Long-distance linear building monitoring method and system

Abstract

The invention relates to a long-distance linear building and structure monitoring method and system, and the method comprises the steps: arranging a total station through a total station setting station, and constructing a monitoring network based on a monitoring point; performing corner measurement on the measurement points through the measurement robot to obtain basic measurement data; performing consistency judgment on the reference transmission points in the measurement points through a traversal method to obtain optimized qualified measurement data; and carrying out adjustment calculation on the qualified measurement data by combining a condition adjustment method with a Heell-Mett adjustment convergence algorithm to obtain coordinate data of the measurement points so as to complete monitoring of the long-distance linear building. According to the invention, the monitoring of the whole-line unified coordinate system of the long-distance linear building is realized, and the accuracy and reliability of the monitoring result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring of buildings and structures, and particularly to a monitoring method and system for long-distance linear buildings and structures. Background Art

[0002] The monitoring technology for long-distance linear buildings and structures is widely used in the field of modern engineering detection. Traditional monitoring methods for linear buildings and structures mainly include manual detection method, GPS technology, InSAR technology, and fiber optic sensor technology. The manual detection method conducts on-site inspections by personnel and uses equipment such as level gauges and theodolites for measurement, but it has low efficiency and is greatly affected by subjective factors. GPS technology can achieve continuous dynamic monitoring, but the signal is limited in closed or semi-closed environments. InSAR technology uses satellite radar images for large-scale monitoring, but it has poor timeliness and is restricted by weather conditions. Fiber optic sensor technology has high sensitivity and anti-electromagnetic interference ability, but the system is complex and the cost is high. These methods have their own advantages in practical applications, but they all face a key problem: in scenarios such as closed long-distance tunnels or semi-closed long-distance bridges, it is difficult to obtain a sufficient number of stable and reliable reference points.

[0003] There are obvious deficiencies in the existing technology during the monitoring process: First, the stability of the reference points cannot be guaranteed, resulting in the accumulation of measurement errors. Second, it is difficult to unify the coordinate systems between multiple monitoring stations, causing data incoherence. Third, there is a lack of an effective reference transfer mechanism, making the long-distance monitoring accuracy rapidly decrease as the distance increases. In addition, traditional methods lack an effective evaluation and screening mechanism for the data quality of reference transfer points and cannot detect and process abnormal data in a timely manner. These deficiencies seriously restrict the development and application of the monitoring technology for long-distance linear buildings and structures. Summary of the Invention

[0004] The present invention provides a monitoring method and system for long-distance linear buildings and structures to solve the defects of the existing technology.

[0005] The present invention provides a monitoring method for long-distance linear buildings and structures, including:

[0006] S1: Set up total stations at the total station setup points and construct a monitoring network based on the layout points;

[0007] S2: Conduct angular and distance measurements on the layout points through a measuring robot to obtain basic measurement data;

[0008] S3: Conduct consistency determination on the reference transfer points among the layout points through a traversal method to obtain optimized qualified measurement data;

[0009] S4: By using the conditional adjustment method in combination with the Helmert adjustment convergence algorithm, perform adjustment calculation on the qualified measurement data to obtain the coordinate data of the setting-out points, so as to complete the monitoring of long-distance linear structures.

[0010] According to a long-distance linear structure monitoring method provided by the present invention, the setting-out points in step S1 include: reference points, reference transfer points, and monitoring points.

[0011] According to a long-distance linear structure monitoring method provided by the present invention, step S1 further includes:

[0012] S11: Set up multiple total station setting-out stations to obtain a measurement base station network, where the straight-line distance between adjacent total station setting-out stations is less than or equal to 100 meters;

[0013] S12: By setting up greater than or equal to 3 reference points at the stable positions of the entrance and exit of the linear structure, obtain a coordinate reference system;

[0014] S13: By setting up multiple reference transfer points between the total station setting-out stations, obtain a coordinate transfer chain;

[0015] S14: Set up monitoring points to obtain a deformation monitoring network.

[0016] According to a long-distance linear structure monitoring method provided by the present invention, the basic measurement data in step S2 includes: distance measurement values, angle measurement values.

