A real-time monitoring system and method for derrick deformation and crown block inclination based on GNSS technology

Abstract

The present application belongs to the technical field of mine safety monitoring, and discloses a kind of real-time monitoring system and method of headframe deformation and crown wheel inclination based on GNSS technology, system includes: GNSS real-time monitoring station network module, data cleaning module, space-time analysis module, headframe and crown wheel inclination monitoring module;GNSS real-time monitoring station network module is used to real-time acquisition headframe three-dimensional deformation monitoring data;Data cleaning module is used to clean headframe three-dimensional deformation monitoring data;Space-time analysis module is used to visualize analysis headframe space-time deformation characteristics in combination with ground state correction model, obtains headframe deformation trend;Headframe and crown wheel inclination monitoring module is used to calculate headframe and crown wheel inclination angle based on GNSS monitoring station network configuration, using the cleaned headframe three-dimensional deformation monitoring data.The present application realizes high-precision headframe deformation and crown wheel inclination real-time monitoring, and provides technical support for mine headframe structure safety evaluation.

Description

Technical Field

[0001] This invention belongs to the field of mine safety monitoring technology, specifically relating to a real-time monitoring system and method for headframe deformation and sheave tilt based on GNSS technology, applicable to health status assessment and safety early warning of headframe structures in mining, metallurgy and other fields. Background Technology

[0002] As a critical load-bearing structure in a mine hoisting system, the safety of the headframe plays a decisive role in the production safety and the safety of personnel and property in the mining area. However, during mining operations, the headframe is subjected to both mechanical loads and environmental factors over a long period of time, making it highly susceptible to three-dimensional cumulative deformation. If millimeter-level displacements or minute structural anomalies are not detected in time, it can lead to major accidents such as headframe tilting, support structure fracture, or even collapse. Therefore, high-precision, real-time headframe deformation monitoring technology has become an urgent need for mine safety management.

[0003] Traditional methods for monitoring derrick deformation primarily rely on ground-based surveying techniques such as total stations and levels, using periodic manual data collection to assess structural stability. However, this approach suffers from three significant drawbacks: first, poor timeliness and long measurement cycles, typically weekly or monthly, making it difficult to capture dynamic deformation processes and meet real-time early warning requirements; second, high labor costs, requiring frequent deployment of measuring points and manual readings, resulting in low efficiency and high risk in harsh mining environments; and third, insufficient spatial coverage, only acquiring discrete point data, failing to comprehensively reflect the overall deformation trend of the derrick and lacking sufficient monitoring accuracy for critical nodes (such as the head sheave). In recent years, the widespread adoption of GNSS technology has brought new opportunities for derrick monitoring, with satellite-based real-time dynamic monitoring increasingly being applied to the health assessment of engineering structures. However, existing solutions still face numerous bottlenecks: limited data quality, significant multipath effects in mining environments, and the difficulty of effectively separating real deformation from noise using conventional filtering methods; a lack of spatiotemporal analysis models, with existing studies mostly employing static benchmark comparison methods, failing to dynamically describe the spatiotemporal evolution of deformation and lacking the ability to distinguish between periodic oscillations and cumulative deformation; and a crude approach to risk assessment, with existing early warning systems relying heavily on single threshold judgments, neglecting the correlation between deformation and structural parameters, resulting in a high false alarm rate. Furthermore, current research suffers from significant scenario limitations. GNSS deformation monitoring technology primarily focuses on large-scale scenarios such as dams, bridges, and landslides, while dedicated monitoring models and methods are lacking for structures like mine derricks—tall structures with concentrated local loads and subject to complex mechanical disturbances. For example, the deformation of the derrick sheave platform, frequently subjected to lateral tension from hoisting ropes, exhibits significant directional and spatial heterogeneity, characteristics that general models struggle to accurately characterize. In summary, existing technologies have failed to effectively address core issues in mine headframe monitoring, such as insufficient data accuracy, coarse spatiotemporal analysis granularity, and lack of scientific rigor in risk assessment. To address these shortcomings, this invention proposes a GNSS monitoring system that integrates data cleaning, dynamic spatiotemporal modeling, deformation and tilt calculations. This system aims to improve the accuracy and efficiency of mine headframe monitoring and provide technical support for mine headframe safety. Summary of the Invention

[0004] To address the challenges of long monitoring cycles and the inability to provide dynamic monitoring and early warning in traditional methods, this invention provides a real-time monitoring system and method for derrick deformation and sheave tilt based on GNSS technology. This system achieves high-precision real-time monitoring of derrick deformation and sheave tilt, providing technical support for safety assessment of derrick structures in mining areas.

[0005] To achieve the above objectives, the present invention provides the following solution: A real-time monitoring system for derrick deformation and sheave tilt based on GNSS technology, the system comprising: a GNSS real-time monitoring station network module, a data cleaning module, a spatiotemporal analysis module, and a derrick and sheave tilt monitoring module; GNSS real-time monitoring station network module is used to collect three-dimensional deformation monitoring data of the derrick in real time; The data cleaning module is used to clean the three-dimensional deformation monitoring data of the derrick. The spatiotemporal analysis module is used to visualize and analyze the spatiotemporal deformation characteristics of the derrick by combining the ground state correction model, and to obtain the deformation trend of the derrick. The derrick and sheave tilt monitoring module is used to calculate the tilt angle of the derrick and sheave based on the GNSS monitoring station network configuration and the three-dimensional deformation monitoring data of the derrick after cleaning.

