple total station sites are first set up to obtain a measurement base station network. The straight-line distance between adjacent total station sites is kept within 100 meters to ensure measurement accuracy and line-of-sight. The total station sites are the locations where the measurement robot is installed. The measurement robot is a device composed of a high-precision total station (in this embodiment, a Leica TM60) and an automated control system, which can automatically complete the aiming, measurement, and data recording of the target point.
[0073] S12: Obtain a coordinate reference system by setting up three or more reference points at the stable entrance and exit positions of a linear building.
[0074] In step S12, a coordinate reference system is obtained by setting up no fewer than three benchmark points at the stable entrance and exit locations of the linear structure. These benchmark points are the coordinate foundation of the entire monitoring system and must be established on stable geological structures. Leica circular prisms are typically used as reflectors. The accuracy of the benchmark points directly affects the accuracy of the entire monitoring network. The point accuracy refers to the standard deviation of the benchmark point coordinates, controlled within ±1mm. Setting up no fewer than three benchmark points is to establish a planar coordinate system and perform network adjustment calculations, thereby improving the reliability of the benchmark point network.
[0075] S13: A coordinate transfer chain is obtained by setting up multiple reference transfer points between the stations of the total station.
[0076] Furthermore, in step S13, the present invention obtains a coordinate transfer chain by setting up multiple reference transfer points between the total station stations. These reference transfer points are a key technical means to solve the problem of insufficient reference points in closed or semi-closed environments. In this embodiment, a 360° small prism is used as a reflector. The reference transfer points serve to transfer the coordinate system from known reference points to the area to be measured. Through observations at the reference transfer points between each station, the entire monitoring network is connected into a whole, achieving a unified coordinate system across the entire line.
[0077] S14: Deploy monitoring points to obtain a deformation monitoring network.
[0078] Furthermore, the monitoring points are set up at locations that can reflect the deformation of linear structures, using single-sided right-angled small prisms as reflectors. The density and location of the monitoring points should be determined based on the structural characteristics and deformation-sensitive areas of the structures. Specifically, the monitoring points should be set up at weak structural parts, deformation-prone areas, and key locations on the load transfer path.
[0079] like Figure 2 The diagram shown illustrates a monitoring network for long-distance linear structures, specifically a tunnel, provided by this invention. Point A is the tunnel entrance reference point, point B is the monitoring point, point C is the reference transfer point, and point D is the total station station. Figure 2 It can be seen that there are two or more reference transfer points between adjacent total station sites, and three or more total stations are used to observe the same reference transfer point.
[0080] S2: The measurement robot measures the edges and corners of the survey points to obtain basic measurement data.
[0081] The basic measurement data in step S2 includes: distance measurement value and angle measurement value.
[0082] Furthermore, a surveying robot refers to an automated surveying device installed at the total station's station. It consists of a high-precision total station and an automated control system, possessing automatic aiming, automatic tracking, and automatic measurement functions. The aforementioned angle measurement refers to a measurement method that simultaneously measures the horizontal angle, vertical angle, and slope distance of a target point, enabling the acquisition of complete data required for three-dimensional spatial coordinates.
[0083] Specifically, in this invention, the measuring robot performs edge and angle measurements on all measurement points (including reference points, reference transfer points, and monitoring points) to obtain two types of basic data: distance measurements and angle measurements. The distance measurement refers to the spatial straight-line distance calculated based on the optical path difference after the laser signal emitted by the measuring robot is reflected back by a prism at the target point; it is also called the slant distance. The angle measurement includes horizontal and vertical angles. The horizontal angle refers to the horizontal rotation angle from the reference direction (in this embodiment, the direction of the known reference point) to the target point direction. The vertical angle refers to the vertical rotation angle from the horizontal plane to the target point direction.
[0084] S3: By using a traversal method, the consistency of the reference transfer points in the survey points is determined, and optimized qualified measurement data is obtained.