[0017] According to a long-distance linear structure monitoring method provided by the present invention, step S3 further includes:

[0018] S31: Calculate the coordinates of the reference transfer points respectively through the reference points on both sides of the structure to obtain two sets of deduced coordinates;

[0019] S32: By calculating the difference between the two sets of deduced coordinates, obtain the coordinate difference amount;

[0020] S33: Compare the coordinate difference amount with the tolerance value to obtain a consistency determination result, where when the coordinate difference amount is greater than the tolerance value, the consistency determination result is inconsistent, and when the coordinate difference amount is less than the tolerance value, the consistency determination result is consistent;

[0021] S34: Eliminate the reference transfer points with the consistency determination result of inconsistent and the corresponding reference transfer point measurement data to obtain qualified measurement data.

[0022] According to a long-distance linear structure monitoring method provided by the present invention, the expression of the tolerance value in step S33 is:

[0023]

[0024] Among them, α is the accuracy of the reference point position, n is the number of total station setup points, and β is the single-station measurement accuracy of the total station.

[0025] According to a long-distance linear structure monitoring method provided by the present invention, step S4 further includes:

[0026] S41: Construct an observation equation including direction values and distance values, and the observation equation is used to characterize the relationship between the observed values and the unknown parameters;

[0027] S42: Obtain the constraint conditions that the observed values should satisfy by introducing a conditional equation of a linear relationship;

[0028] S43: Combine the observation equation and the conditional equation to obtain an error equation;

[0029] S44: Construct a normal equation and solve the normal equation to obtain a correction vector of the unknown parameters;

[0030] S45: Update the unknown parameters according to the correction vector to obtain the adjusted value of the observed value;

[0031] S46: Calculate the unknown parameters through the observation equation according to the adjusted value to obtain the coordinate data of the setting-out points.

[0032] According to a long-distance linear structure 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 is the i-th observed value, v i is the correction of the i-th observed value, a it (t = 1, 2, …, n) are the equation coefficients, and x t (t = 1, 2, …, n) are the unknown parameters;

[0035] The expression of the conditional equation in step S42 is:

[0036] f k (l i ,x j )=0;

[0037] Among them, f k is the k-th condition, and xj is the j-th unknown parameter;

[0038] The expression of 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 of 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 parameter, and l is the observation value vector;

[0043] The expression of 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 parameter is:

[0046] X new = X old + ΔX;

[0047] where X new is the estimated value of the updated unknown parameter, and X old is the estimated value of the unknown parameter before updating.

[0048] The present invention also provides a long-distance linear structure monitoring system, including:

[0049] Monitoring equipment, which is used to perform angular and linear measurements on the survey points based on the constructed monitoring network to obtain basic measurement data;

[0050] A data processing unit, which is used to process the basic measurement data obtained by the monitoring equipment. The data processing unit specifically includes:

[0051] A determination subunit, which is used to perform consistency determination on the reference transfer points among the survey points through the traversal method to obtain optimized qualified measurement data;

[0052] The solver unit is used to adjust the qualified measurement data by combining the conditional adjustment method with the Helmert adjustment convergence algorithm to obtain the coordinate data of the surveying and setting points.

[0053] According to a long-distance linear building monitoring system provided by the present invention, the monitoring equipment specifically includes:

[0054] A total station and a measuring robot for controlling the total station for monitoring, wherein the total station and the measuring robot are both arranged at a total station location;

[0055] A circular prism, wherein the circular prism is arranged at a reference point;

[0056] A 360° small prism, wherein the 360° small prism is arranged at a reference transfer point;

[0057] A single-sided right-angle small prism is arranged at a monitoring point.

[0058] The present invention provides a long-distance linear building monitoring method and system, which solves the key problem of insufficient benchmark points in closed or semi-closed environments by deploying a monitoring system construction method, and realizes accurate monitoring of the unified coordinate system of the entire line. The present invention adopts benchmark transfer points to overcome the technical bottleneck of rapid attenuation of accuracy of traditional monitoring methods during long-distance transmission, so that the monitoring accuracy is maintained at a high level; in addition, the consistency judgment mechanism of the present invention provides a scientific quantitative standard for the quality assessment of benchmark transfer points, effectively identifies and eliminates abnormal data, and significantly improves the reliability of coordinate transmission; and the conditional adjustment method with unknown variables combined with the application of the Helmert adjustment convergence algorithm realizes the overall adjustment and optimization of the observation network, greatly improving the accuracy of the final coordinate solution.

