Method and system for real-time monitoring and early warning of construction stress based on intelligent hanging basket

Abstract

The application discloses a construction stress real-time monitoring and early warning method and system based on a smart hanging basket, and the method comprises the following steps: arranging sensing devices on key stress parts of the hanging basket and calibrating the key stress parts, establishing an empty load stress benchmark, collecting stress and posture data in real time and preprocessing and uploading, identifying time-varying boundary conditions such as post-anchor constraint stiffness, track contact stiffness and hinged clearance based on the collected data, constructing an adaptive finite element mechanics model, iteratively adjusting the constraint equation and the stiffness matrix through a model correction algorithm, and eliminating calculation distortion caused by time-varying boundaries; spatial modal decomposition is performed on stress fields and deformation fields of multiple measuring points, strain modal characteristics of the main truss and the bottom basket system are extracted, the stiffness matrix is inversed, and structural damage is identified through stiffness consistency discrimination; the central control system outputs early warning according to the stress and health state, pushes to multiple terminals and links to safety locking, and forms a monitoring and early warning closed loop.

Description

Technical Field

[0001] This invention belongs to the field of intelligent hanging basket technology, and more specifically, relates to a method and system for real-time monitoring and early warning of construction stress based on intelligent hanging basket. Background Technology

[0002] In the cantilever construction of long-span bridges, the hanging basket, as a temporary structure directly bearing construction loads, has its stress state directly affecting construction safety and project quality. With the continuous increase in bridge span, the stress system of the hanging basket structure becomes increasingly complex. During construction, loads, boundary conditions, and structural states are all dynamically changing. Traditional manual monitoring and periodic inspection methods are no longer sufficient to meet the needs of real-time safety management. Existing hanging basket monitoring technologies mostly rely on fixed stress sensors and limited manual observation. Data acquisition frequency is low, coverage is limited, and there is a lack of dynamic identification capabilities for time-varying boundary conditions such as anchor constraint stiffness, track contact stiffness, and hinge gaps. This makes it difficult to accurately reflect the true stress state of the hanging basket under different construction conditions, easily leading to distortions in mechanical model calculations and errors in structural stress inversion. Furthermore, it fails to effectively identify potential risks such as hidden damage to members, loose connections, and overall stiffness degradation.

[0003] Meanwhile, traditional monitoring systems often focus on single-point stress over-limit alarms, lacking systematic analysis of the overall structural mechanical behavior and long-term tracking of its health status. Early warning mechanisms are mostly limited to single threshold triggering, failing to achieve closed-loop control with simultaneous multi-terminal push notifications and safety lockouts, making it difficult to form a continuous and effective safety protection system during construction. Safety accidents during hanging basket construction often stem from neglecting changes in boundary conditions and structural performance degradation. Existing technologies cannot achieve online perception and dynamic assessment of these factors, resulting in untimely and inaccurate early warning and handling of safety risks, posing significant safety hazards to the construction process.

[0004] Therefore, there is an urgent need for a method that can realize real-time monitoring and early warning of stress during cantilever construction, and solve the problems of incomplete data collection, lagging boundary condition identification, insufficient accuracy of mechanical models, unsystematic health status judgment, and non-closed-loop early warning mechanism in existing technologies. By establishing a complete system from data collection, model correction, status assessment to early warning execution, the real-time performance, accuracy and systematic nature of safety management and control during cantilever construction of long-span bridges can be improved, providing reliable safety assurance. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing hanging basket construction monitoring, such as incomplete data collection, delayed boundary condition identification, insufficient accuracy of mechanical models, and non-closed-loop early warning mechanisms. It provides a method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets, which enables dynamic perception, accurate assessment, and closed-loop control of the stress state throughout the entire hanging basket construction process, thereby improving the level of construction safety assurance.

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a method for real-time monitoring and early warning of construction stress based on a smart hanging basket, comprising: S1. Sensors are deployed and networked to calibrate key stress-bearing parts of the main truss, rear anchor, and suspension points of the smart hanging basket. Initial stress benchmark under no-load is established, and stress and attitude data are collected in real time and uploaded to the central control system and cloud platform after preprocessing. S2. Based on real-time collected stress, attitude and displacement sensing data, online parameter identification is carried out on the time-varying boundary conditions of the smart hanging basket, including the rear anchor constraint stiffness, track contact stiffness and hinge gap; an adaptive finite element mechanical model updated synchronously with the construction status is constructed, and the constraint equations and stiffness matrix are iteratively adjusted in real time through the model correction algorithm to eliminate the calculation distortion and structural stress inversion error caused by the time-varying boundary. S3. Perform spatial modal decomposition on the stress field and deformation field collected synchronously from multiple measuring points to extract the strain mode shapes and frequency characteristics of the main truss and bottom basket system of the smart hanging basket; based on the modal characteristics, invert the overall stiffness matrix distribution of the structure, and identify hidden damage of members, loose connections and overall stiffness degradation trends through stiffness consistency discrimination; S4. The central control system outputs early warning commands based on the actual stress state and health characteristics of the structure, pushes the early warning information to the field terminal and remote terminal simultaneously, and performs safety locking in conjunction to form a complete monitoring and early warning closed loop; The process of real-time iterative adjustment of constraint equations and stiffness matrix is ​​based on real-time stress monitoring data and adaptive finite element mechanical model during the construction phase. Constraint equations with nodal displacement and element internal force as variables are established, and the constraint displacement boundary conditions of the hanging basket support and rear anchor, as well as the stress limit conditions of the measuring points under the construction conditions are used as constraints. The augmented constraint equation is constructed using the Lagrange multiplier method. By iteratively solving the equation, the nodal displacement vector and the overall stiffness matrix are updated online, and the element internal force and stress distribution of the finite element model are corrected simultaneously.

