Stay wire space safety early warning method based on three-dimensional topographic data

Abstract

The invention provides a stay wire space safety early warning method based on three-dimensional topographic data, and relates to the technical field of power transmission line construction safety. The method comprises the following steps: acquiring a point cloud and completing three-dimensional coordinate fusion in combination with a BIM model; constructing multi-body dynamics and a finite element model, and calculating tension and a safety coefficient; adopting a hierarchical bounding box method to extract the minimum distance between the stay wire and the obstacle; sensors are arranged at key parts to collect tension and postures; fusing simulation and actual measurement data, and estimating a structure state through Kalman filtering; evaluating the security level and giving out early warning and adjustment suggestions; displaying the stress vector and the risk thermodynamic diagram on a three-dimensional platform; a real-time monitoring mechanism is established, and an abnormal automatic pushing scheme is provided; and constructing a fault tree and a Bayesian network to identify disaster-causing factors and fault paths. According to the invention, integrated evaluation of stay wire space and stress safety is realized, the detection precision and the intelligent early warning capability are improved, and the method is suitable for multiple scenes of iron tower construction and operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of power transmission line construction safety technology, and in particular to a method for early warning of safety in the guy wire space based on three-dimensional terrain data. Background Technology

[0002] As a crucial safety support system in the construction of suspended pylon towers, the guyed structure plays a key role in ensuring construction safety through its stress stability and rational spatial layout. Currently, the placement and tension control of guyed wires in transmission line construction mainly rely on construction experience and simplified mechanical calculations, lacking a comprehensive analysis mechanism with high precision, multi-scenario capabilities, and dynamic response. This leads to safety hazards such as guyed wire interference, overload, and anchorage instability in complex terrain.

[0003] In the existing technology, the methods for spatial safety and stress analysis of guy wires mainly focus on the following aspects: first, manual wiring and tension estimation based on static geometric models; second, single-point simulation analysis using finite element software; and third, local monitoring by manually deploying single-point sensors such as strain gauges. These methods have the following defects: (1) lacking high-precision modeling methods for complex three-dimensional terrain at the construction site, making it impossible to detect spatial collisions between guy wires and surrounding obstacles; (2) the simulation process is detached from the real-time working conditions at the site, failing to reflect the dynamic changes in tension and the fluctuations in stress at anchor points during the construction process; (3) the distribution of sensing data is sparse, greatly affected by interference, and unable to form a continuous and stable safety assessment system; (4) the assessment results are difficult to present intuitively, lacking real-time visualization and early warning capabilities, making it difficult for construction personnel to grasp potential risks in a timely manner.

[0004] To address the aforementioned issues, some studies have attempted to introduce UAV lidar, BIM modeling, finite element analysis, and sensor monitoring technologies. However, most of these studies remain at the level of single-module applications, lacking a systematic solution that integrates 3D modeling, spatial interference analysis, dynamic tension simulation, sensor data fusion, visualization evaluation, and intelligent early warning. This makes it difficult to meet the comprehensive analysis requirements for both the safety of the guy wire space and the stress in suspended pole construction scenarios. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to propose a spatial safety early warning method for guy wires based on three-dimensional terrain data. This method integrates spatial interference detection and dynamic stress assessment throughout the entire guy wire construction process, while improving the accuracy of guy wire deployment and the real-time performance of safety assessment. It can be widely applied in high-risk construction scenarios such as suspended pole erection for transmission lines, providing reliable technical support and intelligent decision-making basis for on-site operations.

[0006] This invention proposes a spatial safety early warning method based on three-dimensional terrain data, comprising the following steps:

[0007] S1. Use UAV LiDAR to acquire point cloud data of the construction site, generate a three-dimensional terrain model, and import it into the tower BIM model for coordinate fusion.

[0008] S2. Based on multibody dynamics and finite element analysis, a simulation model is constructed to establish the dynamic relationship between the tension of the tension wire and the safety factor, and multiple safety thresholds are set to obtain simulation data.

[0009] S3. Use the hierarchical bounding box method to perform spatial division and collision detection, and extract the minimum safe distance between the wire and the obstacle.

[0010] S4. Install fiber optic grating sensors and IMUs at key locations of the tension line to collect real-time tension and attitude data.

[0011] S5. Merge simulation data and real-time data, use Kalman filtering for state correction and dynamic estimation, and generate fused results;

[0012] S6. Conduct a security assessment based on the fusion results and generate early warning judgment results;

[0013] S7. Display the force vector and risk heat map of the guy wire through a 3D visualization platform, supporting interactive viewing;

[0014] S8. Set up a real-time monitoring and early warning mechanism to automatically push adjustment suggestions when the tension is abnormal;

[0015] S9. Construct a fault tree and transform it into a Bayesian network model. Based on the adjustment suggestions, reason and analyze the system's safety status, and identify key disaster-causing factors and potential failure paths.

[0016] Preferably, step S1 specifically includes:

[0017] S11. Use a drone equipped with a lidar scanning system to conduct multi-angle aerial scanning of the construction site to obtain point cloud data of the construction site with centimeter-level accuracy. The point cloud data of the construction site includes the topography of the construction site, the location of the tower base, and surrounding obstacles.

[0018] S12. The acquired point cloud data of the construction site is registered. The multi-view point cloud data is aligned by the iterative nearest point algorithm to generate a unified and continuous full-scene 3D point cloud model.

[0019] S13. The point cloud data is processed using a surface reconstruction algorithm to generate a continuous and high-precision three-dimensional terrain mesh model. Each point in the model has unique spatial coordinates (x, y, z).

[0020] S14. Import the tower BIM model provided by the design institute. The model includes the tower structural component number, component size, installation location, installation direction, and guy wire anchor point information.

[0021] S15, Extract the coordinates of the guy wire anchor point (x a ,y a ,z a and connecting direction vector The tower BIM model and the 3D terrain model were uniformly converted to the construction coordinate system. s ;

[0022] S16. Establish the initial input parameters for the guy wire spatial path, including the anchoring endpoints. Tower connection point Calculate the unit direction vector of the guy wire

[0023] S17. Complete the data fusion of the tower BIM model, 3D point cloud model and 3D terrain mesh model to provide a unified spatial basis for simulation analysis and collision detection.

[0024] Preferably, in step S2, establishing the dynamic relationship between the tension of the pull wire and the safety factor specifically includes:

[0025] Establish a load model to simulate the external loads borne by the guy wire under actual construction or operation conditions, including the conductor gravity load G and wind pressure load P:

[0026] G=γ1·A·L v

[0027] P=γ4·A·L p ·cosα·sinθ

[0028] Where γ1 and γ4 are the gravity and wind pressure ratios, respectively, and L v L p These represent the vertical span and the length of the wind pressure action, respectively; cosα and sinθ represent the projection of the wind load onto the direction of the tension wire; and θ is the wind direction angle.

[0029] Solving for the tension F of each segment of the tension wire under given load conditions i Calculate the stress σ i :

[0030]

[0031] Construct the initial static form-finding state of the guy wire, assuming that the deformation curve of the guy wire under horizontal tension H under a uniformly distributed load q satisfies:

[0032]

[0033] Where ρ is the guy wire span, used to control the initial guy wire configuration in the simulation, and the horizontal tension H is inferred from the mid-span sag f:

[0034]

[0035] A safety factor calculation model is introduced to determine whether the current tension segment is in a safe state by comparing the ultimate bearing capacity of the material with the actual stress level.

