Tunnel surrounding rock subsidence measurement method based on multi-target cooperative positioning and error compensation

Abstract

This invention discloses a method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation. The method includes: deploying ≥8 optical targets in three dimensions within the tunnel surrounding rock monitoring area; setting ≥3 total station / laser scanner stations at stable benchmark points verified by ground-penetrating radar to establish a global coordinate system; achieving microsecond-level synchronization using GPS timing or the IEEE 1588 protocol; collecting target angle and distance data; eliminating gross errors using an improved Mahalanobis distance criterion; obtaining a consistent dataset through coordinate transformation and spatiotemporal alignment; calculating target coordinates based on spatial resection + LM optimization; calculating preliminary displacement and deformation fields; establishing a comprehensive compensation model for three types of errors: instrument axis, atmospheric refraction, and benchmark point displacement, to obtain a corrected displacement field; generating settlement cloud maps through Kriging interpolation; implementing graded early warning based on dual-threshold triggering; and cyclically optimizing the system. This invention achieves millimeter-level high precision, full-area coverage, and long-term automated monitoring, suitable for safety assessment and early warning throughout the entire tunnel construction and operation cycle.

Description

Technical Field

[0001] This invention belongs to the field of high-precision automated measurement technology of tunnel surrounding rock settlement, specifically involving a method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation. Background Technology

[0002] Tunnels, as underground concealed engineering projects, are an important component of transportation infrastructure networks. Tunnel surrounding rock settlement is a core indicator reflecting the structural stability of a tunnel. Excessive or uneven settlement can lead to lining cracking, water leakage, and even major safety accidents such as collapses and train derailments, causing severe casualties and economic losses. Therefore, achieving high-precision, real-time, and continuous monitoring of tunnel surrounding rock settlement is of paramount importance for ensuring tunnel construction safety and operational lifespan.

[0003] Currently, tunnel surrounding rock settlement monitoring mainly employs techniques such as single-point total station measurement, GNSS measurement, static leveling, and single-point laser scanning. However, these methods all have insurmountable technical defects in the complex environment of tunnels, and cannot meet the requirements of modern tunnel engineering for high precision, high reliability, and full coverage in safety management.

[0004] The single-point total station method is currently the most widely used traditional monitoring method. It involves placing a single reflecting prism on the surrounding rock surface and using a total station to measure the prism's three-dimensional coordinates point by point, calculating settlement by comparing coordinates from different periods. This method has the following fatal flaws: First, its measurement efficiency is extremely low, requiring discrete point-by-point measurements, often taking several hours to complete the measurement of a single cross-section, making real-time monitoring impossible. Second, it lacks redundant geometric constraints; single-point observations lack a verification mechanism, and if the prism is obstructed by construction machinery, dust, or becomes loose, the data will be directly invalidated, and the accuracy of the measurement results cannot be determined. Third, it does not consider systematic errors; instrument axis errors, atmospheric refraction errors, etc., accumulate with increasing measurement distance, with measurement errors at 50 meters reaching over 5 millimeters, failing to meet millimeter-level monitoring requirements. Fourth, it has low automation, requiring manual on-site operation, making long-term continuous monitoring difficult in high-risk areas during construction and in closed tunnels during operation.

[0005] GNSS measurement utilizes the Global Navigation Satellite System to obtain the three-dimensional coordinates of measurement points, offering advantages such as high automation and continuous monitoring. However, this method is entirely dependent on satellite signals, which are completely blocked by rock and soil within tunnels, preventing satellite signals from penetrating. Therefore, it can only be applied to the tunnel entrance area and cannot achieve full-area tunnel monitoring. Even at the entrance area, multipath effects and signal interference can significantly reduce positioning accuracy, typically only reaching centimeter-level accuracy, far from meeting the precision requirements for monitoring tunnel surrounding rock settlement. Furthermore, GNSS equipment is expensive, and large-scale deployment would significantly increase monitoring costs.

[0006] The hydrostatic leveling method, based on the principle of communicating vessels, calculates vertical settlement by measuring changes in liquid level at various measuring points, enabling automated continuous monitoring. However, this method has significant limitations: First, it can only measure vertical settlement, failing to capture horizontal displacement and torsional deformation of the surrounding rock, which is often three-dimensional, and excessive horizontal displacement can also lead to structural instability. Second, it requires a complex interconnected pipeline system, which is easily damaged by construction machinery and vehicles within the tunnel, causing blockages and leaks, resulting in extremely high maintenance costs. Third, the number of measuring points is limited; pipeline length and elevation differences restrict the monitoring range, typically allowing measuring points to be deployed only in a limited number of cross-sections, making full coverage impossible. Fourth, it is greatly affected by temperature changes; the thermal expansion and contraction of liquids can lead to measurement errors, and in environments with large temperature variations within tunnels, measurement accuracy is difficult to guarantee.

[0007] Single-point laser scanning acquires point cloud data of the surrounding rock surface using a laser scanner. By comparing the deformation of point cloud data at different time periods, area monitoring can be achieved. However, this method has the following limitations: First, the field of view of a single device is limited, usually only 360° horizontal and 180° vertical. Areas such as the arch foot and corner of the side wall in the tunnel are easily blocked, forming blind spots in monitoring. Secondly, systematic errors were not considered. Instrument shaft errors, atmospheric refraction errors, etc. were not effectively compensated. When the temperature and humidity changed by 10°C, the measurement error could reach more than 2 mm. Third, the point cloud data volume is huge and the processing speed is slow, making real-time monitoring impossible; Fourth, without multi-source collaborative constraints, the accuracy of single-point scanning decreases rapidly with increasing distance, and the reliability of data calculation is insufficient, making it prone to false deformations.

[0008] In summary, existing tunnel surrounding rock settlement monitoring technologies generally suffer from insufficient accuracy, weak anti-interference capabilities, incomplete regional coverage, difficulty in eliminating systematic errors, low automation, and untimely early warning. They cannot simultaneously meet the demands of modern tunnel engineering for high-precision, high-reliability, full-coverage, and long-term automated monitoring. Especially during the construction of mountain tunnels, the measurement accuracy and reliability of traditional methods are difficult to guarantee in harsh environments such as high dust levels, strong vibrations, and obstructed visibility. Furthermore, during the operation of urban subways, 24-hour unattended monitoring is required to promptly capture millimeter-level settlement anomalies, a task that traditional methods also cannot fulfill. Therefore, there is an urgent need to develop a tunnel surrounding rock settlement measurement technology that can adapt to the complex environment of tunnels, achieve high precision, full-coverage, and long-term automated monitoring, in order to address the many shortcomings of existing technologies and ensure the safety of tunnel construction and operation. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a tunnel surrounding rock settlement measurement method based on multi-target collaborative positioning and error compensation, which is mainly used for real-time monitoring and safety assessment of surrounding rock settlement during tunnel construction and operation. It is used to solve the technical problems of traditional single-target measurement being susceptible to interference, having large errors, and being unable to accurately reflect the overall settlement state of the surrounding rock.

[0010] The present invention adopts the following technical solution: A method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation includes the following steps: S1. Multiple high-precision optical targets are deployed in three dimensions in the tunnel surrounding rock settlement monitoring area, and multiple total station or laser scanner measurement stations are set at stable reference points in the tunnel to establish a unified global coordinate system, thereby obtaining the spatial deployment results of the high-precision optical targets, the position parameters of the measurement stations, and the global coordinate system. S2. Synchronize the measurement equipment at each measurement station using a precision clock, collect the angle and distance data of each measurement station to each high-precision optical target, perform gross error elimination, coordinate system unification and timestamp alignment on the collected raw observation values, and obtain a multi-source observation dataset with a unified timestamp and a unified coordinate system. S3. Based on the multi-source observation dataset, extract the three-dimensional position of each measurement station and the observation direction vector of the corresponding high-precision optical target. Use the spatial resection algorithm combined with the Levenberg-Marquardt optimization algorithm to minimize the sum of squared residuals between the observation direction and the calculation direction, and calculate the three-dimensional coordinates of each high-precision optical target at the current time. S4. Calculate the coordinate difference between the three-dimensional coordinates of each high-precision optical target at the current moment and the initial reference coordinates or the coordinates of the previous cycle to obtain the preliminary three-dimensional displacement of each high-precision optical target, and generate the preliminary deformation field of the surrounding rock surface based on the preliminary three-dimensional displacement of each high-precision optical target. S5. Analyze the preliminary three-dimensional displacement, identify and separate three types of systematic errors: instrument shaft system error, atmospheric refraction error, and small displacement of reference point. Establish corresponding error models for each type and integrate them into a comprehensive error model. Use the comprehensive error model to compensate for the preliminary three-dimensional displacement to obtain the corrected displacement field. S6. Perform spatial interpolation or surface fitting on the vertical settlement data in the corrected displacement field to obtain the continuous settlement field of the monitoring area, and generate a continuous settlement cloud map or contour line based on the continuous settlement field. Based on the preset settlement threshold and change rate threshold, determine the anomaly of the monitoring area and obtain graded early warning information. S7. Repeat S2 to S6 according to the set cycle, and feed back the corrected displacement field, continuous settlement cloud map or contour line and graded early warning information obtained in each cycle to update the comprehensive error model and evaluate the target deployment scheme, so as to obtain the updated comprehensive error model and / or the adjusted target deployment scheme.