[0059] The automated monitoring mode of the system of the present invention greatly reduces human intervention, reduces subjective errors, improves monitoring efficiency and data consistency, and further enhances the geometric strength and anti-interference ability of the monitoring network, making the monitoring results more accurate and reliable. The subsequent mathematical model and iterative calculation mechanism ensure the rigor of the solution process and the optimality of the results. The present invention not only achieves a breakthrough in technology, but also demonstrates its value in practical applications. The characteristics of one-time deployment and long-term use greatly reduce the long-term monitoring cost, and only daily maintenance is required to continuously obtain high-quality monitoring data. In summary, the present invention effectively solves the key technical problems in the field of long-distance linear building and structure monitoring, provides reliable data support for safety assessment and management decisions of related projects, and has broad application prospects and significant social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the attached drawings required in the description of the embodiments or the prior art. Obviously, the attached drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other attached drawings can also be obtained based on these attached drawings.

[0061] Figure 1 Schematic flow chart of a long-distance linear building structure monitoring method provided by the present invention;

[0062] Figure 2 Schematic diagram of the monitoring network provided by the present invention;

[0063] Figure 3 Schematic structural diagram of a long-distance linear building structure monitoring system provided by the present invention.

[0064] Description of the attached drawings: 100, monitoring device; 200, data processing unit; 210, determination sub-unit; 220, de-solver sub-unit. Detailed implementation manners

[0065] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the attached drawings in the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention, and they should not be construed as limiting the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for the purpose of description and cannot be construed as indicating or implying relative importance.

[0066] The following describes the embodiments of the present invention in conjunction with the illustrations.

[0067] As Figure 1 shown, the present invention provides a long-distance linear building structure monitoring method, including:

[0068] S1: Set up total stations at the total station setup points and construct a monitoring network based on the layout points.

[0069] Among them, the layout points in step S1 include: reference points, reference transfer points, and monitoring points.

[0070] Among them, step S1 further includes:

[0071] S11: Set up multiple total station setup points to obtain a measurement base station network, where the straight-line distance between adjacent total station setup points is less than or equal to 100 meters.

[0072] Specifically, multiple total station sites are first deployed to obtain a measurement base station network, wherein the straight-line distance between adjacent total station sites is kept within 100 meters in order to ensure measurement accuracy and line-of-sight conditions. The total station site is the location for installing a measurement robot, which is a device composed of a high-precision total station, and the present embodiment uses 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 arranging three or more reference points at the entrance and exit fixed positions of the linear structure.

[0074] In step S12, a coordinate reference system is obtained by arranging at least three reference points at the entrance and exit of the linear structure. The reference point is the coordinate basis of the entire monitoring system and must be established on a stable geological structure. Leica circular prisms are usually used as reflectors. The accuracy of the reference point directly affects the accuracy of the entire monitoring network. The point accuracy refers to the standard deviation of the reference point coordinates, which is controlled within the range of ±1mm. Arranging at least three reference points is to establish a plane coordinate system and perform network adjustment calculations to improve the reliability of the reference point network.

[0075] S13: A coordinate transfer chain is obtained by arranging multiple reference transfer points between the total station setting sites.

[0076] Further, in step S13, the present invention obtains a coordinate transfer chain by arranging multiple reference transfer points between the total station stations. The reference transfer points are a key technical means to solve the problem of insufficient reference points in a closed or semi-closed environment. This embodiment uses a 360° small prism as a reflector. The reference transfer points play a role in transferring the coordinate system from the known reference points to the area to be measured. Through the observation of the reference transfer points between the measuring stations, the entire monitoring network is connected as a whole to achieve a unified coordinate system for the entire line.

[0077] S14: Deploy monitoring points to obtain a deformation monitoring network.

[0078] Furthermore, the monitoring points are arranged at positions that can reflect the deformation of linear structures, using single-sided right-angle small prisms as reflectors. The density and positions of the monitoring points should be determined according to the structural characteristics and deformation-sensitive areas of the structures. The specific locations are in weak structural parts, deformation-prone areas, and key positions on the load transfer path.

[0079] like Figure 2 As shown in FIG. 1 , a monitoring network diagram of a long-distance linear structure provided by the present invention, specifically a tunnel, is shown. Point A is the entrance reference point, point B is the monitoring point, point C is the reference transfer point, and point D is the total station setting point.Figure 2 It can be seen that there are two or more reference transfer points between adjacent total station setting stations, and the same reference transfer point is observed by three or more total stations.

[0080] S2: Use a measuring robot to perform angular and linear measurements on the setting points to obtain basic measurement data.

[0081] Among them, the basic measurement data in step S2 includes: distance measurement values and angle measurement values.

