A real-time analysis method and an aerial gallery lifting system

Abstract

The embodiment provides a real-time analysis method for aerial corridor lifting and an aerial corridor lifting system, wherein the method comprises the following steps: when starting the synchronous lifting operation of the aerial corridor, synchronously collecting multi-source measured data; fusing and mapping the multi-source measured data to a pre-constructed digital twin model to update the model load and boundary conditions, and outputting the stress field and deformation simulation results of all components; determining a component multi-level early warning threshold set based on the pre-defined component types of all components and / or corresponding simulation results; outputting a component multi-level force early warning signal based on the stress field and deformation simulation results of the component and the corresponding component multi-level early warning threshold set; when the vertical displacement difference between the lifting points exceeds a preset threshold or the component force early warning signal reaches a first target level, determining a lifting compensation amount of the lifting point; and generating a control signal based on the lifting compensation amount of the lifting point. Through implementation of the scheme, the accuracy and reliability of analysis in the aerial corridor lifting process are improved.

Description

Technical Field

[0001] This invention belongs to the field of building construction technology, specifically relating to a real-time analysis method and a system for lifting elevated walkways. Background Technology

[0002] Existing conventional lifting monitoring technologies only deploy a small number of sensors on key members of the connecting corridor, which can only obtain stress and displacement data at local points and cannot cover all components of the connecting corridor. This results in a large number of monitoring blind spots and makes it difficult to fully reflect the stress field and deformation distribution of the entire structure. At the same time, the parameters, loads and boundary conditions of traditional simulation models are all preset fixed values ​​before construction. The finite element simulation results deviate significantly from the actual working conditions on site, and the stress data of the components output by the simulation has low reliability and cannot be used as a reliable basis for synchronous control of lifting points.

[0003] Furthermore, existing early warning mechanisms generally use a uniform stress threshold to assess the risk of all components. When using the same set of assessment criteria, it is easy for slight stress fluctuations in some components to trigger early warnings, resulting in a large number of false alarms. Frequent early warnings cause repeated shutdowns of the hoisting operation, which seriously slows down the construction progress. If the uniform threshold is relaxed, it will lead to the failure to report stress exceeding the limit of permanent components, which may cause major safety accidents such as deformation of members, damage to nodes, or even overall instability, making it difficult to guarantee accuracy. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a real-time analysis method and a system for lifting elevated walkways, so as to meet the need to improve the accuracy and reliability of the analysis during the lifting process of elevated walkways.

[0005] To achieve the above objectives, the present invention provides the following technical solution: According to a first aspect, the present invention provides a real-time analysis method for lifting an aerial corridor, comprising: when initiating a synchronous lifting operation of the aerial corridor, simultaneously collecting multi-source measured data, including the vertical displacement of each lifting point, the internal force deformation of key steel corridor members, and external wind load; fusing and mapping the multi-source measured data to a pre-constructed digital twin model to update the model load and boundary conditions, performing finite element analysis, and outputting the stress field and deformation simulation results of all components of the entire corridor; determining a multi-level early warning threshold set for the corresponding components based on the predefined component types and corresponding simulation results; outputting multi-level stress early warning signals for each component based on the stress field and deformation simulation results of the components and the corresponding multi-level early warning threshold set; determining the vertical displacement difference between multiple lifting points based on the vertical displacement of each lifting point; determining the lifting compensation amount for each lifting point when the vertical displacement difference between lifting points exceeds a preset threshold, or when any component stress early warning signal reaches a first target level; and generating a control signal based on the lifting compensation amount to control the lifting device to lift the lifting points.

[0006] According to a second aspect, the present invention provides an aerial walkway lifting system, comprising: a multimodal sensor for collecting multi-source measured data; a controller for executing a real-time analysis method for aerial walkway lifting as described in the above embodiments; and a lifting device for lifting the suspension point based on control signals.

[0007] According to a third aspect, an embodiment of the present invention provides an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the real-time analysis method for aerial walkway elevation described in the first aspect or any embodiment of the first aspect.

[0008] This embodiment provides a real-time analysis method for the lifting of an aerial walkway. It integrates multi-dimensional measured monitoring data such as suspension point displacement, member internal force, and wind load, and maps the measured information to a pre-built digital twin model. The model load parameters and structural boundary constraints are updated in real time. Based on the updated twin model, a full-domain finite element solution is performed, accurately outputting the complete stress field and overall deformation simulation results of all components of the aerial walkway. The method classifies component types by combining the different bearing capacity standards of permanent and temporary components, matches corresponding multi-level early warning thresholds to determine stress risk, and simultaneously identifies asynchronous deviations in vertical displacement of suspension points. When the displacement difference exceeds the standard or the component stress triggers an early warning, the method automatically calculates the lifting compensation amount of the suspension points and issues control instructions. By differentiating permanent main components from temporary construction components based on component type, and adopting early warning judgment standards adapted to their design load-bearing capacity for each type of component, the problem of false alarms and missed alarms caused by uniform threshold judgment is greatly reduced. It can accurately identify the overload, off-center load, and torsional deformation hazards of different components. Based on the real stress and deformation data of all components, synchronous deviation closed-loop control is carried out. From the overall structure to individual components, layered control is implemented to improve construction risks and enhance the accuracy of risk identification and the reliability of early warning in the overall lifting process of the aerial corridor.

[0009] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0010] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart illustrating a specific example of a real-time analysis method for lifting an aerial walkway according to the present invention. Figure 2 This is a schematic diagram of a module structure of an aerial walkway lifting system according to the present invention; Figure 3 This is a schematic block diagram of a specific example of an electronic device in an embodiment of the present invention. Detailed Implementation

[0011] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0013] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0014] This invention provides a real-time analysis method for the elevation improvement of aerial walkways, such as... Figure 1 As shown, it includes: S101, when the synchronous lifting operation of the aerial corridor is started, multi-source measured data are collected simultaneously. The multi-source measured data includes the vertical displacement of each lifting point, the internal force deformation of the key members of the steel corridor, and the external wind load. S102 fuses and maps multi-source measured data to a pre-built digital twin model to update the model load and boundary conditions, performs finite element solution, and outputs the stress field and deformation simulation results of all components of the entire connecting corridor. S103, Based on the predefined component types of all components and the corresponding simulation results, determine the set of multi-level early warning thresholds for the corresponding components; S104, based on the stress field and deformation simulation results of the component and the corresponding multi-level early warning threshold set of the component, outputs multi-level stress early warning signals for each component; S105, Based on the vertical displacement of each lifting point, determine the vertical displacement difference between multiple lifting points; S106, when the vertical displacement difference between the lifting points exceeds the preset threshold, or when the force warning signal of any component reaches the first target level, the lifting compensation amount of the lifting point is determined. S107 generates a control signal based on the lifting point compensation amount to control the lifting device to lift the lifting point.

