A dynamic adjustment method and system for construction errors of annular cable-supported grid structures

Abstract

The present invention discloses a method and system for dynamically adjusting the construction errors of a ring-cable-supported grid structure, which relates to the field of building construction technology. The method comprises: performing design modeling of the ring-cable-supported grid structure, and constructing calibrated structural state data of the nodes using simulation fitting; deploying perception sensors to obtain a perception data set, and using the data set to generate a structural state vector of the node; analyzing spatial offset, cable force imbalance, and angular elevation error based on the state vector and calibration data, identifying errors and conducting a conduction and source tracing analysis, and establishing a dynamic adjustment strategy based on the analysis results. The present invention solves the technical problem that the existing technology lacks real-time and accurate error identification and dynamic adjustment means, resulting in errors not being corrected in a timely manner, affecting construction accuracy and structural stability. The present invention achieves the technical effect of accurately locating the error source and dynamically adjusting it through real-time perception and error conduction and source tracing analysis, thereby ensuring construction accuracy and structural stability.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to a method and system for dynamically adjusting construction errors of an annular cable-supported grid structure. Background Art

[0002] Annular cable-supported grid structures are widely used in large-span buildings and complex engineering projects due to their high efficiency, lightweight, and excellent mechanical properties. However, during construction, due to various factors (such as installation errors, environmental changes, and equipment precision limitations), the structure may experience problems such as positional deviations, uneven cable tension, and geometric inaccuracies. If these errors are not detected and corrected promptly, they can compromise the stability and safety of the entire structure. Traditional error control methods typically rely on manual inspection and local adjustments, making it difficult to achieve real-time, efficient, and accurate error identification and adjustment. Summary of the Invention

[0003] The present application provides a method and system for dynamically adjusting the construction errors of a ring-cable-supported grid structure, which is used to solve the technical problem that the existing technology lacks real-time and accurate error identification and dynamic adjustment means, resulting in the failure to correct errors in a timely manner, affecting construction accuracy and structural stability.

[0004] The first aspect of the present application provides a method for dynamically adjusting the construction error of a ring-shaped cable-supported grid structure, the method comprising: executing design modeling of the ring-shaped cable-supported grid structure, and performing simulation fitting using the design modeling results to construct calibrated structural state data of position nodes; deploying perception sensors, the perception sensors comprising a total station and a GNSS RTK module deployed at supports and nodes, an FBG optical fiber strain sensor deployed at the tensioning end anchor, and an environmental sensor deployed at the cable segment; after the ring-shaped cable-supported grid structure is assembled, activating the perception sensors, establishing a perception data set, and generating a structural state vector of the node using the perception data set; performing spatial offset error, cable force imbalance error, and angular elevation error analysis based on the structural state vector and the calibrated structural state data to establish an error identification result; performing error conduction source tracing analysis on the error identification result, and establishing a dynamic adjustment strategy using the error conduction source tracing analysis result and the error identification result.

[0005] The second aspect of the present application provides a dynamic adjustment system for construction errors of an annular cable-supported grid structure, the system comprising: a structural state calibration module, the structural state calibration module being used to execute design modeling of the annular cable-supported grid structure, and using the design modeling results for simulation fitting to construct calibrated structural state data of the position nodes; a sensor deployment module, the sensor deployment module being used to deploy perception sensors, the perception sensors comprising a total station and a GNSS RTK module deployed at supports and nodes, an FBG optical fiber strain sensor deployed at the tensioning end anchor, and an environmental sensor deployed at the cable segment; a data perception module, the data perception module being used to activate the perception sensors after the annular cable-supported grid structure is assembled, establish a perception data set, and generate a structural state vector of the node using the perception data set; an error identification module, the error identification module being used to perform spatial offset error, cable force imbalance error, and angular elevation error analysis based on the structural state vector and the calibrated structural state data to establish an error identification result; an error conduction tracing module, the error conduction tracing module being used to perform error conduction tracing analysis on the error identification result, and establish a dynamic adjustment strategy using the error conduction tracing analysis result and the error identification result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The present application provides a method and system for dynamic adjustment of construction errors of a ring-cable-supported grid structure, which relates to the field of building construction technology. Through the collection and analysis of real-time perception data, combined with design modeling and simulation fitting, dynamic error perception is performed in key construction stages such as initial tensioning, synchronous tensioning, and fine-tuning locking. The error source is accurately located by error conduction and tracing analysis, and a dynamic adjustment strategy is formulated to achieve precise control during the construction process. This solves the technical problem that the existing technology lacks real-time and accurate error identification and dynamic adjustment means, resulting in the failure to correct errors in a timely manner, affecting construction accuracy and structural stability. It achieves the technical effect of accurately locating the error source and dynamically adjusting it through real-time perception and error conduction and tracing analysis to ensure construction accuracy and structural stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a method for dynamically adjusting construction errors of a ring-shaped cable-supported grid structure provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of a dynamic adjustment system for construction errors of a ring-shaped cable-supported grid structure provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: structural state calibration module 11 , sensor deployment module 12 , data perception module 13 , error identification module 14 , error conduction tracing module 15 . DETAILED DESCRIPTION

[0012] The present application provides a method and system for dynamically adjusting the construction errors of a ring-cable-supported grid structure, which is used to solve the technical problem that the existing technology lacks real-time and accurate error identification and dynamic adjustment means, resulting in the failure to correct errors in a timely manner, affecting construction accuracy and structural stability.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Example 1, as Figure 1 As shown, the present application provides a method for dynamically adjusting the construction error of a ring-shaped cable-supported grid structure, the method comprising:

[0016] P10: Perform design modeling of the annular cable-supported grid structure, and use the design modeling results for simulation fitting to construct the calibrated structural state data of the position nodes.

[0017] Specifically, the design and modeling of the circular cable-supported lattice structure must first be performed. Using computer-aided design (CAD) software or other specialized structural analysis tools, a detailed geometric model of the structure is constructed according to the project's design requirements and specifications. This model includes the specific locations of all nodes, the connection methods of each cable segment, the layout of supports, and the distribution of cable forces. The design and modeling process must comprehensively consider factors such as the structural functional requirements, loading conditions, and material properties to ensure a scientific and reasonable design.

