Overhead layer climbing frame wall-attached support steel truss stand column safety monitoring system fused with big data analysis

Abstract

The invention discloses an overhead layer climbing frame wall-attached support steel truss stand column safety monitoring system fused with big data analysis, relates to the technical field of construction monitoring, and is used for solving the problem of recognition misjudgment caused by construction disturbance. According to construction behavior control signals, behavior labels are extracted, a semantic mapping relation between construction behaviors and structure responses is constructed, a behavior-driven data set is generated, a disturbance change trend is extracted through node response tensor and time difference analysis, the behavior labels are fused to construct a structure state vector, and a structure behavior distribution model is established. Analyzing the deviation degree between the current state and the model response, identifying the propagation path of the anomaly in space by combining a structure layout diagram, extracting a high-risk abnormal block mass region by adopting a diagram clustering method, analyzing the structural state change and construction behavior of the abnormal region, triggering different monitoring response strategies, and obtaining a high-risk abnormal block mass region; the structural risk identification and control in the construction process are realized, and the structural safety monitoring efficiency in the floor construction stage is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction monitoring. More specifically, the present invention relates to a safety monitoring system for the steel truss columns of the climbing formwork wall-mounted supports in the overhead floor, which integrates big data analysis. Background Art

[0002] In high-rise building construction, the climbing formwork in the overhead floor, as the main formwork support and safety protection device, is connected to the steel truss columns through wall-mounted supports and rises and falls periodically with the construction progress. The climbing formwork lifting behavior not only changes the boundary conditions between itself and the main structure, but also causes sudden changes in the local stress state of the steel truss columns. With the development of big data analysis and intelligent sensing technologies, the method of integrating structural physical state, construction behavior characteristics and time series analysis has become an important direction for intelligent monitoring of high-rise buildings.

[0003] Deficiencies of the prior art: For the structural safety monitoring system of the wall-mounted supports of the climbing formwork in the overhead floor and the steel truss columns, the judgment method based on stress or displacement thresholds is generally adopted, lacking the ability of dynamic identification and fusion analysis of construction behavior states. In the actual construction process, operations such as climbing formwork lifting will cause rapid changes in the structural force boundary conditions, resulting in local stress mutations or displacement responses. However, the structural responses caused by such construction behaviors are often misjudged as abnormal by the system, or conversely, the real abnormalities are masked because they fail to deviate from the construction behaviors. Due to the lack of an associated mapping between structural data and construction behaviors, the existing system cannot effectively distinguish between expected mutations and unexpected abnormalities during the construction process, and also lacks the logical scheduling ability based on multi-source data modeling and state-driven, resulting in insufficient accuracy of monitoring results, lagging risk identification, and single response strategies, making it difficult to meet the safety monitoring requirements in complex dynamic construction environments. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the following solutions are provided to solve the problem of misjudgment of identification caused by construction disturbances in the above-mentioned background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A safety monitoring system for the steel truss columns of the climbing formwork wall-mounted supports in the overhead floor, which integrates big data analysis, includes an overhead floor monitoring and data acquisition module, a structural state change module, a structural area anomaly identification module, and a response strategy module. The modules are connected by signals; The overhead floor monitoring and data acquisition module is used to collect multi-modal response information in the areas of the steel truss columns and the wall-mounted supports, extract behavior labels from the collected data according to the action type and state switching points, establish a mapping relationship between construction behaviors and structural responses, and generate structural response data with behavior-driven semantics; The structural state change module is used to construct a node response tensor based on the structural response data, extract the perturbation change trend of the structural nodes by combining time differences, and inject behavior labels during the expression of the structural node states to generate a node activation function, thereby constructing the structural state vector of the structural nodes; The structural area anomaly recognition module is used to establish a structural behavior distribution model according to the construction behavior conditions, identify the degree of abnormal deviation based on the response difference between the current state and the model, analyze the spatial propagation characteristics of the anomaly based on the structural layout diagram, and use graph clustering to extract the abnormal cluster area; The response strategy module is used to obtain the structural state evolution characteristics in the high-risk abnormal clusters in the abnormal cluster area, analyze the temporal coordination information between the structural response behavior and the construction process, and trigger different monitoring response strategies according to the analysis results.