[0006] Preferably, the GNSS real-time monitoring station network module consists of one GNSS reference station, eight GNSS monitoring stations, and a monitoring and early warning cloud platform; Each GNSS real-time monitoring station is equipped with a GNSS receiver and a wireless communication module. A GNSS reference station is located 1-3 km away from the GNSS monitoring stations to provide reference data. The GNSS monitoring stations are distributed according to a predetermined layout at key parts of the derrick. There are two stations on the upper sheave platform, located on both sides of the sheave axis; and six stations on the lower sheave platform, two on both sides of the lower sheave axis and four at the four corners of the lower sheave platform. Eight GNSS monitoring stations are used to monitor deformation of key parts of the derrick. Simultaneously, the GNSS monitoring stations on both sides of the sheave axis monitor the tilt of the sheave, and the GNSS monitoring stations at the four corners of the lower sheave platform monitor the tilt of the lower sheave platform. The monitoring and early warning cloud platform receives GNSS observation data and uses technologies such as NRTK, RT-PPP, and PPP-RTK to monitor the derrick in real time. Through integrated data management and analysis tools, it achieves real-time monitoring and early warning of derrick deformation. Each GNSS real-time monitoring station connects to the monitoring and early warning cloud platform via a wireless communication module to transmit data in real time, build a monitoring network, acquire derrick deformation data, and automatically collect three-dimensional coordinate data and related monitoring information of the mine derrick at different times according to preset time intervals.

[0007] Preferably, the data cleaning module consists of an outlier detection unit, an interpolation unit, and a high-frequency noise reduction unit; The outlier detection unit is used to identify and remove outlier data points in three-dimensional space based on the Local Outlier Factor (LOF) algorithm. Interpolation unit, used to estimate and fill missing or abnormal monitoring data points while preserving the true micro-oscillations during derrick operation using Savitzky-Golay smoothing interpolation method; The high-frequency denoising unit is used to filter out high-frequency noise using the db4 wavelet soft thresholding denoising technique, and output a high-fidelity clean sequence.

[0008] Preferably, the spatiotemporal analysis module consists of a ground-state data unit, a correction term calculation unit, a spatiotemporal modeling unit, and a deformation trend analysis unit; The ground-state data unit is used to store monitoring data from the initial installation phase as ground-state data. The correction term calculation unit is used to compare subsequent monitoring data with ground state data, calculate the correction term for each monitoring point, and reflect the magnitude and direction of deformation. The spatiotemporal modeling unit is used to construct models using spatiotemporal analysis algorithms to analyze the evolution of deformation in time and space. The deformation trend analysis unit is used to draw deformation trend diagrams, predict future deformation trends, and generate intuitive deformation analysis reports based on correction terms and spatiotemporal models.

[0009] Preferably, the derrick and sheave tilt analysis module consists of a tilt calculation unit; the tilt calculation unit is used to determine the tilt angle of the sheave platform and the sheave. For the sheave platform, by analyzing the three-dimensional coordinate data of the monitoring points around it, a local coordinate system is established with the most stable monitoring point as the origin. A plane is fitted, and the tilt angle of the platform is obtained by calculating the angle between the plane normal vector and the horizontal plane. The projection of the normal vector onto the horizontal plane is the tilt direction. For the top wheel, the tilt angle of the top wheel is calculated using the arctangent function based on the ratio of the elevation difference to the distance between the monitoring points on both sides of the top wheel axle, and the tilt direction is determined by combining the three-dimensional coordinate data.

[0010] This invention also provides a method for real-time monitoring of derrick deformation and sheave tilt based on GNSS technology. The method is implemented using the aforementioned system and includes: By deploying one GNSS reference station and eight GNSS monitoring stations, a monitoring and early warning cloud platform is established to form a GNSS monitoring network and collect real-time three-dimensional deformation monitoring data of the derrick. Savitzky-Golay cubic spline interpolation, local outlier factor LOF anomaly detection, and db4 wavelet soft thresholding denoising method were used to clean the three-dimensional deformation monitoring data of the derrick. By combining the ground state correction model, the spatiotemporal deformation characteristics of the derrick are visualized and analyzed to reveal the deformation trend of the derrick; Based on the GNSS monitoring station network configuration, the tilt angles of the derrick and sheave are calculated using the monitoring data after cleaning.

