Anchoring system life intelligent evaluation method, system, equipment and medium

Abstract

The invention relates to an anchoring system life intelligent evaluation method, system and device and a medium, and the method comprises the steps: constructing a topological network through the space coordinates of anchoring points and the rigidity of components, and quantifying the mechanical correlation between nodes; the load is monitored in real time and compared with a design value, and the dynamic sensitivity of the node is calculated by fusing material strength characteristics; identifying a set of overload source nodes based on a sensitivity threshold; a Monte Carlo path simulation technology is adopted, a material fatigue accumulation effect is combined to model a failure conduction probability, and a risk conduction path and a strength index are generated; integrating historical degradation data to predict single anchor life, and coupling the maximum path risk and the minimum single anchor life to calculate a system attenuation coefficient; and outputting an evaluation report containing the remaining service life and the position of the high-risk unit. The method breaks through the limitation of traditional single-anchor isolated evaluation, realizes dynamic quantification of the failure conduction effect of the multiple anchoring units, and solves the problem of maintenance decision blindness caused by conduction effect deficiency.

Description

Technical Field

[0001] This invention relates to the field of safety assessment technology for anchoring engineering, specifically to a method, system, equipment, and medium for intelligent evaluation of the lifespan of an anchoring system. Background Technology

[0002] In modern civil engineering structures, anchoring systems serve as critical load-bearing components, and their service life directly impacts the safety and reliability of infrastructure such as bridges, slopes, and dams. Anchoring system life assessment methods are technical means to predict the overall remaining service life of the system through a comprehensive analysis of the material property degradation, load conditions, and environmental factors of anchoring units. Traditional methods mainly rely on physical testing and statistical models of individual anchoring units, and standardized procedures have been established in engineering practice, including periodic manual inspections and regression analysis of sensor monitoring data. However, as large-scale engineering structures develop towards ultra-long spans and ultra-high strength, anchoring systems increasingly exhibit the characteristics of multi-unit collaborative force-bearing group anchors, posing new technical challenges to their life assessment methods.

[0003] However, existing technologies have significant drawbacks: Firstly, traditional methods only evaluate individual anchored units in isolation, failing to quantify the failure propagation effects caused by mechanical coupling between units. When an anchored unit fails due to overload or damage, its load is dynamically redistributed to neighboring units. This chain reaction leads to accelerated degradation of the system's lifetime, but existing models lack the ability to model failure propagation paths. Secondly, because the dynamic propagation process of coordinated failure of multiple anchored units is not considered, traditional methods struggle to accurately locate the weakest, high-risk unit in the system, resulting in blind maintenance decisions. This can lead to resource waste due to over-maintenance or systemic collapse risks due to insufficient local reinforcement. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an intelligent evaluation method, system, device and medium for anchoring system life that can dynamically quantify failure transmission effects and accurately assess the overall life of the system.

[0005] The objective of this invention is achieved through the following solution:

[0006] In a first aspect, the present invention provides an intelligent evaluation method for the life of an anchoring system, comprising the following steps:

[0007] S1: Obtain the spatial coordinates of anchor points and stiffness parameters of connecting components in the engineering structure, calculate the Euclidean distance between each anchor point and filter anchor point pairs that meet the preset maximum influence distance, calculate the edge weights between adjacent anchor points based on the stiffness matrix of connecting components and the cross-sectional area of ​​connecting components, and generate an anchor system topology diagram containing node attribute sets and edge weight sets.

[0008] S2: Real-time acquisition of load data in the anchoring system, combined with the anchoring system topology diagram to perform node sensitivity calculation, compare the deviation of load data with the design load in the node attribute set, and combine with the material yield strength parameter to generate the node sensitivity of each node.

[0009] S3: Perform overload source marking on node sensitivity, filter nodes with sensitivity greater than a preset sensitivity threshold, and generate an overload source set;

[0010] S4: Based on Monte Carlo path simulation, the topology of the anchoring system and the set of overload sources are processed to calculate the failure propagation probability between adjacent nodes and generate propagation analysis results containing the set of failure propagation paths and the path risk intensity.

[0011] S5: Based on the transmission analysis results, extract the maximum path risk intensity in the failure transmission path set, combine it with the historical degradation data of the anchoring system to calculate the life decay coefficient of the anchoring system and the remaining life data of each node's single anchor, integrate and generate an anchoring system life assessment report, which is used to indicate the remaining service life of the engineering structure and the location of high-risk anchoring units.

[0012] In one embodiment, S1 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0013] S11: Obtain the spatial coordinates of anchorage points and stiffness parameters of connecting components in the engineering structure, perform Euclidean distance calculation on the spatial coordinates of anchorage points, calculate the straight-line distance between every two anchorage points, and generate a distance matrix between nodes.

[0014] S12: Perform spatial correlation filtering on the distance matrix between nodes, select anchor point pairs whose distance is less than the preset maximum influence distance, and generate a set of associated node pairs;

[0015] S13: Perform edge weight calculation on the set of associated nodes and stiffness parameters of connecting components, calculate the mechanical transfer efficiency based on the stiffness matrix and cross-sectional area, and generate an edge weight set;

[0016] S14: Construct a topology network based on the edge weight set and the preset anchorage node design parameters, integrate the design load, yield strength and anchorage length to form node attributes, map the edge weight set to the topology edge, and generate an anchorage system topology diagram containing the node attribute set and edge weight set.

[0017] In one embodiment, step S2 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0018] S21: Real-time acquisition of load data in the anchoring system, time-domain feature extraction processing of the load data, calculation of load fluctuation amplitude and frequency distribution, and generation of dynamic load feature spectrum;

[0019] S22: Perform deviation quantification processing on the design load of nodes in the dynamic load characteristic spectrum and anchoring system topology diagram, compare the nonlinear difference between the real-time load peak and the design load threshold, and generate load deviation coefficient.

[0020] S23: Perform sensitivity fusion on the load deviation coefficient and yield strength parameters in the node attribute set, and calculate the dynamic vulnerability of the node by combining the material strength attenuation characteristics and the coupling effect of environmental temperature and humidity, and generate the node sensitivity of each node.

[0021] In one embodiment, step S3 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0022] S31: Perform threshold determination processing on node sensitivity, compare the sensitivity value of each node with the preset sensitivity threshold, and generate a node state label set;

[0023] S32: The node state marker set is processed to extract overloaded nodes, and the nodes marked as overloaded are filtered and their spatial positions are recorded to generate a preliminary overloaded node set.

[0024] S33: Perform set optimization processing on the initial screening of overload nodes, merge duplicate nodes and verify the validity of node positions through preset engineering boundary coordinate range, remove abnormal nodes that exceed the structural range, and generate an overload source set.

[0025] In one embodiment, step S4 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0026] S41: Perform transmission probability modeling on the edge weight set and node sensitivity, and calculate the failure propagation probability by combining the material fatigue cumulative effect to generate the failure propagation probability;

[0027] S42: Perform path Monte Carlo generation on the failure propagation probability and overload source set to simulate the propagation path of failure along the topological edge and generate the original propagation path set;

[0028] S43: Perform path risk intensity calculation on the original transmission path set, multiply the transmission probability along the path and compensate for spatial attenuation, correct the cumulative deviation of transmission probability of long paths through the topological distance attenuation factor, and generate transmission analysis results containing the set of failed transmission paths and path risk intensity.