[0036] The safety factor is calculated as follows:

[0037]

[0038] The safety factor is set with multiple thresholds η1, η2, and η3 to determine whether the tension state of the guy wire is normal, warning, or dangerous.

[0039] Preferably, step S2 further includes:

[0040] Output tension change sequence {T (t)}、Safety factor sequence {K (t)}、Minimum safe distance sequence {d (t) The data is used as simulation data for subsequent state fusion and evaluation analysis.

[0041] Preferably, step S3 specifically includes:

[0042] S31, Spatial foundation and coordinates of anchor points based on data fusion (x a ,y a ,z a Construct the wire space path and generate the corresponding wire envelope B. i ;

[0043] S32. Pull the wire envelope B i Perform spatial Boolean intersection with the 3D terrain mesh model to identify the interference area between the path and the terrain, and output the results of potential collision risks;

[0044] S33, For each draw wire envelope B i With obstacle envelope B j Perform a traversal check to determine if there is an intersection relationship.

[0045] S34. For the line segments that are determined to have a spatial proximity relationship, further calculate the spatial path envelope B of the line segments. i With obstacle envelope B j The minimum spatial distance d between them ij The calculation formula is as follows:

[0046]

[0047] in, These represent the wire envelope B. i With obstacle envelope B jThe set of boundary points, where |pq| represents the Euclidean distance between pairs of points;

[0048] S35. Calculate the minimum safe distance d for all pull lines in the current simulation frame. ij Summarized into a safety spacing vector D seg , and the preset minimum distance threshold d min Comparisons are made for subsequent safety assessments and early warning determinations.

[0049] S36. If D exists seg <d min If the risk is identified as a potential spatial interference risk, its spatial location will be marked for use in the risk heatmap of the 3D visualization platform.

[0050] Preferably, step S4 specifically includes:

[0051] S41. Fiber Bragg grating sensors and inertial measurement units are installed at the connection points between the guy wire and the tower and at the anchoring points to form a distributed sensor network for acquiring guy wire operating status information.

[0052] S42. Fiber Bragg grating sensors are used to monitor the axial strain and temperature changes of the draw wire in real time, recording the wavelength shift Δλ. i =λ i -λ0, where λ i To measure the wavelength, λ0 is the initial wavelength;

[0053] S43, through the sensitivity coefficient K s The wavelength drift is converted into a tension value, and the tension calculation formula is as follows:

[0054]

[0055] S44, The inertial measurement unit is used to collect the dynamic response behavior of the guy wire at the guy wire connection point;

[0056] S45. Simultaneously collect tension, acceleration, temperature, and attitude data of each sensing node during the sampling period to construct a sensing observation matrix.

[0057]

[0058] S46. Transfer the sensor observation matrix It is used as the input for subsequent state estimation and Kalman filtering fusion to support real-time identification and dynamic modeling analysis of stress state.

[0059] Preferably, step S5 specifically includes:

[0060] S51, the tension change sequence {T (t)}、Safety factor sequence {K (t)}、Minimum safe distance sequence {d (t)} as the input to the predicted state, forming the predicted state vector

[0061]

[0062] in, T represents the prior predicted state at time k. k SF is the estimated value of the tension in the guy wire calculated by the simulation model. k D is the safety factor estimate obtained through the fusion of simulation analysis and real-time data. k This is the minimum spatial distance estimate calculated based on the fusion of real-time sensor data and simulation data;

[0063] S52, Transfer the sensor observation matrix As the observation input, construct the observation input vector z. k :

[0064]

[0065] in, a is the real-time tension measurement value of the pull wire acquired by the fiber optic sensor. k For real-time acquisition of triaxial acceleration observations, θ k The angle of the pull line attitude;

[0066] S53. Employ the extended Kalman filter algorithm based on the predicted state vector. With the observed input vector z k Perform prediction error calculation and state correction, and update the posterior state estimation vector:

[0067]

[0068] in, For the updated posterior state estimate, K k Here, h is the Kalman gain matrix, and h(·) is the observation function used to map the predicted state to the observation space.

[0069] S54. Predict the state vector using the extended Kalman filter algorithm. With the observed input vector z k Perform fusion processing to output the estimated tension value of the draw wire. Safety factor estimate Minimum spatial distance estimate These three factors together constitute the fusion result of this method, which is used to characterize the changing trend of the mechanical state and spatial safety state of the guy wire structure at the current moment, and serves as the direct input basis for subsequent risk assessment and safety judgment. At the same time, the above estimated values ​​are recorded in chronological order to form a dynamic tension sequence, a dynamic safety factor sequence, and a dynamic spatial distance sequence, which are used for three-dimensional force vector drawing and risk level identification.

[0070] Preferably, step S6 specifically includes:

[0071] S61, Based on tension estimation Safety factor estimate Minimum spatial distance estimate Construct an assessment model for security level classification;

[0072] S62, Introducing the safety factor η i Computational model:

[0073]

[0074] Where σ max Let A be the ultimate stress of the wire material, and A be the cross-sectional area. This is the current tension estimate;

[0075] S63. Set multi-level security thresholds η1, η2, η3 and spatial distance threshold d. min ,d warn The overall risk level R is determined according to the following rules. i (t);

[0076] S64. Calculate the comprehensive risk for all guy wire segments, use it for structural safety assessment, and set the target safety factor η. * η is used to represent the minimum safety margin required for a structure under stress. * Pre-set by user requirements;

[0077] S65. For line segment i with a risk level of 1 or higher, according to the target safety factor η * Calculate tension adjustment recommendations

[0078]

[0079] S66, Risk level R i (t), tension estimate Tension adjustment recommendations The output is simultaneously sent to the visualization system and the security control module.

[0080] Preferably, step S7 specifically includes:

[0081] S71. Construct an integrated 3D visualization platform that integrates 3D terrain models, tower BIM structural models, and tension estimates. Safety factor estimate Minimum spatial distance estimate and risk level R i (t) Spatial fusion of data;

[0082] S72. Based on the unit direction vector of each wire segment and tension estimates Draw a 3D force vector diagram, with the vector representation as follows:

[0083]

[0084] in Let be the force vector at time t, with its direction aligned with the tension line and its length proportional to the magnitude of the tension.

[0085] S73. Risk level R generated based on step S6 i (t), define the heatmap color encoding function:

[0086] H i (t)=f(R i (t))

[0087] Where H i (t) represents the color value of the heat map used for visualization. Different risk levels correspond to different colors: green indicates normal, yellow indicates warning, orange indicates attention, and red indicates danger.

[0088] S74, Transform the force vector It binds the risk heatmap to the spatial path structure of each guy line, realizes time-varying 3D rendering display, and supports user interactive operation, including guy line number selection, hazard level filtering, visibility control, guy line segment highlighting, dynamic scaling and rotation;

[0089] S75. Construct a timeline playback module that supports t∈[t0,t... N The historical tension status and risk level evolution process within the interval are visualized, and the output tension evolution map, risk level time series map, force vector dynamic map and spatial situation thermal animation are provided to assist construction decision-making and safety trend assessment.