[0011] Preferably, the method for cleaning the three-dimensional deformation monitoring data of the derrick using Savitzky-Golay cubic spline interpolation, local outlier factor LOF anomaly detection, and db4 wavelet soft thresholding denoising includes: Missing interpolation and preliminary smoothing: Monitoring data of the derrick , , Three single-dimensional sequences are executed point by point. Savitzky-Golay Filtering: Within a sliding window of width 2m+1=11, where m represents the half-width of the window, a cubic polynomial is used to fit the local data: ; Least squares fit to local data, the coefficient vector is: ; in, For the first in the window i+k The original observations of each epoch, i The index of the current point to be estimated. for Vandermonde matrix, k For relative indexes within the window, p For the order of the polynomial, The coefficients to be determined are: For any missing epoch, the vector of original observations within the window is used. t j ,Will t j Substituting into the cubic polynomial yields the interpolation result. ( t j ); Spatial anomaly detection: Construct a vector from the three-dimensional increments of the same epoch. ,in, They represent the distances in the X, Y, and H directions, respectively. Savitzky-Golay Calculate the deformation increment after processing. k -Locally reachable density in the neighborhood: ; in, The three-dimensional deformation vector of the current center point. It is k - Neighborhood Vector of any neighboring point within, Represents the vector of the nearest point To its first k The Euclidean distance between the nearest neighbor vectors. Then by and The closest k = Consists of 20 points; This leads to the LOF factor: ; when When an outlier is identified, it is removed from the list; the missing location is then re-entered within the same window. Savitzky-GolayPolynomial extrapolation complement; High-frequency noise reduction: The cleaned sequence was decomposed using db4 wavelet J=5 levels, with soft thresholding applied to the detail coefficients. ; in, For the first j Layer k Wavelet detail coefficients, This represents the Euclidean distance, and MAD is the absolute deviation of the median of the detail coefficients. For sequence length, The threshold is used; the reconstructed signal is the following final clean sequence: ; in, The wavelet decomposition level; For the first Layer scaling function basis; For the first j Layer wavelet function basis; These are approximation coefficients.

[0012] Preferably, methods for visually analyzing the spatiotemporal deformation characteristics of the derrick using a ground-state correction model to reveal the derrick deformation trend include: The monitoring data from the initial installation phase is stored as baseline data; By comparing subsequent monitoring data with ground state data, correction terms are calculated for each monitoring point to reflect the magnitude and direction of deformation. A model is constructed using spatiotemporal analysis algorithms to analyze the evolution of deformation in time and space; Based on the correction terms and the spatiotemporal model, a deformation trend diagram is drawn to predict future deformation trends and generate an intuitive deformation analysis report.

[0013] Preferably, the method for calculating the tilt angle of the derrick and sheave using post-cleaning monitoring data, based on the GNSS monitoring station network configuration, includes: Calculation of derrick tilt angle: ; in, a , b These are the parameters to be determined; Calculation of the tilt angle of the sheave wheel: Assume that there are two GNSS monitoring stations, A and B, with a distance of L between them. Obtain the elevation data of A and B from the monitoring data, which are H respectively. A and H B Calculate the elevation difference ΔH = H between the two monitoring points. A -H B ; Assuming the sheave axle was originally horizontal, when it tilts, the angle of tilt is... θ calculate:θ ≈ΔH / L is based on the assumption of a small angle approximation, that is, when the tilt angle is less than a preset threshold, θ ≈tan θ =ΔH / L; If the tilt angle is greater than the preset threshold, the arctangent function is used for calculation, i.e. θ =arctan(ΔH / L).

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention enables high-precision real-time monitoring of derrick deformation and sheave tilt, solving the problems of long monitoring cycles and inability to provide dynamic monitoring and early warning in traditional methods, and providing technical support for safety assessment of derrick structures in mining areas. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a hardware deployment structure diagram of a real-time monitoring system for derrick deformation and sheave tilt based on GNSS technology according to an embodiment of the present invention (the left side is a side view of the derrick monitoring part, and the right side is a front view). Figure 2 This is a flowchart illustrating a real-time monitoring method for derrick deformation and sheave tilt based on GNSS technology, according to an embodiment of the present invention. Detailed Implementation

[0017] 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 embodiments of the present invention, and not all embodiments. 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.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1 like Figure 1 As shown, the present invention provides a real-time monitoring system for derrick deformation and sheave tilt based on GNSS technology, including a GNSS real-time monitoring station network module, a data cleaning module, a spatiotemporal analysis module, and a derrick and sheave tilt monitoring module.