[0029] In one embodiment, the formula for calculating the failure propagation probability of the intelligent evaluation method for the life of an anchoring system provided by the present invention is as follows:

[0030]

[0031] in, Let be the failure propagation probability from node i to node j. The edge weight value. This represents the maximum value of all edge weights in the topological graph. The material attenuation coefficient, Let i be the sensitivity of node i. This represents the maximum sensitivity of all nodes in the topology graph.

[0032] In one embodiment, S5 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0033] S51: Perform critical path extraction processing on the transmission analysis results, screen transmission paths whose risk intensity exceeds the system risk threshold, and generate a set of high-risk paths;

[0034] S52: Extract the maximum value of the path risk intensity in the high-risk path set to obtain the highest risk value among all paths and generate the maximum path risk intensity.

[0035] S53: Perform Weibull distribution modeling on the historical degradation data of the anchoring system, calculate the remaining life of the nodes through the three-parameter Weibull distribution function, and generate the single anchor remaining life data of each node;

[0036] S54: Extract the minimum value from the single anchor remaining lifetime data, combine it with the maximum path risk intensity to couple the system lifetime, calculate the system lifetime decay coefficient through the hyperbolic decay function, and generate the system remaining lifetime value by combining the minimum single anchor remaining lifetime.

[0037] S55: Encapsulates engineering decisions on the set of high-risk paths and the remaining life value of the system, integrates the prediction of remaining service life and the spatial coordinates of high-risk anchoring units, and generates an anchoring system life assessment report.

[0038] Secondly, the present invention provides an intelligent evaluation system for the life of an anchoring system, which is configured with the following modules:

[0039] The anchoring system topology construction module is used to obtain the spatial coordinates of anchoring points and stiffness parameters of connecting components in the engineering structure, calculate the Euclidean distance between each anchoring point and filter anchoring point pairs that meet the preset maximum influence distance, calculate the edge weights between adjacent anchoring points based on the stiffness matrix of connecting components and the cross-sectional area of ​​connecting components, and generate an anchoring system topology diagram containing node attribute sets and edge weight sets.

[0040] The node sensitivity calculation module is used to acquire load data in the anchoring system in real time, perform node sensitivity calculation processing in combination with the anchoring system topology diagram, compare the deviation of load data with the design load in the node attribute set, and generate the node sensitivity of each node in combination with the material yield strength parameter.

[0041] The overload source marking module is used to mark nodes for overload source processing, filter nodes with sensitivity greater than a preset sensitivity threshold, and generate an overload source set.

[0042] The failure propagation path analysis module is used to process the anchoring system topology and overload source set based on Monte Carlo path simulation, calculate the failure propagation probability between adjacent nodes, and generate propagation analysis results containing the failure propagation path set and path risk intensity.

[0043] The life assessment report generation module is used to extract the maximum path risk intensity from the set of failure transmission paths based on the transmission analysis results, calculate the life attenuation coefficient of the anchoring system and the remaining life data of each anchor node by combining it with the historical degradation data of the anchoring system, and integrate and generate the anchoring system life assessment report. The anchoring system life assessment report is used to indicate the remaining service life of the engineering structure and the location of high-risk anchoring units.

[0044] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the above-mentioned intelligent evaluation methods for the life of anchoring systems.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-mentioned intelligent evaluation methods for the life of anchoring systems.

[0046] In summary, the intelligent life evaluation method for anchoring systems provided in this application establishes a node attributed topology graph using spatial coordinates and mechanical parameters, enabling the quantification of the mechanical relationship network between anchoring units. Combined with real-time load monitoring and dynamic sensitivity calculation based on material strength characteristics, it achieves accurate perception of local overload risks. Based on Monte Carlo simulation-based failure propagation path modeling, it quantifies the risk propagation probability and path strength between overloaded nodes, overcoming the limitations of traditional methods in characterizing the collaborative failure of multiple anchoring units. Finally, by integrating historical degradation data and propagation analysis results, and coupling the system-level life decay coefficient through a hyperbolic attenuation model, it achieves dual decision support for dynamic prediction of remaining service life and spatial location of high-risk units. Compared to the traditional isolated single-anchor evaluation mode, this method can establish a mathematical mapping relationship between failure propagation effects and system life decay, realizing a paradigm shift from "point-based risk identification" to "networked failure tracking," significantly improving the accuracy of life prediction for group anchoring systems. Simultaneously, through the visualization of high-risk unit location capabilities, it accurately guides maintenance resource allocation, providing a scientific decision-making basis for the safe operation and maintenance of major engineering structures.

[0047] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0048] Figure 1 A flowchart illustrating an intelligent evaluation method for the lifespan of an anchoring system provided in this application embodiment;

[0049] Figure 2 A schematic diagram of the process for generating transmission analysis results containing a set of failure transmission paths and the intensity of path risk, provided in an embodiment of this application;

[0050] Figure 3 This is a structural schematic diagram of an intelligent evaluation system for the life of an anchoring system, provided as another embodiment of this application. Detailed Implementation

[0051] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0053] In one embodiment, such as Figure 1 As shown, an intelligent evaluation method for the lifespan of an anchoring system is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0054] S1: Obtain the spatial coordinates of anchor points and stiffness parameters of connecting components in the engineering structure, calculate the Euclidean distance between each anchor point and filter anchor point pairs that meet the preset maximum influence distance, calculate the edge weights between adjacent anchor points based on the stiffness matrix and cross-sectional area of ​​the connecting components, and generate an anchoring system topology diagram containing node attribute sets and edge weight sets.

[0055] Specifically, the system acquires the spatial coordinates of anchorage points and the stiffness parameters of connecting components in the engineering structure. The spatial coordinates of anchorage points are obtained through high-precision 3D measurement equipment. For projects with established Building Information Models (BIMs), the system directly exports the 3D coordinate data of anchorage points from the BIM, using the engineering construction coordinate system. The stiffness parameters of connecting components include the elastic modulus, Poisson's ratio, moment of inertia, and component length. The system determines the elastic modulus and Poisson's ratio based on the factory inspection report of the connecting component material, extracts the cross-sectional dimensions from the component design drawings to calculate the cross-sectional area and moment of inertia, and uses a laser rangefinder to measure the length of the connecting component. The system calculates the distance between any two anchorage points based on the Euclidean distance formula in 3D space. The preset maximum influence distance is determined based on the collaborative force characteristics of the anchorage system's group anchors. Through indoor group anchorage model tests combined with numerical simulation verification, the system selects anchorage point pairs that meet the preset maximum influence distance to form a potential edge set of the topological graph.

[0056] Furthermore, the system calculates the edge weights between adjacent anchorage points based on the stiffness matrix and cross-sectional area of ​​the connecting components. Using the finite element method, the connecting components are discretized into beam or rod elements to establish element stiffness matrices. After assembling these into a global stiffness matrix, the mean of the main diagonal elements is extracted as the equivalent stiffness. The edge weights are then calculated using the equivalent stiffness, the actual cross-sectional area of ​​the connecting components, and the standard cross-sectional area. The system uses a graph theory model to generate the anchorage system topology. The node set consists of all anchorage points and includes node attributes, the edge set consists of anchorage point pairs that satisfy the preset maximum influence distance, and the edge weight set is stored in the form of an adjacency matrix. The system generates the topology map using specialized software and supports the visualization of node distribution and edge weight differences.