[0018] After the design model is complete, simulation fitting is performed using this model. This involves converting the structural design model into a mathematical model through numerical simulation techniques such as finite element analysis (FEA). The model is then simulated under various operating conditions. These conditions can include the structure's deadweight, construction loads, environmental loads (such as wind and temperature fluctuations), and possible dynamic loads. Through simulation analysis, the stress distribution, deformation, and stability of the structure under various operating conditions can be predicted, ensuring that the structure maintains optimal performance during actual construction and long-term use.

[0019] The simulation results provide information on the mechanical behavior and positional changes of each node under specific operating conditions. This information is used to evaluate whether the design meets practical requirements. If the simulation results show that the deformation or stress of certain nodes exceeds the design standards, designers can adjust parameters such as node position and cable tension based on the simulation data to further optimize the design and ensure the stability and safety of the structure.

[0020] After completing the simulation fitting, the calibration structural state data of the position nodes is then constructed. These calibration data include key structural information such as the ideal position and actual position of each node, as well as the strain, stress and deformation of the node. By comparing the data obtained by simulation fitting with the design model, the calibration state data of each node can be determined. These data will serve as a reference benchmark in the subsequent construction process to help construction personnel conduct real-time monitoring and adjustments. Specifically, when the construction structure begins to take shape, the actual data on site can be collected through perception sensors and compared with these calibration data to identify errors in the construction process and take corresponding adjustment measures.

[0021] By performing design modeling and simulation fitting, and building calibrated structural state data, every step of the entire construction process can be monitored and adjusted in real time to ensure its consistency with the design state, thereby improving construction accuracy and structural stability.

[0022] P20: Deploy perception sensors, which include total stations and GNSSRTK modules deployed on supports and nodes, FBG fiber optic strain sensors deployed on tensioning end anchors, and environmental sensors deployed on cable segments.

[0023] Optionally, sensor deployment can be performed throughout the construction site to monitor the dynamic parameters of the ring-cable-supported grid structure in real time. This deployment of sensor technology is fundamental to identifying and dynamically adjusting structural errors, ensuring continuous and accurate data collection during construction, allowing for the timely detection and resolution of potential errors.

[0024] Specifically, the deployment of perception sensors first involves installing total stations and GNSS RTK modules at supports and node locations. A total station is a high-precision measurement tool that can record changes in node positions in real time, especially during construction, when node positions may shift slightly due to external forces or installation errors. The total station continuously scans and measures the position of each node, providing accurate spatial coordinate data. The GNSS RTK module is used to provide even more precise positioning data, and is particularly suitable for positioning at wide-area construction sites. GNSS RTK technology uses satellite signals for real-time positioning, achieving millimeter-level accuracy. This ensures the precise alignment of each node throughout the structure during construction, avoiding structural deformation caused by position errors.

[0025] Next, fiber-optic strain sensors (FBGs) are deployed at the anchors at the tensioning ends. Utilizing the principle of fiber Bragg gratings (FBGs), these sensors monitor strain changes in the structure in real time. In cable-supported grid structures, the magnitude and distribution of cable forces are crucial to structural stability. FBG sensors can measure cable forces with high precision, accurately monitoring stress changes in each cable segment during the tensioning process. This allows for timely detection of potential problems such as unbalanced tension or excessive strain, ensuring uniform stress distribution across the structure and improving construction accuracy.

[0026] Furthermore, environmental sensors should be deployed on cable segments to monitor external environmental changes during construction, such as temperature, humidity, and wind speed. These environmental factors significantly impact the tension, strain, and displacement of the structure. Temperature fluctuations can cause structural materials to expand or contract, humidity changes can affect cable segment material properties, and wind speed can influence the distribution of tension forces during construction. These environmental sensors can capture real-time environmental condition data and integrate it with structural strain and position data for analysis, further improving the accuracy of error identification.

[0027] By integrating data from various sensors, various changes throughout the construction process can be fully and accurately perceived, thereby ensuring that the annular cable-supported grid structure can be successfully constructed according to design requirements and ensuring the stability and safety of the structure.

[0028] P30: After the ring-shaped cable-supported grid structure is assembled, the perception sensors are activated, a perception data set is established, and the structural state vector of the node is generated using the perception data set.

[0029] Specifically, after the annular cable-supported grid structure is assembled, the next key step is to put the perception sensors into practical use and obtain real-time data during the construction process.

[0030] Once the ring-shaped cable-supported grid structure is assembled, the deployed sensors must be activated to ensure proper operation of the total station, GNSS RTK module, fiber-optic strain gauge (FBG) strain sensor, and environmental sensor. These sensors then begin collecting real-time data on the structure's status. Once activated, these devices continuously monitor the spatial position changes of supports and nodes, the strain of the anchorage at the tensioning ends, and environmental factors within the cable segments, according to pre-set sampling frequencies and parameters.

[0031] As the sensors operate, the collected data is transmitted to the data processing system, forming a perception dataset. This dataset provides a real-time record of the structural state during construction, encompassing dynamic information from the assembly phase to the locking stage. This data includes not only the 3D coordinates of nodes and cable tension changes, but also the potential impact of environmental factors on the structure. The data acquisition system must be highly precise, reliable, and real-time to ensure the integrity and accuracy of the perception dataset.

[0032] After establishing the perception data set, the next step is to use this data to generate the node's structural state vector. The structural state vector is a mathematical representation of the node's comprehensive state at a given moment. It integrates key information such as the node's spatial position, cable tension, and angular elevation into a single vector. Generating the structural state vector requires processing and analyzing the perception data. Data fusion algorithms are used to integrate data from different sensor types and eliminate noise and errors. The resulting structural state vector intuitively reflects the node's actual state during construction, providing accurate input for subsequent error analysis.

[0033] In practice, the process of generating the structural state vector requires consideration of data synchronization and consistency. Because different sensor types may have varying sampling frequencies and data formats, data synchronization techniques are necessary to ensure that all data is comparable at the same point in time. Furthermore, the data processing system must include data verification and correction capabilities to address potential sensor failures or data anomalies.