[0011] Preferably, in step S1, the number of high-precision optical targets is not less than 8, and the high-precision optical targets are evenly arranged according to a three-dimensional spatial structure to avoid coplanar or linear arrangement, and the distance between any two high-precision optical targets is not less than 0.5 meters; the number of measurement stations is at least 3, distributed at different sections of the tunnel, and each measurement station can simultaneously observe all high-precision optical targets.

[0012] Preferably, in step S1, the measurement station is located at the concrete lining or bedrock, far away from the construction disturbance area; the global coordinate system is established with the tunnel entrance or fixed reference point as the origin, and the local coordinates of each measurement station are mapped to the global coordinate system through a homogeneous transformation matrix; the global coordinates of the high-precision optical target are determined according to the position parameters, rotation matrix, direction unit vector and distance parameters of the measurement station.

[0013] Preferably, in step S2, the precision clock synchronization is achieved using a GPS timing module or the IEEE 1588 precision clock protocol; the original observation values ​​collected by each measurement station for each high-precision optical target include horizontal angle, pitch angle, and slant distance; the synchronized data is aligned with the timestamps and a time deviation compensation model is established to eliminate time deviations caused by device clock jitter.

[0014] Preferably, in step S2, the gross error elimination adopts an improved Mahalanobis distance criterion, specifically including: taking the median of the observations of the same high-precision optical target at multiple measurement stations and constructing a covariance matrix; calculating the standardized residual for the observations at the i-th measurement station; when the standardized residual is greater than a threshold, the corresponding observation is determined as a gross error and eliminated; wherein, the threshold is determined according to the observation dimension.

[0015] Preferably, in step S2, the angle data and distance data are first converted into Cartesian coordinates, and then coordinate system is unified by the transformation matrix from the measurement station to the global coordinate system; and cubic spline interpolation is used to construct an interpolation function for the discrete observation sequence to output the coordinate data of each high-precision optical target at a unified sampling time, thereby obtaining the multi-source observation dataset.

[0016] Preferably, in step S3, for each high-precision optical target, based on the three-dimensional positions of multiple measurement stations and the corresponding observation direction vectors, a residual sum of squares objective function between the observation direction and the calculation direction is constructed, and the Levenberg-Marquardt optimization algorithm is used to iteratively solve the problem; wherein, the calculation direction vector is obtained by normalizing the vector between the coordinates of the high-precision optical target to be solved and the three-dimensional position of the corresponding measurement station; the initial value of the Levenberg-Marquardt optimization algorithm is set as the average value of the three-dimensional positions of multiple measurement stations, and the iteration termination condition is that the residual change is less than a preset threshold or the maximum number of iterations is reached.

[0017] Preferably, in step S5, the instrument shaft system error is modeled using a linear function, the atmospheric refraction error is modeled using a multiple regression model based on temperature and humidity data, and the small displacement of the reference point is monitored in real time using a stable reference target set on the tunnel wall. The instrument shaft system error, the atmospheric refraction error, and the small displacement of the reference point are integrated into a comprehensive error model to compensate for the preliminary three-dimensional displacement, thereby obtaining the corrected displacement field.

[0018] Preferably, in step S6, the vertical settlement data in the corrected displacement field is subjected to Kriging spatial interpolation or quadratic polynomial surface fitting to obtain a continuous settlement field of the monitoring area, and a continuous settlement cloud map or contour line is generated based on the continuous settlement field; wherein, the Kriging spatial interpolation method estimates the settlement value at any location in the monitoring area based on a variogram model, the variogram model including nugget effect, sill value and range parameter; the quadratic polynomial surface fitting solves the fitting coefficients by least squares method; and based on the comparison results between the current settlement amount and rate of change and the preset settlement threshold and rate of change threshold, anomaly judgment is made in the monitoring area and a graded early warning is triggered.

[0019] Secondly, embodiments of the present invention provide a tunnel surrounding rock settlement measurement system based on multi-target collaborative positioning and error compensation, comprising: The target unit is used to deploy multiple high-precision optical targets in the tunnel surrounding rock settlement monitoring area; The measurement station unit is used to set up multiple total station or laser scanner measurement stations at stable benchmark points inside the tunnel, and to collect angle and distance data of each measurement station to each high-precision optical target. The data preprocessing unit is used to perform gross error removal, coordinate system unification, and timestamp alignment on the acquired raw observations to obtain a multi-source observation dataset with a unified timestamp and a unified coordinate system. The collaborative solution unit is used to solve the three-dimensional coordinates of each high-precision optical target at the current moment based on the multi-source observation dataset, and to generate preliminary three-dimensional displacement and preliminary deformation field. The error compensation unit is used to identify and separate three types of systematic errors: instrument shaft system error, atmospheric refraction error, and small displacement of reference point. It establishes a comprehensive error model and uses the comprehensive error model to compensate for the preliminary three-dimensional displacement to obtain the corrected displacement field. The settlement analysis and early warning unit is used to perform spatial interpolation or surface fitting on the vertical settlement data in the corrected displacement field to obtain a continuous settlement field, and generate a continuous settlement cloud map or contour line based on the continuous settlement field. It also performs anomaly judgment based on preset settlement threshold and rate of change threshold to obtain graded early warning information. The iterative optimization unit is used to repeatedly perform data processing, collaborative solution, error compensation and settlement analysis according to a set cycle, and feeds back the corrected displacement field, continuous settlement cloud map or contour line and graded early warning information obtained in each cycle to update the comprehensive error model and evaluate the target deployment scheme.

[0020] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the aforementioned method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation.

[0022] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation.

[0023] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation.

[0024] Compared with the prior art, the present invention has at least the following beneficial effects: A method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation is proposed. This method solves the problems of single-point observation lacking verification and being susceptible to obstruction failure by using a multi-target three-dimensional layout and multi-site collaboration to form redundant geometric constraints. The fully automated processing replaces manual operation, enabling 24-hour continuous monitoring. Comprehensive compensation for multi-source system errors eliminates the influence of environmental interference and instrument drift. A closed-loop iterative mechanism ensures the stability of long-term monitoring. The deep integration of collaborative positioning and dynamic error compensation achieves high-precision and high-reliability monitoring of tunnel surrounding rock settlement across the entire tunnel area.

[0025] Furthermore, the number of targets is no less than eight and they are non-coplanar and nonlinearly distributed, providing sufficient geometric redundancy to avoid overall solution failure due to single-point failure; the spacing between any two targets is ≥0.5 meters to ensure spatial sampling density and capture local deformation characteristics of the surrounding rock; more than three measurement stations are distributed on different cross sections to ensure that each target is observed from multiple angles, effectively suppressing systematic errors from single-direction observations. The installation accuracy is controlled within ±1mm, eliminating the impact of target installation deviations on the measurement results from the source.

[0026] Furthermore, the benchmark points were selected from concrete linings or bedrock verified by ground-penetrating radar, far from construction disturbance areas, fundamentally avoiding measurement errors caused by the displacement of the benchmark points themselves. A homogeneous transformation matrix was used to map the local station coordinates to the global coordinate system, ensuring spatial consistency of multi-station data. Least squares optimization was used to make the coordinate system residual less than 1mm, eliminating the cumulative effect of initial calibration errors. A stable benchmark and a unified coordinate system are prerequisites for comparing data from different periods and accurately calculating the surrounding rock displacement, significantly improving the reliability of the monitoring results.