[0085] Furthermore, in step S3, the consistency determination of the benchmark transfer points in the survey points by the traversal method is a key data processing step in the long-distance linear building monitoring method. The traversal method refers to the algorithm that checks all benchmark transfer points one by one in turn. The consistency determination of benchmark transfer points aims to check the reliability of the coordinates of the benchmark transfer points and eliminate abnormal points caused by measurement errors, instrument failures, prism displacement, etc.
[0086] Step S3 further includes:
[0087] S31: Calculate the coordinates of the reference transfer point by using the reference points on both sides of the building to obtain two sets of calculated coordinates.
[0088] Furthermore, when determining consistency, the coordinates of the reference transfer point are first calculated using the reference points on both sides of the building to obtain two sets of estimated coordinates. The specific calculation method is as follows: the total station stations along the line are divided into two groups. The first group uses the reference point A at the entrance of the linear building (where A represents all reference points) as the known point, and calculates the coordinates of the reference transfer point P forward using the measurement data to obtain the first set of estimated coordinates P1 (X1, Y1, Z1). The second group uses the reference point B at the exit of the linear building (where B represents all reference points) as the known point, and calculates the coordinates of the same reference transfer point P backward using the measurement data to obtain the second set of estimated coordinates P2 (X2, Y2, Z2). Both the forward and backward calculations use the spatial forward intersection method, that is, the spatial coordinates of the unknown point are calculated using the known point coordinates and the measured angle and distance values.
[0089] S32: Obtain the coordinate difference by calculating the difference between the two sets of calculated coordinates.
[0090] Step S32 calculates the difference between the two sets of calculated coordinates to obtain the coordinate difference amount. The coordinate difference amount Δ is defined as the spatial distance between the two sets of calculated coordinates, which reflects the consistency of the coordinates of the same point obtained from both ends. The smaller the difference amount, the better the consistency of the measurement data of the reference transfer point.
[0091] S33: Compare the coordinate difference with the limit value to obtain a consistency determination result, wherein when the coordinate difference is greater than the limit value, the consistency determination result is inconsistent, and when the coordinate difference is less than the limit value, the consistency determination result is consistent.
[0092] The expression for the limit difference value in step S33 is as follows:
[0093]
[0094] Where α is the accuracy of the reference point, n is the number of stations set up by the total station, and β is the measurement accuracy of a single station of the total station.
[0095] In step S33, the coordinate difference is compared with the limit value to obtain a consistency judgment result. The limit value is a discrimination threshold derived based on the error propagation principle, and its expression is as above. When the coordinate difference Δ is greater than the limit value, the consistency judgment result is inconsistent, indicating that the data of the reference transfer point is abnormal; when the coordinate difference Δ is less than the limit value, the consistency judgment result is consistent, indicating that the data of the reference transfer point is reliable. The theoretical basis of the limit formula is that when the difference between the calculated coordinates at both ends exceeds the allowable range caused by error propagation, it is considered that there is a systematic error or gross error at that point, and it should be eliminated.
[0096] S34: Remove the reference transfer points and their corresponding measurement data that are inconsistent in the consistency determination result to obtain qualified measurement data.
[0097] Finally, the reference transfer points with inconsistent consistency judgment results and their corresponding reference transfer point measurement data are removed to obtain qualified measurement data. That is, reference transfer points with inconsistent consistency judgment results will no longer participate in subsequent adjustment calculations. By removing abnormal reference transfer points, the quality of input data for subsequent adjustment calculations is guaranteed.
[0098] S4: By combining the conditional adjustment method with the Hermite adjustment convergence algorithm, the qualified measurement data is adjusted and solved to obtain the coordinate data of the survey points, so as to complete the monitoring of long-distance linear structures.