[0034] By activating sensor-generated structural state vectors at each node, the construction team was able to monitor the structural status in real time at each stage of construction and make necessary adjustments to ensure the circular cable-supported grid structure was completed as intended. This process not only improved construction accuracy but also provided strong data support for dynamic error management during construction.

[0035] P40: Perform spatial offset error, cable force imbalance error, and angle elevation error analysis based on the structural state vector and the calibrated structural state data to establish an error identification result.

[0036] Furthermore, step P40 in this embodiment of the present application further includes:

[0037] P41: Obtain the actual position coordinates of the node, perform spatial offset calculation based on the actual position coordinates and the calibrated theoretical coordinates, and establish the spatial offset error; P42: Obtain the actual tension data of the cable segment, and obtain the cable force imbalance error based on the actual tension data and the calibrated tension calculation; P43: Obtain the actual angle and elevation of the node, calculate the angle and elevation difference of the node based on the calibrated angle and calibrated elevation, the actual angle and elevation, and generate the angle and elevation error.

[0038] It should be understood that by obtaining the structural state vector and calibrating the structural state data, error analysis is started to identify errors that may occur during the construction process and provide a basis for subsequent dynamic adjustment and precise control.

[0039] First, by obtaining the actual position coordinates of each node (provided by the total station and GNSS RTK module) and comparing them with the theoretical coordinates in the calibrated structure state data, the spatial offset of each node can be calculated. This calculation uses the three-dimensional spatial coordinate difference method, subtracting the calibrated position coordinates from the node's actual position coordinates to obtain the spatial offset error vector. This calculation provides a comprehensive understanding of the structure's spatial position deviation, providing data support for subsequent position adjustments.

[0040] Next, cable force imbalance error analysis is performed. By obtaining the actual tension data of the cable segment (provided by FBG fiber optic strain sensors) and comparing it with the theoretical tension from the calibrated structural state data, the cable force imbalance error can be calculated. This calculation uses the tension difference method, which subtracts the calibrated tension from the actual cable segment tension to determine the cable force imbalance error. This calculation clearly identifies any deviations in the cable force distribution, providing a basis for subsequent cable force adjustments.

[0041] Finally, angle and elevation error analysis is performed. By obtaining the actual angles and elevations of the nodes (provided by the total station and environmental sensors) and comparing them with the theoretical angles and elevations in the calibrated structure status data, the angle and elevation errors can be calculated. This calculation uses the angle and elevation difference method, subtracting the calibrated angle and elevation from the actual angle and elevation of the node to obtain the angle error and elevation error, respectively. This calculation provides a comprehensive understanding of the structure's deviations in angle and elevation, providing data support for subsequent angle and elevation adjustments.

[0042] After analyzing the spatial offset error, cable force imbalance error, and angular elevation error, all error data is integrated to form an error identification result. This error identification result includes not only the specific error values ​​for each node and cable segment, but also the error distribution pattern and possible influencing factors. This result will serve as an important basis for subsequent error transmission source analysis and the formulation of dynamic adjustment strategies.

[0043] By identifying and calculating these errors, the construction team can promptly discover various problems during the construction process and make dynamic adjustments, providing a scientific basis for further optimization of construction accuracy and dynamic compensation of errors.

[0044] P50: Perform error conduction source tracing analysis on the error identification result, and establish a dynamic adjustment strategy using the error conduction source tracing analysis result and the error identification result.

[0045] Furthermore, error transmission source analysis is performed on the error identification result. Step P50 of the embodiment of the present application further includes:

[0046] P51: Use the error identification results to establish the error field matrix of each node; P52: Take each node as the center point and establish a node heterogeneous graph based on the physical conduction relationship of the annular cable-supported grid structure; P53: Use the node heterogeneous graph to identify the error reverse conduction path and establish an error tracing path atlas, and the error tracing path atlas is provided with a path contribution degree identifier; P54: Use the path contribution degree in the error tracing path atlas to trace the causal chain back and locate the main tension cable segment and the external disturbance source; P55: Use the positioning results of all nodes to perform global error causal analysis to complete the error conduction tracing analysis.

[0047] Optionally, through in-depth analysis of the error identification results, the error propagation path and root cause can be obtained, and an effective dynamic adjustment strategy can be established accordingly to achieve precise control and efficient adjustment of construction errors.

[0048] After error identification is complete, the first step is to use the error identification results to establish an error field matrix for each node. The error field matrix is ​​a mathematical model that describes the error distribution of each node in terms of spatial position, cable tension, and angular elevation. By integrating the error data for each node into the matrix, the spatial distribution characteristics of the errors can be intuitively displayed, providing a foundation for subsequent error transmission analysis.

[0049] Next, a node heterogeneous graph is constructed, taking each node as the center and incorporating the physical conduction relationships of the ring-cable-supported grid structure. This node heterogeneous graph represents the physical connections and interactions between nodes as a graph structure. In this graph, each node is connected by edges, and the edge weights reflect the physical conduction influences between nodes, such as cable segment interactions and tension force transmission. This graph construction allows for a clearer understanding of the error conduction paths between nodes and provides a framework for subsequent error reverse conduction.

[0050] After establishing a heterogeneous node graph, it is used to identify the error backpropagation paths. This process involves analyzing the error propagation path in reverse order to trace the error source. This process creates an error traceability path atlas, with each path labeled with a contribution. This contribution indicates the contribution of each path to the overall error. By identifying and labeling these paths, it is possible to determine which nodes and paths play a dominant role in the error propagation process, further pinpointing the location of the error source.

[0051] After the error traceability path atlas is constructed, the path contribution is used to perform causal chain backtracking. This process aims to gradually identify the dominant tensioning cable segments and external disturbance sources by analyzing the path contribution. The dominant tensioning cable segment is the critical cable segment that causes error propagation during the tensioning process due to imbalanced tensioning forces or improper control. External disturbance sources include environmental factors (such as temperature fluctuations and wind) or non-ideal factors during construction (such as tool errors and manual operation). These factors can affect the structure and subsequently propagate to the entire structural system.