[0082] Furthermore, the measuring robot refers to an automated measuring device installed at the total station setting station, which consists of a high-precision total station and an automated control system, and has functions of automatic aiming, automatic tracking, and automatic measurement. The angular and linear measurement refers to a measurement method that simultaneously measures the horizontal angle, vertical angle, and inclined distance to the target point, and can obtain complete data required for three-dimensional space coordinates.

[0083] Specifically, in the present invention, the measuring robot performs angular and linear measurements on all setting points (including reference points, reference transfer points, and monitoring points) to obtain two types of basic data: distance measurement values and angle measurement values. The distance measurement value refers to the spatial straight-line distance calculated based on the optical path difference after the measuring robot emits a laser signal and the signal is reflected back by the prism on the target point, which is also called the inclined distance; the angle measurement value includes the horizontal angle and the vertical angle. The horizontal angle refers to the horizontal rotation angle from the reference direction, which is the known reference point direction in this embodiment, to the target point direction, and the vertical angle refers to the vertical rotation angle from the horizontal plane to the target point direction.

[0084] S3: Use the traversal method to perform consistency determination on the reference transfer points among the setting points to obtain optimized qualified measurement data.

[0085] Furthermore, in step S3, the present invention uses the traversal method to perform consistency determination on the reference transfer points among the setting points, which is a key data processing step in the long-distance linear structure monitoring method. The traversal method refers to an algorithm that sequentially checks all reference transfer points one by one, and the consistency determination of the reference transfer points aims to check the reliability of the coordinates of the reference transfer points and eliminate abnormal points caused by measurement errors, instrument failures, prism displacements, etc.

[0086] Among them, step S3 further includes:

[0087] S31: Calculate the coordinates of the reference transfer points respectively through the reference points on both sides of the structure to obtain two sets of deduced coordinates.

[0088] Furthermore, when performing consistency determination, first calculate the coordinates of the reference transfer points through the reference points on both sides of the building or structure to obtain two sets of deduced coordinates. The specific calculation method is as follows: Divide the total station setup points arranged along the line into two groups. The first group takes the reference point A (here A represents all reference points) at the entrance of the linear building or structure as the known point, and calculates the coordinates of the reference transfer point P through forward calculation of the measurement data to obtain the first set of deduced coordinates P1(X1, Y1, Z1); the second group takes the reference point B (here B represents all reference points) at the exit of the linear building or structure as the known point, and calculates the coordinates of the same reference transfer point P through reverse calculation of the measurement data to obtain the second set of deduced coordinates P2(X2, Y2, Z2). Both the forward calculation and the reverse calculation adopt the method of spatial resection, that is, calculate the spatial coordinates of the unknown point through the known point coordinates and the measured angle and distance values.

[0089] S32: Obtain the coordinate difference amount by calculating the difference between the two sets of deduced coordinates.

[0090] Step S32 obtains the coordinate difference amount by calculating the difference between the two sets of deduced coordinates. The coordinate difference amount Δ is defined as the spatial distance between the two sets of deduced coordinates, which reflects the consistency degree of the coordinates of the same point deduced from both ends. The smaller the difference amount, the better the consistency of the measurement data of this reference transfer point.

[0091] S33: Compare the coordinate difference amount with the tolerance value to obtain the consistency determination result. Among them, when the coordinate difference amount is greater than the tolerance value, the consistency determination result is inconsistent; when the coordinate difference amount is less than the tolerance value, the consistency determination result is consistent.

[0092] Among them, the expression of the tolerance value in step S33 is:

[0093]

[0094] Among them, α is the reference point position accuracy, n is the number of total station setup points, and β is the single-station measurement accuracy of the total station.

[0095] In step S33, compare the coordinate difference amount with the tolerance value to obtain the consistency determination result. The tolerance value is a discrimination threshold deduced according to the error propagation principle, and its expression is as above. When the coordinate difference amount Δ is greater than the tolerance value, the consistency determination result is inconsistent, indicating that there is an abnormality in the data of this reference transfer point; when the coordinate difference amount Δ is less than the tolerance value, the consistency determination result is consistent, indicating that the data of this reference transfer point is reliable. The theoretical basis of the tolerance formula is that when the difference between the two sets of deduced coordinates exceeds the allowable range caused by error propagation, it is considered that there is a systematic error or gross error at this point, and it should be excluded.

[0096] S34: Eliminate the reference transfer points with inconsistent consistency determination results and the corresponding reference transfer point measurement data to obtain qualified measurement data.