[0090] S76. All visualization results support linked display with the early warning module. When the risk level changes to reach the set threshold R... i (t)≥R warn When the warning message is displayed, the view will automatically switch to the view of that line segment and highlight the warning message.

[0091] R warnThis is an integer-type warning threshold used to determine whether the risk level of a pull segment has changed drastically. The visualization platform is triggered to respond when the risk level of a pull segment meets the following conditions in the time series:

[0092] |R i (t)-R i (t-Δt)|≥R warn

[0093] Where R i (t) represents the risk level of the i-th wire segment at time t, and Δt is the monitoring time interval.

[0094] Preferably, step S8 specifically includes:

[0095] S81. Set the monitoring refresh cycle Δt for the cable operation status, and periodically read the tension estimate output in step S6. Safety factor estimate Minimum spatial distance estimate Risk level R i (t) and tension adjustment recommendations

[0096] S82. Based on the pre-set multi-level safety factor thresholds η1, η2, η3 and spatial distance threshold d min ,d warn It determines in real time whether the following early warning conditions are met:

[0097]

[0098]

[0099] S83. If the warning conditions are triggered, the risk level R will be adjusted. i (t)≥R warn The pull wire segment numbering record is as follows W(t) is the system's overall risk status indicator function, used to determine whether the system is currently in an overall risk state:

[0100]

[0101] S84. For the tension segment i in the warning state, output the recommended tension optimization value:

[0102]

[0103] Where η * For the target safety factor, σ max Where A is the ultimate stress of the material and A is the cross-sectional area of ​​the draw wire.

[0104] S85, Constructing an early warning data structure W i(t), which contains the following:

[0105]

[0106] Location i The coordinates of the line segment are given.

[0107] S86, W the early warning data structure i (t) Real-time synchronization to the early warning database and visualization platform, W i (t) represents the risk state of the i-th section of the cable at time t, W i (t) takes the value: W i (t)∈{0,1,2,3}: These correspond to four levels: normal, warning, attention, and danger, respectively.

[0108] S87, When R is detected in the system within a continuous ΔT time period i When the state of (t)≥2 remains unchanged, the system will automatically trigger a voice alarm, interface prompts, and adjust the tension in conjunction with the device interface.

[0109] Preferably, step S9 specifically includes:

[0110] S91. Based on the guyed tower structure model, foundation parameters, and geological environmental conditions, construct a system fault tree model that includes events such as structural damage, tension anomalies, and geological slippage. And use logic gates to define the logical relationships between each sub-event;

[0111] S92, Fault tree model Transform into a Bayesian network Where V is the set of event nodes and E is the set of edges of the directed acyclic graph formed by event dependencies;

[0112] S93, to Each root node event R in i According to the early warning data structure W i (t), calculate its prior probability:

[0113]

[0114] Where n fault n represents the number of times the fault occurred. total Indicates the total number of observations;

[0115] S94. Combining practical operating procedures with engineering expert knowledge, construct a conditional probability table for intermediate nodes and result event nodes:

[0116]

[0117] S95, Perform forward inference to calculate target event Ej Failure probability:

[0118] S96. Based on node sensitivity analysis, calculate the impact of each input node on the output failure event, and construct a set of key disaster-causing factors:

[0119]

[0120] Where ∈ represents the set sensitivity threshold for the impact;

[0121] S97, Observations of high-risk node events At that time, perform Bayesian backward inference to calculate the posterior probability:

[0122]

[0123] S98, Key Factors A structural safety assessment report is generated by summarizing fault paths, posterior probability results, etc.

[0124] Beneficial effects:

[0125] This invention integrates high-precision lidar point cloud modeling with tower BIM data to establish a unified 3D model of the construction site with centimeter-level accuracy. This effectively recreates the real environment of the construction site and achieves the fusion of spatial coordinates between the guy wire anchor points and the tower structure, providing a complete digital scene foundation for subsequent stress simulation and spatial interference detection. Secondly, the multibody dynamics and finite element analysis hybrid simulation engine designed in the method not only considers static factors such as the mechanical properties of the guy wire material and initial pretension, but also introduces dynamic conditions such as wind load and equipment sway. It accurately calculates the tension changes and safety factors of the guy wire under multiple loads and sets multi-level safety thresholds to realize an early warning and alarm mechanism for tension exceeding limits, effectively preventing guy wire overload instability.

[0126] This invention employs a distributed sensor network to collect multi-source information such as guy wire tension, temperature, and attitude in real time. It combines this with Kalman filtering algorithms and simulation results for state fusion, ensuring high timeliness and accuracy in safety assessments. Furthermore, regarding spatial safety, it introduces hierarchical bounding box and collision detection tree algorithms to dynamically determine the minimum safe distance between the guy wire and terrain / obstacles, identify potential interference risks, and visualize them as 3D force vector maps and risk heat maps. Construction personnel can interactively view the risk distribution through a visual interface to aid decision-making. Finally, this invention innovatively combines fault tree analysis with Bayesian network inference technology to construct a causal link diagram of structural risks. When risks are known, it can accurately locate the disaster source through reverse reasoning, identify key weak points, and generate a safety diagnostic report, providing a scientific basis for subsequent construction adjustments, structural reinforcement, and operation and maintenance optimization.

[0127] In summary, this invention not only achieves integrated assessment of space safety and stress safety, but also makes significant breakthroughs in simulation accuracy, real-time performance, intelligent reasoning, and visual interaction, possessing broad engineering application value and promising prospects for promotion. Attached Figure Description

[0128] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0129] Figure 1 This is a flowchart of the spatial safety early warning method based on three-dimensional terrain data proposed in this invention.

[0130] Figure 2 This is a flowchart illustrating the simulation calculation process of the method proposed in this invention for tension simulation analysis. Detailed Implementation

[0131] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0132] This embodiment proposes a method for spatial safety early warning based on three-dimensional terrain data using a string line. Figure 1 This method utilizes UAV lidar to collect point cloud data from the construction site, integrates it with the tower's BIM model, and achieves centimeter-level precision in 3D terrain reconstruction and spatial coordinate unification. It constructs a hybrid simulation engine integrating multibody dynamics and the finite element method to dynamically calculate guy wire tension and structural safety factors. A hierarchical bounding box algorithm is used for spatial partitioning and collision detection to obtain the minimum safe distance between the guy wire and obstacles. A distributed monitoring network is constructed using fiber optic grating sensors and IMUs to collect tension and attitude data in real time. An extended Kalman filter algorithm is combined to fuse simulation and measured data, improving the system's dynamic response and accuracy. Finally, a safety assessment report is generated and visualized through 3D force vector maps and risk heat maps, supporting real-time decision-making and tension adjustment by construction personnel. Compared to existing technologies, this method effectively solves problems such as untimely detection of guy wire spatial interference, insufficient accuracy in tension change monitoring, and delayed risk assessment feedback, significantly improving the structural safety management level and emergency response capability of construction sites.

[0133] The specific steps of the safety early warning method for wire space proposed in this embodiment are as follows:

[0134] S1. Use UAV LiDAR to acquire point cloud data of the construction site, generate a three-dimensional terrain model, and import it into the tower BIM model for coordinate fusion.

[0135] S2. Based on multibody dynamics and finite element analysis, a simulation model is constructed to establish the dynamic relationship between the tension of the tension wire and the safety factor, and multiple safety thresholds are set to obtain simulation data.