[0015] For example, the acquisition of multi-source measured data can be based on the pre-installation of laser triangulation displacement gauges at each lifting point, fixed between the bottom of the corridor and the temporary corbel to eliminate support deformation interference, and used to output the vertical displacement of each lifting point; wavelength demodulation type fiber optic grating sensors are attached to the upper chord of the main truss, the mid-span node plate, and the connection of the temporary support, with a three-dimensional strain gauge configured at each point to realize the reconstruction of axial + bending + shear stress; ultrasonic anemometers and thermocouple arrays are deployed at the four corners and the middle of the top of the corridor to construct a three-dimensional wind speed gradient field and temperature field, and to collect temperature and external wind load.

[0016] A preliminary finite element digital twin baseline model of the irregular connecting corridor was pre-built using the BIM-IFC model import engine. The model fully input the corridor's geometry, steel component section parameters, steel mechanical properties, initial constraint boundaries of the lifting points, and temporary support assembly relationships. After receiving multi-source measured data streams in real time, the heterogeneous data fusion module maps real-time vertical displacement of the lifting points, 3D wind field, overall temperature gradient, and internal force and deformation data of the members to the twin model, dynamically updating the model's constraint boundaries, wind loads, and coupled thermal stress loads. A nonlinear finite element solver is then invoked to perform quasi-real-time structural static analysis, traversing all permanent and temporary components of the connecting corridor, and outputting complete 3D stress field cloud maps and component deformation vector simulation results. The model can automatically be set to complete a boundary update and recalculation every short period, such as 5 minutes, achieving dynamic updates.

[0017] The digital twin visualization platform reads the built-in component attribute metadata of the imported BIM model and automatically completes the component classification and labeling. Permanent components can include main trusses, main beams, permanent node connection plates, main load-bearing steel box girders, and other load-bearing components that will be permanently retained after completion. Temporary components can include temporary lifting lugs, temporary supports, temporary connection fixtures, construction reinforcements, and other temporary load-bearing components that will be dismantled after lifting. After batch importing BIM models, the platform can automatically identify the attributes of tens of thousands of structural members.

[0018] Then, the single-component stress field simulation results output by the finite element method are read, and the axial stress values ​​of the component sections are extracted. If the section stress value is positive, the component is determined to be under tension; if the section stress value is negative, the component is determined to be under compression. The binary labels of each component type and stress state are recorded simultaneously as threshold matching indexes.

[0019] The system has four built-in independent multi-level early warning thresholds, all based on the allowable stress of the component design. Each threshold set includes three levels of segmentation standards: blue (attention), yellow (speed reduction), and red (shutdown). The matching logic is as follows: The component is a permanent component under tension, matching the first multi-level threshold set, that is, the benchmark threshold is 70% of the design allowable stress, i.e., the proportion of the allowable stress; safety ratio. 95% of the baseline threshold, or 66.5%, is a blue alert, which is the second target level and represents a safety ratio. 98% of the baseline threshold, or 68.6%, constitutes a yellow alert, which is the first target level and represents a safety ratio. The baseline threshold, 70%, is a red shutdown, which is the third target level. The component is a permanent structure under pressure, matching the second multi-level threshold set, with a baseline threshold of 60% of the design stress; safety ratio 57% Blue Alert 58.8% Yellow Alert 60% of systems are shut down due to red flags; The component is a temporary component under tension, matched with the third multi-level threshold set, with a baseline threshold of 80% of the design stress; safety ratio 76% blue alert, 78.4% Yellow Alert 80% of systems are in red and shut down; The component is a temporary component under pressure, matching the fourth multi-level threshold set, with a baseline threshold of 70% of the design stress; safety ratio 66.5% Blue Alert 68.6% Yellow Alert 70% of systems are in red and shut down.

[0020] The safety ratio is calculated as the ratio of the measured simulated stress of the component to the allowable design stress of the component. The parameter settings in this embodiment are for reference only and can be adjusted according to actual engineering requirements. When the stress warning signal of any component reaches the second target level, an inspection prompt is issued; when the stress warning signal of any component reaches the third target level, the synchronous lifting operation of the aerial walkway is stopped.

[0021] This embodiment first categorizes components into permanent and temporary components. Then, for each type and condition under tension and compression, it configures a separate set of independent multi-level early warning thresholds. All thresholds are based on the component's design allowable stress, with differentiated trigger boundaries for blue, yellow, and red warning levels. This matching method solves the problems of false alarms and missed alarms caused by different design standards for the bearing capacity of permanent and temporary components and significant differences in the failure mechanisms of tension and compression components under the traditional unified threshold: the early warning threshold for permanent components is set more conservatively, prioritizing the safety redundancy of the main building structure; the threshold for temporary components is appropriately relaxed to adapt to the temporary stress characteristics during the construction phase. The entire hierarchical logic fully conforms to the current steel structure engineering design specifications, significantly improving the accuracy of early warning judgments. It avoids unnecessary shutdowns due to slight stress fluctuations and also avoids missing truly dangerous stress conditions.

[0022] Next, the measured vertical displacement values ​​of all the lifting points transmitted by the laser displacement sensors are read in real time, the coordinate differences between each pair of lifting points are traversed in real time, and the vertical displacement difference between all lifting points is continuously calculated. The system presets the critical threshold for displacement difference exceeding the limit to be 3mm.

[0023] Set up dual linkage trigger conditions, and start calculating the lifting point compensation amount when either condition is met: one of them is the vertical displacement difference between any two sets of lifting points. 3mm; secondly: the stress warning signal output by any steel component reaches the first target level (yellow warning). The solution for the lifting point compensation amount can be to simulate the stress and deformation distribution of the entire component under different compensation displacements in a digital twin model, lock the displacement adjustment range that will not cause local stress over-limit, and eliminate the dangerous compensation amount range that will cause a sudden increase in the stress of the member. Based on the compensation fine-tuning amount of each lifting point, a servo hydraulic control command signal is generated and sent to the independent servo hydraulic lifting device equipped with each lifting point to drive the oil cylinder to complete the single-point millimeter-level displacement compensation fine-tuning.

[0024] This invention provides a real-time analysis method for the lifting of aerial walkways. It integrates multi-dimensional measured monitoring data such as suspension point displacement, member internal forces, and wind load, and maps the measured information to a pre-built digital twin model. The model load parameters and structural boundary constraints are updated in real time. Based on the updated twin model, a global finite element method is performed to accurately output the complete stress field and overall deformation simulation results of all components of the aerial walkway. The method classifies component types by combining the different bearing capacity standards of permanent and temporary components, matches corresponding multi-level early warning thresholds to determine stress risk, and simultaneously identifies asynchronous deviations in vertical displacement of suspension points. When the displacement difference exceeds the standard or the component stress triggers an early warning, the method automatically calculates the lifting compensation amount of the suspension points and issues control instructions. By differentiating permanent main components from temporary construction components based on component type, and adopting early warning judgment standards adapted to their design load-bearing capacity for each type of component, the problem of false alarms and missed alarms caused by uniform threshold judgment is greatly reduced. It can accurately identify the overload, off-center load, and torsional deformation hazards of different components. Based on the real stress and deformation data of all components, synchronous deviation closed-loop control is carried out. From the overall structure to individual components, layered control is implemented to improve construction risks and enhance the accuracy of risk identification and the reliability of early warning in the overall lifting process of the aerial corridor.