[0020] The GNSS real-time monitoring station network module is used to deploy multiple GNSS monitoring stations at key locations of mine shafts, deploy a monitoring and early warning cloud platform, and receive satellite signals in real time through GNSS receivers and other equipment to collect three-dimensional coordinate data and related monitoring information of the mine shafts, forming a real-time monitoring station network; The data cleaning module is used to perform preprocessing on the collected monitoring data, such as interpolation, outlier removal, and noise reduction, to improve data quality and retain true deformation information. The spatiotemporal analysis module is used to process and analyze the cleaned data using model methods such as ground state correction, revealing the spatiotemporal evolution law of derrick deformation; The derrick and sheave tilt monitoring module is used to calculate the tilt angle of the derrick and sheave in the mining area, analyze the tilt pattern, and detect anomalies in a timely manner. Furthermore, in this invention, the GNSS real-time monitoring station network module consists of one GNSS reference station, eight GNSS monitoring stations, and a monitoring and early warning cloud platform. Each GNSS real-time monitoring station is equipped with a high-precision GNSS receiver and a wireless communication module. The GNSS reference station is located at a stable position 1-3 km away from the GNSS monitoring stations to provide accurate reference data. The GNSS monitoring stations are distributed in a specific layout at key parts of the derrick. There are two upper sheave platforms, located on both sides of the sheave axis; there are six lower sheave platforms, two on both sides of the lower sheave axis and four at the four corners of the lower sheave platform. The eight GNSS monitoring stations are used to monitor the deformation (surface displacement and uneven settlement) of key parts of the derrick. At the same time, the GNSS monitoring stations on both sides of the sheave axis are used to monitor the tilt of the sheave, and the GNSS monitoring stations at the four corners of the lower sheave platform are used to monitor the tilt of the lower sheave platform. The monitoring and early warning cloud platform receives GNSS observation data and uses technologies such as NRTK, RT-PPP, and PPP-RTK to monitor the derrick in real time. Through integrated data management and analysis tools, it achieves real-time monitoring and early warning of derrick deformation. Each GNSS monitoring station is connected to the monitoring and early warning cloud platform through a wireless communication module to transmit data in real time, building a stable and reliable monitoring network to ensure the acquisition of comprehensive and accurate derrick deformation data. At the same time, it automatically collects three-dimensional coordinate data and related monitoring information of the mine derrick at different times according to preset time intervals, providing complete and continuous raw data support for subsequent data processing and analysis.

[0021] Furthermore, in this invention, the data cleaning module consists of an outlier detection unit, an interpolation unit, and a high-frequency denoising unit. The outlier detection unit, based on the Local Outlier Factor (LOF) algorithm, accurately identifies outlier data points in three-dimensional space and removes them. The interpolation unit uses the Savitzky-Golay smoothing interpolation method to reasonably estimate and fill missing or abnormal monitoring data points while preserving the actual micro-oscillations during derrick operation. Subsequently, the high-frequency denoising unit uses db4 wavelet soft thresholding denoising technology to further filter out high-frequency noise and output a high-fidelity clean sequence.

[0022] Furthermore, in this invention, the spatiotemporal analysis module comprises a ground-state data unit, a correction term calculation unit, a spatiotemporal modeling unit, and a deformation trend analysis unit. The ground-state data unit stores monitoring data from the initial installation phase as ground-state data. The correction term calculation unit compares subsequent monitoring data with the ground-state data to calculate the correction term for each monitoring point, reflecting the magnitude and direction of deformation. The spatiotemporal modeling unit uses spatiotemporal analysis algorithms to construct a model and analyze the evolution of deformation in time and space. The deformation trend analysis unit, based on the correction term and the spatiotemporal model, plots a deformation trend diagram, predicts future deformation trends, and generates an intuitive deformation analysis report, providing a scientific basis for risk assessment and decision-making, and helping to understand the spatiotemporal evolution characteristics of derrick deformation in a timely manner.

[0023] Furthermore, in this invention, the derrick and sheave tilt analysis module is composed of a tilt calculation unit. The tilt calculation unit focuses on determining the tilt angle of the sheave platform and the sheave itself. For the sheave platform, by analyzing the three-dimensional coordinate data of its surrounding monitoring points, a local coordinate system is established using the most stable monitoring point as the origin. A plane is fitted, and the tilt angle of the platform is obtained by calculating the angle between the plane's normal vector and the horizontal plane. The projection of the normal vector onto the horizontal plane is the tilt direction. For the sheave, the focus is on the ratio of the elevation difference to the distance between the monitoring points on both sides of the sheave shaft. The tilt angle of the sheave is calculated using the arctangent function, and the tilt direction is determined by combining this with the three-dimensional coordinate data. The tilt calculation unit integrates this data to provide accurate tilt information, providing crucial basis for subsequent risk assessment and early warning, ensuring the safe and stable operation of the derrick and sheave.

[0024] Example 2 like Figure 2 As shown, this invention provides a real-time monitoring method for derrick deformation and sheave tilt based on GNSS technology, implemented using the system described in Embodiment 1, and includes the following steps: (1) Deployment of GNSS real-time monitoring station network The GNSS real-time monitoring network deployment takes "high-precision, real-time, and automated deformation monitoring of the derrick" as its core objective. Combining the spatial characteristics of the steel structure derrick, the electromagnetic and climatic environment of the mining area, and the need for maintenance-free operation in the later stage, it adopts an architecture of "1 base station + 8 monitoring stations". Through satellite navigation and positioning system monitoring, redundant power supply, wireless communication and high-level waterproof and lightning protection, a long-term stable all-weather monitoring network is constructed.

[0025] The base station is located on a rooftop or bedrock area with a stable foundation, open view, and no electromagnetic interference, within 1–3 km of the derrick. It uses a concrete base, and the antenna installation height meets specifications, keeping it away from strong electromagnetic sources. It is equipped with a high-precision satellite receiver supporting multiple systems and frequencies. The power supply system uses a combination of solar panels, energy storage batteries, and a controller to ensure continuous operation for several days, even during continuous cloudy or rainy weather. Communication uses a wireless data terminal module, supporting remote access to a fixed address. The chassis is made of stainless steel, with built-in surge protectors, and the grounding resistance meets specifications, providing the entire unit with a high level of protection.