[0057] S2: Real-time acquisition of load data in the anchoring system, combined with the anchoring system topology diagram to perform node sensitivity calculation, compare the deviation of load data with the design load in the node attribute set, and combine with the material yield strength parameter to generate the node sensitivity of each node.

[0058] Specifically, the system monitors load data in the anchoring system in real time. It employs a distributed sensor network to collect load data and deploys sensors at key stress points of each anchoring point, including strain gauges or fiber optic sensors at the connection between the anchor head and connecting components, in the middle of the anchor bolt, and pressure sensors at the ends of the connecting components. The system acquires sensor data through a data acquisition unit, filters it to remove noise interference, and then transmits it to the data processing center via an industrial module. The system removes outliers from the transmitted load data and generates a real-time effective load sequence for each node. The system calculates node sensitivity based on the anchoring system topology diagram. Node sensitivity quantifies the overload risk of a single anchoring point. The system compares the deviation between the real-time effective load and the design load in the node attribute set. The design load is determined according to relevant structural design specifications. It also calculates node sensitivity by combining the yield strength parameters of the anchoring unit material and the stress cross-sectional area of ​​the anchoring point. The system uses the load deviation rate to reflect the degree of deviation between the actual load and the design load, and the load stress ratio to reflect the ratio of stress to yield stress corresponding to the real-time load. The system combines the load deviation rate and the load stress ratio to generate the node sensitivity for each node.

[0059] S3: Perform overload source marking on node sensitivity, filter nodes with sensitivity greater than a preset sensitivity threshold, and generate an overload source set.

[0060] Specifically, the system performs overload source marking processing on node sensitivity. The preset sensitivity threshold is determined based on the reliability requirements of the anchoring system and engineering test data. The system selects anchoring units of the same type and specifications as the target project to conduct fatigue load tests to obtain the failure probability under different load levels. Combined with the structural safety level of the project, the preset sensitivity threshold is determined through reliability analysis. The system compares the node sensitivity of each node with the preset sensitivity threshold and selects nodes with sensitivity greater than the preset sensitivity threshold as overload source nodes. The system assigns a unique identifier to each overload source node and records key information of the overload source node, including node identifier, spatial coordinates, real-time load value, sensitivity value, marking time, load deviation rate, and load stress ratio. The system integrates the information of all overload source nodes to generate an overload source set, which is stored in a structured database table to provide data support for subsequent Monte Carlo path simulation.

[0061] S4: Based on Monte Carlo path simulation, the topology of the anchoring system and the set of overload sources are processed to calculate the failure propagation probability between adjacent nodes and generate propagation analysis results containing the set of failure propagation paths and the path risk intensity.

[0062] Specifically, the system processes the anchorage system topology and overload source set based on Monte Carlo path simulation. The system sets the sampling number for the Monte Carlo simulation to ensure the statistical significance of the simulation results. Failure propagation influencing factors are defined as the load transfer coefficient of adjacent nodes, the fatigue reliability of connecting components, and the sensitivity transfer coefficient of nodes. The load transfer coefficient is the ratio of the actual stiffness to the design stiffness of the connecting component. Fatigue reliability is calculated based on Miner's linear cumulative damage theory. The sensitivity transfer coefficient is the ratio of the sensitivities of adjacent nodes. The system sets simulation termination conditions: the simulation stops when failure propagates to non-load-bearing nodes, boundary nodes, or when the path risk intensity falls below a preset minimum risk threshold. The system calculates the failure propagation probability between adjacent nodes, comprehensively considering the load transfer coefficient, fatigue reliability, sensitivity transfer coefficient, and node sensitivity.

[0063] Starting from each node in the overload source set, the system determines the propagation probability of adjacent nodes through random sampling, generating multiple failure propagation paths. The system records the node sequence of each path and calculates the path risk intensity, which is obtained by multiplying the failure propagation probabilities of all adjacent nodes in the path by the path length. The system integrates all simulated paths to generate a failure propagation path set and presents the propagation analysis results in a data table, including the starting point, node sequence, ending point, path risk intensity, and propagation probability of each segment for each path. The system sorts the propagation analysis results according to the path risk intensity.

[0064] S5: Based on the transmission analysis results, extract the maximum path risk intensity in the failure transmission path set, combine it with the historical degradation data of the anchoring system to calculate the life decay coefficient of the anchoring system and the remaining life data of each node's single anchor, integrate and generate an anchoring system life assessment report, which is used to indicate the remaining service life of the engineering structure and the location of high-risk anchoring units.

[0065] Specifically, the system extracts the maximum path risk intensity from the set of failure transmission paths based on the transmission analysis results. The system collects historical degradation data of the anchoring system, including annual inspection data, material performance degradation data, overload event records, and maintenance records. The annual inspection data consists of periodic inspection results of the anchoring units, and the material performance degradation data includes corrosion rate and strength loss rate. The system fits the material performance degradation data into time series data based on a linear degradation model. All historical data is organized by timestamp and stored in a time-series database, supporting queries by node identifier and time range. The system combines the maximum path risk intensity with the strength loss rate from the historical degradation data to calculate the anchoring system's lifespan attenuation coefficient, which reflects the degree of system lifespan attenuation.

[0066] Furthermore, the system calculates the remaining life data of each anchor node based on the material's remaining strength and real-time load. The material's remaining strength is calculated using the material's yield strength and strength loss rate. A load amplification factor is introduced during the calculation process. The load amplification factor is determined based on the maximum path risk intensity to account for the additional load impact caused by failure propagation. The system integrates life decay coefficient and single anchor remaining life data to generate an anchoring system life assessment report. The assessment report includes a system overview, data sources, analysis process summary, core assessment results, maintenance recommendations, and attached diagrams. The system overview covers the project name, anchoring system type, number of anchoring units, and monitoring cycle. Data sources cover spatial coordinate measurement data, load monitoring data, historical degradation data, and simulation parameters. The analysis process summary covers topology map construction results, node sensitivity statistics, number and distribution of overload sources, and failure propagation path ranking results. Core assessment results cover the overall remaining service life of the system and the locations of high-risk anchoring units. The overall remaining service life of the system is obtained by combining the minimum of the remaining life of all single anchors with the life decay coefficient. The locations of high-risk anchoring units include overload source nodes and nodes with the highest risk intensity in the failure propagation path, with relevant information marked. Maintenance recommendations cover a list of high-risk units to be prioritized for reinforcement, maintenance cycle adjustment recommendations, and load optimization allocation schemes. Attached diagrams include the anchoring system topology map, failure propagation path diagram, single anchor remaining life distribution map, and load monitoring time series curve.

[0067] In summary, the intelligent life evaluation method for anchoring systems provided in this application establishes a node attributed topology graph using spatial coordinates and mechanical parameters, enabling the quantification of the mechanical relationship network between anchoring units. Combined with real-time load monitoring and dynamic sensitivity calculation based on material strength characteristics, it achieves accurate perception of local overload risks. Based on Monte Carlo simulation-based failure propagation path modeling, it quantifies the risk propagation probability and path strength between overloaded nodes, overcoming the limitations of traditional methods in characterizing the collaborative failure of multiple anchoring units. Finally, by integrating historical degradation data and propagation analysis results, and coupling the system-level life decay coefficient through a hyperbolic attenuation model, it achieves dual decision support for dynamic prediction of remaining service life and spatial location of high-risk units. Compared to the traditional isolated single-anchor evaluation mode, this method can establish a mathematical mapping relationship between failure propagation effects and system life decay, realizing a paradigm shift from "point-based risk identification" to "networked failure tracking," significantly improving the accuracy of life prediction for group anchoring systems. Simultaneously, through the visualization of high-risk unit location capabilities, it accurately guides maintenance resource allocation, providing a scientific decision-making basis for the safe operation and maintenance of major engineering structures.