[0025] As an optional implementation method, unlike the above embodiments which use the type of component (permanent or temporary) to retrieve the corresponding level, this embodiment proposes to determine a multi-level early warning threshold set for the corresponding component based on the simulation results. This includes: extracting the relative ratio between the actual bearing stress and the ultimate bearing capacity of each component in the corridor at the current moment based on the simulation results; using the amplitude of the relative ratio and the adjacent position relationship of the component in the corridor's spatial topology as dual joint measurement criteria, an unsupervised clustering algorithm is used to automatically divide all components into three cohesive categories: high-risk stress cluster, medium-risk stress cluster, and low-risk stress cluster; statistically analyzing the central tendency and dispersion of the stress data of components within the three risk clusters, and calculating the exclusive baseline warning line for the corresponding risk cluster based on the inherent statistical characteristics of each cluster; and determining a multi-level early warning threshold set based on the differentiated risk level weights of high, medium, and low-risk clusters and the exclusive baseline warning line for each cluster, with all early warning thresholds dynamically updated in real time with each finite element solution update.

[0026] For example, before initiating clustering calculations, the system first prepares two types of basic data. The first type is the ratio of the actual load-bearing stress of each component in the connecting corridor at the current moment to its own material ultimate bearing capacity. Specifically, for each component output by the finite element solution, the material property parameters of that component are retrieved, and its corresponding yield strength or tensile strength design value is obtained as the benchmark for the material's ultimate bearing capacity. The ratio of the absolute value of the real-time simulated stress of the component to the corresponding material ultimate bearing capacity is calculated to obtain a dimensionless stress ratio index. After completing this calculation by traversing all components of the entire connecting corridor, the stress ratio data of all components are sorted by component number and spatial topological index, and summarized to form a complete dataset of the stress distribution characteristics of the entire structure, completely eliminating the dimensional differences between components of different materials and cross-sectional dimensions.

[0027] The second type is the force flow transmission topology matrix, which is pre-constructed based on the mesh topology of the digital twin finite element model. Specifically, during construction, each component is defined as an independent node in the topology graph, and the existence of a direct physical connection between any two components is determined. The criterion is whether the two components share at least one node in the finite element model, and whether that node provides the conditions for force flow transmission. For component pairs with topological adjacency, corresponding connection weight values ​​are pre-assigned based on their connection method, force transmission cross-sectional area, and connection stiffness. Component pairs with higher connection stiffness and more direct force transmission have higher weight values, while non-directly connected component pairs have zero weight values. The connection weight values ​​of all component pairs together constitute a complete weighted adjacency matrix, which quantitatively describes the topological proximity between any two components in the entire corridor in terms of spatial force flow transmission. It is important to emphasize that this weighted adjacency matrix is ​​not static. Considering that changes in the temporary support status, stiffness degradation due to components entering the elastoplastic stage, and loosening of connection nodes during the lifting of the connecting corridor will substantially change the force transmission path, this embodiment introduces a quasi-static update mechanism: whenever the system lifts 5% of the total stroke or detects a sudden change in displacement of any key node exceeding a preset threshold, it re-determines the force transmission cross-sectional area and effective connection stiffness between components based on the current finite element tangent stiffness matrix, and dynamically refreshes the corresponding connection weight values ​​in the weighted adjacency matrix, so that the topological constraints always conform to the actual force transmission path of the current structure.

[0028] After the two types of basic data are prepared, the system initiates clustering calculations. When determining which cluster a component should belong to, the system no longer relies solely on the absolute deviation of the stress ratio between the component and the cluster centroid. Instead, it calculates the comprehensive distance using two factors. The first factor is the numerical distance factor, which is the absolute deviation between the component's current stress ratio and the target cluster centroid's stress ratio, reflecting the similarity in their stress levels. The second factor is the topological association reward factor, which is the accumulated connection weight between the component and all existing components in the target cluster, obtained by querying the weighted adjacency matrix. A higher accumulated value indicates a closer connection between the component and the target cluster along the spatial force flow transmission path.

[0029] To address the serious drawbacks of traditional schemes, such as the inconsistency between the numerical distance and topological reward dimensions, and the tendency for direct subtraction to result in a negative composite distance leading to clustering mathematical failure, this embodiment performs normalization and fusion processing on both. Specifically, firstly, the numerical distance factor is scaled to the [0, 1] interval to obtain the normalized numerical distance; secondly, the topological association reward factor is divided by the maximum possible cumulative connection weight of its target cluster to obtain the normalized topological association degree (value [0, 1]); finally, the composite distance is constructed. The calculation formula is: , in, For normalized numerical distance, To normalize the topological connectivity, α is a preset balance coefficient with a value ranging from 0.5 to 0.7. The specific value is determined by offline calibration of the system based on the type of connecting corridor structure. This formula ensures that the overall distance is always non-negative, and that numerical force deviation and topological connectivity are controlled and balanced under a unified dimension: the larger the numerical distance, the larger the overall distance; and the greater the topological connectivity, the smaller the overall distance.

[0030] The initial centroids are selected using the long-distance seeding strategy of the K-Means++ algorithm, but the distance between any two nodes during the seeding process is calculated using the aforementioned comprehensive distance. Specifically, firstly, a component is randomly selected from all components of the entire corridor as the initial centroid of the first cluster. Then, the comprehensive distance from each of the remaining components to the selected centroid is calculated. The component with the larger comprehensive distance has a higher probability of being selected as the next centroid, thus ensuring that the selected initial centroids are as dispersed as possible in both the numerical and spatial topological dimensions. This process is repeated until three initial centroids are selected, corresponding to the initial centers of the high-risk stress cluster, the medium-risk stress cluster, and the low-risk stress cluster, respectively.

[0031] After the initial centroids are determined, the iterative allocation and centroid update phase begins. In each iteration, for each component to be allocated, its combined distance to the centroids of the three clusters is calculated, and the component is allocated to the cluster with the smallest combined distance. After all components are allocated, the arithmetic mean of the stress ratios of all components within each cluster is calculated, and this mean is used as the new centroid value for that cluster in the next iteration. This update process only updates the numerical dimension of the centroids; the topological dimension information remains unchanged, based on the pre-constructed and quasi-statically refreshed weighted adjacency matrix. The above allocation and update process iterates repeatedly until the proportion of components reassigned to clusters in two consecutive iterations falls below a preset convergence threshold, or the number of iterations reaches a preset maximum iteration limit, at which point the process terminates.