[0026] A total of eight monitoring stations are deployed, arranged according to the principles of key nodes and symmetrical distribution: two monitoring stations are located on the upper sheave platform, symmetrically positioned on both sides of the sheave axis; six monitoring stations are located on the lower sheave platform, with four at the four corners and two on both sides of the sheave axis. These eight monitoring stations are used to monitor the deformation (surface displacement and uneven settlement) of key parts of the derrick. Simultaneously, four monitoring stations around the platform monitor the platform's tilt, and four monitoring stations on both sides of the sheave axis monitor the tilt of the sheave. The four monitoring stations around the platform are welded to the supporting brackets, extending half a meter outward from the derrick. The monitoring stations on both sides of the sheave are also welded to the supporting brackets and extend outward a certain distance. This reduces the impact of multipath interference on the monitoring data and accurately measures the correction value between the antenna phase center and the monitoring position on the derrick surface, facilitating subsequent data processing. The monitoring station's main unit uses a miniaturized, low-power satellite receiver, powered by mains electricity (with cables protected by flexible conduits), and a backup power source of lithium batteries. It supports low-power operation and sleep / wake-up modes, and the sampling frequency is adjustable. Encrypted data is uploaded to the monitoring and early warning cloud platform via a wireless communication module.

[0027] The monitoring and early warning cloud platform receives GNSS observation data and uses technologies such as NRTK, RT-PPP, and PPP-RTK to monitor the derrick in real time. Through integrated data management and analysis tools, it achieves real-time monitoring and early warning of derrick deformation. Each monitoring station is connected to the monitoring and early warning cloud platform through a wireless communication module to transmit data in real time, building a stable and reliable monitoring network to ensure the acquisition of comprehensive and accurate derrick deformation data. At the same time, it automatically collects three-dimensional coordinate data and related monitoring information of the mine derrick at different times according to preset time intervals, providing complete and continuous raw data support for subsequent data processing and analysis.

[0028] (2) Preprocessing of measurement data from monitoring stations Due to the characteristics of the derrick damping pendulum and the influence of GNSS multipath propagation, the original sequence simultaneously contains random missing data, outliers, and high-frequency noise. To ensure the accuracy of subsequent modeling, a method was adopted... Savitzky-Golay The original data is preprocessed using a concatenated processing strategy of "smooth interpolation - LOF spatial anomaly detection - db4 wavelet soft thresholding denoising". The specific process is as follows.

[0029] 1) Missing interpolation and preliminary smoothing Monitoring data of the derrick , , Three single-dimensional sequences are executed point by point. Savitzky-Golay Filtering: Within a sliding window with a width of 2m+1=11 epochs (m=5, representing the half-width of the window), the following cubic polynomial is used to fit the local data: ; in, For the first in the window i+k The original observations of each epoch, i The index of the current point to be estimated. k For relative position index within the window, p For the order of the polynomial, The coefficients to be determined are denoted as .

[0030] Least squares fit to local data, the coefficient vector is: ; in, for Vandermonde matrix, This is the vector of original observations within the window. For any missing epoch... t j ( j (Indicates the index of the missing point in the global time series), t j Substituting into the cubic polynomial yields the interpolation result. For the X direction, this result can be denoted as ( t j ).

[0031] 2) Spatial Anomaly Detection Construct a vector from the three-dimensional increments of the same epoch. ,in They represent the distances in the X, Y, and H directions, respectively. Savitzky-Golay The processed deformation increment is compared to the original within an 11-epoch sliding window. , , A cubic polynomial least squares fitting is performed point-by-point, and the window center value (or extrapolation value) is used as the current result. Missing points are filled in simultaneously, resulting in three clean, continuous, and smooth sequences that retain the true vibration signal, which are then used for subsequent three-dimensional spatial anomaly detection. Then, calculations are performed... k - Neighborhood ( k =20) Locally accessible density: ; in, The three-dimensional deformation vector of the current center point. It is k - Neighborhood Vector of any neighboring point within, Represents the vector of the nearest point To its first k The Euclidean distance between the nearest neighbor vectors. Then by and The closest k = Consists of 20 points.

[0032] This leads to the LOF factor: ; when When an outlier is identified, it is removed from the list; the missing location is then re-entered within the same window. Savitzky-Golay Polynomial extrapolation complement.

[0033] 3) High-frequency noise reduction The cleaned sequence was decomposed using db4 wavelet J=5 levels, with soft thresholding applied to the detail coefficients. ; in, For the first j Layer k Wavelet detail coefficients, This represents the Euclidean distance, and MAD is the absolute deviation of the median of the detail coefficients. For sequence length, The threshold is used; the reconstructed signal is the following final clean sequence: ; in, The wavelet decomposition level; For the first Layer scaling function basis; For the first j Layer wavelet function basis; These are approximation coefficients.