[0068] In one embodiment, S1 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0069] S11: Obtain the spatial coordinates of anchor points and stiffness parameters of connecting components in the engineering structure, perform Euclidean distance calculation on the spatial coordinates of anchor points, calculate the straight-line distance between every two anchor points, and generate a distance matrix between nodes.

[0070] Specifically, the system acquires the spatial coordinates of anchorage points and the stiffness parameters of connecting components in the engineering structure. The spatial coordinates of anchorage points are collected using high-precision 3D measuring equipment. For projects with an established Building Information Model (BIM), the system directly exports the 3D coordinate data of anchorage points from the BIM, using the engineering construction coordinate system. The stiffness parameters of the connecting components include the elastic modulus, Poisson's ratio, moment of inertia, and component length. The system determines the elastic modulus and Poisson's ratio based on the factory inspection report of the connecting component material, extracts the cross-sectional dimensions from the component design drawings to calculate the cross-sectional area and moment of inertia, and uses a distance measuring device to measure the length of the connecting components. The system performs Euclidean distance calculation on the acquired spatial coordinates of the anchorage points. Using the 3D coordinates of any two anchorage points as input, the system calculates the straight-line distance between the two points based on the Euclidean distance formula, traversing all anchorage point combinations to complete the distance calculation. The system organizes all calculated straight-line distances between anchorage points into a matrix, where the rows and columns of the matrix correspond to different anchorage point identifiers, and the matrix elements represent the straight-line distance between corresponding two anchorage points, generating a node distance matrix.

[0071] S12: Perform spatial association filtering on the distance matrix between nodes, select anchor point pairs whose distance is less than the preset maximum influence distance, and generate a set of associated node pairs.

[0072] Specifically, the system performs spatial correlation filtering on the distance matrix between nodes. The preset maximum influence distance is determined based on the collaborative force characteristics of the anchorage system's group anchors, and verified through indoor group anchorage model tests combined with numerical simulations, serving as the filtering criterion. The system iterates through each element in the distance matrix between nodes, comparing the element value with the preset maximum influence distance, and filtering out matrix elements whose values ​​are less than the preset maximum influence distance. The system extracts the anchor point identifiers corresponding to the filtered matrix elements; each matching matrix element corresponds to a pair of anchor points, forming an anchor point pair. The system organizes all matching anchor point pairs in a structured format, with each anchor point pair containing unique identifiers for both anchor points, generating a set of associated node pairs.

[0073] S13: Perform edge weight calculation on the set of associated nodes and stiffness parameters of connecting components, calculate the mechanical transfer efficiency based on the stiffness matrix and cross-sectional area, and generate an edge weight set.

[0074] Specifically, the system performs edge weight calculations on the set of associated node pairs and the stiffness parameters of connecting components. Based on the stiffness parameters of the connecting components, the system constructs a stiffness matrix. Using the finite element method, the connecting components between associated node pairs are discretized into beam or rod elements. Element stiffness matrices are established based on the material properties and cross-sectional parameters of the components, and then assembled into a global stiffness matrix through coordinate transformation. The system extracts the mean value of the main diagonal elements of the global stiffness matrix as the equivalent stiffness. Combined with the cross-sectional area of ​​the connecting components between associated node pairs, the system calculates the mechanical transfer efficiency. The mechanical transfer efficiency is determined by multiplying the equivalent stiffness by the cross-sectional area and then dividing it by the ratio of the standard parameters of that type of connecting component. The system uses the mechanical transfer efficiency as edge weights, assigning corresponding edge weights to each anchor point pair in the set of associated node pairs. Each edge weight is associated one-to-one with its corresponding anchor point pair, sorted by anchor point pair identifiers, generating an edge weight set. The edge weight set is stored in a data table, recording the anchor point pair identifiers and corresponding edge weight values.

[0075] S14: Construct a topology network based on the edge weight set and the preset anchorage node design parameters, integrate the design load, yield strength and anchorage length to form node attributes, map the edge weight set to the topology edge, and generate an anchorage system topology diagram containing the node attribute set and edge weight set.

[0076] Specifically, the system constructs a topology network based on the edge weight set and preset anchorage node design parameters. The preset anchorage node design parameters include design load, yield strength, and anchorage length. The design load is determined according to relevant structural design specifications, the yield strength is obtained from the factory test report of the anchorage unit material, and the anchorage length is obtained from engineering construction records or component design drawings. The system integrates the design load, yield strength, and anchorage length, assigning corresponding attribute information to each anchorage point to form node attributes. The node attributes of all anchorage points are then compiled into a node attribute set.

[0077] Furthermore, the system uses anchor point pairs in the set of associated node pairs as the basis of topological edges, mapping the edge weight values ​​in the edge weight set to the corresponding topological edges, thus establishing the association between topological edges and edge weights. The system uses a graph theory model to construct the topological network. The node set consists of all anchor points in the engineering structure, with each node associated with the attribute information in the node attribute set. The edge set consists of anchor point pairs in the set of associated node pairs, with each edge associated with the edge weight value in the edge weight set, generating an anchoring system topology graph containing the node attribute set and the edge weight set.

[0078] In one embodiment, step S2 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0079] S21: Real-time acquisition of load data in the anchoring system, time-domain feature extraction processing of the load data, calculation of load fluctuation amplitude and frequency distribution, and generation of dynamic load feature spectrum.

[0080] Specifically, the system monitors load data in the anchoring system in real time. Adapted load monitoring sensors are deployed at key stress points of each anchoring node. The sensor type is selected based on the type of load (tensile / compressive load, shear load, etc.) The sensors upload the collected load data to the system's data processing module in real time via wireless or wired transmission links. The system performs time-domain feature extraction on the uploaded raw load data, using signal processing algorithms to separate the effective load signal from environmental interference signals. Time-domain analysis methods are used to calculate the load fluctuation amplitude and frequency distribution. The load fluctuation amplitude reflects the range of load variation within the monitoring period, while the frequency distribution reflects the periodicity of load changes over time. The system integrates the calculated load fluctuation amplitude and frequency distribution according to time series and node numbers, presenting the dynamic change characteristics of the load at each node in a graphical form, generating a dynamic load characteristic spectrum. This characteristic spectrum completely records the dynamic attributes of the load at each node.

[0081] S22: Perform deviation quantification processing on the design load of nodes in the dynamic load characteristic spectrum and anchoring system topology diagram, compare the degree of nonlinear difference between the real-time load peak and the design load threshold, and generate load deviation coefficient.

[0082] Specifically, the system performs deviation quantification processing on the dynamic load characteristic spectrum and the node design loads in the anchoring system topology diagram. The node design loads are taken from the design load parameters in the node attribute set of the topology diagram. The design load parameters are determined according to relevant structural design specifications, and the system sets design load thresholds based on the design load parameters. The system extracts the real-time load peak values ​​for each time period from the dynamic load characteristic spectrum, compares the real-time load peak values ​​with the corresponding node's design load threshold, and calculates the degree of nonlinear difference between the two based on nonlinear analysis theory. The degree of nonlinear difference is determined by the deviation of the real-time load peak value from the design load threshold, combined with the nonlinear coefficient of the load change trend. The system quantifies the degree of nonlinear difference of each node in different time periods to obtain the load deviation coefficient corresponding to each node. Each load deviation coefficient is associated with the node identifier and time period. The system groups and organizes the load deviation coefficients by node identifier, generating a load deviation coefficient set. The load deviation coefficient set is stored as an associated data table, recording the node identifier, time period, and corresponding load deviation coefficient.