[0032] Before moving on to the next centroid update after each round of iteration allocation, the system simultaneously performs cluster capacity verification and anti-degradation mechanisms. Specifically, it counts the number of components contained in each risk cluster in the current round. If it finds that the number of components in a certain cluster is less than 5% of the total number of components in the entire corridor, it determines that the cluster is at risk of degrading into an empty cluster. The system immediately forces the reselection of the centroid of the cluster from the actual components corresponding to the high, medium, and low quantiles in the global stress ratio ranking, and jumps back to the allocation step to re-execute the current round of iteration. This ensures that the clustering results always maintain the three risk granularities and avoids the collapse of the cluster dimension due to improper initial centroid selection.

[0033] During each iteration of the allocation process, the system synchronously executes an outlier detection mechanism. When the combined distance from a component to the centroids of all three clusters exceeds a preset global distance threshold, the component is identified as an outlier component with abnormal stress. To avoid frequent false shutdowns caused by stress singularity numerical distortions commonly found at support nodes and sharp corners in finite element simulations, this embodiment introduces a spatiotemporal continuous dual verification mechanism for outlier determination. Specifically, a component must be identified as having a combined distance exceeding the limit in both the current moment and the previous two consecutive finite element solution steps (a total of three consecutive time steps) to be considered a valid component with abnormal stress. In this case, it is not forcibly assigned to any risk cluster, but is instead marked as a component of special concern and a high-level warning signal is directly triggered and pushed to the administrator terminal. If there is only a single outlier or the conditions for three consecutive time steps are not met, it is only marked as a suspected outlier, and a first-level inspection prompt signal is output for manual review by the administrator, without triggering a shutdown. The global distance threshold is automatically determined based on the statistical distribution of the comprehensive distances between all components in the current iteration. Specifically, it is taken as a preset multiple of the average global comprehensive distance as the judgment threshold, and this multiple is adaptively adjusted with each iteration (relaxed in the early stage and tightened in the later stage). This mechanism effectively avoids the problem of extreme points distorting the overall cluster boundary and raising or lowering the warning baseline of normal components caused by local stress concentration or numerical singularity.

[0034] After iterative convergence, the three clusters are output as high-risk stress cluster, medium-risk stress cluster, and low-risk stress cluster, respectively. Each component obtains a unique and definite cluster affiliation label, which is subject to both numerical similarity and spatial connectivity constraints, ensuring that the clustering results have clear physical and mechanical significance. After clustering, the system then statistically analyzes the central tendency and dispersion of the stress ratio data of components within each cluster.

[0035] To avoid the drawbacks of traditional arithmetic mean and standard deviation, which are easily skewed by a few extreme outliers within a cluster, leading to passive increases or decreases in the baseline warning line, this embodiment adopts a robust statistical strategy: using the median of the stress ratio data for each cluster as a measure of central tendency and the interquartile range (IQR) as a measure of dispersion, and automatically calculating a specific baseline warning line based on the robust statistical characteristics of each cluster. Specifically, the specific baseline warning line for high-risk stress clusters is "median + 1.0 times IQR"; the specific baseline warning line for medium-risk stress clusters is "median + 1.5 times IQR"; and the specific baseline warning line for low-risk stress clusters is "median + 2.0 times IQR". The entire calculation process does not require any manual input of preset thresholds and is entirely driven by the actual stress data under the current lifting posture. Furthermore, the median and IQR are naturally immune to outliers, accurately matching the normal stress level of the cluster components under the current working conditions.

[0036] Based on the exclusive baseline warning line of each cluster, while generating a differentiated set of warning thresholds, an absolute physical red line is introduced as the bottom-line defense to completely solve the problem that low-risk clusters may fail to report high-risk absolute stress when the global stress is generally high in relative clustering, and effectively correct the logical paradox that the absolute warning threshold of high-risk clusters is higher than that of low-risk clusters in the traditional multiple method.

[0037] Specifically, the warning thresholds for high-risk clusters are as follows: Level 1: min (absolute safety limit 0.40, baseline × 0.8); Level 2: min (0.50, baseline × 1.0); Level 3: min (0.65, baseline × 1.2); Level 1: min (0.50, baseline × 0.9); Level 2: min (0.65, baseline × 1.1); Level 3: min (0.75, baseline × 1.3); Level 1: min (0.60, baseline × 1.0); Level 2: min (0.75, baseline × 1.2); Level 3: min (0.85, baseline × 1.4). These absolute safety limits are examples and can be calibrated according to the actual strength grade of the engineering materials.

[0038] Simultaneously, a mandatory global absolute physical red line is established: when the absolute stress ratio of any component in the connecting corridor exceeds 0.85, regardless of which risk cluster the component currently belongs to or what its relative threshold is within that cluster, the system directly triggers the highest-level emergency shutdown signal and pushes it to the administrator terminal. All generated warning thresholds are updated synchronously with each finite element solution refresh, ensuring that the warning sensitivity always adapts to the current stress state of the structure. High-risk cluster warnings have the lowest absolute threshold and the most sensitive response, while low-risk cluster warnings have the highest absolute threshold and effectively filter false alarms.

[0039] Each connecting corridor component is traversed, and its risk cluster category is retrieved. Its real-time stress ratio is compared one by one with the corresponding risk cluster's multi-level warning threshold set: when the stress ratio is below the first-level warning threshold, a safety signal is output; when the stress ratio is between the first and second-level warning thresholds (i.e., reaching the second target level), a second-level warning inspection signal is output; when the stress ratio is between the second and third-level warning thresholds (i.e., reaching the first target level), a hoisting point lifting control signal is output; when the stress ratio exceeds the third-level warning threshold (i.e., reaching the third target level), a third-level warning shutdown signal is output. Specifically, once the aforementioned global absolute physical red line (stress ratio > 0.85) is triggered, all the above-mentioned hierarchical logic is unconditionally overridden, and the highest-priority emergency shutdown command is directly output. This command has the highest interrupt priority at the hardware level and is unaffected by software iteration delays.

[0040] The method proposed in this embodiment breaks away from the reliance on manually preset fixed warning thresholds in traditional schemes. It achieves the physical meaning division of risk clusters through unsupervised clustering with spatial topological constraints, and the warning threshold is dynamically and adaptively adjusted according to the improvement conditions.