[0052] Finally, the positioning results of all nodes are used to perform a global error causal analysis to complete the error transmission source analysis. Global error causal analysis is a comprehensive analysis of the causes of errors in the entire structure. By integrating the error source tracing results of all nodes, we can fully understand the distribution and propagation of errors in the structure, thus providing a comprehensive basis for the formulation of dynamic adjustment strategies.

[0053] After completing the error transmission and source tracing analysis, a dynamic adjustment strategy is established by combining the error identification results and the source tracing analysis results. The dynamic adjustment strategy is a specific adjustment measure and method proposed for errors that occur during the construction process. These strategies should be designed according to the type, size, and cause of the error to achieve precise control and efficient adjustment of construction errors. For example, for spatial offset errors, they can be corrected by adjusting the support position or re-tensioning the cable segment; for cable force imbalance errors, they can be resolved by adjusting the prestress of the cable or optimizing the tensioning sequence; for angular elevation errors, they can be improved by adjusting the support structure of the node or optimizing the construction process.

[0054] Through error transmission and tracing analysis, not only can the source of the error be accurately identified, but it can also provide data support and decision-making basis for subsequent adjustments, ensuring that errors in the construction process are effectively controlled and ultimately completing high-quality structural construction.

[0055] Furthermore, step P54 of the embodiment of the present application further includes:

[0056] P54-1: Perform sequential screening based on the path contribution to establish a set of candidate dominant error sources; P54-2: Perform error data matching verification on the candidate dominant error source set, including calling real-time tension data for data verification on candidate tension cable segment nodes and calling environmental sensors for external disturbance verification on candidate source nodes; P54-3: Use data verification results and external disturbance verification results to perform directional consistency analysis of path error transmission to locate the dominant tension cable segment and external disturbance source.

[0057] Specifically, the source tracing analysis process of error transmission can be further refined, and the main tension cable segment and external disturbance source can be accurately located by screening, verifying and analyzing the contribution of the error path.

[0058] First, a set of candidate dominant error sources is established by sequentially screening based on path contribution. Path contribution identifies the importance of each error transmission path in the error propagation process; a higher contribution indicates a greater impact on the error transmission. Therefore, by sorting by path contribution, the paths with the greatest impact on the error can be identified, and the nodes or cable segments corresponding to these paths are considered the set of candidate dominant error sources. This step aims to narrow the analysis scope, focusing on the nodes and paths most likely to be error sources, providing key clues for subsequent verification and analysis.

[0059] Next, error data matching verification is performed on the candidate set of dominant error sources. This process involves two aspects: first, verifying the data by using real-time tension data from candidate cable segment nodes; second, verifying the external disturbance quantity by using environmental sensors from candidate source nodes. By using real-time tension data, it is possible to verify whether the actual tension of the candidate cable segment is consistent with the expected error transmission path; by using environmental sensor data, it is possible to verify whether external disturbances (such as temperature changes and wind loads) have affected the error. Error data matching verification requires the use of real-time tension data and environmental sensor data. Real-time tension data should be obtained using FBG fiber optic strain sensors to ensure real-time and accuracy. Environmental sensor data should cover key environmental factors such as temperature, humidity, and wind speed to comprehensively assess the impact of external disturbances. Data verification and external disturbance quantity verification should be based on rigorous statistical analysis methods to ensure the scientific nature of the verification results.

[0060] Finally, the data verification results and the external disturbance verification results are used to conduct a directional consistency analysis of the path error transmission. The purpose of this analysis is to check whether the direction of error propagation in the structure is consistent with expectations. Through a comprehensive analysis of the verification results, it is determined whether the error transmission path is consistent with the actual situation. For example, if the tension data and environmental disturbance quantities of certain nodes show consistent abnormal patterns, it can be determined that these nodes are the dominant sources of error, and these errors are transmitted to other nodes along a specific path. Through consistency analysis, the construction team can clearly determine the direction of error transmission and ultimately accurately locate the dominant tension cable segment and the external disturbance source, ensuring that these sources are adjusted and addressed in a timely manner.

[0061] Through the above steps, combined with path contribution, real-time data verification and external disturbance verification, it is possible to accurately trace the source of the error and clarify its impact path, providing scientific guidance for subsequent dynamic adjustment strategies, ensuring that errors can be effectively identified and corrected during the construction process, thereby maintaining the accuracy and stability of the structure.

[0062] Furthermore, step P55 of the embodiment of the present application further includes:

[0063] P55-1: A global error causal graph is established based on the dominant error source tracing results and tracing paths of all nodes. The global error causal graph is composed of a node set consisting of structural nodes, tensioned cable segment nodes, and external disturbance source nodes. The tracing path relationship constructs a directed edge set, and the edge weight is defined based on the error path contribution; P55-2: Node influence calculation, error source influence clustering analysis, and error chain cascade identification are performed in the global error causal graph to complete the error conduction tracing analysis.

[0064] Optionally, through further global error causal analysis, a global error causal map is constructed and in-depth error source analysis is performed to complete the error conduction source analysis and provide a basis for precise adjustment of the entire structure.

[0065] First, a global error causal graph is constructed based on the dominant error source location and tracing paths for all nodes. This graph consists of a node set consisting of structural nodes, tensioned cable segment nodes, and external disturbance source nodes. These nodes represent key elements of the structure, including node location, cable segment stress conditions, and the influence of external disturbance sources. Comprehensive analysis of these nodes provides a comprehensive understanding of how errors propagate and expand within the structure.

[0066] In the graph, traceability paths are constructed using a set of directed edges. The direction of each edge represents the path along which the error propagates from one node to another, and the edge weight is defined based on the contribution of the error path. Specifically, error transmission paths with greater contribution are assigned a higher weight, reflecting their importance in the error propagation process. By constructing such a directed graph and defining path weights, we can clearly demonstrate the error transmission process and its impact range, providing a clear diagram for subsequent analysis.

[0067] Next, node influence calculation, error source impact clustering analysis, and error chain cascade identification are performed within the global error causal graph. Node influence calculation involves evaluating the influence of each node in the global error causal graph to determine which nodes play a key role in error propagation within the entire structure. By calculating the influence of each node, it is possible to identify the core nodes in the error transmission chain. These nodes have the greatest impact on the entire structure and therefore require priority adjustment.