[0099] Furthermore, the conditional adjustment method is a basic method of measurement adjustment, applicable to situations where there are geometric or physical conditions that must be met between observations. The conditional adjustment method with unknowns is an extension of the conditional adjustment method, considering both observations and unknown parameters. The Hermite adjustment convergence algorithm is an iterative optimization algorithm that achieves the transformation between coordinate systems and the convergence of adjustment results through continuous seven-parameter transformation.
[0100] Step S4 further includes:
[0101] S41: Construct an observation equation that includes direction and distance values, which is used to characterize the relationship between the observed values and the unknown parameters.
[0102] The expression for the observation equation in step S41 is as follows:
[0103] l i +v i =a i1 x1+a i2 x2 + ... + a it x t ;
[0104] Among them, l i For the i-th observation, v i Let a be the correction number for the i-th observation. it (t = 1, 2, ..., n) are the coefficients of the equation, x t (t = 1, 2, ..., n) are unknown parameters.
[0105] In step S41, an observation equation containing direction and distance values is first constructed. The observation equation is used to characterize the relationship between the observed values and the unknown parameters. Specifically, in the monitoring of long-distance linear structures, the observed values described in this invention are the direction values (horizontal angle and vertical angle) and distance values (slope distance) measured by the measuring robot, while the unknown parameters are the three-dimensional coordinates of the reference transfer point, the monitoring point, and the total station setting point.
[0106] S42: By introducing conditional equations for linear relationships, the constraints that the observed values should satisfy are obtained.
[0107] The expression for the conditional equation in step S42 is as follows:
[0108] f k (l i ,x j ) = 0;
[0109] Among them, f k For the k-th condition, x j Let j be the j-th unknown parameter.
[0110] Furthermore, the condition equation refers to the geometric or physical relationship that the observed values must satisfy, and the expression of the condition equation in this invention is as above.
[0111] S43: Combine the observation equation and the condition equation to obtain the error equation.
[0112] The expression for the error equation in step S43 is as follows:
[0113] v i =a i1 x1+a i2 x2 + ... + a it x t ―l i .
[0114] Furthermore, the error equation combines the observation equation and the condition equation to express the relationship between the observed value correction and the unknown parameter correction.
[0115] S44: Construct the normal equations and solve them to obtain the correction vector of the unknown parameters.
[0116] The expression for the normal equation in step S44 is as follows:
[0117] A T PAΔX+A T PL=0;
[0118] The expression for the correction vector in step S44 is:
[0119] ΔX=(AT PA) ―1 A T PL;
[0120] Where A is the coefficient matrix, P is the weight matrix, ΔX is the correction vector of the unknown parameters, and L is the observation vector.
[0121] Furthermore, the normal equations are derived using the least squares principle, where P is the weight matrix, which aims to minimize the weighted sum of squares of the observation corrections. P represents the weight of each observation, typically the square of the reciprocal of the observation precision, with higher precision observations receiving larger weights. By differentiating and setting the derivative to zero, the normal equations as shown above are obtained. Solving this system of linear equations yields the correction vector ΔX for the unknown parameters.
[0122] S45: Update the unknown parameters according to the correction vector to obtain the adjusted values of the observations.
[0123] In step S45, the expression for updating the unknown parameters is:
[0124] X new =X old +ΔX;
[0125] Among them, X new X is the updated estimate of the unknown parameters. old These are the estimated values of the unknown parameters before the update.
[0126] In step S45, the unknown parameters are updated based on the correction vector to obtain the adjusted values of the observations. The obtained corrections for the unknown parameters are added to the approximate values to obtain new parameter values. Then, the adjusted values of the observations are calculated based on the new parameter values. The expression for the adjusted values is:
[0127] L new =L0+V;
[0128] Among them, L new For the updated adjustment value, L0 is the original observation value, and V is the observation correction calculated according to the error equation. Due to the nonlinear nature of the observation equation, one iteration often cannot obtain the optimal solution, and multiple iterations are required. The termination condition of the iteration is: when the maximum component of the parameter correction is less than the preset threshold, the calculation is considered to have converged and the iteration ends.