[0034] (3) Spatiotemporal analysis of monitoring station measurement data This system performs a three-step process analysis on the monitoring station data: first, preliminary surface modeling; then, further analysis using a ground-state correction model; and finally, on-site optimization of the receiver installation location based on the analysis results. The process is as follows: ① For the eight GNSS monitoring stations on the derrick, the aim is to visualize and analyze their three-dimensional deformation. , , In cases where the deformation data is not fully understood, preliminary modeling of the 3D deformation data needs to be considered. Based on the cleaned GNSS 3D deformation time series, a cubic spline interpolation surface model is established for the 3D deformation of the upper and lower sheaves and the lower sheave platform monitoring station using visualization software. Discrete observations are resampled into continuous surfaces. Subsequently, a cumulative deformation surface map is drawn in the same coordinate system, and the spatial undulations of each monitoring point are visually displayed using contour line color differences. Displacement and settlement are assessed based on the overall deformation undulation image. During the assessment, the deformation magnitude is first determined by the surface extrema and gradient: if the deformation is small and the gradient change is gentle, it is considered "small and uniform"; if local convexity or gradient abruptness occurs, it is delineated as an "significant settlement / lateral displacement" anomaly area. ② To better analyze the deformation patterns, for deformation data involving temporal and spatial changes, a spatiotemporal big data model is specifically considered for analysis. For each spatiotemporal model, considering cost, data redundancy, and intuitiveness, the ground state correction model is used to process the monitoring data. Based on the theory of the ground-state correction model, in analyzing the variation characteristics of monitoring station time-series data relative to a specified time, only the position information at that time and the position changes at other times relative to that specified time need to be stored. This eliminates the need to store all position information at other times. Furthermore, thanks to the freedom in selecting ground-state data points, this model allows for the selection of different starting points and the observation of the movement trajectory relative to that point over subsequent time periods, highlighting the spatiotemporal visualization of deformation monitoring. A model is established to quantitatively study the displacement of each monitoring station relative to the ground-state data (assuming no position change, and setting the first epoch of data received after equipment installation as the ground state), i.e., the correction term. The degree of derrick deformation is then visualized and analyzed based on the cohesiveness of the correction term. Finally, addressing the identified problems, the GNSS monitoring stations around the derrick's undercarriage platform and on both sides of the undercarriage were adjusted to suitable positions. The monitoring stations were extended and fixed outwards by welding T-shaped steel structure support brackets, reducing signal obstruction at the source and optimizing monitoring conditions.

[0035] (4) Monitoring the tilt of the derrick and sheave Displacement and uneven settlement of the derrick surface need to be obtained through monitoring data from the monitoring and early warning cloud platform, and the monitoring of the tilt of the derrick and sheave needs to be implemented based on this monitoring data.

[0036] 1) Derrick tilt calculation ① Select a reference point Stability Assessment: Historical data from four monitoring stations on the derrick's sheave platform were analyzed to assess its stability. A monitoring station with minimal deformation and stable data fluctuations was selected as a reference point. For example, assuming that the deformation data of monitoring station JC01 fluctuated the least during the monitoring period and its position was relatively fixed compared to other monitoring stations, JC01 could be selected as the reference point.

[0037] Record initial coordinates: Record the three-dimensional coordinates of the reference point at the start of monitoring, as the benchmark for subsequent deformation calculations. Assume that at the initial moment, the coordinates of the reference point are ( , , ).

[0038] ② Calculate relative displacement Data Acquisition: Acquire the three-dimensional coordinate data of the other three monitoring stations (JC02, JC03, and JC04) during the monitoring period.

[0039] Calculate relative displacement: For each monitoring station, at each monitoring time Calculate its relative displacement with respect to the reference point.

[0040] ; ③ Establish a local coordinate system Set the origin: Set the reference point JC01 as the origin of the local coordinate system, and set its coordinates to (0, 0, 0).

[0041] Determine the coordinate axis orientation: Based on the structure of the derrick and the distribution of monitoring stations, determine the orientation of the X, Y, and Z axes of the local coordinate system. For example, the X-axis can be pointed to one of the main extension directions of the derrick, the Y-axis can be pointed to another main extension direction perpendicular to the X-axis, and the Z-axis can be perpendicular to the XY plane and point towards the zenith.

[0042] Coordinate transformation: The relative displacement data of the other three monitoring stations are transformed to this local coordinate system, that is, the coordinates in the local coordinate system are: ; ④ Fitting the inclined surface Data point collection: Collect the coordinate data of the three monitoring stations in the local coordinate system to form three data points.

[0043] Fitting method selection: The least squares method can be used to fit the inclined surface. Assume the equation of the inclined surface is: ; in, a , b , c These are the parameters to be determined.

[0044] Constructing a system of equations: Based on the coordinate data from the three monitoring stations, construct the following system of equations: ; By solving this system of equations, the parameters can be obtained. a , b , c The value of is used to determine the equation of the inclined surface.

[0045] ⑤ Calculate the tilt value Calculate the normal vector: Based on the fitted equation of the inclined surface, its normal vector can be obtained as ( a , b , -1).