[0027] Furthermore, by employing GPS time synchronization or the IEEE 1588 precision clock protocol, microsecond-level time synchronization accuracy can be achieved, ensuring strict time alignment of observation data from different stations on the same target. A time deviation compensation model is established to effectively eliminate time errors caused by equipment clock jitter. Synchronously acquired horizontal angle, elevation angle, and slant range data provide complete observational information for subsequent spatial rendezvous calculations.

[0028] Furthermore, by calculating the median and covariance matrix of observations from multiple stations on the same target, a standardized residual is constructed as a criterion. Compared to the traditional 3σ criterion, this method can adapt to the measurement accuracy of different devices and effectively resist outlier contamination of up to 30%. It does not require prior knowledge of the data distribution characteristics, exhibits stronger robustness in complex tunnel environments, and can accurately eliminate gross errors caused by momentary occlusion, equipment noise, etc., ensuring the quality of the input data.

[0029] Furthermore, the angular distance observations were converted to Cartesian coordinates before coordinate system transformation, avoiding the nonlinear errors caused by direct polar coordinate conversion. Cubic spline interpolation was used to align the timestamps of the discrete observation sequences, accurately reconstructing the target coordinates at any given time, offering higher accuracy compared to linear interpolation. Ultimately, the spatiotemporal consistency error was controlled within 0.1 milliseconds and 0.2 millimeters, ensuring seamless fusion of data from multiple stations.

[0030] Furthermore, a spatial resection algorithm combined with Levenberg-Marquardt optimization is employed to solve for the target coordinates by minimizing the sum of squared residuals between the observation and computation directions. Geometric constraints from multi-site observations are used to control the typical single-point localization error to within 1 mm. Using the average position of the observation points as the initial value ensures rapid convergence of the algorithm; strict iteration termination conditions ensure the stability of the solution results. Accuracy is improved by 3-5 times, and reliable solutions are still available even with partially missing data.

[0031] Furthermore, wavelet transform or Kalman filtering was employed to separate low-frequency systematic error components, accurately identifying three main error sources: instrument axis system, atmospheric refraction, and reference point displacement. Linear correction models, temperature and humidity multivariate regression models, and reference target monitoring models were established to achieve targeted compensation for different types of errors. After comprehensive compensation, the accuracy of the displacement field was improved by more than 15%, effectively eliminating the influence of environmental temperature and humidity changes, instrument calibration deviations, and reference drift, significantly reducing the false alarm rate.

[0032] Furthermore, by employing Kriging spatial interpolation or quadratic polynomial surface fitting, the spatial variation characteristics of surrounding rock settlement can be accurately captured, generating continuous settlement cloud maps or contour lines to intuitively display the deformation distribution across the entire area. A three-level early warning mechanism with dual thresholds for settlement amount and rate of change can be established, which can promptly trigger different levels of early warning based on the degree of anomaly. The early warning information is pushed to the monitoring platform through a real-time interface, enabling early detection and early handling of abnormal settlement.

[0033] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0034] In summary, this invention improves positioning accuracy and anti-interference capability through multi-target and multi-site collaborative observation, eliminates environmental and instrument deviations through multi-source error dynamic compensation, realizes full-domain deformation visualization through spatial fusion technology, and ensures long-term monitoring reliability through a closed-loop iterative mechanism. It comprehensively solves the problems of low accuracy, poor coverage, susceptibility to interference, and low automation of traditional methods.

[0035] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention; Figure 2 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 3 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0037] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0040] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0041] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0042] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0043] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0044] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0045] This invention provides a method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation, deploying a multi-target collaborative positioning system. Multiple high-precision optical targets are pre-installed in the surrounding rock area within the tunnel where settlement needs to be monitored. These targets should be arranged according to a three-dimensional spatial structure to ensure simultaneous observation from multiple measurement stations. Simultaneously, a total station or laser scanner is installed as a measurement station at a stable reference point within the tunnel, and a stable coordinate system is established. This step lays the physical foundation for subsequent collaborative observation and data acquisition, ensuring that the target area is covered by multiple measurement sources. Multi-source data synchronous acquisition and preprocessing are then performed. All measurement equipment is activated to synchronously or quasi-synchronously acquire coordinate data from the deployed targets. The acquired data includes the angle and distance information of each target relative to different measurement stations. Subsequently, the raw observation values ​​are preprocessed to remove gross errors caused by momentary occlusion or equipment noise, and all data are unified to the same timestamp and coordinate system, providing a clean and consistent dataset for subsequent collaborative calculations. Collaborative positioning calculations and preliminary deformation analysis are then performed. Using preprocessed multi-source observation data, the three-dimensional spatial coordinates of each target are collaboratively calculated based on spatial resection or similar algorithms. This process utilizes geometric constraints from multiple observation directions, effectively improving the accuracy and reliability of single-point positioning. After obtaining the high-precision coordinates of all targets at a certain moment, they are compared with their initial coordinates or coordinates from the previous cycle to calculate the preliminary three-dimensional displacement of each target, thus obtaining the preliminary deformation field of the surrounding rock surface. Systematic error modeling and compensation are implemented. The preliminary deformation data is analyzed to identify and separate systematic errors caused by the measurement system itself. These errors may include instrument axis errors, atmospheric refraction errors, and small displacements of reference points due to changes in the tunnel environment. Mathematical models for these error sources are established, for example, by setting up additional stable reference targets to monitor reference displacement, or by training an error correction model using historical data. Then, this error model is applied to the preliminary deformation data to compensate for it, obtaining a corrected displacement field that more closely approximates the actual surrounding rock deformation. Settlement information fusion, early warning, and system iteration are completed. After error compensation, the displacement data of each target, especially the vertical settlement data, are spatially interpolated or fitted to surfaces to generate a continuous settlement cloud map or contour lines for the entire monitoring area. The system automatically analyzes the data based on preset settlement and rate-of-change thresholds, triggering a tiered early warning system upon detecting any abnormal areas. The entire process is executed cyclically according to a set schedule, enabling long-term automated monitoring. The results of each cycle are fed back to optimize the error model and evaluate the target deployment scheme, making adjustments as necessary to achieve self-optimization of the measurement system.

[0046] This invention is applied to the monitoring of surrounding rock settlement throughout the entire lifecycle of tunnel construction and operation. Utilizing multi-target collaborative positioning and error compensation technology, multiple monitoring targets are deployed at key stress-bearing locations in the surrounding rock, such as the arch crown, left and right arch waists, and wall toes, during the tunnel excavation phase in mountainous areas. Spatial location data is collected synchronously from multiple targets and collaboratively calculated. Simultaneously, errors caused by interference factors such as construction vibration, changes in environmental temperature and humidity, and instrument drift are dynamically compensated for, enabling real-time and accurate acquisition of the settlement deformation, deformation rate, and overall deformation trend of the surrounding rock. In the operation phase of urban subway tunnels, this system can achieve 24-hour unmanned continuous monitoring, promptly capturing millimeter-level minute settlement anomalies, providing high-precision data support for tunnel structural safety assessment, disease early warning, and maintenance plan formulation. For the investigation of diseases and the evaluation of reinforcement effects in aging tunnels, the collaborative measurement of multiple targets can also accurately grasp the surrounding rock settlement repair status, ensuring tunnel operational safety. This invention has wide applications in tunnel engineering and urban rail transit engineering.

[0047] Please see Figure 1 The present invention discloses a method for measuring tunnel surrounding rock settlement based on multi-target collaborative positioning and error compensation, comprising the following steps: S1. Deploy a multi-target cooperative localization system In the surrounding rock area within the tunnel where settlement needs to be monitored, multiple high-precision optical targets are pre-installed. These targets should be arranged according to a three-dimensional spatial structure to ensure simultaneous observation from multiple measurement stations. Simultaneously, total stations or laser scanners are installed at stable benchmark points within the tunnel as measurement stations, and a stable coordinate system is established. This step lays the physical foundation for subsequent collaborative observation and data acquisition, ensuring that the target area is covered by multiple measurement sources. The specific implementation method of this step is as follows: In the tunnel surrounding rock settlement monitoring area, high-precision optical targets, such as reflecting prisms or coded markers, are installed at key locations. The number of targets should be no less than eight to cover three-dimensional space. The target positions are arranged based on geometric optimization principles to ensure that any target point... coordinates The targets are evenly distributed in the global coordinate system, avoiding coplanar or linear arrangement, and minimizing the observation blind zone. Specifically, a spatial sampling algorithm is used to calculate the target spacing. satisfy Rice, of which For target indexing, This enhances three-dimensional coverage. This deployment method allows for simultaneous observation from multiple angles, significantly improving subsequent positioning accuracy and robustness, and reducing the impact of single-point errors through redundancy. During installation, anchor bolts are used to fix the target to the surrounding rock surface, ensuring the target remains stable under vibration or deformation, with an installation accuracy error of less than ±1 mm.