[0136] S3. Use the hierarchical bounding box method to perform spatial division and collision detection, and extract the minimum safe distance between the wire and the obstacle.

[0137] S4. Install fiber optic grating sensors and IMUs at key locations of the tension line to collect real-time tension and attitude data.

[0138] S5. Merge simulation data and real-time data, use Kalman filtering for state correction and dynamic estimation, and generate fused results;

[0139] S6. Conduct a security assessment based on the fusion results and generate early warning judgment results;

[0140] S7. Display the force vector and risk heat map of the guy wire through a 3D visualization platform, supporting interactive viewing;

[0141] S8. Set up a real-time monitoring and early warning mechanism to automatically push adjustment suggestions when the tension is abnormal;

[0142] S9. Construct a fault tree and transform it into a Bayesian network model. Based on the adjustment suggestions, reason and analyze the system's safety status, and identify key disaster-causing factors and potential failure paths.

[0143] In this embodiment, S1 specifically includes:

[0144] S11. Use a drone equipped with a lidar scanning system to conduct multi-angle aerial scanning of the construction site to obtain point cloud data of the construction site with centimeter-level accuracy. The point cloud data of the construction site includes the topography of the construction site, the location of the tower base, and surrounding obstacles.

[0145] S12. The acquired point cloud data of the construction site is registered. The multi-view point cloud data is aligned by the iterative nearest point algorithm to generate a unified and continuous full-scene 3D point cloud model.

[0146] S13. The point cloud data is processed using a surface reconstruction algorithm to generate a continuous and high-precision three-dimensional terrain mesh model. Each point in the model has unique spatial coordinates (x, y, z).

[0147] S14. Import the tower BIM model provided by the design institute. The model includes the tower structural component number, component size, installation location, installation direction, and guy wire anchor point information.

[0148] S15, Extract the coordinates of the guy wire anchor point (x a ,y a ,z a and connecting direction vector The tower BIM model and the 3D terrain model were uniformly converted to the construction coordinate system. s ;

[0149] S16. Establish the initial input parameters for the guy wire spatial path, including the anchoring endpoints. Tower connection point Calculate the unit direction vector of the guy wire

[0150] S17. Complete the data fusion of the tower BIM model, 3D point cloud model and 3D terrain mesh model to provide a unified spatial basis for simulation analysis and collision detection.

[0151] In this embodiment, S2 specifically includes:

[0152] S21. Construct a simulation engine that integrates multibody dynamics and the finite element method. For the guy wire anchor point, use the shear surface model to estimate the soil volume V:

[0153]

[0154] Where h is the shear surface height, i.e., the anchoring depth of the ground anchor; d is the short side width of the upper shear surface, corresponding to the surface side; l is the long side length of the upper shear surface, corresponding to the unfolded surface perpendicular to the direction of the guy wire; d1 and l1 correspond to the layering of the ground anchor shear region, representing the side length parameters of the lower shear surface. From this, the estimated value of the ground anchor pull-out force Q can be obtained:

[0155]

[0156] Where γ is the soil unit weight, representing the self-weight strength of a unit volume of soil under gravity, and K is the pull-out safety reduction factor. If the installation angle of the guy wire and the frictional properties of the soil are considered, then the pull-out force Q is:

[0157]

[0158] Among them, φ1 is the internal friction angle of the soil, which is an important physical quantity that characterizes the shear resistance of the soil. The larger the value, the more stable the soil structure and the more resistant to sliding. α represents the angle between the guy line and the horizontal plane, which represents the spatial orientation of the tension force on the ground anchor.

[0159] S22. Establish a load model to simulate the external loads borne by the guy wire under actual construction conditions, including the conductor gravity load G and wind pressure load P. The calculation formulas are as follows:

[0160] G=γ1·A·Lv ;

[0161] P=γ4·A·L p ·cosα·sinθ;

[0162] Where γ1 and γ4 are the gravity and wind pressure ratios, respectively, and L v , L p These represent the vertical span and the length of the wind pressure action, respectively; cosα and sinθ represent the projection of the wind load onto the direction of the tension wire; and θ is the wind direction angle.

[0163] S23. Solve for the tension F of each segment of the tension wire under the given load conditions. i Calculate the stress:

[0164]

[0165] Furthermore, the initial state of static form-finding for the guy wire is constructed, assuming that the deformation curve of the guy wire under horizontal tension H under a uniformly distributed load q satisfies:

[0166]

[0167] Where ρ is the guy wire span, used to control the initial guy wire configuration in the simulation, and the horizontal tension H is inferred from the mid-span sag f:

[0168]

[0169] S24. Introduce a safety factor calculation model, defined as follows:

[0170]

[0171] The safety factor calculation model is a core indicator model used to assess whether the guy wire structure is safe under stress. Its goal is to determine whether the current guy wire segment is in a safe state by comparing the ultimate bearing capacity of the material with the actual stress level.

[0172] To assess the ultimate tension of the cable, the maximum tension value T is calculated using the following formula. max :

[0173]

[0174] in It is used to describe the mechanical properties of cable-like structures under uniformly distributed loads, and is an important function for more accurately evaluating the maximum tension distribution after considering the cable curvature;

[0175] S25. Set multi-level thresholds η1, η2, and η3 for safety factors to determine whether the tension state of the guy wire is normal or in warning mode;

[0176] S26, Output tension change sequence {T(t)}、Safety factor sequence {K (t)}、Minimum safe distance sequence {d (t) The data is used as simulation data for subsequent state fusion and evaluation analysis.

[0177] In this embodiment, S3 specifically includes:

[0178] S31, Spatial foundation and coordinates of anchor points based on data fusion (x a ,y a ,z a Construct the wire space path and generate the corresponding wire envelope B. i ;

[0179] S32. Pull the wire envelope B i Perform spatial Boolean intersection with the 3D terrain mesh model to identify the interference area between the path and the terrain, and output the results of potential collision risks.

[0180] S33, For each draw wire envelope B i With obstacle envelope B j Perform a traversal check to determine if there is an intersection relationship.

[0181] S34. For the line segments that are determined to have a spatial proximity relationship, further calculate the spatial path envelope B of the line segments. i With obstacle envelope B j The minimum spatial distance d between them ij The calculation formula is as follows:

[0182]

[0183] in, These represent the wire envelope B. i With obstacle envelope B j The set of boundary points, where |pq| represents the Euclidean distance between pairs of points;

[0184] S35. Calculate the minimum safe distance d for all pull lines in the current simulation frame. ij Summarized into a safety spacing vector D seg , and the preset minimum distance threshold d min Comparisons are made for subsequent safety assessments and early warning determinations.

[0185] S36. If D exists seg <d min If the risk is identified as a potential spatial interference risk, its spatial location will be marked for use in the risk heatmap of the 3D visualization platform.

[0186] In this embodiment, S4 specifically includes:

[0187] S41. Fiber Bragg grating sensors and inertial measurement units are installed at the connection points between the guy wire and the tower and at the anchoring points to form a distributed sensor network for acquiring guy wire operating status information.