[0007] Furthermore, the sensor deployment and network calibration process in S1 is as follows: Sensing units are installed on the main truss, rear anchor point, and suspension point stress node of the smart hanging basket. Sensing units are installed at intervals along the axial direction on the main truss. Sensing units are installed one-to-one on the rear anchor point and the suspension point stress node. The sensing units are connected to the smart hanging basket structure body by bolt fixing. All sensing units are connected to the data acquisition module via an industrial bus. Each sensing unit is assigned a unique communication address and a unified sampling frequency is configured. Communication debugging between the sensing units and the data acquisition module is completed, and a point-to-point data transmission link is established. The intelligent hanging basket is controlled to be in a completely unloaded and static stable state. The data acquisition module is started to continuously collect the raw data output by each sensor unit. The acquisition time is set, and outlier removal processing is performed on the raw data. The initial stress value of each measuring point is determined through calculation and integrated to form the initial stress benchmark of the system. Based on the initial stress reference of the system, the amplitude deviation calibration and zero-point calibration of the acquisition channels of each sensing unit are performed to eliminate the inherent error of the sensing unit and the installation deviation, and to complete the network calibration of the sensing system.

[0008] Furthermore, the calibration process for the unloaded initial stress reference in S1 is as follows: The intelligent hanging basket is placed in a static and stable state without external loads or construction disturbances. The original stress signals of each sensing unit are collected synchronously to construct the stress vector at the measuring point. The stress vector at the measuring point is decomposed into singular spectrum, retaining the dominant singular components of the structural response and removing noise components to reconstruct a pure stress sequence. Based on the material constitutive relation, the pure stress sequence is converted into a strain vector. Using the minimum potential energy of the structure as the objective function and the constraint displacement boundary conditions of the hanging basket support and the rear anchor as constraints, the objective function is optimized to obtain the strain equilibrium solution. The optimal stress equilibrium solution of each measuring point is obtained by back calculation through the material constitutive relation. The set of optimal stress equilibrium solutions of each measuring point is used as the initial stress reference of the system under no-load conditions to complete the calibration.

[0009] Furthermore, the online parameter identification process in S2 is as follows: Based on the initial stress benchmark under no-load and the stress time series data collected in real time during the construction phase, a stress increment vector is constructed; a state space model is established with the stiffness parameters of the components to be identified as state variables and the stress increments as observed values; a recursive extended Kalman filter algorithm is used to iteratively correct the stiffness parameters through prediction and update steps; the identified stiffness parameters are physically consistent, and invalid parameter values ​​that exceed the theoretical stiffness range corresponding to the elastic modulus of the component material are eliminated to obtain the real-time effective stiffness parameter vector of each component, thus completing the online identification of key parameters of the hanging basket structure.

[0010] Furthermore, the adaptive finite element mechanical model construction process in S2 is as follows: Real-time effective stiffness parameter vector based on key components of the hanging basket structure Update the overall stiffness matrix of the hanging basket. Components of the element stiffness matrix corresponding to each component ,in For the first The state vector is composed of the real-time effective stiffness parameters of each component at each sampling time. For the first The sampling time of the first sampling moment Real-time effective stiffness parameters of each component after identification Assign component numbers, The total number of components to be identified. For the first The element stiffness matrix components of the i-th component are derived from the i-th... Sectional parameters and material elastic modulus of each component With real-time effective stiffness parameters jointly determined, The elastic modulus of the material corresponding to the component; Construct the updated global stiffness matrix ,in The overall stiffness matrix of the hanging basket; the updated overall stiffness matrix Substituting the equilibrium equations into the finite element mechanical model ,in For the model node displacement vectors, This represents the load vector during the construction phase; combined with the constraint displacement boundary conditions of the hanging basket support and rear anchor. Solving the equilibrium equations yields the model's nodal displacement vectors. Theoretical stress vectors at each measuring point ,in Let be the constraint displacement vector of the hanging basket support and the rear anchor. These are the theoretical stress vectors at each measuring point calculated using the finite element model. It is a unit geometric matrix; Theoretical stress vector With real-time collected stress data at measurement points Perform residual verification; if the residual exceeds a preset threshold, readjust the element stiffness matrix components. And update the overall stiffness matrix. This continues until the residual between the theoretical stress and the measured stress meets the preset requirements, thus forming an adaptive finite element mechanical model that can synchronously reflect the real-time stress state of the hanging basket.

[0011] Furthermore, the Lagrange multiplier method is used to construct the augmented constraint equations as follows: in, For the first The overall stiffness matrix of the hanging basket at each sampling time is obtained by updating the real-time effective stiffness parameters of each component; For the first The model node displacement vector at each sampling time; For the first The construction stage load vector at each sampling time; This is the constraint matrix corresponding to the constrained displacement; Let be the Lagrange multiplier vector; solve the augmented constraint equations iteratively.

[0012] Furthermore, the method for extracting the strain mode shapes and frequency characteristics in S3 is as follows: A stress-displacement joint observation matrix is ​​constructed based on stress field and deformation field data acquired synchronously from multiple measurement points; singular value decomposition is performed on the matrix, and the first few dominant components are selected according to the energy proportion of the singular values. The strain mode shape vectors are extracted from them, and the natural frequency characteristics of each mode are obtained through frequency domain transformation. The orthogonality of the extracted mode shapes is verified by the modal confidence criterion, and highly correlated pseudo-modal components are eliminated. At the same time, the physical consistency of the modes is verified by combining the theoretical mode shape characteristics of the hanging basket structure, and abnormal modes that deviate from the theoretical mode shape beyond the preset range are eliminated. Finally, the effective strain mode shapes and frequency characteristics reflecting the stress characteristics of the hanging basket main truss and bottom basket system are obtained.