[0048] Measurement equipment is installed at stable benchmark points within the tunnel. These benchmark points are selected on concrete lining or bedrock, far from areas prone to construction disturbance, and their stability is verified using ground-penetrating radar. At least three total stations or laser scanners are installed as measurement stations, distributed across different sections of the tunnel, ensuring that each station can simultaneously observe all targets. Station coordinates. Pre-calibrate in the global coordinate system, and use a high-precision level and theodolite for initial positioning, with errors controlled within ±2 mm. Ensure unobstructed line of sight between the measurement station and the target; verify by laser ranging that the maximum observation distance does not exceed 50 meters to guarantee data acquisition quality.

[0049] Establish a stable global coordinate system, with the tunnel entrance or a fixed reference point as the origin. Define an orthogonal axis system. Coordinate system transformation uses a homogeneous transformation matrix to map local station coordinates to the global system. Let the station... The observation data is for angles and distance Target global coordinates with station coordinates The relationship is:

[0050] in, It is a rotation matrix. Let be the direction unit vector. This model ensures coordinate system consistency, facilitating subsequent collaborative computation. Finally, the initial coordinate error is optimized using least squares, and the objective function is... ,in, To estimate the coordinates, iterative solutions are used to make the residual less than 1 mm, thus providing a highly reliable physical basis for data acquisition.

[0051] In the tunnel surrounding rock settlement monitoring area, high-precision optical targets (reflective prisms or coded markers) are installed at key stress-bearing locations such as the arch crown, left and right arch waists, and wall toes. At least eight targets are required, evenly distributed in a three-dimensional spatial structure, avoiding coplanar or linear arrangements. The distance between any two targets should be no less than 0.5 meters. Anchor bolts are used to fix the targets to the surrounding rock surface, with installation accuracy controlled within ±1 mm. Inside the tunnel, concrete lining or bedrock away from construction disturbance areas is selected as a reference point. Its stability is verified using ground-penetrating radar. At least three total stations or laser scanners are installed at the reference point as measurement stations, distributed across different tunnel sections, ensuring that each station can simultaneously observe all targets. The maximum observation distance does not exceed 50 meters, and the initial positioning error of the station is controlled within ±2 mm. A global coordinate system is established using the tunnel entrance or a fixed reference point as the origin. A homogeneous transformation matrix is ​​used to map the local coordinates of each measurement station to the global coordinate system. The initial coordinate error is optimized using least squares, ensuring the coordinate system residual is less than 1 mm. The system achieved blind-spot-free coverage of the monitoring area, with target installation accuracy reaching ±0.8 mm and coordinate system residuals remaining stable within 0.7 mm. Multi-site, multi-angle observations provided sufficient geometric constraints, offering a reliable physical basis for subsequent high-precision calculations. Benchmark points verified by ground-penetrating radar exhibited a maximum displacement of less than 0.3 mm under vibration during construction, ensuring the long-term stability of the benchmark.

[0052] S2. Perform multi-source data synchronous acquisition and preprocessing. Start all measuring equipment to synchronously or quasi-synchronously acquire coordinate data from multiple deployed targets. The acquired data includes the angle and distance information of each target relative to different measuring stations. Subsequently, the raw observations are preprocessed to remove gross errors caused by momentary occlusion or equipment noise, and all data are unified to the same timestamp and coordinate system to provide a clean and consistent dataset for subsequent collaborative calculations. The specific implementation method of this step is as follows: Multi-device microsecond-level time synchronization is achieved using a GPS time synchronization module or the IEEE 1588 precision clock protocol. This triggers total stations, laser trackers, and other surveying equipment to simultaneously collect data from the target object. Data for each reflective target. Each site. Target The original observation vector is ,in It is a horizontal angle. The pitch angle, This is the slant range. For non-cooperative targets, the lidar outputs point cloud coordinates. A time deviation compensation model is established by processing quasi-synchronous data using a timestamp alignment algorithm:

[0053] in, For reference clock, This is for device clock jitter.

[0054] Gross error detection employs an improved Mahalanobis distance criterion: calculating the distance for the same target. exist Median of observations at each station Construct the covariance matrix For the first Calculate the standardized residuals from the observations at each station:

[0055] Set threshold ( (for the observation dimension), when If an error is detected, it is immediately classified as a gross error and discarded. This criterion is adaptable to different equipment precision levels and effectively resists errors as high as [presumably referring to a specific type of error]. Outlier contamination.

[0056] The coordinate system is achieved through site extrinsic parameter calibration. Let the site be... To the global coordinate system The transformation matrix is:

[0057] in, For rotation matrix, This is a translation vector. It represents the local observations. Convert to global coordinates: For angular distance observations, first convert them to Cartesian coordinates:

[0058]

[0059] Then perform a coordinate system transformation. Timestamp alignment uses cubic spline interpolation: for discrete observation sequences. Construct interpolation function ,in Given B-spline basis functions, output uniform sampling times. coordinates The final spatiotemporally aligned dataset is generated:

[0060] Its spatiotemporal consistency error is controlled within , This provides highly complete input for subsequent calculations.

[0061] A GPS timing module or the IEEE 1588 precision clock protocol is used to achieve microsecond-level time synchronization among multiple measuring devices, triggering all devices to simultaneously acquire horizontal angle, pitch angle, and slant range data for each target. An improved Mahalanobis distance criterion is used to detect gross errors in the raw observations: the median of observations of the same target at multiple stations is calculated, a covariance matrix is ​​constructed, and the standardized residual for each observation is calculated. When the residual exceeds the chi-square distribution threshold (99% confidence level), it is identified as a gross error and discarded. The angular distance observations are converted to Cartesian coordinates, and all local coordinates are converted to global coordinates using a homogeneous transformation matrix from the measuring stations to the global coordinate system. Cubic spline interpolation is used to timestamp-align the discrete observation sequences, and an interpolation function is constructed to output target coordinate data at a unified sampling time. The system achieves a multi-device time synchronization error of less than 0.1 milliseconds; it can effectively resist outlier contamination of up to 30%, with a gross error removal accuracy of over 98%; coordinate transformation error of less than 0.1 millimeters; timestamp alignment error of less than 0.2 millimeters; and the spatiotemporal consistency error of the final multi-source observation dataset is controlled within 0.1 milliseconds and 0.2 millimeters, with data integrity reaching over 99%.

[0062] S3. Perform cooperative positioning calculation and preliminary deformation analysis. Using preprocessed multi-source observation data, and based on spatial resection or similar algorithms, the three-dimensional spatial coordinates of each target are collaboratively calculated. This process utilizes geometric constraints from multiple observation directions, effectively improving the accuracy and reliability of single-point positioning. After obtaining the high-precision coordinates of all targets at a certain moment, they are compared with their initial coordinates or coordinates from the previous period to calculate the preliminary three-dimensional displacement of each target, thereby obtaining the preliminary deformation field of the surrounding rock surface. The specific implementation method of this step is as follows: Input preprocessed multi-source observation data, including the three-dimensional positions of multiple observation points (such as total stations or camera stations). ( , (Number of observation points) and the corresponding observation direction vector of the target. (Unit vector). For each target, a spatial resection algorithm is applied to calculate the target's three-dimensional coordinates by minimizing the sum of squared residuals between the observed and calculated directions. .

[0063] The direction vector is defined as follows: ,in, Let represent the Euclidean norm. The error function is... The overall objective function is to minimize The Levenberg-Marquardt optimization algorithm was used iteratively to solve the problem. The initial value was set as the average position of the observation points, and the convergence condition was that the residual change was less than a threshold. The maximum number of iterations is 100 or 1 meter. This process utilizes... Geometric constraints in all directions significantly improve positioning accuracy (typical error less than 1 mm) and enhance reliability, especially in multi-source data fusion, effectively suppressing the effects of single-point noise or occlusion. After calculation, high-precision coordinates of all targets at the current moment are obtained. . and initial reference coordinates (e.g., measurements taken during installation) or coordinates from the previous period Compare and calculate the three-dimensional displacement vector for each target:

[0064] in, The reference coordinates are selected based on monitoring requirements. The displacement components are represented as follows:

[0065]

[0066]

[0067] Based on the displacement data of all targets, a preliminary deformation field of the surrounding rock surface is generated through spatial interpolation or meshing methods. The output is a displacement vector map or contour map, which is used for real-time monitoring of rock mass stability.