[0041] As an optional implementation, multi-source measured data is fused and mapped to a pre-built digital twin model to update the model loads and boundary conditions, and finite element analysis is performed to output the stress field and deformation simulation results of all components of the entire connecting corridor, including: Multi-source measured data is divided into driving group data and verification group data. The driving group data is fused and mapped to a pre-built digital twin model to update the model load and boundary conditions, and finite element analysis is performed to obtain the stress field and deformation simulation results of all components of the entire corridor. Based on the target components included in the verification group data, the stress field and deformation simulation results of multiple corresponding target components are searched in the stress field and deformation simulation results of all components of the entire corridor. The measured data of multiple target components in the verification group data are compared with the stress field and deformation simulation results of the corresponding target components to determine the comprehensive deviation. When the comprehensive deviation is less than a preset threshold, the stress field and deformation simulation results of all components of the entire corridor are considered reliable output results and are output. When the comprehensive deviation is greater than or equal to the preset threshold, the target algorithm is used to adaptively correct the digital twin model until the comprehensive deviation is less than the preset threshold. Then, the driving group data is fused and mapped to the corrected digital twin model to update the model load and boundary conditions, and the stress field and deformation simulation results of all components of the entire corridor are output.

[0042] For example, the system automatically divides all monitoring data synchronously collected by the sensing layer into two types of datasets: driving data and verification data. The driving data includes all vertical displacements of the suspension points, instantaneous wind speeds across the field, and overall temperature gradient data. Its function is to update the constraint boundaries of the digital twin model and external wind and temperature loads, and to drive the finite element iterative calculation. The verification data selects the measured values ​​of fiber optic strain of key verification rods and the measured true values ​​of displacement of key control points. Its function is to serve as a benchmark to verify the accuracy of the simulation output results.

[0043] The displacement, wind speed, and temperature data of the drive group are integrated and mapped to the pre-built digital twin model in real time. The model constraint boundaries and coupled loads are updated, and the nonlinear finite element solver is started. The initial calculation outputs the complete stress field and deformation vector simulation results of all components of the entire connecting corridor.

[0044] From the initial finite element output of the full-domain simulation results, the simulated stress and displacement values ​​of the target rods and verification points of the verification group are accurately extracted; the actual measured values ​​of the sensors in the verification group are matched and compared with the corresponding simulated values ​​point by point, and the mean square error algorithm is used to calculate the comprehensive deviation value of all verification points. The engineering preset comprehensive deviation critical threshold is 5%.

[0045] When the overall deviation A 5% deviation indicates the finite element stress field and deformation simulation results are reliable, and the simulation data is directly output for subsequent component early warning and lifting point compensation calculation; when the overall deviation is within 5%, the results are considered reliable. If the value is 5%, the model simulation is determined to be distorted, and the adaptive correction process is initiated.

[0046] Specifically, the preset model correction algorithm is invoked to automatically iterate and adjust the key parameters of the model: the stiffness reduction factor of steel components, the wind load reduction factor, the temperature stress coupling factor, and the nodal constraint stiffness parameters. Each time the parameters are adjusted, the finite element solution is run again to calculate the comprehensive deviation, and the iteration is repeated until the comprehensive deviation is less than the 5% threshold.

[0047] Specifically, the algorithm process for correcting the target model is as follows: First, set initial values ​​and legal value ranges that conform to engineering specifications for the four key parameters to be adjusted. At the same time, import the actual measured data of suspension point displacement, member internal force, wind load, and temperature. Set 5% as the deviation acceptance line and set the maximum number of iterations to avoid infinite loop.

[0048] After each parameter adjustment, finite element analysis (FEM) is performed to obtain simulation results. The relative errors between the simulated and measured values ​​of displacements at all lifting points and internal forces in key members are statistically analyzed. The overall comprehensive deviation is calculated using a weighted average of displacement and internal force. The influence of each parameter on the comprehensive deviation is calculated. Along the direction that minimizes the deviation, four parameters are adjusted synchronously in preset small steps, ensuring that the parameter values ​​do not exceed the reasonable range allowed by the project throughout the process. After each parameter adjustment, the comprehensive deviation is recalculated. Iteration stops immediately if the deviation is less than 5%. If the target is not met, the parameter adjustment and recalculation steps are repeated until the deviation is acceptable. Finally, a calibrated high-precision mechanical model is output.

[0049] After the model parameters are iteratively corrected to meet the standards, the real-time driving group data is remapped to the updated digital twin model, and the finite element solution is executed again to output the full component stress field and deformation simulation results that meet the accuracy requirements, and then enter the subsequent early warning and control process.

[0050] This invention provides a real-time analysis method for the lifting of aerial walkways. The method splits all on-site monitoring data into two independent groups: a driving group specifically for updating the load boundary conditions of the digital twin model, and a verification group specifically for checking the deviation between the simulation results and the actual on-site conditions. When the overall deviation exceeds the allowable range, the system automatically iteratively corrects key variables such as the model's stiffness parameters, wind load coupling coefficient, and temperature influence coefficient until the consistency between the simulation results and the measured data meets the standard. Only then will subsequent mechanical analysis results be output. This solves the problems of fixed parameters in traditional digital twin models, large deviations between simulation results and actual on-site stress, and distorted and unusable output results. It allows the twin model to adaptively calibrate according to changes in on-site conditions, ensuring that the full-domain stress and deformation results output by the finite element method perfectly match the actual on-site stress state. Subsequent early warning and suspension point compensation adjustments based on these accurate results have sufficient engineering reliability, completely avoiding the safety hazards of making erroneous control operations based on distorted simulation data.

[0051] As an optional implementation, a control signal is generated based on the lifting point compensation amount to control the lifting device to lift the lifting point, including: Based on the lifting compensation amount of the lifting point, the target displacement setpoint and velocity limit value of the lifting point are output; the target displacement setpoint and velocity limit value are then sent to the PID fine-tuning controller of the corresponding lifting point, so that the PID fine-tuning controller executes the following steps: The system collects real-time measured values ​​of the lifting point displacement at a preset frequency scanning cycle. Based on the deviation between the target displacement setpoint and the measured displacement value, it calculates and generates a basic control quantity for the servo valve opening. Using the speed limit value as the maximum allowable rate of change constraint for the current control cycle, it performs rate limiting processing on the basic control quantity for the servo valve opening, generating a rate-limited servo valve opening control signal to drive the hydraulic lifting device. When the rate of change of speed corresponding to the basic control quantity exceeds the speed limit value, the excess portion is truncated and output at the maximum rate corresponding to the limit value. When the measured displacement value approaches the target displacement setpoint, the servo valve opening is reduced.

[0052] For example, after the digital twin platform completes the verification of the compensation amount of the lifting point, it will send the target displacement setpoint and the speed limit value adapted to the current working condition for each lifting point to the PID fine-tuning controller of the corresponding lifting point through an independent hard channel. The physical links of each controller are completely isolated, and there will be no cross-lifting point command crosstalk.