[0083] S23: Perform sensitivity fusion on the load deviation coefficient and yield strength parameters in the node attribute set, and calculate the dynamic vulnerability of the node by combining the material strength attenuation characteristics and the coupling effect of environmental temperature and humidity, and generate the node sensitivity of each node.

[0084] Specifically, the system performs sensitivity fusion on the load deviation coefficient and the yield strength parameter in the node attribute set. The yield strength parameter is taken from the node attribute set. The system acquires material strength decay characteristic data, which is based on historical test data of the anchoring system and analyzed in conjunction with material aging theory. The system collects environmental temperature and humidity data, analyzes the impact of temperature and humidity coupling effect on the mechanical properties of the anchoring unit based on environmental engineering theory, and determines the temperature and humidity coupling effect coefficient. The system fuses the load deviation coefficient and yield strength parameter, adjusts the actual effective value of the yield strength parameter in conjunction with the material strength decay characteristic data, introduces the temperature and humidity coupling effect coefficient to correct the load deviation coefficient, and calculates the dynamic vulnerability of the node based on mechanical coupling theory. The dynamic vulnerability is determined by the ratio of the corrected load deviation coefficient to the adjusted effective value of the yield strength. The system quantifies the dynamic vulnerability of each node, with each node corresponding to a unique dynamic vulnerability value. The system associates and organizes the node identifier with the corresponding dynamic vulnerability value to generate the node sensitivity of each node.

[0085] In one embodiment, step S3 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0086] S31: Perform threshold determination processing on node sensitivity, compare the sensitivity value of each node with the preset sensitivity threshold, and generate a node state label set.

[0087] Specifically, the system performs threshold determination on node sensitivity. The preset sensitivity threshold is determined based on the reliability requirements of the anchoring system and engineering test data. Fatigue load tests are conducted on anchoring units of the same type and specifications as the target project to obtain the failure probability under different load levels. This threshold is then determined after reliability analysis combined with the structural safety level of the project. The system retrieves the sensitivity values ​​of each node, iterates through all nodes, and compares the sensitivity value of each node with the preset sensitivity threshold one by one. Based on the comparison results, the node status is marked. If the node sensitivity value is greater than the preset sensitivity threshold, the node is marked as overloaded; if the node sensitivity value is less than or equal to the preset sensitivity threshold, the node is marked as normal. The system organizes all node identifiers, sensitivity values, and corresponding status markers in a structured format. Each entry contains a unique node identifier, sensitivity value, and status marker information, generating a node status marker set.

[0088] S32: The node state marker set is processed to extract overloaded nodes, and the nodes marked as overloaded are filtered and their spatial locations are recorded to generate a preliminary overloaded node set.

[0089] Specifically, the system performs overload node extraction processing on the node status marker set. The system iterates through all entries in the node status marker set, filters out entries marked as overloaded, and extracts the corresponding node identifiers from these entries. The system calls the previously acquired anchor point spatial coordinate data, matches the corresponding node spatial coordinates based on the filtered overloaded node identifiers, and records the spatial coordinate information of each overloaded node. The system associates and integrates the identifiers of the overloaded nodes with their corresponding spatial coordinates, forming a record for each overloaded node containing both the node identifier and spatial coordinates. All such records are sorted and organized by node identifier to generate an initial set of overloaded nodes.

[0090] S33: Perform set optimization processing on the initial screening of overload nodes, merge duplicate nodes and verify the validity of node positions through preset engineering boundary coordinate range, remove abnormal nodes that exceed the structural range, and generate an overload source set.

[0091] Specifically, the system performs set optimization on the initial screening of overload nodes. The system iterates through all records in the initial screening set, checking for duplicate records based on the node's unique identifier. If multiple records correspond to the same node identifier, one complete record is retained, and the remaining duplicate records are deleted, thus merging duplicate nodes. The system calls the preset engineering boundary coordinate range data for the engineering structure. This data is taken from engineering design drawings or building information models and includes the coordinate boundary range of the anchorage area of ​​the engineering structure. The system compares the spatial coordinates of each node in the initial screening set of overload nodes with the preset engineering boundary coordinate range to determine if the node's spatial coordinates are within the engineering boundary coordinate range. If the node's spatial coordinates exceed the preset engineering boundary coordinate range, it is identified as an abnormal node and removed from the set; if the node's spatial coordinates are within the preset engineering boundary coordinate range, the node record is retained. The system integrates the optimized and retained node records to generate an overload source set. The overload source set contains node identifiers, spatial coordinates, and sensitivity values, and is stored in a standardized database table.

[0092] In one embodiment, such as Figure 2 As shown, S4 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0093] S41: Perform transmission probability modeling on the edge weight set and node sensitivity, and calculate the failure propagation probability by combining the material fatigue cumulative effect.

[0094] Specifically, the system performs probabilistic modeling on the edge weight set and node sensitivity, traverses all entries in the edge weight set, extracts the edge weight value corresponding to each edge, and determines the maximum value of all edge weights in the topology graph by comparing values ​​one by one. Simultaneously, the system collects the sensitivity values ​​of all nodes, sorts and compares these values, and determines the maximum sensitivity value among all nodes in the topology graph. The system introduces a material attenuation coefficient k, which is determined based on fatigue characteristic test data of the anchoring unit material. The test data covers the strength retention rate of the material under different cycles of loading. The k value reflects the rate of performance degradation of the material with the accumulation of cyclic loads. The system uses a preset formula to calculate the failure propagation probability. The formula for calculating the failure propagation probability is:

[0095]

[0096] in, Let be the failure propagation probability from node i to node j. The edge weight value. This represents the maximum value of all edge weights in the topological graph. The material attenuation coefficient, Let i be the sensitivity of node i. This represents the maximum sensitivity of all nodes in the topology graph. The system iterates through all associated node pairs, calling this formula to calculate the corresponding sensitivity for each pair. The calculation results are associated with node pair identifiers, sorted and stored according to node pair identifiers, and a failure propagation probability set is generated. This set is used to quickly query the propagation probability data between nodes during subsequent path simulation.

[0097] S42: Perform path Monte Carlo generation on the failure propagation probability and overload source set to simulate the propagation path of failure along the topological edges and generate the original propagation path set.

[0098] Specifically, the system performs Monte Carlo path generation processing on the failure propagation probability and the overload source set. The system first reads the overload source set and extracts the identifiers and spatial coordinates of all overload source nodes. The system sets the number of sampling iterations for the Monte Carlo simulation, determined based on the total number of nodes and associated node pairs in the anchoring system, ensuring that the sampling results cover the main possible failure propagation paths in the system. For each overload source node, the system initiates the sampling process, setting that node as the starting node for failure propagation during each sampling, and querying all node pairs associated with that starting node and their corresponding failure propagation probabilities. The system generates a random number r. If the random number r is less than... If the propagation condition is met, then the corresponding node j is set as the next propagation node.