[0188] S42. Fiber Bragg grating sensors are used to monitor the axial strain and temperature changes of the draw wire in real time, recording the wavelength shift Δλ. i =λ i -λ0, where λ i To measure the wavelength, λ0 is the initial wavelength;

[0189] S43, through the sensitivity coefficient K s The wavelength drift is converted into a tension value, and the tension calculation formula is as follows:

[0190]

[0191] S44, The inertial measurement unit is used to collect the dynamic response behavior of the guy wire at the guy wire connection point;

[0192] S45. Simultaneously collect tension, acceleration, temperature, and attitude data of each sensing node during the sampling period to construct a sensing observation matrix.

[0193]

[0194] S46. Transfer the sensor observation matrix It is used as the input for subsequent state estimation and Kalman filtering fusion to support real-time identification and dynamic modeling analysis of stress state.

[0195] In this embodiment, S5 specifically includes:

[0196] S51, {T} (t)}、Safety factor sequence {K (t)}、Minimum safe distance sequence {d (t)} as the input to the predicted state, forming the predicted state vector

[0197]

[0198] in, T represents the prior predicted state at time k. k SF is the estimated value of the tension in the guy wire calculated by the simulation model. k D is the safety factor estimate obtained through the fusion of simulation analysis and real-time data. k This is the minimum spatial distance estimate calculated based on the fusion of real-time sensor data and simulation data;

[0199] S52, Transfer the sensor observation matrix As the observation input, construct the observation input vector z. k :

[0200]

[0201] in, a is the real-time tension measurement value of the pull wire acquired by the fiber optic sensor. k For real-time acquisition of triaxial acceleration observations, θ k The angle of the pull line attitude;

[0202] S53. Employ the extended Kalman filter algorithm based on the predicted state vector. With the observed input vector z k Perform prediction error calculation and state correction, and update the posterior state estimation vector:

[0203]

[0204] in, For the updated posterior state estimate, K k Here, h is the Kalman gain matrix, and h(·) is the observation function used to map the predicted state to the observation space.

[0205] S54. Predict the state vector using the extended Kalman filter algorithm. With the observed input vector z k Perform fusion processing to output the estimated tension value of the draw wire. Safety factor estimate Minimum spatial distance estimate These three factors together constitute the fusion result of this method, which is used to characterize the mechanical state and spatial safety state of the guy wire structure at the current moment and serve as the direct input basis for subsequent risk assessment and safety judgment. At the same time, the above estimated values ​​are recorded in chronological order to form a dynamic tension sequence, a dynamic safety factor sequence, and a dynamic spatial distance sequence, which are used for three-dimensional force vector drawing and risk level identification.

[0206] In this embodiment, S6 specifically includes:

[0207] S61, Based on tension estimation Safety factor estimate Minimum spatial distance estimate Construct an assessment model for security level classification;

[0208] S62, Introducing the safety factor η i Computational model:

[0209]

[0210] Where σ max Let A be the ultimate stress of the wire material, and A be the cross-sectional area. This is the current tension estimate;

[0211] S63. Set multi-level security thresholds η1, η2, η3 and spatial distance threshold d. min ,d warn The overall risk level R is determined according to the following rules. i (t);

[0212] S64. Calculate the comprehensive risk for all guy wire segments, use it for structural safety assessment, and set the target safety factor η. * η is used to represent the minimum safety margin required for a structure under stress. * Pre-set by user requirements;

[0213] S65. For line segment i with a risk level of 1 or higher, according to the target safety factor η * Calculate tension adjustment recommendations

[0214] S66, Risk level R i (t), tension estimate Tension adjustment recommendations The output is simultaneously sent to the visualization system and the security control module.

[0215] In this embodiment, S7 specifically includes:

[0216] S71. Construct an integrated 3D visualization platform that integrates 3D terrain models, tower BIM structural models, and tension estimates. Safety factor estimate Minimum spatial distance estimate and risk level R i (t) Spatial fusion of data;

[0217] S72. Based on the unit direction vector of each wire segment and tension estimates Draw a 3D force vector diagram, with the vector representation as follows:

[0218]

[0219] in Let be the force vector at time t, with its direction aligned with the tension line and its length proportional to the magnitude of the tension.

[0220] S73. Risk level R generated based on step S6 i (t), define the heatmap color encoding function:

[0221] H i (t)=f(R i (t));

[0222] Where H i (t) represents the color value of the heat map used for visualization. Different risk levels correspond to different colors: green indicates normal, yellow indicates warning, orange indicates attention, and red indicates danger.

[0223] S74, Transform the force vector It binds the risk heatmap to the spatial path structure of each guy line, realizes time-varying 3D rendering display, and supports user interactive operation, including guy line number selection, hazard level filtering, visibility control, guy line segment highlighting, dynamic scaling and rotation;

[0224] S75. Construct a timeline playback module that supports t∈[t0,t... N The historical tension status and risk level evolution process within the interval are visualized, and the output tension evolution map, risk level time series map, force vector dynamic map and spatial situation thermal animation are provided to assist construction decision-making and safety trend assessment.

[0225] S76. All visualization results support linked display with the early warning module. When the risk level changes to reach the set threshold R... i (t)≥R warn When the warning message is displayed, the view will automatically switch to the view of that line segment and highlight the warning message.

[0226] R warn This is an integer-type warning threshold used to determine whether the risk level of a pull segment has changed drastically. The visualization platform is triggered to respond when the risk level of a pull segment meets the following conditions in the time series:

[0227] |R i (t)-R i (t-Δt)|≥R warn ;

[0228] Where R i (t) represents the risk level of the i-th wire segment at time t, and Δt is the monitoring time interval.

[0229] In this embodiment, S8 specifically includes:

[0230] S81. Set the monitoring refresh cycle Δt for the cable operation status, and periodically read the tension estimate output in step S6. Safety factor estimate Minimum spatial distance estimate Risk level R i (t) and tension adjustment recommendations

[0231] S82. Based on the pre-set multi-level safety factor thresholds η1, η2, η3 and spatial distance threshold d min ,d warn It determines in real time whether the following early warning conditions are met:

[0232]

[0233]

[0234] S83. If the warning conditions are triggered, the risk level R will be adjusted. i (t)≥R warn The line segment number is recorded as W(t), where W(t) is the system's overall risk status indicator function, used to determine whether the system is currently in an overall risk state.

[0235]

[0236] S84. For the tension segment i in the warning state, output the recommended tension optimization value:

[0237]

[0238] Where η * For the target safety factor, σ max Where A is the ultimate stress of the material and A is the cross-sectional area of ​​the draw wire.

[0239] S85, Constructing an early warning data structure W i (t), which contains the following:

[0240]

[0241] Location i The coordinates of the line segment are given.

[0242] S86, W the early warning data structure i (t) Real-time synchronization to the early warning database and visualization platform, W i (t) represents the risk state of the i-th section of the cable at time t, W i (t) can take the following values: W i (t)∈{0,1,2,3}: These correspond to four levels: normal, warning, attention, and danger, respectively.

[0243] S87, When R is detected in the system within a continuous ΔT time period i When the state of (t)≥2 remains unchanged, the system will automatically trigger a voice alarm, interface prompts, and adjust the tension in conjunction with the device interface.