[0068] The preprocessed multi-source observation dataset is input, and the 3D positions of each measurement station and the corresponding observation direction vectors of the targets are extracted. For each target, a spatial resection algorithm is applied to construct the objective function of the sum of squared residuals between the observation and calculation directions. The Levenberg-Marquardt optimization algorithm is then used to iteratively solve for the 3D coordinates of the target. The initial values ​​of the algorithm are set as the average of the 3D positions of multiple measurement stations, and the convergence condition for the iteration is that the residual change is less than 10. -6 The maximum number of iterations is 100 meters or 100. After calculating the current coordinates of all targets, the difference between these coordinates and the initial reference coordinates or the coordinates of the previous cycle is calculated to obtain the preliminary three-dimensional displacement of each target. The preliminary deformation field of the surrounding rock surface is then generated through spatial interpolation.

[0069] Quantifiable results: The typical error of single-point positioning is less than 0.8 mm, and the maximum error does not exceed 1 mm; the average number of algorithm iterations is 12, and the convergence speed is 3 times faster than the traditional Gauss-Newton method; even with 30% missing data, it can still complete reliable calculations with a success rate of over 95%; the preliminary deformation field can accurately reflect the overall deformation trend of the surrounding rock, and the consistency with the actual deformation is over 90%.

[0070] S4. Implement system error modeling and compensation Analyze the preliminary deformation data to identify and separate systematic errors caused by the measurement system itself. These errors may include instrument shafting errors, atmospheric refraction errors, and minute displacements of the reference point due to changes in the tunnel environment. Establish mathematical models for these error sources, for example, by setting up additional stable reference targets to monitor reference displacement, or by training an error correction model using historical data. Then, apply this error model to the preliminary deformation data to compensate for it, obtaining a corrected displacement field that more closely approximates the actual surrounding rock deformation. The specific implementation method of this step is as follows: Analysis of preliminary deformation data ,in, Indicates a time index. These are the original displacement measurements. Low-frequency systematic error components are separated using wavelet transform or Kalman filtering, identifying three main error sources: instrument shaft system error, atmospheric refraction error, and reference point displacement. The instrument shaft system error originates from the calibration deviation of the measuring equipment and is modeled as a linear function:

[0071] parameter and Determined through equipment calibration experiments, It is a proportionality coefficient. It's an offset. Atmospheric refraction error is caused by changes in temperature and humidity inside the tunnel, based on environmental sensor data (temperature). ,humidity Establish a multiple regression model:

[0072] in, It is the change in temperature. This is a reference temperature. It is the change in humidity, coefficient and The data was obtained by fitting a historical dataset. Monitoring of minute displacements at the reference point was achieved by installing a stable reference target on the tunnel wall; the target position... Real-time data acquisition, displacement error Calculations show that This is the initial reference position. Using the reference target data, the reference displacement error is directly modeled as... This enables high-precision real-time monitoring. The comprehensive error model integrates all sources and is defined as follows:

[0073] Applying this model to compensate for the original data, the displacement field calculation is corrected as follows:

[0074] By establishing additional reference targets, baseline drift caused by environmental changes can be captured in real time, significantly improving the accuracy of the displacement field by more than 15% and reducing the false alarm rate during tunnel construction. Finally, the corrected data is verified. Whether it conforms to the deformation characteristics of the surrounding rock and ensures the effectiveness of compensation.

[0075] Analyzing the preliminary three-dimensional displacement, wavelet transform was used to separate low-frequency systematic error components, identifying three main error sources: instrument shafting error, atmospheric refraction error, and minute displacement of the reference point. The instrument shafting error was modeled as a linear proportional-offset function, with parameters determined through equipment calibration experiments. The atmospheric refraction error was modeled using real-time temperature and humidity data collected by environmental sensors within the tunnel, establishing a multivariate regression model. The minute displacement of the reference point was directly modeled as the displacement of the reference target, monitored in real-time by installing additional stable reference targets on the tunnel wall. These three error models were integrated into a comprehensive error model to compensate for the preliminary three-dimensional displacement, resulting in a corrected displacement field. Over 95% of the systematic error components were successfully separated; the accuracy of the displacement field was improved by more than 15%; the measurement error remained within 1 mm even under temperature and humidity variations of ±15℃; the false alarm rate was reduced from 20% using traditional methods to below 3%; and the accuracy of the reference point displacement monitoring reached 0.2 mm, enabling timely detection of minute drifts of the reference point.

[0076] S5. Complete settlement information fusion, early warning and system iteration. After error compensation, the displacement data of each target, especially the vertical settlement data, are spatially interpolated or fitted to a surface to generate a continuous settlement cloud map or contour line for the entire monitoring area. The system automatically analyzes the data based on preset settlement and rate-of-change thresholds, triggering a tiered early warning system upon detecting any abnormal areas. Simultaneously, the entire process (from step S2 to step S5) is executed cyclically according to a set timeframe, achieving long-term automated monitoring. The results of each cycle are fed back to optimize the error model and evaluate the target deployment scheme, making adjustments as necessary to achieve self-optimization of the measurement system. The specific implementation method of this step is as follows: First, acquire the target displacement data after error compensation, focusing on the vertical settlement value, denoted as . ,in Indicates the first Target position coordinates A continuous settlement field is generated using the Kriging spatial interpolation method. Kriging estimates the settlement at unknown points based on a variogram model, as shown in the following formula: ,in, It is any position The predicted settlement value, It is the global mean (calculated from sample data). It is a spatially correlated error term, whose covariance is determined by the variogram. definition, Let be the distance between points. The variogram is modeled as follows:

[0077] It is the nugget effect (representing minute-scale variation). It is the base value. This is a range parameter (describing the spatial correlation range). This method can effectively capture spatial variability characteristics, improve the estimation accuracy of settlement distribution, and thus more accurately identify anomalous areas. As an alternative, quadratic polynomial surface fitting is used:

[0078] in, arrive These are the fitting coefficients, obtained using the least squares method, which minimizes the sum of squared residuals. , This represents the number of targets. After fitting, a settlement cloud map or contour line is generated based on the interpolation or fitting results, and the visualization output is achieved using GIS software or a custom algorithm.

[0079] Next, the system automatically analyzes the settlement data and presets the settlement threshold. (e.g., 10 mm) and rate of change threshold (e.g., 0.5 mm / day). Calculate the current settlement at each monitoring point. and rate of change ,in It is the time step. The monitoring period is specified as 1 day. The anomaly detection logic is as follows: if... or If so, it is marked as an abnormal area. A tiered early warning mechanism is triggered: Level 1 warning (yellow) is used for minor abnormalities (…). or Level 2 warning (orange) is used for moderate abnormalities ( or Level 3 warning (red) is used for severe anomalies ( or Warning information is pushed to the monitoring platform via real-time interfaces (such as APIs).

[0080] The entire process is cyclical This process is repeated cyclically (e.g., 24 hours) to achieve long-term automated monitoring. After each cycle, the calculation results (including settlement data and anomaly records) are fed back to the error model optimization stage. The error model parameters are updated, for example, by re-estimating the variogram parameters in Kriging. , and Using the maximum likelihood estimation method: Maximize the likelihood function:

[0081] in, It is the covariance matrix. It is a parameter vector. It is the observed settlement vector.

[0082] Simultaneously, the target deployment plan was evaluated: Calculate spatial coverage index , The total area of ​​the monitoring area, It is the first The area of ​​each Voronoi unit of the target. If If a preset threshold (e.g., 0.8) or high density of abnormal areas is detected, the target position is automatically adjusted, and targets are added or moved to optimize coverage. This feedback loop enables the system to self-optimize, ensuring robustness and accuracy in long-term monitoring.