[0013] Furthermore, the process of stiffness consistency determination in S3 is as follows: Based on the extracted strain mode shapes and frequency characteristics, the modal stiffness inversion formula is used. Calculate the overall stiffness matrix distribution of the hanging basket structure, where The global stiffness matrix obtained by inversion, For the first The natural angular frequencies of the first mode. The mass matrix of the hanging basket structure is predetermined by theoretical modal analysis. For the first First strain mode shape vector; The stiffness matrix obtained by inversion is compared with the initial design stiffness matrix, and the stiffness residuals are calculated element by element. ,in For the first The inverse stiffness value of each member. For the first The initial design stiffness value of each member; when If the value exceeds the preset threshold, it is determined that the member has hidden damage or loose connection; if the average residual of the overall stiffness matrix exceeds the preset range, it is determined that the hanging basket has an overall stiffness degradation trend, and the stiffness consistency judgment is completed.

[0014] As a second aspect of the present invention, a system for real-time monitoring and early warning of construction stress based on intelligent hanging baskets is also provided, comprising: The sensor deployment and data acquisition unit is used to deploy and network the sensors on the main truss, rear anchor, and key stress-bearing parts of the smart hanging basket, establish the initial stress benchmark under no-load conditions, collect stress and attitude data in real time, and upload the data to the central control system and cloud platform after preprocessing. The boundary identification and model correction unit is used to identify time-varying boundary conditions of the smart hanging basket, including rear anchor constraint stiffness, track contact stiffness, and hinge gap, based on real-time collected stress, attitude, and displacement sensing data. It constructs an adaptive finite element mechanical model that is updated synchronously with the construction status, and iteratively adjusts the constraint equations and stiffness matrix in real time through the model correction algorithm to eliminate calculation distortion and structural stress inversion errors caused by time-varying boundaries. The modal decomposition and stiffness discrimination unit is used to perform spatial modal decomposition on the stress field and deformation field simultaneously acquired from multiple measuring points, extract the strain mode shapes and frequency characteristics of the main truss and bottom basket system of the smart hanging basket; based on the modal characteristics, the overall stiffness matrix distribution of the structure is inverted, and the hidden damage of the members, loosening of connections and overall stiffness degradation trend are identified through stiffness consistency discrimination. The multi-terminal linkage early warning closed-loop unit is used by the central control system to output early warning commands based on the actual stress state and health characteristics of the structure, and push the early warning information to the field terminal and remote terminal simultaneously and link to execute safety locking, forming a complete monitoring and early warning closed loop; The process of real-time iterative adjustment of constraint equations and stiffness matrix is ​​based on real-time stress monitoring data and adaptive finite element mechanical model during the construction phase. Constraint equations with nodal displacement and element internal force as variables are established, and the constraint displacement boundary conditions of the hanging basket support and rear anchor, as well as the stress limit conditions of the measuring points under the construction conditions are used as constraints. The augmented constraint equation is constructed using the Lagrange multiplier method. By iteratively solving the equation, the nodal displacement vector and the overall stiffness matrix are updated online, and the element internal force and stress distribution of the finite element model are corrected simultaneously.

[0015] As a third aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, the computer program being executed by a processor of any one of the methods for real-time monitoring and early warning of construction stress based on a smart hanging basket.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The present invention provides a method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets. This method establishes an initial stress benchmark under no-load conditions by deploying and calibrating sensors on key stress-bearing components such as the main truss, rear anchor, and lifting points of the hanging basket. It then collects and preprocesses stress and attitude-related data in real time, and simultaneously uploads the valid data to the central control system and cloud platform. This achieves stable collection and standardized transmission of stress and attitude information throughout the entire hanging basket construction process. This step relies on a reasonable sensor deployment and calibration process to ensure the authenticity and representativeness of the original monitoring data. Simultaneously, standardized preprocessing reduces data noise interference, providing fundamental data support for subsequent boundary condition identification, mechanical model construction, and structural health status assessment. This ensures the entire monitoring system has reliable data input conditions, preventing deviations in subsequent analysis due to data anomalies or missing data, and guaranteeing the continuous and stable operation of monitoring work.

[0017] 2. The present invention provides a method for real-time monitoring and early warning of construction stress based on a smart hanging basket. By utilizing real-time collected stress, attitude, and displacement sensor data, it identifies online parameters of time-varying boundary conditions such as the stiffness of the hanging basket's rear anchor constraint, track contact stiffness, and hinge gap. This constructs an adaptive finite element mechanical model that updates synchronously with the construction status, and uses a model correction algorithm to iteratively adjust the constraint equations and stiffness matrix in real time. This method effectively solves the problem that traditional fixed models cannot adapt to changes in boundary conditions during construction, eliminates calculation distortions and structural stress inversion errors caused by time-varying boundaries, and enables the mechanical model to accurately match the actual construction state of the hanging basket. This improves the accuracy of structural stress calculation and state inversion, provides a reasonable analytical basis for accurately judging the structural safety status, and ensures that the evaluation results reflect the actual working performance of the hanging basket.