[0046] Calculate the tilt angle: Tilt angle θ It can be calculated using the angle between the normal vector and the vertical direction (Z-axis direction), as shown in the formula: ; This angle is the tilt angle of the sheave platform.

[0047] 2) Calculation of the tilt of the sheave wheel Determine the specific locations of the monitoring stations on both sides of the sheave shaft, ensuring they are on the same axis and equidistant from the center of the sheave shaft. Assume these two monitoring stations are A and B, and the distance between them is L. Obtain the elevation data of A and B from the monitoring data, denoted as H. A and H B Calculate the elevation difference ΔH = H between the two monitoring points. A -H B .

[0048] Assuming the sheave shaft was originally horizontal, when it tilts, the tilt angle θ can be calculated using the following formula: θ ≈ΔH / L, this formula is based on the assumption of a small angle approximation, that is, when the tilt angle is small, θ ≈tan θ =ΔH / L. If the tilt angle is large, the arctangent function is used for calculation, i.e. θ =arctan(ΔH / L).

[0049] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A real-time monitoring system for derrick deformation and sheave tilt based on GNSS technology, characterized in that, The system includes: a GNSS real-time monitoring station network module, a data cleaning module, a spatiotemporal analysis module, and a derrick and sheave tilt monitoring module; GNSS real-time monitoring station network module is used to collect three-dimensional deformation monitoring data of the derrick in real time; The data cleaning module is used to clean the three-dimensional deformation monitoring data of the derrick. The spatiotemporal analysis module is used to visualize and analyze the spatiotemporal deformation characteristics of the derrick by combining the ground state correction model, and to obtain the deformation trend of the derrick. The derrick and sheave tilt monitoring module is used to calculate the tilt angle of the derrick and sheave based on the GNSS monitoring station network configuration and the three-dimensional deformation monitoring data of the derrick after cleaning. The derrick and sheave tilt analysis module consists of a tilt calculation unit; the tilt calculation unit is used to determine the tilt angle of the sheave platform and the sheave. For the sheave platform, by analyzing the three-dimensional coordinate data of its surrounding monitoring points, a local coordinate system is established with the most stable monitoring point as the origin, and a plane is fitted. The equation of the plane is: ;in, a , b , c These are the parameters to be determined; Based on the coordinate data from the three monitoring stations, a system of equations was constructed and solved to obtain the parameters. a , b , c The value of is used to determine the equation of the inclined surface; Based on the fitted equation of the inclined surface, its normal vector is obtained as ( a , b (, -1); The tilt angle of the platform can be obtained by calculating the angle between the plane normal vector and the horizontal plane. The projection of the normal vector onto the horizontal plane is the tilt direction. For the sheave wheel, the tilt angle of the sheave wheel is calculated using the arctangent function based on the ratio of the elevation difference to the distance between the monitoring points on both sides of the sheave wheel axle, and the tilt direction is determined by combining the three-dimensional coordinate data. Specifically: Calculation of derrick tilt angle: ; in, a , b The parameter to be determined; The calculation of the tilt angle of the crown wheel: assuming that two GNSS monitoring stations are A and B respectively, the distance between them is L, the elevation data of A and B are obtained from the monitoring data, which are H A and H B respectively, and the elevation difference ΔH=H A -H B of the two monitoring points is calculated. Assuming the sheave axle was originally horizontal, when it tilts, the angle of tilt is... θ calculate: θ ≈ΔH / L is based on the assumption of a small angle approximation, that is, when the tilt angle is less than a preset threshold, θ ≈tan θ =ΔH / L; If the tilt angle is greater than the preset threshold, the arctangent function is used for calculation, i.e. θ =arctan(ΔH / L).

2. The system according to claim 1, characterized in that, The GNSS real-time monitoring network module consists of one GNSS reference station, eight GNSS monitoring stations, and a monitoring and early warning cloud platform. Each GNSS real-time monitoring station is equipped with a GNSS receiver and a wireless communication module. A GNSS reference station is located 1-3 km away from the GNSS monitoring stations to provide reference data. The GNSS monitoring stations are distributed according to a predetermined layout at key parts of the derrick. There are two stations on the upper sheave platform, located on both sides of the sheave axis; and six stations on the lower sheave platform, two on both sides of the lower sheave axis and four at the four corner points of the lower sheave platform. Eight GNSS monitoring stations are used to monitor deformation of key parts of the derrick. Simultaneously, the GNSS monitoring stations on both sides of the sheave axis monitor the tilt of the sheave, and the GNSS monitoring stations at the four corner points of the lower sheave platform monitor the tilt of the lower sheave platform. The monitoring and early warning cloud platform receives GNSS observation data and uses network real-time dynamic positioning (NRTK), real-time precise single-point positioning (RT-PPP), and precise single-point real-time dynamic positioning (PPP-RTK) technologies to monitor the derrick in real time. Through integrated data management and analysis tools, it achieves real-time monitoring and early warning of derrick deformation. Each GNSS real-time monitoring station connects to the monitoring and early warning cloud platform via a wireless communication module to transmit data in real time, build a monitoring network, acquire derrick deformation data, and automatically collect three-dimensional coordinate data and related monitoring information of the mine derrick at different times according to preset time intervals.