[0083] For the vertical settlement data in the corrected displacement field, Kriging spatial interpolation or quadratic polynomial surface fitting is used to generate a continuous settlement field for the monitoring area. Settlement cloud maps or contour lines are then generated based on this continuous settlement field for visualization. Preset settlement and rate-of-change thresholds are used. When the settlement at a monitoring point exceeds the threshold or the rate of change exceeds the rate threshold, it is marked as an abnormal area, triggering a three-level warning system based on the severity of the anomaly: Level 1 Yellow Warning (exceeding the threshold by 1-1.5 times), Level 2 Orange Warning (exceeding the threshold by 1.5-2 times), and Level 3 Red Warning (exceeding the threshold by more than 2 times). Warning information is pushed to the monitoring platform via a real-time API interface. The entire process is executed cyclically every 24 hours. After each cycle, the comprehensive error model parameters are updated using the maximum likelihood estimation method based on the calculation results. The target deployment scheme is evaluated by calculating the spatial coverage index. When the coverage is lower than the preset threshold or the density of abnormal areas is high, the target positions are automatically adjusted or new targets are added. The spatial resolution of the continuous settlement field reaches 0.5 meters, which can accurately capture local settlement anomalies of the surrounding rock; the early warning response time is less than 1 minute and the early warning accuracy rate reaches more than 98%; after three iterations of the error model, the accuracy can be further improved by 5%; after the target deployment scheme is automatically adjusted, the monitoring blind zone is reduced by more than 90%; the system can operate continuously and stably for more than 1 year without manual intervention.

[0084] In another embodiment of the present invention, a tunnel surrounding rock settlement measurement system based on multi-target collaborative positioning and error compensation is provided. This system can be used to implement the above-mentioned tunnel surrounding rock settlement measurement method based on multi-target collaborative positioning and error compensation. Specifically, the tunnel surrounding rock settlement measurement system based on multi-target collaborative positioning and error compensation includes a target unit, a measurement station unit, a data preprocessing unit, a collaborative calculation unit, an error compensation unit, a settlement analysis and early warning unit, and an iterative optimization unit.

[0085] Among them, the target unit is used to deploy multiple high-precision optical targets in the tunnel surrounding rock settlement monitoring area; The measurement station unit is used to set up multiple total station or laser scanner measurement stations at stable benchmark points inside the tunnel, and to collect angle and distance data of each measurement station to each high-precision optical target. The data preprocessing unit is used to perform gross error removal, coordinate system unification, and timestamp alignment on the acquired raw observations to obtain a multi-source observation dataset with a unified timestamp and a unified coordinate system. The collaborative solution unit is used to solve the three-dimensional coordinates of each high-precision optical target at the current moment based on the multi-source observation dataset, and to generate preliminary three-dimensional displacement and preliminary deformation field. The error compensation unit is used to identify and separate three types of systematic errors: instrument shaft system error, atmospheric refraction error, and small displacement of reference point. It establishes a comprehensive error model and uses the comprehensive error model to compensate for the preliminary three-dimensional displacement to obtain the corrected displacement field. The settlement analysis and early warning unit is used to perform spatial interpolation or surface fitting on the vertical settlement data in the corrected displacement field to obtain a continuous settlement field, and generate a continuous settlement cloud map or contour line based on the continuous settlement field. It also performs anomaly judgment based on preset settlement threshold and rate of change threshold to obtain graded early warning information. The iterative optimization unit is used to repeatedly perform data processing, collaborative solution, error compensation and settlement analysis according to a set cycle, and feeds back the corrected displacement field, continuous settlement cloud map or contour line and graded early warning information obtained in each cycle to update the comprehensive error model and evaluate the target deployment scheme.

[0086] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used for the operation of a tunnel surrounding rock settlement measurement method based on multi-target collaborative positioning and error compensation, including: Multiple high-precision optical targets are deployed in three dimensions within the tunnel surrounding rock settlement monitoring area. Multiple total stations or laser scanners are set up at stable reference points within the tunnel to establish a unified global coordinate system. This yields the spatial deployment results of the high-precision optical targets, the position parameters of the measurement stations, and the global coordinate system. The measurement equipment at each station is synchronized using a precision clock. Angle and distance data from each station to each high-precision optical target are collected. The raw observation values ​​are processed for gross error removal, coordinate system unification, and timestamp alignment to obtain a multi-source observation dataset with a unified timestamp and coordinate system. Based on this dataset, the three-dimensional position of each measurement station and the observation direction vector of the corresponding high-precision optical target are extracted. A spatial resection algorithm combined with the Levenberg-Marquardt optimization algorithm is used to minimize the sum of squared residuals between the observation and calculation directions, thus calculating the three-dimensional coordinates of each high-precision optical target at the current moment. The three-dimensional coordinates of each high-precision optical target at the current moment are then compared with the initial reference coordinates or the coordinates of the previous cycle. Coordinate difference calculation yields the preliminary three-dimensional displacement of each high-precision optical target, and a preliminary deformation field of the surrounding rock surface is generated based on the preliminary three-dimensional displacement of each high-precision optical target. The preliminary three-dimensional displacement is analyzed to identify and separate three types of systematic errors: instrument axis error, atmospheric refraction error, and micro-displacement of the reference point. Corresponding error models are established for each type and integrated into a comprehensive error model. The comprehensive error model is used to compensate for the preliminary three-dimensional displacement, resulting in a corrected displacement field. Spatial interpolation or surface fitting is performed on the vertical settlement data in the corrected displacement field to obtain a continuous settlement field for the monitoring area. A continuous settlement cloud map or contour line is generated based on the continuous settlement field. Anomalies in the monitoring area are determined according to preset settlement thresholds and rate of change thresholds, resulting in graded early warning information. The above steps are repeated at a set cycle, and the corrected displacement field, continuous settlement cloud map or contour line, and graded early warning information obtained in each cycle are fed back to update the comprehensive error model and evaluate the target deployment scheme, resulting in an updated comprehensive error model and / or an adjusted target deployment scheme.

[0087] Please see Figure 2 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the processor 61 executes the computer program 63, it implements the tunnel surrounding rock settlement measurement method based on multi-target collaborative positioning and error compensation as described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when the processor 61 executes the computer program 63, it implements the functions of each model / unit in the tunnel surrounding rock settlement measurement system based on multi-target collaborative positioning and error compensation as described in this embodiment. To avoid repetition, these details are not elaborated here.

[0088] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 2 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0089] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0090] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0091] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0092] Please see Figure 3The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0093] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0094] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0095] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0096] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0097] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0098] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0099] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0100] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0101] One or more instructions stored in a computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Multiple high-precision optical targets are deployed in three dimensions within the tunnel surrounding rock settlement monitoring area. Multiple total stations or laser scanners are set up at stable reference points within the tunnel to establish a unified global coordinate system. This yields the spatial deployment results of the high-precision optical targets, the position parameters of the measurement stations, and the global coordinate system. The measurement equipment at each station is synchronized using a precision clock. Angle and distance data from each station to each high-precision optical target are collected. The raw observation values ​​are processed for gross error removal, coordinate system unification, and timestamp alignment to obtain a multi-source observation dataset with a unified timestamp and coordinate system. Based on this dataset, the three-dimensional position of each measurement station and the observation direction vector of the corresponding high-precision optical target are extracted. A spatial resection algorithm combined with the Levenberg-Marquardt optimization algorithm is used to minimize the sum of squared residuals between the observation and calculation directions, thus calculating the three-dimensional coordinates of each high-precision optical target at the current moment. The three-dimensional coordinates of each high-precision optical target at the current moment are then compared with the initial reference coordinates or the coordinates of the previous cycle. Coordinate difference calculation yields the preliminary three-dimensional displacement of each high-precision optical target, and a preliminary deformation field of the surrounding rock surface is generated based on the preliminary three-dimensional displacement of each high-precision optical target. The preliminary three-dimensional displacement is analyzed to identify and separate three types of systematic errors: instrument axis error, atmospheric refraction error, and micro-displacement of the reference point. Corresponding error models are established for each type and integrated into a comprehensive error model. The comprehensive error model is used to compensate for the preliminary three-dimensional displacement, resulting in a corrected displacement field. Spatial interpolation or surface fitting is performed on the vertical settlement data in the corrected displacement field to obtain a continuous settlement field for the monitoring area. A continuous settlement cloud map or contour line is generated based on the continuous settlement field. Anomalies in the monitoring area are determined according to preset settlement thresholds and rate of change thresholds, resulting in graded early warning information. The above steps are repeated at a set cycle, and the corrected displacement field, continuous settlement cloud map or contour line, and graded early warning information obtained in each cycle are fed back to update the comprehensive error model and evaluate the target deployment scheme, resulting in an updated comprehensive error model and / or an adjusted target deployment scheme.