[0097] Finally, eliminate the reference transfer points with inconsistent consistency determination results and the corresponding reference transfer point measurement data to obtain qualified measurement data, that is, the reference transfer points with a consistency determination of no will no longer participate in the subsequent adjustment calculation. By eliminating abnormal reference transfer points, the quality of the input data for the subsequent adjustment calculation is ensured.

[0098] S4: Perform adjustment calculation on the qualified measurement data through the conditional adjustment method combined with the Helmert adjustment convergence algorithm to obtain the coordinate data of the setting-out points, so as to complete the monitoring of long-distance linear structures.

[0099] Furthermore, the conditional adjustment method is a basic method of surveying adjustment, applicable to situations where there are geometric or physical conditions that must be satisfied among the observed values. The conditional adjustment method with unknowns is an extension of the conditional adjustment method, considering both the observed values and the unknown parameters. The Helmert adjustment convergence algorithm is an iterative optimization algorithm that realizes the coordinate system conversion and the convergence of the adjustment results through continuous seven-parameter transformations.

[0100] Among them, step S4 further includes:

[0101] S41: Construct an observation equation containing direction values and distance values, and the observation equation is used to represent the relationship between the observed values and the unknown parameters.

[0102] Among them, the expression of the observation equation in step S41 is:

[0103] l i +v i =a i1 x1+a i2 x2+…+a it x t ;

[0104] Among them, l i is the i-th observed value, v i is the correction of the i-th observed value, a it (t = 1, 2, …, n) are the equation coefficients, and x t (t = 1, 2, …, n) are the unknown parameters.

[0105] In step S41, an observation equation including direction values 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 long-distance linear structure monitoring, the observed values in the present invention are the direction values (horizontal angle and vertical angle) and distance values (slant distance) measured by the total station, while the unknown parameters are the three-dimensional coordinates of the reference transfer points, monitoring points, and total station setup points.

[0106] S42: By introducing the conditional equation of the linear relationship, obtain the constraint conditions that the observed values should satisfy.

[0107] Among them, the expression of the conditional equation in step S42 is:

[0108] f k (l i ,x j ) = 0;

[0109] Among them, f k is the k-th condition, and x j is the j-th unknown parameter.

[0110] Furthermore, the conditional equation refers to the geometric or physical relationship that the observed values must satisfy. The expression of the conditional equation of the present invention is as above.

[0111] S43: Combine the observation equation and the conditional equation to obtain the error equation.

[0112] Among them, the expression of the error equation in step S43 is:

[0113] v i = a i1 x1 + a i2 x2 + … + a it x t ―l i .

[0114] Furthermore, the error equation is an equation that combines the observation equation and the conditional equation to express the relationship between the correction of the observed value and the correction of the unknown parameter.

[0115] S44: Construct the normal equation and solve the normal equation to obtain the correction vector of the unknown parameter.

[0116] Among them, the expression of the normal equation in step S44 is:

[0117] A T PAΔX + A T PL = 0;

[0118] The expression of the correction vector in step S44 is:

[0119] ΔX = (AT PA) ―1 A T PL;

[0120] Among them, A is the coefficient matrix, P is the weight matrix, ΔX is the correction vector of the unknown parameters, and L is the observation value vector.

[0121] Furthermore, the normal equation is derived by the least squares principle. P is the weight matrix, that is, it is required to minimize the weighted sum of squares of the observation corrections, representing the weights of each observation value. Generally, the weights are taken as the squares of the reciprocals of the observation accuracies, and larger weights are given to the observation values with higher accuracies. By taking the derivative and setting the derivative to zero, the normal equation as shown in the above expression is obtained. By solving this linear equation system, the correction vector ΔX of the unknown parameters is obtained.

[0122] S45: Update the unknown parameters according to the correction vector to obtain the adjusted value of the observation values.

[0123] Among them, in step S45, the expression for updating the unknown parameters is:

[0124] X new = X old + ΔX;

[0125] Among them, X new is the estimated value of the updated unknown parameters, and X old is the estimated value of the unknown parameters before updating.