[0129] S46: Based on the adjustment values, calculate the unknown parameters using the observation equation to obtain the coordinate data of the survey points.
[0130] Finally, based on the adjusted values, the final adjusted values of the observations are substituted into the original observation equations to calculate the final values of all unknown parameters, i.e., the three-dimensional coordinates of the survey points, including the reference transfer points, monitoring points, and total station stations.
[0131] like Figure 3 As shown, the present invention also provides a long-distance linear building monitoring system, comprising:
[0132] The monitoring device 100 is used to perform edge and corner measurements on the survey points based on the constructed monitoring network to obtain basic measurement data.
[0133] The monitoring equipment 100 specifically includes: a total station and a measuring robot for controlling the total station to perform monitoring, wherein the total station and the measuring robot are both deployed at the total station's station; a circular prism, which is deployed at a reference point; a 360° small prism, which is deployed at a reference transfer point; and a single-sided right-angle small prism, which is deployed at a monitoring point.
[0134] In practical implementation, this invention uses a high-precision, stable total station, the Leica TM60, and a high-precision 360-degree small prism for the transition point, with an angle measurement accuracy of 2″. In addition, to improve the coupling of the transition point, the following measures can be taken: this invention also sets up as many reference transition points as possible and increases redundant observations. The same reference transition point is observed by three or more total stations as much as possible.
[0135] Data processing unit 200, which is used to process the basic measurement data obtained by the monitoring equipment, specifically includes:
[0136] The determination subunit 210 is used to determine the consistency of the reference transfer points in the survey points by traversal method, so as to obtain optimized qualified measurement data.
[0137] Solution subunit 220 is used to perform adjustment calculations on the qualified measurement data by combining the conditional adjustment method with the Hermès adjustment convergence algorithm to obtain the coordinate data of the survey points.
[0138] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0140] This invention provides a long-distance linear structure monitoring method and system, which can be used for tunnel deformation monitoring: applicable to structural deformation monitoring in long-distance enclosed or semi-enclosed environments such as high-speed railway tunnels, highway tunnels, and subway tunnels. For example, in a high-speed railway tunnel traversing a complex geological area, this method establishes a complete monitoring network to monitor the convergence deformation, settlement, and displacement of the tunnel lining in real time, promptly identifying abnormal deformation sections and providing data support for safe tunnel operation. It can also be used for bridge health monitoring: applicable to the health status monitoring of long-distance linear structures such as cross-sea bridges and viaducts. For example, in a cross-sea bridge several kilometers long, this method monitors the health status of the main beams and piers. Displacement and deformation analysis is used to assess the dynamic response of bridges under load, temperature changes, and wind loads, and to evaluate structural safety. It can also be used for pipeline monitoring: suitable for monitoring the deformation and displacement of long-distance pipelines in oil, natural gas, and water conservancy projects. For example, when an oil pipeline crosses mountainous areas, this method can monitor settlement and displacement along the pipeline route to prevent pipeline rupture and leakage accidents caused by geological activity. Furthermore, it can be used for railway line monitoring: suitable for precise monitoring of high-speed and heavy-haul railway tracks. For example, when a high-speed railway line passes through a soft soil foundation area, this method can accurately monitor the planar position and elevation changes of the track to ensure that the track geometry meets the safety requirements for high-speed train operation.