[0018] 3. The present invention provides a method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets. This method performs spatial modal decomposition on stress and deformation field data collected synchronously from multiple measuring points, extracting the strain mode shapes and frequency characteristics of the main truss and bottom basket system. Based on these modal characteristics, the overall stiffness matrix distribution of the structure is inverted, and latent damage to members, loose connections, and overall stiffness degradation trends are identified through stiffness consistency discrimination. The central control system outputs corresponding early warning commands based on the actual stress state and health status characteristics of the structure, synchronously pushing the early warning information to field terminals and remote terminals, and simultaneously executing safety locking operations to form a complete monitoring and early warning closed loop. This process enables effective identification of latent structural defects and timely handling of safety risks, improving the safety of hanging basket construction without adding additional construction interference, and ensuring a smooth and orderly construction process. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for real-time monitoring and early warning of construction stress based on a smart hanging basket, according to an embodiment of the present invention. Figure 2This is a schematic diagram of the overall model of the smart hanging basket according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the deployment of the intelligent hanging basket monitoring and control system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0021] Example 1 Please refer to Figure 1 This embodiment 1 provides a method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets, including: S1. Sensors are deployed and networked to calibrate key stress-bearing parts of the main truss, rear anchor, and suspension points of the smart hanging basket. Initial stress benchmark under no-load is established, and stress and attitude data are collected in real time and uploaded to the central control system and cloud platform after preprocessing. S2. Based on real-time collected stress, attitude and displacement sensing data, online parameter identification is carried out on the time-varying boundary conditions of the smart hanging basket, including the rear anchor constraint stiffness, track contact stiffness and hinge gap; an adaptive finite element mechanical model updated synchronously with the construction status is constructed, and the constraint equations and stiffness matrix are iteratively adjusted in real time through the model correction algorithm to eliminate the calculation distortion and structural stress inversion error caused by the time-varying boundary. S3. Perform spatial modal decomposition on the stress field and deformation field collected synchronously from multiple measuring points to extract the strain mode shapes and frequency characteristics of the main truss and bottom basket system of the smart hanging basket; based on the modal characteristics, invert the overall stiffness matrix distribution of the structure, and identify hidden damage of members, loose connections and overall stiffness degradation trends through stiffness consistency discrimination; S4. The central control system outputs early warning commands based on the actual stress state and health characteristics of the structure, pushes the early warning information to the field terminal and remote terminal simultaneously, and performs safety locking in conjunction to form a complete monitoring and early warning closed loop; The process of real-time iterative adjustment of constraint equations and stiffness matrix is ​​based on real-time stress monitoring data and adaptive finite element mechanical model during the construction phase. Constraint equations with nodal displacement and element internal force as variables are established, and the constraint displacement boundary conditions of the hanging basket support and rear anchor, as well as the stress limit conditions of the measuring points under the construction conditions are used as constraints. The augmented constraint equation is constructed using the Lagrange multiplier method. By iteratively solving the equation, the nodal displacement vector and the overall stiffness matrix are updated online, and the element internal force and stress distribution of the finite element model are corrected simultaneously.

[0022] This embodiment further elaborates on the above steps.

[0023] (1) Sensor deployment and data acquisition Please refer to Figure 2 as well as Figure 3 In the cantilever construction of long-span bridges, the initial stress state of the hanging basket serves as the benchmark for subsequent stress monitoring and safety assessment, directly affecting the accuracy of stress monitoring data during construction. To establish a reliable initial stress benchmark, it is first necessary to systematically deploy and network sensors at key stress-bearing components of the hanging basket. Sensor units are deployed at the main truss, rear anchor points, and lifting point stress nodes of the intelligent hanging basket. Sensor units are spaced out along the axial direction of the main truss, and sensor units are deployed one-to-one between the rear anchor points and lifting point stress nodes. The sensor units are connected to the intelligent hanging basket structure using bolt fixing. All sensing units are connected to the data acquisition module via an industrial bus. Each sensing unit is assigned a unique communication address and a unified sampling frequency is configured. Communication debugging between the sensing units and the data acquisition module is completed, and a point-to-point data transmission link is established. The intelligent hanging basket is controlled to be in a completely unloaded and static stable state. The data acquisition module is started to continuously collect the raw data output by each sensor unit. The acquisition time is set, and outlier removal processing is performed on the raw data. The initial stress value of each measuring point is determined through calculation and integrated to form the initial stress benchmark of the system. Based on the initial stress reference of the system, the amplitude deviation calibration and zero-point calibration of the acquisition channels of each sensing unit are performed to eliminate the inherent error of the sensing unit and the installation deviation, and to complete the network calibration of the sensing system.

[0024] The calibration process for the initial stress benchmark under no-load conditions is as follows: the smart hanging basket is placed in a static and stable state without external loads or construction disturbances, and the original stress signals of each sensing unit are collected simultaneously to construct the stress vector at the measuring points. ,in For the first Instantaneous stress sampling values ​​at each measuring point This represents the total number of sensing units; right Perform singular spectrum decomposition ,in It is a left singular vector matrix. It is a singular value diagonal matrix. The right singular vector matrix is ​​used to reconstruct a pure stress sequence by preserving the dominant singular components of the structural response and removing noise components. Based on material constitutive relations , pure stress sequence Convert to strain vector ,in The elastic modulus of the material corresponding to the component; The objective function is to minimize the structural potential energy. ,in The overall stiffness matrix of the hanging basket is determined by the section parameters and material elastic moduli of the main truss, rear anchor, and suspension point components, with the constraint displacement boundary conditions of the hanging basket supports and rear anchor. As a constraint, for the objective function By performing extreme value optimization, a strain equilibrium solution satisfying the minimum potential energy principle is obtained. The optimal stress equilibrium solution at each measuring point was obtained by inverse calculation using constitutive relations. , all measuring points The set serves as the initial stress reference for the system under no-load conditions, and the calibration is completed.