[0126] In step S45, update the unknown parameters according to the correction vector to obtain the adjusted value of the observation values. Add the obtained correction of the unknown parameters to the approximate value to get a new parameter value, and then calculate the adjusted value of the observation values according to the new parameter value. The expression of the adjusted value is:

[0127] L new = L0 + V;

[0128] Among them, L new is the updated adjusted value, L0 is the original observation value, and V is the correction of the observation value calculated according to the error equation. Due to the non - linear nature of the observation equation, a single iteration often cannot obtain the optimal solution, and multiple iterative calculations are required. The termination condition of the iteration is: when the maximum component of the parameter correction is less than the preset threshold, it is considered that the calculation converges and the iteration ends.

[0129] S46: Calculate the unknown parameters according to the adjusted value through the observation equation to obtain the coordinate data of the layout points.

[0130] Finally, according to the adjusted values, substituting the adjusted values of the final observed values into the original observation equations, the final values of all unknown parameters can be calculated, that is, the layout points, including the three-dimensional coordinates of the reference transfer points, monitoring points, and total station layout points.

[0131] As Figure 3 shown, the present invention also provides a long-distance linear structure monitoring system, including:

[0132] A monitoring device 100, which is used to perform angular and linear measurements on the layout points based on the constructed monitoring network to obtain basic measurement data.

[0133] Among them, the monitoring device 100 specifically includes: a total station and a measuring robot for controlling the total station for monitoring. The total station and the measuring robot are evenly arranged at the total station layout point; a circular prism, which is arranged at the reference point; a 360° small prism, which is arranged at the reference transfer point; and a single-sided right-angle small prism, which is arranged at the monitoring point.

[0134] In specific implementation, the present invention uses a high-precision and stable total station Leica TM60, and a high-precision 360-degree small prism is used for the transfer point, with an angular measurement accuracy of 2″. In addition, the following measures can be taken to improve the coupling degree of the transfer point. The present invention also tries to arrange more reference transfer points and increase redundant observations, and the same reference transfer point is preferably observed by 3 or more total stations.

[0135] A data processing unit 200, which is used to process the basic measurement data obtained by the monitoring device measurement. The data processing unit 200 specifically includes:

[0136] A determination subunit 210, which is used to perform consistency determination on the reference transfer points in the layout points by the traversal method to obtain optimized qualified measurement data.

[0137] A solution operator subunit 220, which is used to perform adjustment and calculation on the qualified measurement data by the conditional adjustment method in combination with the Helmert adjustment convergence algorithm to obtain the coordinate data of the layout points.

[0138] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0139] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that makes a contribution to the prior art can be embodied in the form of a software product, and this computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0140] A long-distance linear structure monitoring method and system provided by the present invention can be used for tunnel deformation monitoring: applicable to the structural deformation monitoring in long-distance closed or semi-closed environments such as high-speed railway tunnels, highway tunnels, and subway tunnels. For example, a certain high-speed railway tunnel passes through a complex geological area. By using this method, a complete monitoring network is established to monitor the convergence deformation, settlement, and displacement of the tunnel lining in real time, and abnormal deformation sections can be discovered in time, providing data support for the safe operation of the tunnel; 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, a cross-sea bridge is several kilometers long. By using this method, the displacement deformation of the main girder and piers is monitored, and the dynamic response of the bridge under the action of load, temperature change, and wind load is analyzed to evaluate the structural safety; it can also be used for pipeline line monitoring: applicable to the deformation and displacement monitoring of long-distance pipelines such as oil, natural gas, and water conservancy. For example, an oil pipeline passes through a mountainous area. By using this method, the settlement and displacement conditions along the pipeline are monitored to prevent pipeline rupture and leakage accidents caused by geological activities; it can also be used for railway line monitoring: applicable to the precise monitoring of high-speed railway and heavy-haul railway tracks. For example, a high-speed railway line passes through a soft soil foundation area. By using this method, the plane position and elevation changes of the track are accurately monitored to ensure that the geometric state of the line 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, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A long-distance linear structure monitoring method, characterized in that Including: S1: Set up total stations at the total station setup points, and construct a monitoring network based on the surveyed points; S2: Use a measuring robot to perform angular and linear measurements on the surveyed points to obtain basic measurement data; S3: Use the traversal method to judge the consistency of the reference transfer points among the surveyed points to obtain optimized qualified measurement data; S4: Use the conditional adjustment method combined with the Helmert adjustment convergence algorithm to perform adjustment calculations on the qualified measurement data to obtain the coordinate data of the surveyed points, so as to complete the monitoring of long-distance linear structures.

2. The long-distance linear structure monitoring method according to claim 1, characterized in that, The surveyed points in step S1 include: reference points, reference transfer points, and monitoring points.