3. The system according to claim 1, characterized in that, The data cleaning module consists of an outlier detection unit, an interpolation unit, and a high-frequency noise reduction unit; The outlier detection unit is used to identify and remove outlier data points in three-dimensional space based on the Local Outlier Factor (LOF) algorithm. Interpolation unit, used to estimate and fill missing or abnormal monitoring data points while preserving the true micro-oscillations during derrick operation using Savitzky-Golay smoothing interpolation method; The high-frequency denoising unit is used to filter out high-frequency noise using the db4 wavelet soft thresholding denoising technique, and output a high-fidelity clean sequence.

4. The system according to claim 1, characterized in that, The spatiotemporal analysis module consists of a ground-state data unit, a correction term calculation unit, a spatiotemporal modeling unit, and a deformation trend analysis unit; The ground-state data unit is used to store monitoring data from the initial installation phase as ground-state data. The correction term calculation unit is used to compare subsequent monitoring data with ground state data, calculate the correction term for each monitoring point, and reflect the magnitude and direction of deformation. The spatiotemporal modeling unit is used to construct models using spatiotemporal analysis algorithms to analyze the evolution of deformation in time and space. The deformation trend analysis unit is used to draw deformation trend diagrams, predict future deformation trends, and generate intuitive deformation analysis reports based on correction terms and spatiotemporal models.

5. A method for real-time monitoring of derrick deformation and sheave tilt based on GNSS technology, wherein the method is implemented using the system described in any one of claims 1-4, characterized in that... The method includes: By deploying one GNSS reference station and eight GNSS monitoring stations, a monitoring and early warning cloud platform is established to form a GNSS monitoring network and collect real-time three-dimensional deformation monitoring data of the derrick. Savitzky-Golay cubic spline interpolation, local outlier factor LOF anomaly detection, and db4 wavelet soft thresholding denoising method were used to clean the three-dimensional deformation monitoring data of the derrick. By combining the ground state correction model, the spatiotemporal deformation characteristics of the derrick are visualized and analyzed to reveal the deformation trend of the derrick; Based on the GNSS monitoring station network configuration, the tilt angles of the derrick and sheave are calculated using the monitoring data after cleaning.

6. The method according to claim 5, characterized in that, The methods for cleaning derrick three-dimensional deformation monitoring data using Savitzky-Golay cubic spline interpolation, local outlier factor LOF anomaly detection, and db4 wavelet soft thresholding denoising include: Missing interpolation and preliminary smoothing: Monitoring data of the derrick , , Three single-dimensional sequences are executed point by point. Savitzky-Golay Filtering: Within a sliding window of width 2m+1=11, where m represents the half-width of the window, a cubic polynomial is used to fit the local data: ； Least squares fit to local data, the coefficient vector is: ； in, For the first in the window i+k The original observations of each epoch, i The index of the current point to be estimated. for Vandermonde matrix, k For relative indexes within the window, p For the order of the polynomial, The coefficients to be determined are: For any missing epoch, the vector of original observations within the window is used. t j ,Will t j Substituting into the cubic polynomial yields the interpolation result. ( t j ); Spatial anomaly detection: Construct a vector from the three-dimensional increments of the same epoch. ,in, They represent the distances in the X, Y, and H directions, respectively. Savitzky-Golay Calculate the deformation increment after processing. k -Locally reachable density in the neighborhood: ； in, The three-dimensional deformation vector of the current center point. It is k - Neighborhood Vector of any neighboring point within, Represents the vector of the nearest point To its first k The Euclidean distance between the nearest neighbor vectors. Then by and The closest k = Consists of 20 points; This leads to the LOF factor: ； when When an outlier is identified, it is removed from the list; the missing location is then re-entered within the same window. Savitzky- Golay Polynomial extrapolation complement; High-frequency noise reduction: The cleaned sequence was decomposed using db4 wavelet J=5 levels, with soft thresholding applied to the detail coefficients. ； in, For the first j Layer k Wavelet detail coefficients, This represents the Euclidean distance, and MAD is the absolute deviation of the median of the detail coefficients. For sequence length, The threshold is used; the reconstructed signal is the following final clean sequence: ； in, The wavelet decomposition level; For the first Layer scaling function basis; For the first j Layer wavelet function basis; These are approximation coefficients.

7. The method according to claim 5, characterized in that, Methods for visually analyzing the spatiotemporal deformation characteristics of the derrick using a ground-state correction model to reveal its deformation trend include: The monitoring data from the initial installation phase is stored as baseline data; By comparing subsequent monitoring data with ground state data, correction terms are calculated for each monitoring point to reflect the magnitude and direction of deformation. A model is constructed using spatiotemporal analysis algorithms to analyze the evolution of deformation in time and space; Based on the correction terms and the spatiotemporal model, a deformation trend diagram is drawn to predict future deformation trends and generate an intuitive deformation analysis report.

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