[0099] Furthermore, the system repeats the above operations, each time querying its associated node pairs and... The system generates random numbers to determine whether to continue propagating until it reaches non-carrying nodes or boundary nodes, forming a complete failure propagation path. After completing all sampling, the system collects all generated propagation paths. Each path records the node propagation sequence and the corresponding associated node pairs. These are then categorized and organized by the starting node to generate an original propagation path set. The path set is stored in a structured table, containing fields for path ID, starting node identifier, node sequence, and path length.

[0100] S43: Perform path risk intensity calculation on the original transmission path set, multiply the transmission probability along the path and compensate for spatial attenuation, correct the cumulative deviation of transmission probability of long paths through the topological distance attenuation factor, and generate transmission analysis results containing the set of failed transmission paths and path risk intensity.

[0101] Specifically, the system performs path risk intensity calculation on the original propagation path set, traverses each path in the original propagation path set, extracts the node propagation sequence in the path, and determines the topological distance between each pair of adjacent nodes in the path. The topological distance is the Euclidean distance between adjacent nodes. The system calculates the spatial attenuation coefficient. The formula is as follows:

[0102]

[0103] in, Let i be the spatial attenuation coefficient between nodes i and j. The topological distance between nodes i and j is represented by this coefficient, which reflects the weakening effect of spatial distance on failure propagation. The system calculates the basic risk intensity of the path. It is obtained by multiplying the failure propagation probabilities of each pair of adjacent nodes in the path, as shown in the formula:

[0104]

[0105] Where ∏ represents the sum of all adjacent nodes in the path. Perform cumulative multiplication. The system introduces a topological distance attenuation factor. Using formula Where D is the total topological distance of the path, i.e., all the paths in the path. The sum of the sums, This is the distance attenuation coefficient. Based on the structural characteristics of the anchoring system, this is used to correct for deviations in the cumulative transmission probability along long paths. The system calculates the final risk intensity. ,in This is the cumulative product of all spatial attenuation coefficients along the path. The system will include the identifier, node sequence, and... Associative storage, by The transmission analysis results are generated by sorting in descending order. The results include a ranking of path risk intensity, path details, and key node identification fields.

[0106] In one embodiment, S5 of the intelligent evaluation method for the life of an anchoring system provided by the present invention specifically includes the following steps:

[0107] S51: Perform critical path extraction processing on the transmission analysis results, screen transmission paths whose risk intensity exceeds the system risk threshold, and generate a set of high-risk paths.

[0108] Specifically, the system performs critical path extraction on the transmission analysis results. The results include a set of failure transmission paths and the corresponding path risk intensity for each path. The system first determines a system risk threshold, based on the engineering structure safety level requirements and the anchoring system failure consequence assessment results, reflecting the maximum acceptable path risk level. The system iterates through each failure transmission path in the results, comparing the path risk intensity with the system risk threshold, and filters out transmission paths whose risk intensity exceeds the threshold. The system records complete information about these filtered paths, including the node propagation sequence, the failure transmission probability of each node pair, and the total path length. These paths are then sorted in descending order of risk intensity to form a high-risk path set. This high-risk path set is stored in a structured data table, with fields including path identifier, path risk intensity, node sequence, and path length.

[0109] S52: Extract the maximum value of the path risk intensity in the set of high-risk paths to obtain the highest risk value among all paths and generate the maximum path risk intensity.

[0110] Specifically, the system extracts the maximum value of the path risk intensity from the high-risk path set. It reads all path risk intensity data from the high-risk path set data table and establishes a risk intensity data sequence. The system can use a traversal comparison method, starting from the beginning of the risk intensity data sequence and comparing the risk intensity value at the current position with the risk intensity values ​​at subsequent positions. After each round of comparison, the system retains the larger risk intensity value, continuing this process until all risk intensity data has been traversed. The system records the final retained risk intensity value, which is the highest risk value among all high-risk paths, generating the maximum path risk intensity. The system associates and stores the maximum path risk intensity with the corresponding path identifier, while simultaneously verifying the integrity of the path information corresponding to the maximum path risk intensity, ensuring that no data such as the node sequence and failure propagation probability in the path is missing.

[0111] S53: Perform Weibull distribution modeling on the historical degradation data of the anchoring system, calculate the remaining lifetime of the nodes using the three-parameter Weibull distribution function, and generate the single anchor remaining lifetime data for each node.

[0112] Specifically, the system uses Weibull distribution modeling to process the historical degradation data of the anchoring system. This historical degradation data includes corrosion depth data, material strength loss data, and mechanical property degradation data for each anchoring unit over the years. The system first preprocesses the historical degradation data, removing outliers and then organizing it according to time series. The system uses a three-parameter Weibull distribution function to fit the preprocessed historical degradation data. The expression for the three-parameter Weibull distribution function is:

[0113]

[0114] in, For a moment The probability of degradation and failure of anchoring elements. For service time, The position parameter reflects the delay time at which degradation begins. The scale parameter reflects the characteristic lifetime of the degradation process. The shape parameter reflects the degradation rate. The system is calculated using the maximum likelihood estimation method. , , The value is used to calculate the remaining service life when the degradation failure probability reaches the preset failure probability threshold based on the distribution function. All anchoring nodes are traversed to complete the remaining life calculation and generate the single anchor remaining life data for each node.

[0115] S54: Extract the minimum value from the single anchor remaining lifetime data, combine it with the maximum path risk intensity to couple the system lifetime, calculate the system lifetime decay coefficient through the hyperbolic decay function, and generate the system remaining lifetime value by combining the minimum single anchor remaining lifetime.

[0116] Specifically, the system extracts the minimum value from the remaining service life data of a single anchor. This minimum value represents the shortest remaining service time in a single anchoring unit, denoted as... The system combines the maximum path risk intensity. To perform system lifetime coupling, the hyperbolic decay function is used to calculate the system lifetime decay coefficient. The hyperbolic decay function is expressed as:

[0117]

[0118] in, The attenuation coefficient is determined based on the structural coupling characteristics of the anchoring system, reflecting the degree of influence of the maximum path risk intensity on the system's lifetime attenuation. The system will have a minimum single anchor remaining life. With system lifespan decay coefficient Multiply by each other to obtain the remaining lifetime of the system. The calculation expression is:

[0119]

[0120] The system verifies the validity of each parameter during the calculation process to ensure... The value is within a reasonable range. By meeting the structural characteristics requirements, the remaining lifespan of the system is determined and stored, providing core lifespan parameters for subsequent engineering decisions.

[0121] S55: Encapsulates engineering decisions on the set of high-risk paths and the remaining life value of the system, integrates the prediction of remaining service life and the spatial coordinates of high-risk anchoring units, and generates an anchoring system life assessment report.

[0122] Specifically, the system encapsulates the high-risk path set and the system's remaining lifespan value into an engineering decision. First, the system extracts all anchorage units involved in the high-risk path set, organizes the spatial coordinates of these anchorage units, and combines this with the single-anchor remaining lifespan data of each unit to mark high-risk anchorage units. High-risk anchorage units are those with the highest risk intensity among the high-risk paths. The system then integrates the remaining service life prediction results to obtain the system's remaining lifespan value. Simultaneously, the system integrates basic information about the anchoring system, including project name, anchoring system type, total number of anchoring units, and monitoring cycle. This information is organized according to a preset report structure, which includes project overview, high-risk path analysis, system life prediction, distribution of high-risk anchoring units, and maintenance recommendations. These recommendations are based on the location and remaining lifespan of the high-risk units. The system converts this integrated information into a standard document format, generating an anchoring system life assessment report. The report includes a visual chart showing the spatial coordinates of high-risk anchoring units, allowing engineers to directly locate high-risk areas and providing comprehensive data support for maintenance decisions.