[0025] (2) Boundary identification and model correction During the construction of the hanging basket, the working states of the rear anchor constraint, track contact, and hinged joints continuously change as construction progresses. Relying solely on a mechanical model with fixed parameters is insufficient to accurately reflect the actual stress conditions of the structure, easily leading to deviations between calculation results and actual working conditions, thus affecting the reliability of safety assessments. To address this issue, the system relies on an established unloaded initial stress benchmark, combined with real-time stress, attitude, and displacement data collected during the construction phase, to identify boundary parameters. Firstly, based on the unloaded initial stress benchmark… The data collected in real time during the construction phase The measuring point, the first Stress time series data at each sampling time point Construct stress increment vector ,in Number the measurement points. , The total number of sensing units. The sampling time sequence number; Establish a state-space model and define the state variable vector. ,in For the first The stiffness parameters to be identified for each component Assign component numbers, , The total number of components to be identified; Define state equations ,in Let be the process noise vector, and its covariance matrix be... Define the observation equation ,in Based on the overall stiffness matrix of the hanging basket The established mapping function between stress increment and stiffness parameter, The observation noise vector has the following covariance matrix: Meanwhile, the mapping function Based on the structural finite element model, by using state vectors... Substitute the stiffness parameters into the stiffness matrix of the hanging basket construction stage. Combined with measured load increments Using equations Calculate the stress increment, where For the material elasticity matrix, It is a geometric matrix; The recursive extended Kalman filter algorithm is used to iteratively correct the accuracy parameters online through the following steps: Prediction step: ,in For the first The sampling time is based on the first sampling time. The prior state estimation vector obtained from the state variables at each sampling time; ,in For the first The prior error covariance matrix at each sampling time. For the first The posterior error covariance matrix at each sampling time point The process noise covariance matrix; Update steps: ,in For the first The Kalman gain matrix at each sampling time. For mapping functions exist Jacobian matrix at the location, To observe the noise covariance matrix; ,in For the first The posterior state estimation vector at each sampling time, the The state vector is composed of the real-time effective stiffness parameters of each component; For the first The stress increment vector at each sampling time; ,in For the first The posterior error covariance matrix at each sampling time point It is the identity matrix; The identified stiffness parameter vector Each parameter in Physical consistency verification is performed, and the verification basis is the elastic modulus of the component material. By eliminating invalid parameter values ​​that exceed the corresponding theoretical stiffness range, the real-time effective stiffness parameter vector of each component is obtained, thus completing the online identification of key parameters of the hanging basket structure.

[0026] After acquiring the real-time effective stiffness parameters, the system updates the stiffness matrix of each element in the finite element model based on these parameters, recombines them to form the overall stiffness matrix of the hanging basket, and substitutes it into the structural mechanics equilibrium equations. Combined with the displacement constraints of the supports and rear anchors, the system solves the problem to obtain the nodal displacements and theoretical stress distributions under the corresponding working conditions. The theoretical calculation results are compared and verified with the field measured data. If the deviation exceeds the allowable range, the element stiffness is adjusted again and the calculation is iterated until the error meets the accuracy requirements, thus forming an adaptive finite element mechanical model that is synchronously matched with the construction state. Specifically: Real-time effective stiffness parameter vector based on key components of the hanging basket structure Update the overall stiffness matrix of the hanging basket. Components of the element stiffness matrix corresponding to each component ,in For the first The state vector is composed of the real-time effective stiffness parameters of each component at each sampling time. For the first The sampling time of the first sampling moment Real-time effective stiffness parameters of each component after identification Assign component numbers, The total number of components to be identified. For the first The element stiffness matrix components of the i-th component are derived from the i-th... Sectional parameters and material elastic modulus of each component With real-time effective stiffness parameters jointly determined, The elastic modulus of the material corresponding to the component; Construct the updated global stiffness matrix ,in The overall stiffness matrix of the hanging basket; the updated overall stiffness matrix Substituting the equilibrium equations into the finite element mechanical model ,in For the model node displacement vectors, This represents the load vector during the construction phase; combined with the constraint displacement boundary conditions of the hanging basket support and rear anchor. Solving the equilibrium equations yields the model's nodal displacement vectors. Theoretical stress vectors at each measuring point ,in Let be the constraint displacement vector of the hanging basket support and the rear anchor. These are the theoretical stress vectors at each measuring point calculated using the finite element model. It is a unit geometric matrix; Theoretical stress vector With real-time collected stress data at measurement points Perform residual verification; if the residual exceeds a preset threshold, readjust the element stiffness matrix components. And update the overall stiffness matrix. This continues until the residual between the theoretical stress and the measured stress meets the preset requirements, thus forming an adaptive finite element mechanical model that can synchronously reflect the real-time stress state of the hanging basket.

[0027] To further improve the model's calculation accuracy, constraint equations with nodal displacements and element internal forces as variables are constructed based on real-time stress monitoring data during the construction phase and an adaptive finite element mechanical model. ;in, For the first The overall stiffness matrix of the hanging basket at each sampling time is obtained by updating the real-time effective stiffness parameters of each component; For the first The model node displacement vector at each sampling time; For the first The construction stage load vector at each sampling time; Constrained displacement boundary conditions of the hanging basket support and rear anchor and stress limit conditions under construction conditions As a constraint, where For the first The first measuring point The measured stress at each sampling time, For the first Allowable material stress at each measuring point; The augmented constraint equations are constructed using the Lagrange multiplier method: ;in, This is the constraint matrix corresponding to the constraint displacement. The vectors are Lagrange multipliers; the nodal displacement vectors are updated online by iteratively solving the augmented constraint equations. With the overall stiffness matrix Simultaneously, the element internal forces and stress distribution of the finite element model are corrected, and the constraint equations and stiffness matrix are adjusted in real time.

[0028] (3) Modal decomposition and stiffness determination During cantilever construction, issues such as hidden damage to structural members, loosening of connections, and overall stiffness degradation are difficult to detect directly through conventional stress monitoring. These hidden dangers gradually alter the structural dynamic characteristics and stress distribution, and if not identified in time, can easily lead to safety risks. To achieve accurate assessment of the structural health status, it is necessary to conduct analysis at the level of overall mechanical properties, relying on stress and deformation field data collected simultaneously from multiple measuring points to extract modal features and determine stiffness consistency.