[0244] In this embodiment, S9 specifically includes:

[0245] S91. Based on the guyed tower structure model, foundation parameters, and geological environmental conditions, construct a system fault tree model that includes events such as structural damage, tension anomalies, and geological slippage. And use logic gates to define the logical relationships between each sub-event;

[0246] S92, Fault tree model Transform into a Bayesian network Where V is the set of event nodes and E is the set of edges of the directed acyclic graph formed by event dependencies;

[0247] S93, to Each root node event R in i According to the early warning data structure W i (t), calculate its prior probability:

[0248]

[0249] Where n fault n represents the number of times the fault occurred. total Indicates the total number of observations;

[0250] S94. Combining practical operating procedures with engineering expert knowledge, construct a conditional probability table for intermediate nodes and result event nodes:

[0251]

[0252] S95, Perform forward inference to calculate target event E j Failure probability:

[0253] S96. Based on node sensitivity analysis, calculate the impact of each input node on the output failure event, and construct a set of key disaster-causing factors:

[0254]

[0255] Where ∈ represents the set sensitivity threshold for the impact;

[0256] S97, Observations of high-risk node events At that time, perform Bayesian backward inference to calculate the posterior probability:

[0257]

[0258] S98, Key Factors A structural safety assessment report is generated by summarizing fault paths, posterior probability results, etc. Used for risk management decisions and maintenance optimization strategy formulation.

[0259] To verify the feasibility and engineering value of this invention in the construction of suspended pole-mounted transmission lines in complex terrain, it was applied to the construction site of tower No. 47 of the 500kV transmission line project in Huidong County, Liangshan Prefecture, Sichuan Province, by the State Grid Sichuan Company in 2024. The terrain where this tower is located is a typical plateau and mountainous landscape, with many steep slopes, cliffs, and obstacles such as mixed shrubs and forests. The space for the guy wire anchoring points is also limited, making it a typical "difficult to construct and high-risk" operation scenario in the industry.

[0260] In this project, the traditional two-dimensional vector calculation method could not accurately describe the true trajectory of the guy wire in three-dimensional space. The construction team was unable to predict potential interference between the guy wire and the bedrock protrusion on the south side. Furthermore, tension distribution data relied heavily on manual experience for estimation, lacking on-site dynamic response data, which severely hampered the efficiency of safety assessments. To overcome these limitations, the project team deployed the three-dimensional integrated analysis system described in this invention for the first time at this tower location.

[0261] Equipped with a DJI M300 RTK drone and a Livox AVIA LiDAR module, a panoramic scan of a 300-meter radius area around the tower was performed, acquiring point cloud data in just 1.5 hours with an accuracy within ±2.5cm. After fusing the collected point cloud data with the tower's BIM model, the system quickly established a 3D spatial model including terrain obstacles, tower structure, and anchor point relationships. The system automatically extracted the vectors from the anchor points to the tower connection points and generated the guy wire path envelopes. Using hierarchical bounding box technology, the simulation detected three guy wires with a minimum distance of less than 0.6 meters between them and the surrounding rock boundary, below the set warning threshold of 0.8 meters. Among these, the guy wire numbered C2 on the south side had the smallest distance to the rocky slope, only 0.42 meters, posing a serious risk of interference.

[0262] To further conduct stress analysis, the system invoked the embedded finite element-multibody dynamics co-simulation engine, inputting the wire parameters (steel-cored aluminum stranded wire, cross-sectional area 28.3 mm²). 2 Initial tension 1100N, elastic modulus 1.95×10 11 The system calculates the tension changes of the nine guy wires in real time, taking into account dynamic load variables such as wind pressure, conductor load, and tower sway during construction. Simulations show that, under wind speeds up to 15.6 m / s, the tension of the third guy wire reaches a maximum of 1634 N, far exceeding the design recommendation. Based on this, the system assesses its safety factor η = 1.94, which is lower than the lower safety limit η = 2.0, automatically triggering an early warning mechanism and recommending an adjustment of the anchoring angle to 5.6°, increasing the tension by 130 N.

[0263] Ten fiber Bragg grating sensors and three IMU modules were deployed simultaneously on-site, distributed at the tower base, tower top, and guy wire connection points, with a data sampling frequency set to 10Hz. After fusing the actual monitoring results and simulation results using Kalman filtering, the tension error decreased from an average of ±95N to ±27N, and the attitude estimation error decreased by approximately 31.4%. Furthermore, the Bayesian network constructed by the system performed online inference analysis on potential disaster-causing factors, identifying a significant increase in the posterior probabilities of three nodes: "wind speed fluctuations," "loosening of rock mass at anchor points," and "tower vibration." Ultimately, "unbalanced stress on the second-layer guy wire" was determined to be the primary cause of the high-risk path, with a posterior probability of 0.46. It was recommended that foundation reinforcement treatment be carried out on the rock foundation of the south anchor section before construction.

[0264] Based on the system's efficient early warning capabilities and spatial perception accuracy, construction personnel completed adjustments within just two hours of identifying a risk, avoiding subsequent strong wind-induced pull-out risks. The on-site chief engineer reported that the system significantly outperformed the previous combination of manual calculation and CAD simulation in terms of 3D spatial modeling, real-time tension analysis, and visualized risk warning, saving approximately 34% of the initial analysis time and improving risk response speed by at least 40%. All system results are rendered through a local visualization platform, including heatmaps and vector graphics. Figure 1 The demonstration showed that on-site technicians could quickly identify risk areas and sections with abnormal tension without needing a professional simulation background. The implementation results are shown in Table 1.

[0265] Table 1. Statistical data on the implementation effect of the method of the present invention on tower M47 of the 500kV Liangshan power transmission project.

[0266]

[0267] As shown in Table 1, the method of the present invention improves the accuracy to ±2.5cm and the efficiency by 50% in 3D modeling; it achieves automatic identification of interference risks within 2.4 seconds in space safety detection, breaking through the limitations of traditional manual judgment; the tension estimation error is reduced to ±27N, and the accuracy is improved by more than 70%; the risk warning response is shortened to 2.2 seconds, which is 6 times faster than manual; the visualization rendering only takes 3.5 seconds, and the risk identification accuracy rate is improved to 94.2%, supporting fast, accurate and safe decision-making.

[0268] This embodiment verifies the comprehensive application capability and technical effect of the invention in actual construction scenarios through methods such as UAV lidar modeling, BIM model fusion, finite element-multibody dynamics simulation, Kalman filter data fusion, and Bayesian network inference. It fully solves the problems of low spatial recognition accuracy, lagging stress simulation, slow early warning response, and insufficient result visualization in traditional guy wire safety assessment. It provides an efficient, accurate, and operable solution for intelligent safety analysis and decision-making in transmission line construction under complex terrain conditions.

[0269] This invention employs a 3D modeling method that integrates lidar point cloud scanning with the tower's BIM model. This method accurately recreates the terrain and structural information of the construction site, achieving high-precision modeling of guy wire anchor points and surrounding obstacles. It fundamentally solves the problems of traditional 2D vector methods, which cannot reflect the true spatial orientation of guy wires and struggle to identify spatial interference. This method provides centimeter-level spatial data, significantly improving the accuracy of spatial safety assessments, and is particularly suitable for high-risk construction environments such as complex mountainous terrain and river crossings.