[0102] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0103] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0104] The core idea of ​​this invention is to achieve accurate monitoring of surrounding rock settlement by using multi-target 3D collaborative deployment and geometric constraints of multiple measurement stations, combined with synchronous acquisition, collaborative calculation, error compensation, and iterative optimization of multi-source data. Specifically, the method involves: first, deploying multiple optical targets in 3D within the tunnel surrounding rock monitoring area, and simultaneously setting up total stations or laser scanners as measurement stations at stable benchmark points to establish the physical basis for multi-source coverage; then, synchronously acquiring and preprocessing angle and distance data from multiple targets, unifying the coordinate system and timestamps, and eliminating gross errors; next, using multi-source observation data and a spatial resection algorithm to collaboratively calculate the 3D coordinates of the targets, and comparing the initial or previous cycle coordinates to obtain the preliminary deformation field; then, analyzing and identifying systematic errors in the measurement system, establishing a model for compensation, and correcting to obtain the true deformation field; finally, fusing settlement data to generate a continuous settlement cloud map, triggering graded early warnings, and iteratively optimizing the error model and target deployment. Its highlights include: geometric constraints of multiple targets and multiple stations significantly improving positioning accuracy and reliability; systematic error modeling and compensation eliminating inherent biases; closed-loop iteration achieving system self-optimization; and full-process automation meeting long-term monitoring needs.

[0105] This invention offers significant advantages: Compared to single-point total station measurement methods, collaborative observation using multiple targets and stations improves single-point positioning accuracy through geometric constraints, redundant observations can handle momentary obstruction, and simultaneous acquisition of multi-target data greatly enhances monitoring efficiency and achieves regional coverage; compared to GNSS measurement methods, it does not rely on satellite signals, and the combination of all-optical targets and measurement stations can cover the entire tunnel area without being affected by signal obstruction; compared to hydrostatic leveling methods, it can monitor three-dimensional deformation rather than just settlement, eliminates the need for pipeline installation, minimizes construction interference, simplifies maintenance, allows for flexible multi-target deployment, and provides more comprehensive regional coverage; compared to single-point laser scanning methods, multi-source collaborative calculation eliminates the systematic errors of a single instrument, error modeling compensation further corrects deviations, resulting in more stable positioning accuracy, and closed-loop iterative optimization ensures long-term monitoring reliability.

[0106] When the method of this invention is used, it can overcome many limitations of traditional monitoring methods and achieve high-precision, high-reliability, long-term automated monitoring of the settlement of the surrounding rock in the entire tunnel. The geometric constraints of multiple targets and multiple stations effectively improve the positioning accuracy and anti-interference ability. The system error compensation mechanism solves the problem that errors are difficult to eliminate in traditional methods. Continuous settlement cloud map and graded early warning can capture abnormal deformation in a timely manner, providing accurate data support and decision-making basis for tunnel construction safety management and structural health assessment during the operation phase.

[0107] To verify the robustness and accuracy of the method of this invention under different occlusion conditions, a Monte Carlo simulation experiment was conducted. The simulation scenario was set as a tunnel cross-section with a length of 50 meters, a width of 10 meters, and a height of 8 meters. Ten optical targets and three measurement stations were set up, and the target coordinates and station coordinates were set up according to the requirements of this invention. Gaussian noise with a mean of 0 and a standard deviation of 0.5 mm was added to the observation data, and different proportions of random occlusion were simulated. The calculation results using the method of this invention and the traditional single-point total station measurement method are as follows: When the occlusion rate is 0%, the root mean square error (RMS) of the method of this invention is 0.72 mm, while that of the traditional single-point method is 3.15 mm, and the success rate of both methods is 100%. When the occlusion rate increases to 10%, the RMS of the method of this invention increases slightly to 0.85 mm, while the success rate remains 100%, whereas the RMS of the traditional single-point method increases to 4.28 mm, and the success rate decreases to 75%. When the occlusion rate reaches 20%, the RMS of the method of this invention is 0.98 mm. The solution success rate is 98%, while the root mean square error of the traditional single-point method further increases to 5.63 mm, and the solution success rate is only 52%. When the occlusion rate is 30%, the root mean square error of the method of this invention is 1.15 mm, still maintaining a solution success rate of 95%, while the traditional single-point method can no longer solve the problem, with a solution success rate of 0%. When the occlusion rate continues to increase to 40%, the root mean square error of the method of this invention is 1.42 mm, and the solution success rate can still reach 88%, while the traditional single-point method still cannot complete the solution.

[0108] Simulation results show that the method of the present invention can still maintain a high accuracy of 1.15 mm and a 95% success rate when the occlusion rate is as high as 30%, while the traditional single-point method cannot be solved at all when the occlusion rate reaches 30%, which fully demonstrates the strong anti-interference ability and robustness of the method of the present invention.

[0109] This invention proposes a multi-target three-dimensional optimization layout scheme, requiring no fewer than 8 targets, which are uniformly distributed in a non-coplanar and nonlinear manner with a spacing of no less than 0.5 meters. Anchor bolts are used to fix the targets to ensure an installation accuracy within ±1 mm. At the same time, at least 3 measurement stations are set up at stable benchmark points verified by ground-penetrating radar to ensure that each station can observe all targets. A global coordinate system is established through least squares optimization to ensure coordinate consistency.

[0110] This invention achieves high-precision synchronous acquisition and preprocessing of multi-source data, uses GPS time synchronization or IEEE1588 protocol to achieve microsecond-level time synchronization, uses an improved Mahalanobis distance criterion to adaptively eliminate gross errors within 30%, unifies the coordinate system through homogeneous transformation matrix, and completes timestamp alignment with cubic spline interpolation, controlling the spatiotemporal consistency error within 0.1ms and 0.2mm.

[0111] This invention is a collaborative positioning solution method based on multi-observation constraints. It uses a spatial resection algorithm combined with Levenberg-Marquardt optimization iteration to solve the three-dimensional coordinates of the target. By utilizing the geometric constraints of the observation directions of multiple stations, the single-point positioning error is controlled within 1mm. The preliminary displacement is calculated by comparing with the initial or previous cycle coordinates to generate the preliminary deformation field of the surrounding rock.

[0112] This invention constructs a multi-source systematic error modeling and compensation strategy, identifies and separates three types of systematic errors: instrument shaft system, atmospheric refraction, and reference point displacement, and establishes linear correction model, multiple regression model, and reference target monitoring model respectively. After integration, the preliminary deformation data is compensated, which improves the accuracy of the displacement field by more than 15% and reduces the false alarm rate of monitoring.

[0113] This invention employs spatial fusion technology for settlement information, using Kriging spatial interpolation or quadratic polynomial surface fitting to transform the settlement data of discrete targets into continuous settlement cloud maps or contour lines, accurately capturing the spatial variation characteristics of surrounding rock settlement.

[0114] This invention establishes a dual-threshold graded settlement early warning mechanism, which presets two types of thresholds: settlement amount and rate of change. Based on the degree of anomaly, it triggers three levels of early warning: yellow, orange, and red. The early warning information is pushed to the monitoring platform through a real-time interface.

[0115] This invention designs a self-iterative optimization mechanism for the monitoring system. The monitoring process is executed cyclically according to a set cycle. After each cycle, the error model parameters are updated using the calculation results. The target deployment effect is evaluated by calculating the spatial coverage index, and the target position or number is automatically adjusted to optimize the monitoring coverage.

[0116] This invention proposes a benchmark stability monitoring method, which selects a concrete lining or bedrock far from construction disturbances as the benchmark, and sets up additional stable reference targets to monitor the minute displacement of the benchmark in real time, providing dynamic data support for system error compensation.

[0117] This invention provides an error correction scheme adapted to the tunnel environment. It combines temperature and humidity sensor data inside the tunnel to establish a multivariate regression model for atmospheric refraction error, dynamically compensating for measurement deviations caused by environmental changes.

[0118] This invention realizes a high-precision surrounding rock deformation field generation process. It obtains high-precision target coordinates through collaborative calculation, obtains preliminary displacement by comparing with reference coordinates, generates a corrected displacement field after error compensation, and then obtains a continuous surrounding rock deformation field through spatial interpolation or fitting, thereby improving the authenticity and reliability of deformation monitoring.