[0029] Based on stress and deformation field data acquired synchronously from multiple measuring points, a joint stress-displacement observation matrix for measuring points is constructed. ,in This is the joint observation matrix of stress and displacement at the measuring points. For the first Stress time-series vector at each measurement point For the first Displacement time-series vector of each measuring point , This represents the total number of measurement points. For joint observation matrix Perform singular value decomposition ,in The left singular vector matrix represents the eigenvectors of the structural modes. This is a singular value diagonal matrix, where the diagonal elements are singular values. The magnitude of each singular value directly reflects the energy proportion of the corresponding component in the structural response. It is a right singular vector matrix; Sort by singular value from largest to smallest, and select the top... The first dominant singular component, and the corresponding left singular vector, are the strain mode shape vectors of the structure. , ,in Representation matrix The The column vector; its corresponding singular values, after being normalized, are transformed using the frequency domain transformation formula. The natural frequency characteristics of each mode are obtained. ,in The effective modal order is preset based on the structural degrees of freedom, response energy ratio, and noise level. For the first Singular values ​​of order, The equivalent mass corresponding to the mode is predetermined by the theoretical modal analysis of the hanging basket structure; The orthogonality of the extracted mode shapes is verified using the Modal Confidence Criterion (MAC). The MAC coefficients are calculated as follows: ,in , For the first , The first-order mode shape vector is used to remove pseudo-mode components and noise mode components whose MAC coefficients are below a preset threshold. Simultaneously, by combining the theoretical modal vibration characteristics of the hanging basket structure, the physical consistency of the extracted effective modes is verified, and abnormal modes that deviate from the theoretical vibration modes beyond the preset range are eliminated. Finally, effective strain mode vibration modes and frequency characteristics that can reflect the stress characteristics of the main truss and bottom basket system of the smart hanging basket are obtained.

[0030] After obtaining stable modal characteristics, based on the extracted strain mode shapes and frequency characteristics, the modal stiffness inversion formula is used. Calculate the overall stiffness matrix distribution of the hanging basket structure, where The global stiffness matrix obtained by inversion, For the first The natural angular frequencies of the first mode. The mass matrix of the hanging basket structure is predetermined by theoretical modal analysis. For the first First strain mode shape vector; The stiffness matrix obtained by inversion is compared with the initial design stiffness matrix, and the stiffness residuals are calculated element by element. ,in For the first The inverse stiffness value of each member. For the first The initial design stiffness value of each member; when If the value exceeds the preset threshold, it is determined that the member has hidden damage or loose connection; if the average residual of the overall stiffness matrix exceeds the preset range, it is determined that the hanging basket has an overall stiffness degradation trend, and the stiffness consistency judgment is completed.

[0031] (4) Multi-terminal linkage early warning closed loop The core objective of safety monitoring during hanging basket construction is to promptly address structural safety hazards and prevent risks from escalating. Monitoring data and health assessment results must ultimately be translated into actual safety protection actions through closed-loop management to truly ensure construction safety. Previously, through modal feature extraction and stiffness consistency assessment, the actual stress state of the hanging basket structure, the health status of its members, and the overall stiffness change trend have been clearly identified. This information will be simultaneously transmitted to the central control system, serving as the core basis for generating early warning commands.

[0032] The central control system comprehensively analyzes the received structural stress data and health assessment results, and combines them with preset safety thresholds to determine whether an early warning needs to be activated and the warning level, thereby outputting the corresponding early warning command. After the early warning command is generated, it will be simultaneously pushed to the on-site construction terminal and the remote management terminal through a preset communication link, ensuring that on-site construction personnel and remote management personnel can simultaneously obtain the early warning information. On-site personnel can promptly carry out on-site handling, and remote personnel can monitor the handling process in real time, achieving two-way collaborative management and control.

[0033] Simultaneously, the central control system will link with the hanging basket safety locking device, initiating the locking procedure while issuing early warning commands. This locks key stress-bearing parts and moving mechanisms of the hanging basket, restricting its continued operation or movement and preventing further structural damage due to abnormal stress, thus curbing safety accidents at their source. Early warning information dissemination, on-site handling, remote monitoring, and safety locking form an organic whole, completing the entire process control from hazard identification and early warning issuance to risk management. This establishes a complete monitoring and early warning closed loop, ensuring the safety and controllability of the entire hanging basket construction process.

[0034] Example 2 Please refer to Figure 4 This embodiment 2 provides a system for real-time monitoring and early warning of construction stress based on intelligent hanging baskets, including: The sensor deployment and data acquisition unit is used to deploy and network the sensors on the main truss, rear anchor, and key stress-bearing parts of the smart hanging basket, establish the initial stress benchmark under no-load conditions, collect stress and attitude data in real time, and upload the data to the central control system and cloud platform after preprocessing. The boundary identification and model correction unit is used to identify time-varying boundary conditions of the smart hanging basket, including rear anchor constraint stiffness, track contact stiffness, and hinge gap, based on real-time collected stress, attitude, and displacement sensing data. It constructs an adaptive finite element mechanical model that is updated synchronously with the construction status, and iteratively adjusts the constraint equations and stiffness matrix in real time through the model correction algorithm to eliminate calculation distortion and structural stress inversion errors caused by time-varying boundaries. The modal decomposition and stiffness discrimination unit is used to perform spatial modal decomposition on the stress field and deformation field simultaneously acquired from multiple measuring points, extract the strain mode shapes and frequency characteristics of the main truss and bottom basket system of the smart hanging basket; based on the modal characteristics, the overall stiffness matrix distribution of the structure is inverted, and the hidden damage of the members, loosening of connections and overall stiffness degradation trend are identified through stiffness consistency discrimination. The multi-terminal linkage early warning closed-loop unit is used by the central control system to output early warning commands based on the actual stress state and health characteristics of the structure, and push the early warning information to the field terminal and remote terminal simultaneously and link to execute safety locking, forming a complete monitoring and early warning closed loop; The process of real-time iterative adjustment of constraint equations and stiffness matrix is ​​based on real-time stress monitoring data and adaptive finite element mechanical model during the construction phase. Constraint equations with nodal displacement and element internal force as variables are established, and the constraint displacement boundary conditions of the hanging basket support and rear anchor, as well as the stress limit conditions of the measuring points under the construction conditions are used as constraints. The augmented constraint equation is constructed using the Lagrange multiplier method. By iteratively solving the equation, the nodal displacement vector and the overall stiffness matrix are updated online, and the element internal force and stress distribution of the finite element model are corrected simultaneously.