[0270] This invention constructs a wire-stayed simulation engine that integrates finite element analysis and multibody dynamics to simulate the dynamic tension changes of wires under different load conditions. By introducing a safety factor grading threshold model, it can accurately determine the stress risk level of the structure. Compared to traditional methods that only perform static mechanical analysis, this invention can dynamically reflect the tension change trend during construction, effectively solving the problem of lacking quantitative evidence of real-time mechanical state during construction, and providing scientific support for adjusting tension parameters and optimizing anchoring points.

[0271] This invention employs a real-time monitoring network composed of distributed fiber Bragg grating sensors and inertial measurement units, and combines an adaptive Kalman filter algorithm to dynamically fuse simulation data and measured data, constructing a reliable stress state estimation model. This method achieves a closed-loop calculation process from "simulation-measurement-correction," significantly improving the system's response to sudden loads and on-site disturbances, and solving the problem of large deviations between simulation models and reality in traditional methods.

[0272] This invention innovatively introduces a safety reasoning model based on fault trees and Bayesian networks. Combining guy wire structural parameters, environmental conditions, and monitoring data, it establishes a safety assessment map for guy wire systems, enabling the identification of key disaster-causing factors and the prediction of risk levels. This method overcomes the limitations of traditional methods that separate spatial interference and stress assessment, achieving integrated analysis of structural safety and spatial safety. It provides a visualized, intelligent, and real-time comprehensive safety management solution for high-risk power transmission construction.

[0273] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0274] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for spatial safety early warning based on three-dimensional terrain data using a string line, characterized in that, Includes the following steps: S1. Use UAV LiDAR to acquire point cloud data of the construction site, generate a three-dimensional terrain model, and import it into the tower BIM model for coordinate fusion. S2. Based on multibody dynamics and finite element analysis, a simulation model is constructed to establish the dynamic relationship between the tension of the tension wire and the safety factor, and multiple safety thresholds are set to obtain simulation data. S3. Use the hierarchical bounding box method to perform spatial division and collision detection, and extract the minimum safe distance between the wire and the obstacle. S4. Install fiber optic grating sensors and inertial measurement units at key locations on the cable to collect real-time tension and attitude data. S5. Merge simulation data and real-time data, use Kalman filtering for state correction and dynamic estimation, and generate fused results; S6. Conduct a security assessment based on the fusion results and generate early warning judgment results; S7. Display the force vector and risk heat map of the guy wire through a 3D visualization platform; S8. Set up a real-time monitoring and early warning mechanism to automatically push adjustment suggestions when tension or spacing is abnormal; S9. Construct a fault tree and transform it into a Bayesian network model. Based on the adjustment suggestions, reason and analyze the system's safety status, and identify key disaster-causing factors and potential failure paths.

2. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S1 specifically includes: S11. Use a drone equipped with a lidar scanning system to conduct multi-angle aerial scanning of the construction site to obtain point cloud data of the construction site with centimeter-level accuracy. The point cloud data of the construction site includes the topography of the construction site, the location of the tower base, and surrounding obstacles. S12. The acquired point cloud data of the construction site is registered. The multi-view point cloud data is aligned by the iterative nearest point algorithm to generate a unified and continuous full-scene 3D point cloud model. S13. The point cloud data is processed using a surface reconstruction algorithm to generate a continuous and high-precision three-dimensional terrain mesh model. Each point in the model has unique spatial coordinates (x, y, z). S14. Import the tower BIM model provided by the design institute. The model includes the tower structural component number, component size, installation location, installation direction, and guy wire anchor point information. S15, Extract the coordinates of the guy wire anchor point (x a ,y a ,z a and connecting direction vector The tower BIM model and the 3D terrain model were uniformly converted to the construction coordinate system. s ; S16. Establish the initial input parameters for the guy wire spatial path, including the anchoring endpoints. Tower connection point Calculate the unit direction vector of the guy wire S17. Complete the data fusion of the tower BIM model, 3D point cloud model and 3D terrain mesh model to provide a unified spatial basis for simulation analysis and collision detection.

3. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, In step S2, the dynamic relationship between the tension of the pull wire and the safety factor is established, specifically including: Establish a load model to simulate the external loads borne by the guy wire under actual construction or operation conditions, including the conductor gravity load G and wind pressure load P: G=γ1·A·L v P=γ4·A·L p ·cosα·sinθ Where γ1 and γ4 are the gravity and wind pressure ratios, respectively, and L v , L p These represent the vertical span and the length of the wind pressure action, respectively; cosα and sinθ represent the projection of the wind load onto the direction of the tension wire; and θ is the wind direction angle. Solving for the tension F of each segment of the tension wire under given load conditions i Calculate the stress σ i : Construct the initial static form-finding state of the guy wire, assuming that the deformation curve of the guy wire under horizontal tension H under a uniformly distributed load q satisfies: Where ρ is the guy wire span, used to control the initial guy wire configuration in the simulation, and the horizontal tension H is inferred from the mid-span sag f: A safety factor calculation model is introduced to determine whether the current tension segment is in a safe state by comparing the ultimate bearing capacity of the material with the actual stress level. The safety factor is calculated as follows: The safety factor is set with multiple thresholds η1, η2, and η3 to determine whether the tension state of the guy wire is normal, warning, or dangerous.

4. The method for spatial safety early warning based on three-dimensional terrain data according to claim 3, characterized in that, Step S2 also includes: Output tension change sequence {T (t) }、Safety factor sequence {K (t) }、Minimum safe distance sequence {d (t) The data is used as simulation data for subsequent state fusion and evaluation analysis.

5. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S3 specifically includes: S31, Spatial foundation and coordinates of anchor points based on data fusion (x a ,y a ,z a Construct the wire space path and generate the corresponding wire envelope B. i ; S32. Pull the wire envelope B i Perform spatial Boolean intersection with the 3D terrain mesh model to identify the interference area between the path and the terrain, and output the results of potential collision risks; S33, For each draw wire envelope B i With obstacle envelope B j Perform a traversal check to determine if there is an intersection relationship. S34. For the line segments that are determined to have a spatial proximity relationship, further calculate the spatial path envelope B of the line segments. i With obstacle envelope B j The minimum spatial distance d between them ij The calculation formula is as follows: in, These represent the wire envelope B. i With obstacle envelope B j The set of boundary points, where |pq| represents the Euclidean distance between pairs of points; S35. Calculate the minimum safe distance d for all pull lines in the current simulation frame. ij Summarized into a safety spacing vector D seg , and the preset minimum distance threshold d min Comparisons are made for subsequent safety assessments and early warning determinations. S36. If D exists seg <d min If the risk is identified as a potential spatial interference risk, its spatial location will be marked for use in the risk heatmap of the 3D visualization platform.

6. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S4 specifically includes: S41. Fiber Bragg grating sensors and inertial measurement units are installed at the connection points between the guy wire and the tower and at the anchoring points to form a distributed sensor network for acquiring guy wire operating status information. S42. Fiber Bragg grating sensors are used to monitor the axial strain and temperature changes of the draw wire in real time, recording the wavelength shift Δλ. i =λ i -λ0, where λ i To measure the wavelength, λ0 is the initial wavelength; S43, through the sensitivity coefficient K s The wavelength drift is converted into a tension value, and the tension calculation formula is as follows: S44, The inertial measurement unit is used to collect the dynamic response behavior of the guy wire at the guy wire connection point; S45. Simultaneously collect tension, acceleration, temperature, and attitude data of each sensing node during the sampling period to construct a sensing observation matrix. S46. Transfer the sensor observation matrix It is used as the input for subsequent state estimation and Kalman filtering fusion to support real-time identification and dynamic modeling analysis of stress state.