[0068] Subsequently, a cluster analysis of the error source impact was performed. This analysis, based on the nodes and paths in the global error causal graph, identified clusters of error sources. Cluster analysis revealed correlations between error sources, helping the construction team identify interrelated error sources and subsequently implement coordinated adjustments to optimize the error handling process.

[0069] Finally, error chain cascade identification is performed. This process traces the error propagation chain and identifies the cascading effects of errors within the structure. In a structure, an error at one node may affect multiple adjacent nodes, triggering a wider impact. Cascade identification clearly illustrates how errors propagate from one node to others, exacerbating the impact on the structure through the cascade effect of errors.

[0070] Through the above analysis, we can clearly identify which nodes, cable segments, and external disturbance sources have the greatest impact on the overall error, so that targeted adjustment strategies can be formulated to ensure that the structure can always maintain the design accuracy and stability during the construction process.

[0071] Furthermore, the execution node influence calculation, step P52-2 of the embodiment of the present application further includes:

[0072] P55-21: Perform influence calculation on each node. The influence calculation includes calculating the influence score of the error source node on all paths based on the contribution of the error source node and the corresponding traceability path; P55-22: Use the influence score to locate the key error source node.

[0073] It should be understood that the process of calculating the node influence can be further refined to identify the key nodes that have the greatest impact on the structural error by accurately evaluating the role of each node in error propagation.

[0074] First, an influence calculation must be performed for each node. This involves calculating the influence score of the error source node on all paths based on the contribution of the error source node and the corresponding traceability path. This process requires comprehensive consideration of the error source node's position in the global error causal graph and its influence on other nodes through each traceability path. The specific calculation method is as follows: Determine the error source node and its traceability path. Identify each error source node and its corresponding traceability path in the global error causal graph. These paths reflect the direction and intensity of error propagation from the source to other nodes. Calculate the path contribution. For each traceability path, quantify the importance of the path in error propagation based on the path contribution identifier. The path contribution can be derived through a comprehensive analysis of the error data, physical conduction relationships, and environmental factors of each node on the path. Calculate the impact score. For each error source node, perform a weighted sum of its contributions through all traceability paths to obtain the node's comprehensive influence score on all paths.

[0075] The key to this calculation step is to quantify the influence of each node into an influence score through mathematical models and graph theory. This score not only considers the node's direct effect but also comprehensively considers the error propagation paths between it and other nodes. If a node's error propagates through multiple paths, and these paths have a significant impact on the overall error, the node will receive a relatively high score, demonstrating its central role in error propagation.

[0076] Next, after completing the impact score calculation, these scores are used to locate the key error source nodes. Key error source nodes refer to those nodes that have the greatest impact on the propagation and distribution of structural errors. For example, first, by setting a threshold, a reasonable impact score threshold is set according to the project requirements and error control objectives. This threshold is used to distinguish between key error source nodes and ordinary nodes. Next, the key nodes are screened, and all error source nodes are sorted according to their impact scores. Nodes with scores higher than the set threshold are screened as key error source nodes. These nodes play a leading role in error propagation and have the most significant impact on the construction accuracy of the structure. Further, verification and adjustment are performed to verify the selected key error source nodes, and their accuracy and reliability are confirmed in combination with the actual construction conditions and error data. If necessary, the key nodes are adjusted according to the verification results to ensure that the identification results of the key error sources meet the actual needs of the project.

[0077] Through the above steps, the analysis of errors in the entire construction process is no longer a single point-to-point detection, but through global impact scoring, the key nodes in the error transmission chain are identified, providing a scientific basis for dynamic adjustment and precise control.

[0078] Furthermore, the error source affects the cluster analysis, and step P52-2 of the embodiment of the present application further includes:

[0079] P55-23: Based on the similarity between the error source nodes and the influencing paths, perform error source area clustering and establish clustering results; P55-24: Based on the clustering results, analyze the path sharing rate and error propagation overlap rate between nodes to locate the error concentration area; P55-25: Use the error concentration area to complete the error conduction source analysis.

[0080] Specifically, the error source impact clustering analysis process can be further refined. By analyzing the similarity of error source nodes and their impact paths, the error source nodes can be regionally clustered, and the concentrated areas of error propagation can be further analyzed, thereby providing accurate positioning for error tracing and correction.

[0081] First, error source regions are clustered based on the similarity between error source nodes and impact paths. This process requires classifying error source nodes, grouping nodes with similar impact paths and propagation characteristics into the same category. Cluster analysis can be implemented using a variety of algorithms, such as distance-based clustering methods (such as the K-means algorithm) or density-based clustering methods (such as the DBSCAN algorithm). During the clustering process, it is necessary to define appropriate similarity metrics, such as the length of the error propagation path, the path contribution, and the spatial distance between error source nodes. Cluster analysis can partition a complex error propagation network into several regions with similar characteristics, providing structured information for further analysis.

[0082] Based on the clustering results, the path sharing ratio and error propagation overlap ratio between nodes are analyzed to locate areas of concentrated error. The path sharing ratio refers to the proportion of error propagation paths shared between different nodes within the same clustering area, while the error propagation overlap ratio refers to the degree of spatial overlap of error propagation paths between different nodes. By calculating these metrics, areas with the most concentrated error propagation can be identified, which are often key areas for error accumulation and amplification. For example, the path sharing ratio between nodes within each clustering area is first calculated, and the ratio of the number of shared paths to the total number of paths is calculated. Secondly, the spatial distribution of error propagation paths is analyzed, and the overlap ratio between paths is calculated to identify areas with high error propagation overlap. These analyses can clarify the location and scope of error concentration areas, providing key intervention targets for subsequent adjustment measures.

[0083] Furthermore, after identifying the error concentration areas, the error propagation chains and cascade effects within these areas are further analyzed. By analyzing the error propagation paths and impact levels within these concentrated areas, the main propagation directions and key nodes of the errors can be identified, thereby completing the error transmission source traceability analysis. This process requires the integration of a global error causal map, comprehensively considering factors such as error source nodes, propagation paths, and impact levels to form a systematic traceability result. The results of the traceability analysis will provide a scientific basis for the formulation of dynamic adjustment strategies, helping construction units to accurately locate error sources, optimize construction processes, and reduce error accumulation and propagation.