[0123] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0124] Based on the same inventive concept, this application also provides an intelligent life evaluation system for anchoring systems to implement the aforementioned intelligent life evaluation method for anchoring systems. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the intelligent life evaluation system for anchoring systems provided below can be found in the limitations of the intelligent life evaluation method for anchoring systems described above, and will not be repeated here.

[0125] Preferably, such as Figure 3 As shown, the present invention provides an intelligent evaluation system 600 for the life of an anchoring system, which is configured with the following modules:

[0126] The anchoring system topology construction module 610 is used to obtain the spatial coordinates of anchoring points and stiffness parameters of connecting members in the engineering structure, calculate the Euclidean distance between each anchoring point and filter anchoring point pairs that meet the preset maximum influence distance, calculate the edge weights between adjacent anchoring points based on the stiffness matrix of connecting members and the cross-sectional area of ​​connecting members, and generate an anchoring system topology diagram containing a set of node attributes and a set of edge weights.

[0127] The node sensitivity calculation module 620 is used to acquire load data in the anchoring system in real time, perform node sensitivity calculation processing in combination with the anchoring system topology diagram, compare the deviation of load data with the design load in the node attribute set, and generate the node sensitivity of each node in combination with the material yield strength parameter.

[0128] The overload source marking module 630 is used to perform overload source marking processing on node sensitivity, filter nodes with sensitivity greater than a preset sensitivity threshold, and generate an overload source set.

[0129] The failure propagation path analysis module 640 is used to process the topology map and overload source set of the anchoring system based on Monte Carlo path simulation, calculate the failure propagation probability between adjacent nodes, and generate propagation analysis results containing the failure propagation path set and path risk intensity.

[0130] The life assessment report generation module 650 is used to extract the maximum path risk intensity in the failure transmission path set based on the transmission analysis results, calculate the life attenuation coefficient of the anchoring system and the single anchor remaining life data of each node by combining it with the historical degradation data of the anchoring system, and integrate to generate the anchoring system life assessment report. The anchoring system life assessment report is used to indicate the remaining service life of the engineering structure and the location of high-risk anchoring units.

[0131] Preferably, the anchoring system topology construction module 610 provided in this application is configured with the following units:

[0132] The node Euclidean distance calculation unit is used to obtain the spatial coordinates of anchor points and stiffness parameters of connecting components in the engineering structure, perform Euclidean distance calculation on the spatial coordinates of anchor points, calculate the straight-line distance between every two anchor points, and generate a node distance matrix.

[0133] The associated node pair filtering unit is used to perform spatial association filtering on the distance matrix between nodes, select anchor point pairs whose distance is less than the preset maximum influence distance, and generate a set of associated node pairs.

[0134] The edge weight calculation unit is used to calculate the edge weights of the set of associated node pairs and the stiffness parameters of the connecting components, and to calculate the mechanical transfer efficiency based on the stiffness matrix and cross-sectional area to generate the edge weight set.

[0135] The topology network construction unit is used to construct the topology network based on the edge weight set and the preset anchoring node design parameters. It integrates the design load, yield strength and anchoring length to form node attributes, maps the edge weight set to the topology edge, and generates an anchoring system topology diagram containing the node attribute set and the edge weight set.

[0136] Preferably, the node sensitivity calculation module 620 provided in this application is configured with the following units:

[0137] The dynamic load feature extraction unit is used to acquire load data in the anchoring system in real time, perform time-domain feature extraction processing on the load data, calculate the load fluctuation amplitude and frequency distribution, and generate a dynamic load feature spectrum.

[0138] The load deviation coefficient calculation unit is used to perform deviation quantification processing on the design load of nodes in the dynamic load characteristic spectrum and anchoring system topology diagram, compare the degree of nonlinear difference between the real-time load peak and the design load threshold, and generate the load deviation coefficient.

[0139] The node sensitivity fusion calculation unit is used to perform sensitivity fusion on the load deviation coefficient and the yield strength parameter in the node attribute set. It combines the material strength attenuation characteristics and the coupling effect of environmental temperature and humidity to calculate the dynamic vulnerability of the node and generate the node sensitivity of each node.

[0140] Preferably, the overload source marking module 630 provided in this application is configured with the following units:

[0141] The sensitivity threshold determination unit is used to perform threshold determination processing on the sensitivity of nodes, compare the sensitivity value of each node with the preset sensitivity threshold, and generate a node status label set.

[0142] The overload node initial screening and extraction unit is used to perform overload node extraction processing on the node state label set, filter the nodes marked as overloaded and record their spatial positions to generate an initial screening overload node set.

[0143] The overload node set optimization unit is used to perform set optimization processing on the initially screened overload node set, merge duplicate nodes and verify the validity of node positions through preset engineering boundary coordinate range, remove abnormal nodes that exceed the structural range, and generate an overload source set.

[0144] Preferably, the failure propagation path analysis module 640 provided in this application is configured with the following units:

[0145] The failure propagation probability modeling unit is used to perform propagation probability modeling on edge weight sets and node sensitivity, and calculates the possibility of failure propagation by combining the material fatigue accumulation effect to generate the failure propagation probability.

[0146] The Monte Carlo simulation unit for propagation paths is used to perform Monte Carlo generation processing on the failure propagation probability and the set of overload sources, simulate the propagation path of failure along the topological edge, and generate the original propagation path set;

[0147] The path risk intensity calculation and correction unit is used to calculate the path risk intensity of the original transmission path set, multiply the transmission probability along the path and compensate for spatial attenuation, and correct the cumulative deviation of the transmission probability of long paths through the topological distance attenuation factor to generate transmission analysis results containing the set of failed transmission paths and the path risk intensity.

[0148] Preferably, the life assessment report generation module 650 provided in this application is configured with the following units:

[0149] The high-risk path screening and extraction unit is used to extract critical paths from the transmission analysis results, screen transmission paths whose risk intensity exceeds the system risk threshold, and generate a set of high-risk paths.

[0150] The maximum path risk intensity extraction unit is used to extract the maximum value of the path risk intensity in the set of high-risk paths, obtain the highest risk value among all paths, and generate the maximum path risk intensity.

[0151] The single anchor remaining life modeling and calculation unit is used to perform Weibull distribution modeling on the historical degradation data of the anchoring system, calculate the remaining life of the node through the three-parameter Weibull distribution function, and generate single anchor remaining life data for each node.

[0152] The system remaining lifetime coupling calculation unit is used to extract the minimum value in the single anchor remaining lifetime data, combine it with the maximum path risk intensity to perform system lifetime coupling, calculate the system lifetime decay coefficient through the hyperbolic decay function, and generate the system remaining lifetime value by combining the minimum single anchor remaining lifetime.

[0153] The life assessment report engineering encapsulation unit is used to encapsulate the high-risk path set and the system's remaining life value for engineering decisions, integrate the remaining service life prediction and the spatial coordinates of the high-risk anchoring units, and generate an anchoring system life assessment report.

[0154] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described intelligent evaluation method for the life of the anchoring system.

[0155] In one embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described intelligent evaluation method for the lifespan of an anchoring system.