7. The method for spatial safety early warning based on three-dimensional terrain data according to claim 4, characterized in that, Step S5 specifically includes: S51, the tension change sequence {T (t) }、Safety factor sequence {K (t) }、Minimum safe distance sequence {d (t) } as the input to the predicted state, forming the predicted state vector in, T represents the prior predicted state at time k. k SF is the estimated value of the tension in the guy wire calculated by the simulation model. k D is the safety factor estimate obtained through the fusion of simulation analysis and real-time data. k This is the minimum spatial distance estimate calculated based on the fusion of real-time sensor data and simulation data; S52, Transfer the sensor observation matrix As the observation input, construct the observation input vector z. k : in, a is the real-time tension measurement value of the pull wire acquired by the fiber optic sensor. k For real-time acquisition of triaxial acceleration observations, θ k The angle of the pull line attitude; S53. Employ the extended Kalman filter algorithm based on the predicted state vector. With the observed input vector z k Perform prediction error calculation and state correction, and update the posterior state estimation vector: in, For the updated posterior state estimate, K k Here, h is the Kalman gain matrix, and h(·) is the observation function used to map the predicted state to the observation space. S54. Predict the state vector using the extended Kalman filter algorithm. With the observed input vector z k Perform fusion processing to output the estimated tension value of the draw wire. Safety factor estimate Minimum spatial distance estimate These three factors together constitute the fusion result of this method, which is used to characterize the changing trend of the mechanical state and spatial safety state of the guy wire structure at the current moment, and serves as the direct input basis for subsequent risk assessment and safety judgment. At the same time, the above estimated values ​​are recorded in chronological order to form a dynamic tension sequence, a dynamic safety factor sequence, and a dynamic spatial distance sequence, which are used for three-dimensional force vector drawing and risk level identification.

8. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S6 specifically includes: S61, Based on tension estimation Safety factor estimate Minimum spatial distance estimate Construct an assessment model for security level classification; S62, Introducing the safety factor η i Computational model: Where σ max Let A be the ultimate stress of the wire material, and A be the cross-sectional area. This is the current tension estimate; S63. Set multi-level security thresholds η1, η2, η3 and spatial distance threshold d. min ,d warn The overall risk level R is determined according to the following rules. i (t); S64. Calculate the comprehensive risk for all guy wire segments, use it for structural safety assessment, and set the target safety factor η. * η is used to represent the minimum safety margin required for a structure under stress. * Pre-set by user requirements; S65. For line segment i with a risk level of 1 or higher, according to the target safety factor η * Calculate tension adjustment recommendations S66, Risk level R i (t), tension estimate Tension adjustment recommendations The output is simultaneously sent to the visualization system and the security control module.

9. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S7 specifically includes: S71. Construct an integrated 3D visualization platform that integrates 3D terrain models, tower BIM structural models, and tension estimates. Safety factor estimate Minimum spatial distance estimate and risk level R i (t) Spatial fusion of data; S72. Based on the unit direction vector of each wire segment and tension estimates Draw a 3D force vector diagram, with the vector representation as follows: in Let be the force vector at time t, with its direction aligned with the tension line and its length proportional to the magnitude of the tension. S73. Risk level R generated based on step S6 i (t), define the heatmap color encoding function: H i (t)=f(R i (t)) Where H i (t) represents the color value of the heat map used for visualization. Different risk levels correspond to different colors: green indicates normal, yellow indicates warning, orange indicates attention, and red indicates danger. S74, Transform the force vector It binds the risk heatmap to the spatial path structure of each guy line, realizes time-varying 3D rendering display, and supports user interactive operation, including guy line number selection, hazard level filtering, visibility control, guy line segment highlighting, dynamic scaling and rotation; S75. Construct a timeline playback module that supports t∈[t0,t... N The historical tension status and risk level evolution process within the interval are visualized, and the output tension evolution map, risk level time series map, force vector dynamic map and spatial situation thermal animation are provided to assist construction decision-making and safety trend assessment. S76. All visualization results support linked display with the early warning module. When the risk level changes to reach the set threshold R... i (t)≥R warn When the warning message is displayed, the view will automatically switch to the view of that line segment and the warning message will be highlighted. R warn The warning threshold is an integer type, used to determine whether the risk level of a pull line segment has changed drastically; when a pull line segment's risk level changes drastically... The visualization platform will trigger a response when the risk level meets the following conditions in the time series: |R i (t)-R i (t-Δt)|≥R warn where R i (t) represents the risk level of the i-th wire segment at time t, and Δt is the monitoring time interval.

10. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S8 specifically includes: S81. Set the monitoring refresh cycle Δt for the cable operation status, and periodically read the tension estimate output in step S6. Safety factor estimate Minimum spatial distance estimate Risk level R i (t) and tension adjustment recommendations S82. Based on the pre-set multi-level safety factor thresholds η1, η2, η i and spatial distance threshold d min ,d warn It determines in real time whether the following early warning conditions are met: S83. If the warning conditions are triggered, the risk level R will be adjusted. i (t)≥R warn The pull wire segment numbering record is as follows W(t) is the system's overall risk status indicator function, used to determine whether the system is currently in an overall risk state: S84. For the tension segment i in the warning state, output the recommended tension optimization value: Where η * For the target safety factor, σ max Where A is the ultimate stress of the material and A is the cross-sectional area of ​​the draw wire. S85, Constructing an early warning data structure W i (t), which contains the following: Location i The coordinates of the line segment are given. S86, W the early warning data structure i (t) Real-time synchronization to the early warning database and visualization platform, W i (t) represents the risk state of the i-th section of the cable at time t, W i (t) takes the value: W i (t)∈{0,1,2,3}: These correspond to four levels: normal, warning, attention, and danger, respectively. S87, When R is detected in the system within a continuous ΔT time period i When the state of (t)≥2 remains unchanged, the system will automatically trigger a voice alarm, interface prompts, and adjust the tension in conjunction with the device interface.

11. The method for spatial safety early warning based on three-dimensional terrain data according to claim 1, characterized in that, Step S9 specifically includes: S91. Based on the guyed tower structure model, foundation parameters, and geological environmental conditions, construct a system fault tree model that includes events such as structural damage, tension anomalies, and geological slippage. And use logic gates to define the logical relationships between each sub-event; S92, Fault tree model Transform into a Bayesian network Where V is the set of event nodes and E is the set of edges of the directed acyclic graph formed by event dependencies; S93, to Each root node event R in i According to the early warning data structure W i (t), calculate its prior probability: Where n fault n represents the number of times the fault occurred. total Indicates the total number of observations; S94. Combining practical operating procedures with engineering expert knowledge, construct a conditional probability table for intermediate nodes and result event nodes: S95, Perform forward inference to calculate target event E j Failure probability: S96. Based on node sensitivity analysis, calculate the impact of each input node on the output failure event, and construct a set of key disaster-causing factors: Where ∈ represents the set sensitivity threshold for the impact; S97, Observations of high-risk node events At that time, perform Bayesian backward inference to calculate the posterior probability: S98, Key Factors A structural safety assessment report is generated by summarizing fault paths, posterior probability results, etc.

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