3. A long-distance linear structure monitoring method according to claim 2, characterized in that Step S1 further includes: S11: Set up multiple total station setup points to obtain a measurement base station network, where the straight-line distance between adjacent total station setup points is less than or equal to 100 meters; S12: Set up at least 3 reference points at the stable positions of the entrance and exit of the linear structure to obtain a coordinate reference system; S13: Set up multiple reference transfer points between the total station setup points to obtain a coordinate transfer chain; S14: Set up monitoring points to obtain a deformation monitoring network.

4. A long-distance linear structure monitoring method according to claim 1, characterized in that, The basic measurement data in step S2 includes: distance measurement values and angle measurement values.

5. A long-distance linear structure monitoring method according to claim 1, characterized in that Step S3 further includes: S31: Calculate the coordinates of the reference transfer points respectively through the reference points on both sides of the structure to obtain two sets of deduced coordinates; S32: Calculate the difference between the two sets of deduced coordinates to obtain the coordinate difference; S33: Compare the coordinate difference with the tolerance value to obtain a consistency judgment result. When the coordinate difference is greater than the tolerance value, the consistency judgment result is inconsistent; when the coordinate difference is less than the tolerance value, the consistency judgment result is consistent; S34: Eliminate the reference transfer points with inconsistent consistency judgment results and the corresponding reference transfer point measurement data to obtain qualified measurement data.

6. The long-distance linear structure monitoring method according to claim 5, characterized in that The expression of the tolerance value in step S33 is: where α is the reference point position accuracy, n is the number of total station setup points, and β is the single-station measurement accuracy of the total station.

7. A long-distance linear structure monitoring method according to claim 1, characterized in that Step S4 further includes: S41: Construct an observation equation including direction values and distance values, which is used to represent the relationship between the observed values and the unknown parameters; S42: Introduce a conditional equation of linear relationship to obtain the constraint conditions that the observed values should satisfy; S43: Combine the observation equation and the conditional equation to obtain an error equation; S44: Construct a normal equation and solve the normal equation to obtain the correction vector of the unknown parameters; S45: Update the unknown parameters according to the correction vector to obtain the adjusted value of the observed values; S46: Calculate the unknown parameters through the observation equation according to the adjusted value to obtain the coordinate data of the surveyed points.

8. A long-distance linear structure monitoring method according to claim 7, characterized in that, The expression of the observation equation in step S41 is: l i +v i = a i1 x1 + a i2 x2 + … + a it x t ; where l i is the i-th observed value, v i is the correction of the i-th observed value, a it (t = 1, 2, …, n) are the equation coefficients, and x t (t = 1, 2, …, n) are the unknown parameters; The expression of the conditional equation in step S42 is: f k (l i ,x j ) = 0; where f k is the k-th condition, and x j is the j-th unknown parameter; The expression of the error equation in step S43 is: v i = a i1 x1 + a i2 x2 + … + a it x t − l i ; The expression of the normal equation in step S44 is: A T PAΔX + A T PL = 0; where A is the coefficient matrix, P is the weight matrix, ΔX is the correction vector of the unknown parameters, and L is the observed value vector; The expression of the correction vector in step S44 is as follows: ΔX = (A T PA) ―1 A T PL; In step S45, the expression for updating the unknown parameters is as follows: X new = X old + ΔX; where X new is the updated estimated value of the unknown parameter, and X old is the estimated value of the unknown parameter before the update.

9. A long-distance linear structure monitoring system, characterized in that Including: A monitoring device, which is used to perform angular and linear measurements on the survey points based on the constructed monitoring network to obtain basic measurement data; A data processing unit, which is used to process the basic measurement data obtained by the monitoring device. Specifically, the data processing unit includes: A determination subunit, which is used to perform consistency determination on the reference transfer points in the survey points through the traversal method to obtain optimized qualified measurement data; A solution operator subunit, which is used to perform adjustment calculation on the qualified measurement data through the conditional adjustment method combined with the Helmert adjustment convergence algorithm to obtain the coordinate data of the survey points.

10. A long-distance linear structure monitoring system according to claim 9, characterized in that, The monitoring device specifically includes: A total station and a measuring robot for controlling the total station for monitoring. The total station and the measuring robot are evenly arranged at the total station setup point; A circular prism, which is arranged at the reference point; A 360° small prism, which is arranged at the reference transfer point; A single-sided right-angled small prism, which is arranged at the monitoring point.

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