[0053] After receiving the instruction, the controller first performs a double verification, that is, confirms that the target displacement does not exceed the remaining stroke of the cylinder and the speed limit does not exceed the rated safe flow rate of the hydraulic system. After the verification is passed, it automatically enters the real-time acquisition state. The controller reads the absolute displacement value of the high-precision laser displacement sensor at a fixed scanning cycle, performs digital filtering to eliminate instantaneous jumps caused by vibration, and obtains a stable measured value of the lifting point displacement.

[0054] The deviation between the target displacement setpoint and the measured value is calculated in real time and the calculation is completed by the PID algorithm: the proportional element responds to the current deviation and outputs the basic opening quickly, the integral element eliminates small steady-state errors, and the derivative element predicts the trend of deviation changes. The final generated basic control quantity of the servo valve opening is directly mapped to the real-time lifting speed of the hydraulic cylinder.

[0055] The controller analyzes the instantaneous speed change rate of the hydraulic cylinder corresponding to the basic control quantity in real time and compares it with the preset speed limit value cycle by cycle. If the rate of change of velocity Limit value: Directly outputs the original basic control quantity to ensure regulation response efficiency; If the rate of change of velocity Limit value: Automatically cuts off the overspeed portion and generates the final control signal strictly according to the maximum rate corresponding to the limit value, completely avoiding sudden rises and falls of the suspension points and preventing impact loads from damaging the corridor structure.

[0056] When the difference between the measured displacement and the target given value enters the critical range of 1mm, the controller automatically starts the gradient deceleration logic: as the difference decreases, the opening of the servo valve is linearly reduced, and the movement speed of the cylinder is gradually reduced to 1 / 5 of the initial speed. The cylinder moves towards the target position without impact throughout the process, and finally the displacement of the lifting point is stably converged within the high-precision range of ±0.5mm, completing this round of compensation action.

[0057] This invention provides a real-time analysis method for lifting aerial walkways. Based on the calculated lifting compensation amount of the lifting points, the method directly outputs the target displacement value and the corresponding speed change limit value. A basic opening command for the servo valve is generated through PID calculation, while an additional hard constraint on the speed change rate is added. When the lifting point displacement approaches the target value, the opening of the servo valve is automatically reduced in a gradient manner. This control method solves the problem that traditional hydraulic lifting, which directly uses ordinary PID regulation, is prone to sudden rises and falls of the lifting points and sudden speed changes that cause impact loads that damage the walkway components. By strictly limiting the acceleration of the cylinder lifting and lowering through rate limiting, and combined with a gradient deceleration strategy at the endpoint, millimeter-level stable positioning of the lifting points is achieved, completely eliminating fatigue damage to the walkway components and nodes caused by dynamic impacts. Furthermore, each lifting point is equipped with an independent PID isolation control loop, preventing crosstalk between control commands from different lifting points and significantly improving the stability of the synchronous fine-tuning process.

[0058] As an optional implementation method, a real-time analysis method for improving aerial walkways further includes: The system monitors the hydraulic system pressure at each lifting point and the rate of change of vertical displacement difference between the lifting points in real time. If the PID fine-tuning controller controls the lifting point to reach the target displacement setpoint, it sends a position signal to the digital twin model, triggering the next round of finite element solution and compensation update. If the hydraulic system pressure exceeds a preset pressure threshold, or the rate of change of vertical displacement difference between the lifting points exceeds a preset rate of change threshold, it triggers a hard interlock protection action. The hard interlock protection action includes: physically cutting off the power supply circuit of the servo valve of the independent hydraulic lifting device of the corresponding lifting point and starting the hydraulic lock to lock the current pressure of the cylinder; sending an interrupt takeover signal to the digital twin model to freeze the finite element solution process; and releasing the hard interlock protection action in response to a manual confirmation reset command. The digital twin model obtains the actual displacement value of each lifting point at the current moment, recalculates the next round of lifting point compensation based on the actual displacement value at the current moment, and sends the recalculated compensation to the PID fine-tuning controller for execution.

[0059] For example, high-frequency pressure sensors with a precision of 0.2 are built into the rodless chamber and rod chamber of the hydraulic cylinder at all lifting points. The sampling frequency is set to 100Hz to continuously collect the real-time working pressure of the upper and lower chambers of the cylinder and synchronously calculate the actual load-bearing tension of the current lifting point. The data delay is controlled within 5ms.

[0060] Meanwhile, high-precision magnetostrictive displacement sensors are installed next to the lifting anchor points of each hoisting point to collect the real-time vertical absolute displacement of the hoisting point at a frequency of 50Hz. The system background automatically performs differential calculations on the displacement data of adjacent hoisting points and generates the real-time rate of change of the vertical displacement difference between the hoisting points every second. The two core safety parameters are directly uploaded to the independent safety interlocking logic module through a hard-wired link, without going through the ordinary industrial control network, thus avoiding response delays caused by network latency.

[0061] All collected raw data will be filtered for outliers in real time to remove invalid data from instantaneous sensor fluctuations, ensuring that every set of data entering the interlocking judgment logic is true and reliable.

[0062] After the PID fine-tuning controller receives the target displacement setpoint from the previous digital twin platform, it will dynamically adjust the flow output of the hydraulic system with a control cycle of 10ms, track the actual displacement change of the lifting point throughout the process, and gradually reduce the deviation between the measured displacement and the target value through small step increment adjustment.

[0063] When the PID controller detects that the measured displacement of the current lifting point has been stably within the ±0.5mm error range of the target setpoint for three consecutive control cycles, it determines that the lifting point has reached the designated position and then sends a time-stamped completion signal to the digital twin platform.

[0064] After receiving all the positioning signals from all lifting points, the platform will not immediately start calculations. Instead, it will first synchronously retrieve all the measured data at the current moment—including the actual displacement of all lifting points, stress data of key members, and real-time wind speed and ambient temperature—and automatically complete a parameter micro-correction of the digital twin model to ensure that the current model state is completely aligned with the actual site.

[0065] Subsequently, the platform automatically starts a new round of full finite element analysis, completes stress and deformation verification of all components of the entire connecting corridor, recalculates the next round of lifting point compensation to adapt to the current working conditions, and after verifying that all compensation amounts will not cause local stress to exceed the limit, sends the new target displacement command to the PID controller of each lifting point, and the system automatically enters the next round of synchronous lifting closed-loop control with a 5-minute cycle.

[0066] The system pre-sets two levels of safety thresholds based on the structural design load-bearing capacity of the connecting corridor and the rated working pressure of the hydraulic system: the first level is the warning threshold, which triggers an audible and visual alert when the pressure or displacement change rate approaches the critical value; the second level is the hard interlock trigger threshold, which is the set limit safety value. As long as the real-time parameters of any hoisting point exceed the hard interlock threshold, the safety interlock logic module will trigger the full-link hard protection action within 15ms, without going through the software logic relay, and the response is achieved entirely by the hardware circuit.