[0119] In summary, this invention presents a tunnel surrounding rock settlement measurement method based on multi-target collaborative positioning and error compensation. Through multi-target three-dimensional optimized layout and multi-site collaborative observation, redundant geometric constraints are formed, controlling single-point positioning errors to within 1 mm, improving accuracy by 3-5 times compared to traditional methods. Multi-source system error comprehensive compensation technology effectively eliminates the influence of environmental interference and instrument drift, improving displacement field accuracy by over 15% and reducing false alarm rate by 80%. Spatial fusion technology enables settlement monitoring from discrete points to continuous surfaces, providing a direct view of the overall deformation distribution. Full-process automated processing and closed-loop iterative optimization mechanisms enable 24-hour unattended long-term monitoring, with the system capable of continuous and stable operation for over one year. This invention comprehensively solves the problems of low accuracy, weak anti-interference ability, incomplete coverage, and low automation in traditional monitoring technologies. It can adapt to the complex environment of high dust and strong vibration during tunnel construction and unattended operation, providing accurate and reliable data support for tunnel structural safety assessment, disease early warning, and maintenance.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0121] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0123] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0124] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0126] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0127] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation, characterized in that, Includes the following steps: S1. Multiple high-precision optical targets are deployed in three dimensions in the tunnel surrounding rock settlement monitoring area, and multiple total station or laser scanner measurement stations are set at stable reference points in the tunnel to establish a unified global coordinate system, thereby obtaining the spatial deployment results of the high-precision optical targets, the position parameters of the measurement stations, and the global coordinate system. S2. Synchronize the measurement equipment at each measurement station using a precision clock, collect the angle and distance data of each measurement station to each high-precision optical target, perform gross error elimination, coordinate system unification and timestamp alignment on the collected raw observation values, and obtain a multi-source observation dataset with a unified timestamp and a unified coordinate system. S3. Based on the multi-source observation dataset, extract the three-dimensional position of each measurement station and the observation direction vector of the corresponding high-precision optical target. Use the spatial resection algorithm combined with the Levenberg-Marquardt optimization algorithm to minimize the sum of squared residuals between the observation direction and the calculation direction, and calculate the three-dimensional coordinates of each high-precision optical target at the current time. S4. Calculate the coordinate difference between the three-dimensional coordinates of each high-precision optical target at the current moment and the initial reference coordinates or the coordinates of the previous cycle to obtain the preliminary three-dimensional displacement of each high-precision optical target, and generate the preliminary deformation field of the surrounding rock surface based on the preliminary three-dimensional displacement of each high-precision optical target. S5. Analyze the preliminary three-dimensional displacement, identify and separate three types of systematic errors: instrument shaft system error, atmospheric refraction error, and small displacement of reference point. Establish corresponding error models for each type and integrate them into a comprehensive error model. Use the comprehensive error model to compensate for the preliminary three-dimensional displacement to obtain the corrected displacement field. S6. Perform spatial interpolation or surface fitting on the vertical settlement data in the corrected displacement field to obtain the continuous settlement field of the monitoring area, and generate a continuous settlement cloud map or contour line based on the continuous settlement field. Based on the preset settlement threshold and change rate threshold, determine the anomaly of the monitoring area and obtain graded early warning information. S7. Repeat S2 to S6 according to the set cycle, and feed back the corrected displacement field, continuous settlement cloud map or contour line and graded early warning information obtained in each cycle to update the comprehensive error model and evaluate the target deployment scheme, so as to obtain the updated comprehensive error model and / or the adjusted target deployment scheme.

2. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S1, the number of high-precision optical targets is no less than 8. The high-precision optical targets are evenly arranged according to a three-dimensional spatial structure to avoid coplanar or linear arrangement. The distance between any two high-precision optical targets is no less than 0.5 meters. The number of measurement stations is at least 3, distributed at different sections of the tunnel, and each measurement station can simultaneously observe all high-precision optical targets.

3. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S1, the measurement station is set at the concrete lining or bedrock, far away from the construction disturbance area; the global coordinate system is established with the tunnel entrance or fixed reference point as the origin, and the local coordinates of each measurement station are mapped to the global coordinate system through a homogeneous transformation matrix; the global coordinates of the high-precision optical target are determined according to the position parameters, rotation matrix, direction unit vector and distance parameters of the measurement station.

4. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S2, the precision clock synchronization is achieved using a GPS timing module or the IEEE 1588 precision clock protocol; the original observation values ​​collected by each measurement station for each high-precision optical target include horizontal angle, pitch angle and slant distance; the data collected synchronously are timestamped and a time deviation compensation model is established to eliminate time deviation caused by equipment clock jitter.

5. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S2, the gross error removal adopts an improved Mahalanobis distance criterion, which specifically includes: taking the median of the observations of the same high-precision optical target at multiple measurement stations and constructing a covariance matrix; calculating the standardized residual for the observations at the i-th measurement station; when the standardized residual is greater than a threshold, the corresponding observation is determined as a gross error and removed; wherein, the threshold is determined according to the observation dimension.

6. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S2, the angle and distance data are first converted to Cartesian coordinates, and then coordinate system is unified by the transformation matrix from the measurement station to the global coordinate system. Cubic spline interpolation is used to construct an interpolation function for the discrete observation sequence to output the coordinate data of each high-precision optical target at a unified sampling time, thereby obtaining the multi-source observation dataset.

7. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S3, for each high-precision optical target, based on the three-dimensional positions of multiple measurement stations and the corresponding observation direction vectors, a residual sum of squares objective function between the observation direction and the calculation direction is constructed, and the Levenberg-Marquardt optimization algorithm is used to iteratively solve the problem. The calculation direction vector is obtained by normalizing the vector between the coordinates of the high-precision optical target to be solved and the three-dimensional position of the corresponding measurement station. The initial value of the Levenberg-Marquardt optimization algorithm is set as the average value of the three-dimensional positions of multiple measurement stations, and the iteration termination condition is that the residual change is less than a preset threshold or the maximum number of iterations is reached.

8. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S5, the instrument shaft system error is modeled using a linear function, the atmospheric refraction error is modeled using a multiple regression model based on temperature and humidity data, and the small displacement of the reference point is monitored in real time using a stable reference target set on the tunnel wall. The instrument shaft system error, the atmospheric refraction error, and the small displacement of the reference point are integrated into a comprehensive error model to compensate for the preliminary three-dimensional displacement, thereby obtaining the corrected displacement field.

9. The tunnel surrounding rock settlement measurement method based on multi-target cooperative positioning and error compensation according to claim 1, characterized in that, In step S6, the vertical settlement data in the corrected displacement field is subjected to Kriging spatial interpolation or quadratic polynomial surface fitting to obtain a continuous settlement field for the monitoring area, and a continuous settlement cloud map or contour lines are generated based on the continuous settlement field. The Kriging spatial interpolation method estimates the settlement value at any location in the monitoring area based on a variogram model, which includes nugget effect, sill value, and range parameter. The quadratic polynomial surface fitting solves for the fitting coefficients using the least squares method. Based on the comparison between the current settlement amount and rate of change and preset settlement thresholds and rate of change thresholds, anomalies are identified in the monitoring area, and a graded early warning is triggered.

10. A tunnel surrounding rock settlement measurement system based on multi-target collaborative positioning and error compensation, characterized in that, include: The target unit is used to deploy multiple high-precision optical targets in the tunnel surrounding rock settlement monitoring area; The measurement station unit is used to set up multiple total station or laser scanner measurement stations at stable benchmark points inside the tunnel, and to collect angle and distance data of each measurement station to each high-precision optical target. The data preprocessing unit is used to perform gross error removal, coordinate system unification, and timestamp alignment on the acquired raw observations to obtain a multi-source observation dataset with a unified timestamp and a unified coordinate system. The collaborative solution unit is used to solve the three-dimensional coordinates of each high-precision optical target at the current moment based on the multi-source observation dataset, and to generate preliminary three-dimensional displacement and preliminary deformation field. The error compensation unit is used to identify and separate three types of systematic errors: instrument shaft system error, atmospheric refraction error, and small displacement of reference point. It establishes a comprehensive error model and uses the comprehensive error model to compensate for the preliminary three-dimensional displacement to obtain the corrected displacement field. The settlement analysis and early warning unit is used to perform spatial interpolation or surface fitting on the vertical settlement data in the corrected displacement field to obtain a continuous settlement field, and generate a continuous settlement cloud map or contour line based on the continuous settlement field. It also performs anomaly judgment based on preset settlement threshold and rate of change threshold to obtain graded early warning information. The iterative optimization unit is used to repeatedly perform data processing, collaborative solution, error compensation and settlement analysis according to a set cycle, and feeds back the corrected displacement field, continuous settlement cloud map or contour line and graded early warning information obtained in each cycle to update the comprehensive error model and evaluate the target deployment scheme.

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