[0084] By implementing the above steps, not only can the error source nodes be identified, but also the concentrated error propagation areas can be effectively identified through regional clustering and path analysis. This provides a scientific basis for further dynamic adjustment and precise control, ensuring that key error sources can be efficiently located and resolved during the construction process, thereby improving the overall structural stability and construction accuracy.

[0085] Furthermore, in the error chain cascade identification, step P52-2 of the embodiment of the present application further includes:

[0086] P55-26: Taking the error source node as the starting point, identify the error propagation chain through depth-first traversal; P55-27: Calculate the cascade score of the error propagation chain according to the contribution of each node in the path; P55-28: Identify the chain risk based on the cascade score to complete the error conduction tracing analysis.

[0087] In a possible embodiment of the present application, the process of error chain cascade identification is further refined, and the error propagation chain from the source node to the target node is identified through a depth-first traversal method, and the impact range and risk of the error are quantitatively analyzed through contribution scores and cascade scores.

[0088] First, starting from the error source node, a depth-first search (DFS) is used to identify error propagation chains. Depth-first search is a commonly used graph traversal algorithm suitable for exploring complex network structures. In the global error causal graph, starting from each error source node, the traceability path is gradually deepened until the end of the path is reached or a specific termination condition is met. This method can systematically identify the complete path of each error propagation chain, providing a foundation for subsequent cascading effect analysis.

[0089] Next, the cascade score of the error propagation chain is calculated based on the contribution of each node in the path. The cascade score is used to quantify the importance and risk of the error propagation chain within the entire structure. The specific calculation method is as follows: For each identified error propagation chain, all nodes on it are traversed and the path contribution of each node is accumulated to obtain the total contribution of the chain. The cascade score is calculated based on the total contribution. A weighted summation method can be used to assign different weights based on factors such as node importance and the length of the error propagation path. The higher the cascade score, the greater the impact of the error propagation chain on the structure and the higher the risk.

[0090] Finally, chain risk identification is performed based on the cascade score to complete the error transmission source analysis. The error propagation chain is risk-graded based on the cascade score. Different risk thresholds can be set to categorize the chain into three levels: high risk, medium risk, and low risk. For high-risk error propagation chains, their paths and key nodes should be identified and recorded so that they can be prioritized for intervention and adjustment in subsequent dynamic adjustment strategies. Through risk identification, construction units can clearly understand which error propagation paths require special attention, allowing them to take proactive measures to prevent the further spread and accumulation of errors.

[0091] The above steps not only identify the critical path of the error propagation chain but also quantify its risk level, providing an accurate basis for the dynamic adjustment of construction errors in the annular cable-supported grid structure. This ensures that errors during construction are effectively controlled and the stability and accuracy of the structure are maximized.

[0092] Furthermore, the dynamic adjustment strategy is established by utilizing the error conduction tracing analysis results and the error identification results. In the embodiment of the present application, step P50 further includes:

[0093] P56: Establish a phased strategy mapping, which includes initial tensioning phase mapping, synchronous tensioning phase mapping, and fine-tuning phase mapping; P57: Utilize the strategy mapping configuration phase strategy feedback mechanism to establish a dynamic adjustment strategy based on the configuration results, error conduction tracing analysis results, and error identification results.

[0094] Specifically, the process of formulating dynamic adjustment strategies can be further refined. By establishing a phased strategy mapping and configuring a phased strategy feedback mechanism, it can be ensured that the adjustment strategy can accurately respond to error problems in different construction stages, thereby achieving dynamic optimization of the construction process.

[0095] First, a phased strategy mapping is established, including mapping for the initial tensioning stage, synchronous tensioning stage, and fine-tuning stage. The construction of annular cable-supported grid structures is usually divided into these three stages, and the construction characteristics and error control requirements of each stage are different. In the initial tensioning stage, the main focus is on the initial tension distribution of the cable and the overall shape of the structure. The strategy mapping should clarify how to adjust the pre-tension of the cable based on the error identification results, and how to correct the initial shape deviation by adjusting the support position. In the synchronous tensioning stage, the focus is on the uniform distribution of cable forces and the stability of the structure. The strategy mapping should cover how to adjust the tensioning sequence and tensioning force based on the error conduction tracing analysis results to ensure the balance of cable forces and the stability of the structure. In the fine-tuning stage, the main goal is to optimize the final shape and accuracy of the structure. The strategy mapping should include how to make local adjustments based on the error identification results, and how to achieve high-precision forming of the structure by fine-tuning the cable forces and support positions.

[0096] Based on the established phased strategy mapping, the strategy mapping is further utilized to configure a phased strategy feedback mechanism. The purpose of this phased strategy feedback mechanism is to dynamically adjust the construction strategy based on error data and source analysis results from the actual construction process. The feedback mechanism should include real-time data acquisition and analysis. Sensors collect real-time structural status data, combined with error transmission and source analysis results, to quickly identify error trends and potential risks. Furthermore, the construction strategy can be dynamically adjusted based on real-time data and source analysis results. For example, during the initial tensioning stage, if a cable segment's tension deviation is detected, the tensioning force or tensioning sequence can be adjusted based on the strategy mapping. During the simultaneous tensioning stage, if cable force imbalance is detected, the tensioning equipment parameters can be adjusted based on the strategy mapping. During the fine-tuning stage, if local node position deviation is detected, fine-tuning can be performed based on the strategy mapping. Furthermore, the adjusted construction strategy is applied to actual construction, and the results of the adjustments are continuously monitored. Based on the monitoring results, the adjustment strategy is further optimized, forming a closed-loop feedback mechanism. This process must be considered in conjunction with the feasibility and cost-effectiveness of the construction process to ensure the effective implementation of the adjustment measures.

[0097] By configuring this phased strategy mapping and feedback mechanism, the construction team was able to dynamically adjust strategies based on real-time data, ensuring effective implementation of error control measures at each construction stage. This approach ensured the construction accuracy of the circular cable-supported grid structure and avoided the risk of structural instability caused by accumulated errors. This flexible, dynamic adjustment approach not only improved construction reliability and accuracy, but also ensured efficient and refined management of the construction process.