[0067] If the hydraulic system pressure at a single lifting point exceeds the preset limit pressure, it indicates that the actual load on that lifting point has exceeded the design allowable range, most likely due to abnormal stress or jamming of local members causing overload. If the rate of change of vertical displacement difference between lifting points exceeds the threshold, it indicates that the synchronization of the lifting points has suddenly lost control, and the connecting corridor structure has experienced unexpected torsion or eccentric loading risks. In either of these situations, a three-level linkage hard protection will be immediately implemented. First-level hardware cutoff: Through an independent physical relay circuit, the power supply circuit of the servo valve of the hydraulic lifting device at the corresponding abnormal lifting point is directly cut off, completely stopping the power output of the lifting point and preventing the abnormal working condition from continuing to expand from the root cause; Second-stage cylinder locking: The hydraulic system's built-in hydraulic control check valve will automatically lock the upper and lower oil chambers of the cylinder the moment power is lost, completely sealing the oil circuit and firmly locking the current extension length of the cylinder. Even if there is leakage in the oil pipe later, it can ensure that the lifting point will not slide or move, reducing the risk of local instability of the connecting corridor. Level 3 Simulation Freeze: The safety interlock module simultaneously sends a high-priority interrupt takeover signal to the digital twin platform. The platform immediately suspends the currently running finite element solution process, freezes all pending lifting and control commands, and prevents the system from continuing to output erroneous control commands under abnormal conditions. At the same time, it automatically pops up a window to lock the operation interface and simultaneously displays a red alarm prompt in the field control room and remote monitoring terminal, indicating the abnormal lifting point number, the parameter type that triggered the interlock, and the real-time value.

[0068] Once the interlock is triggered, the on-site safety control process will automatically start: the system will automatically generate an abnormal working condition snapshot and archive and save all data on the pressure, displacement, and stress of the lifting points and members in the 10 minutes before the interlock is triggered, so that technicians can retrospectively analyze the cause of the failure.

[0069] On-site maintenance and structural engineers immediately compared the snapshot data to check the status of the hydraulic system at the abnormal lifting points and the stress on the members at the corresponding parts of the connecting corridor. After confirming that there was no structural damage and the hydraulic fault was completely eliminated, the project manager entered a unique password on the back-end control platform and issued a manual confirmation and reset command.

[0070] After receiving the reset command, the system first gradually restores the power supply path of the servo valve at the abnormal lifting point, automatically detects whether the hydraulic system circuit pressure is stable, and releases the hardware interlock after confirming that there is no abnormality.

[0071] Subsequently, the digital twin platform automatically retrieves the true absolute displacement values ​​of all lifting points at the moment of interlocking triggering, completely discarding the old calculation benchmark before interlocking. Taking the current actual position of all lifting points as a new starting point, it re-runs the full finite element verification, iteratively calculates the new lifting compensation amount adapted to the current state, and verifies that the new target displacement of each lifting point will not cause structural stress exceeding the limit or synchronization deviation exceeding the standard. Then, the new control command is sent to the PID fine-tuning controller of all lifting points, and the system automatically restores the normal synchronous lifting closed-loop control process, and the connecting corridor continues to lift smoothly upward.

[0072] This invention provides a real-time analysis method for lifting aerial walkways. During system operation, it continuously monitors the rate of change of hydraulic system pressure and lifting point displacement difference. If either indicator exceeds a set threshold, the power supply to the servo valve is directly cut off via a hardware circuit, simultaneously triggering a hydraulic lock cylinder and freezing the twin model's solution process. All protection actions are completed at the hardware level, without relying on software calculations. After manual reset following fault clearance, the system directly recalculates the subsequent lifting compensation based on the actual displacement of the current lifting point. Compared to traditional pure software alarm protection modes, this hardware interlocking circuit achieves millisecond-level fault response, completely avoiding the risks of network latency and software crashes leading to protection failure. Locking the cylinder directly prevents the lifting point from slipping and becoming unstable. After fault clearance, there is no need for manual recalibration of the displacement benchmark; the system can quickly resume synchronous lifting operations, while simultaneously ensuring structural safety under extreme conditions and the efficiency of continuous construction operations.

[0073] As an optional implementation, the lifting device collects multi-source measured data every time the lifting point is raised a target distance. This acquisition mode departs from the traditional method of triggering data acquisition at fixed time intervals. Instead, it sets a fixed lifting distance threshold, and the system automatically triggers a round of full-dimensional multi-source monitoring data acquisition every time the lifting point is raised a preset target distance. This acquisition logic perfectly matches the displacement changes during the lifting of the connecting corridor. The greater the lifting height of the connecting corridor, the more significant the changes in structural stress state and lifting point synchronization deviation. By triggering acquisition based on displacement, the true stress state of each key construction node can be accurately captured. Simultaneously, during the stable lifting phase of the connecting corridor, the system automatically reduces unnecessary data acquisition frequency, which not only reduces the computing power consumption of various sensors and backend computing equipment but also ensures that the twin model can be updated and the structural safety status verified promptly after each displacement change.

[0074] This embodiment provides an elevated walkway lifting system, such as... Figure 2 As shown, it includes: The multimodal sensor 201 is used to collect multi-source measured data; Controller 202 is used to execute a real-time analysis method for lifting an aerial walkway as described in the above embodiments; Lifting device 203 is used to lift the hoisting point based on control signals.

[0075] For example, the lifting device includes a PID fine-tuning controller and a hydraulic lifting device. Specific examples are given in the corresponding sections of the above method embodiments, and will not be repeated here.

[0076] This application also provides an electronic device, such as... Figure 3 As shown, processor 501 and memory 502 are connected via a bus or other means.

[0077] Processor 501 can be a central processing unit (CPU). Processor 501 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0078] The memory 502, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to a real-time analysis method for aerial walkway lifting in this embodiment of the invention. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory.

[0079] Memory 502 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 502 may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0080] The one or more modules are stored in the memory 502, and when executed by the processor 501, they perform actions such as... Figure 1 The embodiment shown presents a real-time analysis method and a system for lifting elevated walkways.

[0081] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figure 1 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0082] This embodiment also provides a computer storage medium storing computer-executable instructions that can execute a real-time analysis method for aerial walkway lifting in any of the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0083] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A real-time analysis method for the elevation improvement of an aerial walkway, characterized in that, include: When the synchronous lifting operation of the aerial corridor is started, multi-source measured data are collected simultaneously. The multi-source measured data includes the vertical displacement of each lifting point, the internal force deformation of the key members of the steel corridor, and the external wind load. Multi-source measured data are fused and mapped to a pre-built digital twin model to update the model load and boundary conditions, and finite element solution is performed to output the stress field and deformation simulation results of all components of the entire connecting corridor. Based on the predefined component types and / or corresponding simulation results of all components, determine the set of multi-level early warning thresholds for the corresponding components; Based on the stress field and deformation simulation results of the components and the corresponding multi-level early warning threshold set of the components, multi-level stress early warning signals of each component are output. Based on the vertical displacement of each lifting point, determine the vertical displacement difference between multiple lifting points; When the vertical displacement difference between the lifting points exceeds the preset threshold, or when the force warning signal of any component reaches the first target level, the lifting compensation amount of the lifting point is determined. Based on the lifting compensation amount of the lifting point, a control signal is generated to control the lifting device to lift the lifting point.