[0035] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement any step of a method for real-time monitoring and early warning of construction stress based on a smart hanging basket.

[0036] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0037] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0038] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets, characterized in that, include: S1. Sensors are deployed and networked to calibrate key stress-bearing parts of the main truss, rear anchor, and suspension points of the smart hanging basket. Initial stress benchmark under no-load is established, and stress and attitude data are collected in real time and uploaded to the central control system and cloud platform after preprocessing. S2. Based on real-time collected stress, attitude and displacement sensing data, online parameter identification is carried out on the time-varying boundary conditions of the smart hanging basket, including the rear anchor constraint stiffness, track contact stiffness and hinge gap; an adaptive finite element mechanical model updated synchronously with the construction status is constructed, and the constraint equations and stiffness matrix are iteratively adjusted in real time through the model correction algorithm to eliminate the calculation distortion and structural stress inversion error caused by the time-varying boundary. S3. Perform spatial modal decomposition on the stress field and deformation field collected synchronously from multiple measuring points to extract the strain mode shapes and frequency characteristics of the main truss and bottom basket system of the smart hanging basket; Based on the modal feature inversion of the overall stiffness matrix distribution of the structure, the hidden damage of the members, loosening of connections and overall stiffness degradation trend are identified by stiffness consistency discrimination. S4. The central control system outputs early warning commands based on the actual stress state and health characteristics of the structure, pushes the early warning information to the field terminal and remote terminal simultaneously, and performs safety locking in conjunction to form a complete monitoring and early warning closed loop; The process of real-time iterative adjustment of constraint equations and stiffness matrix is ​​based on real-time stress monitoring data and adaptive finite element mechanical model during the construction phase. Constraint equations with nodal displacement and element internal force as variables are established, and the constraint displacement boundary conditions of the hanging basket support and rear anchor, as well as the stress limit conditions of the measuring points under the construction conditions are used as constraints. The augmented constraint equation is constructed using the Lagrange multiplier method. By iteratively solving the equation, the nodal displacement vector and the overall stiffness matrix are updated online, and the element internal force and stress distribution of the finite element model are corrected simultaneously.

2. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 1, characterized in that, The process of sensor deployment and network calibration in S1 is as follows: Sensing units are installed on the main truss, rear anchor point, and suspension point stress node of the smart hanging basket. Sensing units are installed at intervals along the axial direction of the main truss. Sensing units are installed one-to-one with the rear anchor point and the suspension point stress node. The sensing units are connected to the smart hanging basket structure body by bolt fixing. All sensing units are connected to the data acquisition module via an industrial bus. Each sensing unit is assigned a unique communication address and a unified sampling frequency is configured. Communication debugging between the sensing units and the data acquisition module is completed, and a point-to-point data transmission link is established. The intelligent hanging basket is controlled to be in a completely unloaded and static stable state. The data acquisition module is started to continuously collect the raw data output by each sensor unit. The acquisition time is set, and outlier removal processing is performed on the raw data. The initial stress value of each measuring point is determined through calculation and integrated to form the initial stress benchmark of the system. Based on the initial stress reference of the system, the amplitude deviation calibration and zero-point calibration of the acquisition channels of each sensing unit are performed to eliminate the inherent error of the sensing unit and the installation deviation, and to complete the network calibration of the sensing system.

3. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 2, characterized in that, The calibration process for the initial stress reference under no-load conditions in S1 is as follows: The intelligent hanging basket is placed in a static and stable state without external loads or construction disturbances. The original stress signals of each sensing unit are collected synchronously to construct the stress vector at the measuring point. The stress vector at the measuring point is decomposed into singular spectrum, retaining the dominant singular components of the structural response and removing noise components to reconstruct a pure stress sequence. Based on the material constitutive relation, the pure stress sequence is converted into a strain vector. Using the minimum potential energy of the structure as the objective function and the constraint displacement boundary conditions of the hanging basket support and the rear anchor as constraints, the objective function is optimized to obtain the strain equilibrium solution. The optimal stress equilibrium solution of each measuring point is obtained by back calculation through the material constitutive relation. The set of optimal stress equilibrium solutions of each measuring point is used as the initial stress reference of the system under no-load conditions to complete the calibration.

4. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 1, characterized in that, The online parameter identification process in S2 is as follows: Based on the initial stress benchmark under no-load and the stress time series data collected in real time during the construction phase, a stress increment vector is constructed; a state space model is established with the stiffness parameters of the components to be identified as state variables and the stress increments as observed values; a recursive extended Kalman filter algorithm is used to iteratively correct the stiffness parameters through prediction and update steps; the identified stiffness parameters are physically consistent, and invalid parameter values ​​that exceed the theoretical stiffness range corresponding to the elastic modulus of the component material are eliminated to obtain the real-time effective stiffness parameter vector of each component, thus completing the online identification of key parameters of the hanging basket structure.

5. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 1, characterized in that, The process of constructing the adaptive finite element mechanical model in S2 is as follows: Real-time effective stiffness parameter vector based on key components of the hanging basket structure Update the overall stiffness matrix of the hanging basket. Components of the element stiffness matrix corresponding to each component ,in For the first The state vector is composed of the real-time effective stiffness parameters of each component at each sampling time. For the first The sampling time of the first sampling moment Real-time effective stiffness parameters of each component after identification Assign component numbers, The total number of components to be identified. For the first The element stiffness matrix components of the i-th component are derived from the i-th... Sectional parameters and material elastic modulus of each component With real-time effective stiffness parameters jointly determined, The elastic modulus of the material corresponding to the component; Construct the updated global stiffness matrix ,in The overall stiffness matrix of the hanging basket; the updated overall stiffness matrix Substituting the equilibrium equations into the finite element mechanical model ,in For the model node displacement vectors, This represents the load vector during the construction phase; combined with the constraint displacement boundary conditions of the hanging basket support and rear anchor. Solving the equilibrium equations yields the model's nodal displacement vectors. Theoretical stress vectors at each measuring point ,in Let be the constraint displacement vector of the hanging basket support and the rear anchor. These are the theoretical stress vectors at each measuring point calculated using the finite element model. It is a unit geometric matrix; Theoretical stress vector With real-time collected stress data at measurement points Perform residual verification; if the residual exceeds a preset threshold, readjust the element stiffness matrix components. And update the overall stiffness matrix. This continues until the residual between the theoretical stress and the measured stress meets the preset requirements, thus forming an adaptive finite element mechanical model that can synchronously reflect the real-time stress state of the hanging basket.

6. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 1, characterized in that, The Lagrange multiplier method is used to construct the augmented constraint equations as follows: in, For the first The overall stiffness matrix of the hanging basket at each sampling time is obtained by updating the real-time effective stiffness parameters of each component; For the first The model node displacement vector at each sampling time; For the first The construction stage load vector at each sampling time; This is the constraint matrix corresponding to the constrained displacement; Let be the Lagrange multiplier vector; solve the augmented constraint equations iteratively.

7. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 1, characterized in that, The method for extracting the strain mode shape and frequency characteristics in S3 is as follows: A stress-displacement joint observation matrix is ​​constructed based on stress field and deformation field data acquired synchronously from multiple measurement points; singular value decomposition is performed on the matrix, and the first few dominant components are selected according to the energy proportion of the singular values. The strain mode shape vectors are extracted from them, and the natural frequency characteristics of each mode are obtained through frequency domain transformation. The orthogonality of the extracted mode shapes is verified by the modal confidence criterion, and highly correlated pseudo-modal components are eliminated. At the same time, the physical consistency of the modes is verified by combining the theoretical mode shape characteristics of the hanging basket structure, and abnormal modes that deviate from the theoretical mode shape beyond the preset range are eliminated. Finally, the effective strain mode shapes and frequency characteristics reflecting the stress characteristics of the hanging basket main truss and bottom basket system are obtained.

8. The method for real-time monitoring and early warning of construction stress based on intelligent hanging baskets according to claim 1, characterized in that, The process of stiffness consistency determination in S3 is as follows: Based on the extracted strain mode shapes and frequency characteristics, the modal stiffness inversion formula is used. Calculate the overall stiffness matrix distribution of the hanging basket structure, where The global stiffness matrix obtained by inversion, For the first The natural angular frequencies of the first mode. The mass matrix of the hanging basket structure is predetermined by theoretical modal analysis. For the first First strain mode shape vector; The stiffness matrix obtained by inversion is compared with the initial design stiffness matrix, and the stiffness residuals are calculated element by element. ,in For the first The inverse stiffness value of each member. For the first The initial design stiffness value of each member; when If the value exceeds the preset threshold, it is determined that the member has hidden damage or loose connection; if the average residual of the overall stiffness matrix exceeds the preset range, it is determined that the hanging basket has an overall stiffness degradation trend, and the stiffness consistency judgment is completed.

9. A system for real-time monitoring and early warning of construction stress based on intelligent hanging baskets, characterized in that, include: The sensor deployment and data acquisition unit is used to deploy and network the sensors on the main truss, rear anchor, and key stress-bearing parts of the smart hanging basket, establish the initial stress benchmark under no-load conditions, collect stress and attitude data in real time, and upload the data to the central control system and cloud platform after preprocessing. The boundary identification and model correction unit is used to identify time-varying boundary conditions of the smart hanging basket, including rear anchor constraint stiffness, track contact stiffness, and hinge gap, based on real-time collected stress, attitude, and displacement sensing data. It constructs an adaptive finite element mechanical model that is updated synchronously with the construction status, and iteratively adjusts the constraint equations and stiffness matrix in real time through the model correction algorithm to eliminate calculation distortion and structural stress inversion errors caused by time-varying boundaries. The modal decomposition and stiffness discrimination unit is used to perform spatial modal decomposition on the stress field and deformation field acquired synchronously from multiple measuring points, and to extract the strain mode shapes and frequency characteristics of the main truss and bottom basket system of the smart hanging basket. Based on the modal feature inversion of the overall stiffness matrix distribution of the structure, the hidden damage of the members, loosening of connections and overall stiffness degradation trend are identified by stiffness consistency discrimination. The multi-terminal linkage early warning closed-loop unit is used by the central control system to output early warning commands based on the actual stress state and health characteristics of the structure, and push the early warning information to the field terminal and remote terminal simultaneously and link to execute safety locking, forming a complete monitoring and early warning closed loop; The process of real-time iterative adjustment of constraint equations and stiffness matrix is ​​based on real-time stress monitoring data and adaptive finite element mechanical model during the construction phase. Constraint equations with nodal displacement and element internal force as variables are established, and the constraint displacement boundary conditions of the hanging basket support and rear anchor, as well as the stress limit conditions of the measuring points under the construction conditions are used as constraints. The augmented constraint equation is constructed using the Lagrange multiplier method. By iteratively solving the equation, the nodal displacement vector and the overall stiffness matrix are updated online, and the element internal force and stress distribution of the finite element model are corrected simultaneously.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-8: a method for real-time monitoring and early warning of construction stress based on a smart hanging basket.

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