[0156] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0157] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0158] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent evaluation of the service life of an anchoring system, characterized in that, The method comprises the following steps: S1: obtaining the spatial coordinates of anchorage points and the stiffness parameters of connecting members in an engineering structure, calculating the Euclidean distances between the anchorage points, and screening anchorage point pairs that meet a preset maximum influence distance, combining the stiffness matrix of the connecting members and the cross-sectional area of the connecting members to calculate the edge weight between adjacent anchorage points, and generating an anchorage system topology graph containing a node attribute set and an edge weight set; S2: real-time acquisition of load data in the anchorage system, node sensitivity calculation and processing in combination with the anchorage system topology graph, comparison of the deviation of the load data from the design load in the node attribute set and combination of the material yield strength parameter, generation of the node sensitivity of each node; S3: overload source marking processing of the node sensitivity, screening of nodes with a sensitivity greater than a preset sensitivity threshold, and generation of an overload source set; S4: processing of the anchorage system topology graph and the overload source set based on Monte Carlo path simulation, calculation of the failure transmission probability between adjacent nodes, and generation of a transmission analysis result containing a failure transmission path set and a path risk intensity; S5: extraction of the maximum path risk intensity in the failure transmission path set based on the transmission analysis result, combination with historical degradation data of the anchorage system to calculate the life attenuation coefficient of the anchorage system and the single-anchor residual life data of each node, and integration to generate an anchorage system life evaluation report, which is used to indicate the remaining service life of the engineering structure and the position of the high-risk anchorage unit.

2. The method of claim 1, wherein, The S1 comprises: S11: obtaining the spatial coordinates of anchorage points and the stiffness parameters of connecting members in an engineering structure, performing Euclidean distance calculation processing on the spatial coordinates of the anchorage points, calculating the straight-line distance between each two anchorage points, and generating an inter-node distance matrix; S12: spatial correlation screening processing of the inter-node distance matrix, selection of anchorage point pairs with a distance less than a preset maximum influence distance, and generation of an associated node pair set; S13: edge weight calculation processing of the associated node pair set and the connecting member stiffness parameters, calculation of the mechanical transmission efficiency according to the stiffness matrix and the cross-sectional area, and generation of an edge weight set; S14: topology network construction of the edge weight set and the preset anchorage node design parameters, integration of the design load, yield strength and anchorage length to form node attributes, mapping of the edge weight set to the topology edges, and generation of an anchorage system topology graph containing a node attribute set and an edge weight set.

3. The method of claim 1, wherein, The S2 comprises: S21: real-time acquisition of load data in the anchorage system, time-domain feature extraction processing of the load data, calculation of the load fluctuation amplitude and frequency distribution, and generation of a dynamic load feature spectrum; S22: deviation quantization processing of the dynamic load feature spectrum and the node design load in the anchorage system topology graph, comparison of the nonlinear difference degree between the real-time load peak value and the design load threshold value, and generation of a load deviation coefficient; S23: sensitivity fusion of the load deviation coefficient and the yield strength parameter in the node attribute set, calculation of the node dynamic vulnerability in combination with the material strength attenuation characteristics and the environmental temperature and humidity coupling effect, and generation of the node sensitivity of each node.

4. The method of claim 1, wherein, The S3 comprises: S31: threshold determination processing is performed on the node sensitivity, the sensitivity value of each node is compared with a preset sensitivity threshold, and a node state marker set is generated; S32: the node state marker set is subjected to overload node extraction processing, nodes marked as overload state are screened and their spatial positions are recorded, and a preliminary screening overload node set is generated; S33: the preliminary screening overload node set is subjected to set optimization processing, repeated nodes are merged, and node position validity is verified through a preset engineering boundary coordinate range, abnormal nodes exceeding the structural range are removed, and an overload source set is generated.

5. The method of claim 1, wherein, The S4 comprises: S41: conduct probability modeling processing is performed on the edge weight set and the node sensitivity, the failure propagation possibility is calculated in combination with the material fatigue cumulative effect, and a failure conduction probability is generated; S42: path Monte Carlo generation processing is performed on the failure conduction probability and the overload source set, the propagation path of failure along the topological edge is simulated, and an original conduction path set is generated; S43: path risk intensity calculation processing is performed on the original conduction path set, the conduction probability is multiplied along the path and the spatial attenuation is compensated, the conduction probability accumulation deviation of long paths is corrected through a topological distance attenuation factor, and a conduction analysis result containing the failure conduction path set and the path risk intensity is generated.

6. The method of claim 5, wherein, The calculation formula of the failure conduction probability is: wherein, is the failure propagation probability from node i to node j, is the edge weight value, is the maximum value of all edge weights in the topology graph, is the material attenuation coefficient, is the sensitivity of node i, is the maximum value of all node sensitivities in the topology graph.

7. The method according to any one of claims 1 to 6, characterized in that, The S5 comprises: S51: key path extraction processing is performed on the conduction analysis result, the conduction paths whose path risk intensity exceeds the system risk threshold are screened, and a high-risk path set is generated; S52: maximum value extraction processing is performed on the path risk intensity in the high-risk path set, the highest risk value in all paths is obtained, and a maximum path risk intensity is generated; S53: Weibull distribution modeling processing is performed on the historical degradation data of the anchoring system, the residual life of the node is calculated through a three-parameter Weibull distribution function, and single-anchor residual life data of each node is generated; S54: the minimum in the single-anchor residual life data is extracted, the system life coupling is performed in combination with the maximum path risk intensity, the system life attenuation coefficient is calculated through a hyperbolic decay function, and the system residual life value is generated in combination with the minimum single-anchor residual life; S55: engineering decision encapsulation is performed on the high-risk path set and the system residual life value, the residual service life prediction and the high-risk anchoring unit spatial coordinates are integrated, and an anchoring system life evaluation report is generated.

8. A system for intelligent evaluation of the life of an anchoring system, characterized in that it comprises: The system comprises: An anchoring system topology graph construction module is configured to obtain the spatial coordinates of anchoring points and the rigidity parameters of connecting members in an engineering structure, calculate the Euclidean distances between the anchoring points, screen anchoring point pairs satisfying a preset maximum influence distance, calculate the edge weights between adjacent anchoring points according to the rigidity matrix of the connecting members and the cross-sectional area of the connecting members, and generate an anchoring system topology graph containing a node attribute set and an edge weight set; A node sensitivity calculation module is configured to obtain load data in the anchoring system in real time, perform node sensitivity calculation processing in combination with the anchoring system topology graph, compare the deviation of the load data from the design load in the node attribute set and combine the material yield strength parameter, and generate the node sensitivity of each node. An overload source marking module is configured to perform overload source marking processing on the node sensitivity, filter nodes with a sensitivity greater than a preset sensitivity threshold, and generate an overload source set; An invalidation conduction path analysis module is configured to perform processing on the anchoring system topology graph and the overload source set based on Monte Carlo path simulation, calculate invalidation conduction probabilities between adjacent nodes, and generate a conduction analysis result containing an invalidation conduction path set and a path risk intensity; A life evaluation report generation module is configured to extract a maximum path risk intensity from the invalidation conduction path set based on the conduction analysis result, calculate a life attenuation coefficient of the anchoring system and single-anchor residual life data of each node in combination with historical degradation data of the anchoring system, and integrate to generate an anchoring system life evaluation report, which is configured to indicate a residual service life of the engineering structure and a high-risk anchoring unit position. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method in any one of claims 1 to 7.

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