2. The real-time analysis method for the elevation improvement of an aerial walkway according to claim 1, characterized in that, Based on the predefined component types and corresponding simulation results of all components, a multi-level early warning threshold set for each component is determined, including: Get the component type of the component; the attributes include permanent components and temporary components. Determine the stress state of the component in the simulation results, including tensile or compressive stress. When a component is a permanent component and is under tension, the multi-level early warning threshold of the component is the first multi-level threshold set; When the component is a permanent component and is under pressure, the multi-level early warning threshold of the component is the second multi-level threshold set; When a component is a temporary component and is under tension, the multi-level early warning threshold of the component is the third multi-level threshold set; When a component is a temporary component and is under pressure, the multi-level early warning threshold of the component is the fourth multi-level threshold set.

3. The real-time analysis method for the elevation improvement of an aerial walkway according to claim 1, characterized in that, Based on the corresponding simulation results, a multi-level early warning threshold set for the corresponding component is determined, including: Based on the simulation results, the relative ratio between the actual bearing stress of each component in the connecting corridor at the current moment and its own material ultimate bearing capacity is extracted to form a dataset reflecting the current stress distribution of the entire connecting corridor. Using the magnitude of stress on components and the adjacent positional relationship of components in the corridor space topology as dual joint measurement criteria, an unsupervised clustering algorithm is used to automatically classify all components into three cohesive categories: high-risk stress cluster, medium-risk stress cluster, and low-risk stress cluster. The central tendency and dispersion of the stress data of the internal components of the three risk clusters are statistically analyzed. Based on the inherent statistical characteristics of each cluster, the specific baseline warning line of the corresponding risk cluster is calculated. Based on the differentiated risk level weights of high, medium and low risk clusters, a multi-level early warning threshold set is determined on the basis of the exclusive baseline warning line of each cluster, and all early warning thresholds are dynamically refreshed in real time with each finite element solution update.

4. The real-time analysis method for the elevation improvement of an aerial walkway according to claim 1, characterized in that, Multi-source measured data are fused and mapped onto a pre-built digital twin model to update the model loads and boundary conditions. Finite element analysis is then performed to output the stress field and deformation simulation results for all components of the entire connecting corridor, including: The multi-source measured data is divided into driver group data and verification group data; The driving group data is fused and mapped to a pre-built digital twin model to update the model load and boundary conditions, and finite element solution is performed to obtain the stress field and deformation simulation results of all components of the entire connecting corridor. Based on the target components included in the verification group data, the stress field and deformation simulation results of the corresponding multiple target components are searched in the stress field and deformation simulation results of all components in the entire connecting corridor. The measured data of multiple target components in the verification group data are compared with the stress field and deformation simulation results of the corresponding target components to determine the comprehensive deviation. When the overall deviation is less than the preset threshold, the stress field and deformation simulation results of all components of the entire connecting corridor are reliable output results and are output. When the overall deviation is greater than or equal to the preset threshold, the target algorithm is used to adaptively correct the digital twin model until the overall deviation is less than the preset threshold. Then, the driving group data is re-fused and mapped to the corrected digital twin model to update the model load and boundary conditions, and output the stress field and deformation simulation results of all components of the entire connecting corridor.

5. The real-time analysis method for the elevation improvement of an aerial walkway according to claim 1, characterized in that, Based on the lifting compensation amount of the hoisting point, a control signal is generated to control the lifting device to lift the hoisting point, including: Based on the lifting compensation amount of the lifting point, output the target displacement setpoint and velocity limit value of the lifting point; The target displacement setpoint and velocity limit value are sent to the PID fine-tuning controller at the corresponding lifting point, so that the PID fine-tuning controller performs the following steps: The measured values ​​of the lifting point displacement are collected in real time at a preset frequency scanning cycle. The basic control quantity of the servo valve opening is generated based on the deviation between the target displacement set value and the measured displacement value. Using the speed limit value as the maximum allowable rate of change constraint for the current control cycle, the basic control quantity of the servo valve opening is rate-limited to generate a servo valve opening control signal with a rate limit, which drives the hydraulic lifting device to move. When the rate of change of speed corresponding to the basic control quantity exceeds the speed limit value, the excess portion is truncated and output at the maximum rate corresponding to the limit value. When the measured displacement value approaches the target displacement value, reduce the opening of the servo valve.

6. The real-time analysis method for the elevation improvement of an aerial walkway according to claim 5, characterized in that, Also includes: Real-time monitoring of hydraulic system pressure values ​​at each lifting point and the rate of change of vertical displacement difference between lifting points; If the PID fine-tuning controller controls the lifting point to reach the target displacement setpoint, it sends a position signal to the digital twin model to trigger the next round of finite element solution and compensation update. If the hydraulic system pressure value exceeds the preset pressure threshold, or the rate of change of vertical displacement difference between lifting points exceeds the preset rate of change threshold, the hard interlock protection action is triggered. The hard interlock protection action includes: physically cutting off the power supply circuit of the servo valve of the independent hydraulic lifting device of the corresponding lifting point, and starting the hydraulic lock to lock the current pressure of the oil cylinder. Send an interrupt takeover signal to the digital twin model to freeze the finite element solution process; In response to a manually confirmed reset command, the hard interlock protection action is released; The digital twin model obtains the actual displacement value of each lifting point at the current moment, recalculates the lifting compensation amount of the next round of lifting points based on the actual displacement value at the current moment, and sends the recalculated compensation amount to the PID fine-tuning controller for execution.

7. The real-time analysis method for the elevation improvement of an aerial walkway according to claim 1, characterized in that, Also includes: When the stress warning signal of any component reaches the second target level, an inspection prompt is issued. The warning level corresponding to the second target level is lower than that of the first target level. When the stress warning signal of any component reaches the third target level, the synchronous lifting operation of the aerial corridor will be stopped. The warning level corresponding to the third target level is higher than that of the first target level.

8. An elevated walkway lifting system, characterized in that, include: Multimodal sensors are used to collect multi-source measured data; A controller for executing a real-time analysis method for lifting an aerial walkway as described in any one of claims 1-7; A lifting device used to lift the hoisting point based on control signals.

9. The elevated walkway lifting system according to claim 8, characterized in that, The lifting device includes a PID fine-tuning controller and a hydraulic lifting device.

10. An electronic device, the device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor performs the steps of the real-time analysis method for the elevation of an aerial walkway as described in any one of claims 1-7.

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