[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A long distance linear infrastructure monitoring method, characterized by, include: S1: Total stations are deployed at the survey points, and a monitoring network is built based on the survey points; Step S1 further includes: S11: Deploying multiple total station sites to obtain a measurement base station network, wherein the straight-line distance between adjacent total station sites is less than or equal to 100 meters; S12: Obtaining a coordinate reference system by deploying more than or equal to 3 reference points at the stable entrance and exit locations of linear structures; S13: Obtaining a coordinate transfer chain by deploying multiple reference transfer points between total station sites; S14: Deploying monitoring points to obtain a deformation monitoring network. S2: The measurement robot measures the edges and corners of the survey points to obtain basic measurement data; S3: By using the traversal method, the consistency of the reference transfer points in the survey points is determined, and optimized qualified measurement data is obtained; Step S3 further includes: S31: Calculating the coordinates of the reference transfer points using reference points on both sides of the structure to obtain two sets of estimated coordinates; S32: Calculating the difference between the two sets of estimated coordinates to obtain the coordinate difference amount; S33: Comparing the coordinate difference amount with the limit value to obtain a consistency judgment result, wherein when the coordinate difference amount is greater than the limit value, the consistency judgment result is inconsistent, and when the coordinate difference amount is less than the limit value, the consistency judgment result is consistent; S34: Removing the reference transfer points with inconsistent consistency judgment results and their corresponding reference transfer point measurement data to obtain qualified measurement data. The expression for the limit difference value in step S33 is: in, For the accuracy of the reference point position, The number of stations to set for the total station. This refers to the single-station measurement accuracy of a total station. S4: By combining the conditional adjustment method with the Hermès adjustment convergence algorithm, the qualified measurement data is adjusted and solved to obtain the coordinate data of the survey points, so as to complete the monitoring of long-distance linear structures. Step S4 further includes: S41: Constructing an observation equation containing direction and distance values, the observation equation being used to characterize the relationship between the observed values and unknown parameters; S42: Obtaining the constraints that the observed values should satisfy by introducing a conditional equation for linear relationships; S43: Combining the observation equation and the conditional equation to obtain an error equation; S44: Constructing a normal equation and solving the normal equation to obtain a correction vector for the unknown parameters; S45: Updating the unknown parameters according to the correction vector to obtain the adjusted values of the observed values; S46: Calculating the unknown parameters using the observation equation based on the adjusted values to obtain the coordinate data of the survey points. The expression for the observation equation in step S41 is: in, For the first One observation value, For the first Correction for each observation, The coefficients of the equation, For unknown parameters; The expression for the conditional equation in step S42 is: in, For the first One condition, For the first One unknown parameter; The expression for the error equation in step S43 is: The expression for the normal equation in step S44 is: in, The coefficient matrix, For the weight matrix, For the correction vector of unknown parameters, A vector of observations; The expression for the correction vector in step S44 is: In step S45, the expression for updating the unknown parameters is: in, For the updated estimates of the unknown parameters, These are the estimated values of the unknown parameters before the update.
2. The method for monitoring long-distance linear buildings and structures according to claim 1, characterized in that, The survey points mentioned in step S1 include: benchmark points, benchmark transfer points, and monitoring points.
3. The method for monitoring long-distance linear buildings and structures according to claim 1, characterized in that, The basic measurement data in step S2 includes: distance measurement value and angle measurement value.
4. A long-distance linear building monitoring system, used to execute the long-distance linear building monitoring method as described in any one of claims 1 to 3, characterized in that, include: The monitoring equipment is used to perform edge and corner measurements on the survey points based on the constructed monitoring network to obtain basic measurement data; A data processing unit is used to process the basic measurement data obtained by the monitoring equipment. The data processing unit specifically includes: The determination subunit is used to determine the consistency of the reference transfer points in the survey points by traversal method, so as to obtain optimized qualified measurement data. The solution subunit is used to perform adjustment calculations on the qualified measurement data using the conditional adjustment method combined with the Hermès adjustment convergence algorithm to obtain the coordinate data of the survey points.
5. A long-distance linear building monitoring system according to claim 4, characterized in that, The monitoring equipment specifically includes: The total station and the surveying robot used to control the total station for monitoring are both deployed at the total station's stations. A circular prism, wherein the circular prism is positioned at a reference point; A 360° small prism, wherein the 360° small prism is positioned at the reference transfer point; A single-sided right-angled small prism is placed at the monitoring point.