[0098] In summary, the embodiments of the present application have at least the following technical effects:

[0099] This application deploys total stations, GNSS RTK modules, FBG fiber optic strain sensors, and environmental sensors to monitor node positions, cable segment tension, and environmental factors during the construction of a circular cable-supported grid structure in real time, ensuring timely capture of errors during construction. The structural state vector of the node is generated using the perception data set and compared with the calibration data to analyze spatial offset errors, cable force imbalance errors, and angular elevation errors to accurately identify construction errors. Through error transmission and source tracing analysis, the source of the error is traced, the key error source nodes are identified, and their impact paths on the entire structure are analyzed. Based on the error identification and transmission and source tracing analysis results, a phased dynamic adjustment strategy is formulated to ensure that errors at each stage of the construction process are corrected in a timely manner to avoid error accumulation. By adjusting the tension, node position, and other construction parameters in real time, construction errors are effectively controlled to ensure the high precision, stability, and safety of the circular cable-supported grid structure, while reducing rework and material waste, improving construction efficiency, and reducing costs.

[0100] The technical effect of ensuring construction accuracy and structural stability is achieved by accurately locating the error source and making dynamic adjustments through real-time perception and error transmission tracing analysis.

[0101] The second embodiment is based on the same inventive concept as the method for dynamically adjusting the construction error of a ring-shaped cable-supported grid structure in the above embodiment. Figure 2 As shown, the present application provides a dynamic adjustment system for the construction error of a ring-shaped cable-supported grid structure. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0102] The structural state calibration module 11 is used to perform design modeling of the annular cable-supported grid structure, and use the design modeling results to perform simulation fitting to construct calibration structural state data of position nodes.

[0103] The sensor deployment module 12 is used to deploy perception sensors, including total stations and GNSS RTK modules deployed on supports and nodes, FBG optical fiber strain sensors deployed on tensioning end anchors, and environmental sensors deployed on cable segments.

[0104] The data perception module 13 is used to activate the perception sensor after the annular cable-supported grid structure is assembled, establish a perception data set, and use the perception data set to generate a structural state vector of the node.

[0105] The error identification module 14 is used to analyze the spatial offset error, the cable force imbalance error, and the angle elevation error according to the structural state vector and the calibration structural state data, and establish an error identification result.

[0106] The error conduction tracing module 15 is used to perform error conduction tracing analysis on the error identification result, and establish a dynamic adjustment strategy using the error conduction tracing analysis result and the error identification result.

[0107] Furthermore, the error identification module 14 is further configured to perform the following steps:

[0108] The actual position coordinates of the node are obtained, and a spatial offset calculation is performed based on the actual position coordinates and the calibrated theoretical coordinates to establish a spatial offset error. The actual tension data of the cable segment is obtained, and the cable force imbalance error is obtained based on the actual tension data and the calibrated tension calculation. The actual angle and elevation of the node are obtained, and the angle and elevation differences of the node are calculated based on the calibrated angle and calibrated elevation, and the actual angle and elevation, respectively, to generate an angle and elevation error.

[0109] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0110] The error identification results are used to establish an error field matrix for each node; with each node as the center point, a node heterogeneous graph is established according to the physical conduction relationship of the annular cable-supported grid structure; the node heterogeneous graph is used to identify the error reverse conduction path, and an error tracing path atlas is established, wherein the error tracing path atlas is provided with a path contribution degree identifier; the path contribution degree in the error tracing path atlas is used to trace the causal chain back to locate the main tension cable segment and the external disturbance source; and the positioning results of all nodes are used to perform global error causal analysis to complete the error conduction tracing analysis.

[0111] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0112] A sequential screening is performed based on the path contribution to establish a set of candidate dominant error sources; an error data matching verification is performed on the candidate dominant error source set, including data verification by calling real-time tension data on candidate tension cable segment nodes and external disturbance verification by calling environmental sensors on candidate source nodes; and a directional consistency analysis of path error conduction is performed using the data verification results and the external disturbance verification results to locate the dominant tension cable segment and the external disturbance source.

[0113] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0114] A global error causal graph is established based on the dominant error source tracing results and tracing paths of all nodes. The global error causal graph consists of a node set consisting of structural nodes, tensioned cable segment nodes, and external disturbance source nodes. The tracing path relationship constructs a directed edge set, and the edge weight is defined based on the error path contribution. Node influence calculation, error source influence clustering analysis, and error chain cascade identification are performed in the global error causal graph to complete the error conduction tracing analysis.

[0115] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0116] An influence calculation is performed on each node. The influence calculation includes calculating the influence score of the error source node on all paths based on the contribution of the error source node and the corresponding traceability path; and locating the key error source node using the influence score.

[0117] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0118] Based on the similarity between the error source nodes and the influencing paths, error source region clustering is performed to establish clustering results. Based on the clustering results, the path sharing rate and error propagation overlap rate between nodes are analyzed to locate the error concentration area. The error concentration area is used to complete the error conduction source tracing analysis.

[0119] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0120] Taking the error source node as the starting point, the error propagation chain is identified through depth-first traversal; the cascade score of the error propagation chain is calculated according to the contribution of each node in the path; and the chain risk is identified based on the cascade score to complete the error conduction traceability analysis.

[0121] Furthermore, the error conduction tracing module 15 is further configured to perform the following steps:

[0122] Establish a phased strategy mapping, which includes initial tensioning phase mapping, synchronous tensioning phase mapping, and fine-tuning phase mapping; utilize the strategy mapping to configure the phase strategy feedback mechanism, and establish a dynamic adjustment strategy based on the configuration results, error conduction tracing analysis results, and error identification results.

[0123] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0125] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for dynamically adjusting the construction error of an annular cable-supported grid structure, characterized in that: The method comprises: Perform design modeling of annular cable-supported grid structures, and use the design modeling results for simulation fitting to construct calibrated structural state data for location nodes; Deploy perception sensors, including total stations and GNSS RTK modules deployed on supports and nodes, FBG fiber optic strain sensors deployed on anchors at tensioning ends, and environmental sensors deployed on cable segments; After the ring cable-supported grid structure is assembled, the sensing sensors are activated to establish a sensing data set, and the structural state vector of the node is generated using the sensing data set; Perform spatial offset error, cable force imbalance error, and angle elevation error analysis based on the structural state vector and the calibrated structural state data, and establish an error identification result; Performing error transmission source analysis on the error identification result, and establishing a dynamic adjustment strategy using the error transmission source analysis result and the error identification result; The error identification result is subjected to error transmission source analysis, including: Establishing an error field matrix for each node using the error identification result; Taking each node as the center point, a node heterogeneous graph is established based on the physical conduction relationship of the ring cable-supported grid structure; Using the node heterogeneous graph to identify the error reverse conduction path, and establishing an error tracing path atlas, wherein the error tracing path atlas is provided with a path contribution degree identifier; The path contribution in the error traceability path atlas is used to trace the causal chain and locate the main tension cable segment and external disturbance source. The positioning results of all nodes are used to perform global error causal analysis to complete the error transmission source analysis.

2. A method for dynamically adjusting construction errors of an annular cable-supported grid structure according to claim 1, characterized in that: The method of using the path contribution in the error tracing path atlas to perform causal chain backtracking and locate the main tension cable segment and the external disturbance source includes: Performing sequential screening according to the path contribution to establish a set of candidate dominant error sources; Perform error data matching verification on the candidate dominant error source set, including calling real-time tension data for candidate tension cable segment nodes for data verification, and calling environmental sensors for external disturbance verification on candidate source nodes; The data verification results and the external disturbance verification results are used to conduct directional consistency analysis of path error transmission to locate the main tension cable segment and the external disturbance source.

3. The method for dynamically adjusting the construction error of an annular cable-supported grid structure according to claim 1, characterized in that: The global error causal analysis using the positioning results of all nodes to complete the error transmission source analysis includes: A global error causal graph is established based on the dominant error source location results and tracing paths of all nodes. The global error causal graph consists of a node set consisting of structural nodes, tensioning cable segment nodes, and external disturbance source nodes. The tracing path relationship constructs a directed edge set, and the edge weight is defined based on the error path contribution. Node influence calculation, error source impact cluster analysis, and error chain cascade identification are performed in the global error causal graph to complete error conduction source tracing analysis.

4. A method for dynamically adjusting construction errors of an annular cable-supported grid structure according to claim 3, characterized in that: The execution node influence calculation includes: Perform influence calculation on each node. The influence calculation includes calculating the influence score of the error source node on all paths based on the contribution of the error source node and the corresponding traceability path. The impact score is used to locate key error source nodes.

5. The method for dynamically adjusting the construction error of an annular cable-supported grid structure according to claim 3, characterized in that: The error sources that affect cluster analysis include: According to the similarity between the error source nodes and the impact path, the error source region clustering is performed to establish the clustering results; Based on the clustering results, the path sharing rate and error propagation overlap rate between nodes are analyzed to locate the error concentration area; The error concentration area is used to complete the error transmission source analysis.

6. A method for dynamically adjusting construction errors of an annular cable-supported grid structure according to claim 3, characterized in that: The error chain cascade identification includes: Taking the error source node as the starting point, the error propagation chain is identified through depth-first traversal; Calculate the cascade score of the error propagation chain based on the contribution of each node in the path; Chain risk identification is performed based on the cascade score to complete error transmission source analysis.

7. The method for dynamically adjusting the construction error of an annular cable-supported grid structure according to claim 1, characterized in that: The establishing of the error identification result includes: Obtaining the actual position coordinates of the node, performing spatial offset calculation based on the actual position coordinates and the calibrated theoretical coordinates, and establishing a spatial offset error; Acquiring actual tension data of the cable segment, and calculating the cable force imbalance error based on the actual tension data and the calibrated tension; The actual angle and elevation of the node are obtained, and the angle and elevation differences of the node are calculated based on the calibrated angle and calibrated elevation, the actual angle and elevation, and the angle and elevation error is generated.

8. The method for dynamically adjusting the construction error of an annular cable-supported grid structure according to claim 1, characterized in that: The method of establishing a dynamic adjustment strategy using the error conduction source analysis results and the error identification results includes: Establishing a phased strategy mapping, wherein the strategy mapping includes an initial tensioning phase mapping, a synchronous tensioning phase mapping, and a fine-tuning phase mapping; By utilizing the policy feedback mechanism in the policy mapping configuration phase, a dynamic adjustment policy is established according to the configuration results, the error conduction tracing analysis results, and the error identification results.

9. A dynamic adjustment system for construction errors of annular cable-supported grid structures, characterized in that: The system comprises: A structural state calibration module, which is used to perform design modeling of the annular cable-supported grid structure and perform simulation fitting using the design modeling results to construct calibrated structural state data of the position nodes; A sensor deployment module, which is used to deploy perception sensors, including total stations and GNSS RTK modules deployed on supports and nodes, FBG optical fiber strain sensors deployed on tensioning end anchors, and environmental sensors deployed on cable segments; A data perception module, which is used to activate perception sensors after the annular cable-supported grid structure is assembled, establish a perception data set, and use the perception data set to generate a structural state vector of the node; An error identification module, the error identification module is used to perform spatial offset error, cable force imbalance error, and angle elevation error analysis based on the structural state vector and the calibration structural state data, and establish an error identification result; An error conduction tracing module, the error conduction tracing module is used to perform error conduction tracing analysis on the error identification result, and establish a dynamic adjustment strategy using the error conduction tracing analysis result and the error identification result; The error conduction tracing module is also used to perform the following steps: using the error identification results to establish an error field matrix for each node; using each node as the center point, establishing a node heterogeneous graph based on the physical conduction relationship of the annular cable-supported grid structure; using the node heterogeneous graph to identify the error reverse conduction path, and establishing an error tracing path atlas, wherein the error tracing path atlas is provided with a path contribution degree identifier; using the path contribution degree in the error tracing path atlas to trace the causal chain back and locate the main tension cable segment and the external disturbance source; using the positioning results of all nodes to perform global error causal analysis to complete the error conduction tracing analysis.

Images