[0006] In a preferred embodiment, it is used to collect multi-modal response information in the areas of the steel truss columns and the wall-attached supports, and extract behavior labels from the collected data according to the action type and the state switching point. The specific steps are as follows: Collect multi-modal response information in the areas of the steel truss columns and the wall-attached supports. The monitoring and collection units include the upper, middle, and lower three sections of the steel truss columns and the node connection points, the rigid contact surface and the sliding area of the wall-attached supports, and the lifting operation process of the climbing formwork; Deploy sensors in the collection units, including strain gauges arranged in the upper, middle, and lower three sections of the steel truss columns to obtain the strain responses of the columns under vertical loads and horizontal perturbations; Displacement sensors are arranged between the wall-attached supports and the structural wall to monitor the micro-sliding, loosening, and relative displacements of the wall-attached points; High-frequency accelerometers are arranged at the node connection points to capture the rapid impacts and vibration responses during the lifting process of the climbing formwork; The behavior collection device is connected to the electric control system of the climbing formwork to collect lifting execution commands, action start and end signals, and lifting displacement change data; Set a unified global sampling time series, and resample or interpolate all the collected data based on the least common multiple sampling step size.

[0007] When parsing the collected data, extract the action type and the state switching point to form a behavior state label. The formed behavior state label includes whether it is currently in the state of lifting the formwork, steady state, unlocking, or stopping.

[0008] In a preferred embodiment, establish the mapping relationship between the construction behavior and the structural response, and generate the structural response data with behavior-driven semantics. The specific steps include: Establish a mapping relationship between construction behaviors and structural response units. The mapping relationship is determined based on the structural layout diagram, construction equipment layout, and on-site experience, and is represented in the system in the form of a response influence diagram, mapping the behavior state to a set of structural response node sets; Fuse the time-synchronized collected data, behavior labels, and the set of structural response nodes to form structural response data; The structural response data includes the modal response values of all controlled nodes at the current time point, the current behavior label, the active response area corresponding to the behavior, the node number and physical location data.

[0009] In a preferred embodiment, it is used to construct a node response tensor based on the structural response data, extract the perturbation change trend of the structural nodes by combining time differences, and inject behavior labels during the expression of the structural node state to generate a node activation function, and construct the structural state vector of the structural node. The specific steps are as follows: Unify and integrate the collected data into a modal response tensor; Calculate the time differences of the response values in the modal response tensor, extract the speed and acceleration changing with time, and given the modal dimensions at adjacent times, determine the response mutation function; Extract the trend feature sequence from the original collected data and perform structural state evolution modeling according to time; Introduce behavior label driving for each node response to construct the structural state vector of the node.

[0010] In a preferred embodiment, introduce behavior label driving for each node response to construct the structural state vector of the node. The specific steps are as follows: Obtain the behavior label at each time ; ; Find the structural area affected by the behavior label , and obtain the set of nodes perturbed by the behavior; If the node , then set the node activation coefficient to , otherwise, set the node activation coefficient , where is the action intensity parameter of the current node relative to the behavior state, is the response influence area corresponding to the behavior; , where T represents the number of time steps for which the construction behavior lasts, , are respectively the total structural response values of node i at time and ; Combine the modal response tensor, trend perturbation function, and node activation function to construct the node at time Structural state vector: , where is the modal response tensor, is the trend perturbation feature.

[0011] In a preferred embodiment, it is used to establish a structural behavior distribution model according to construction behavior conditions, and identify the degree of abnormal deviation based on the response difference between the current state and the model. The specific steps are as follows: Establish a structural behavior distribution model under normal working conditions, conduct deviation judgment, and set the state distribution function as: , where represents the state vector of the i-th structural node at time , represents the behavior label corresponding to time , is a specific construction behavior; represents the conditional probability distribution of the state vector of node i when the construction behavior is in the state; Conduct real-time deviation detection of the state vector and identify the degree of abnormal deviation. For each structural node, extract the typical response trajectory of the node under this behavior according to the behavior label at the current moment, and calculate the deviation intensity score , where represents the m-th modal dimension of the state vector; represents the behavior condition under the m-th dimensional reference trajectory value; is the deviation mapping function; The actual state response value is obtained through real-time sensors at time by collecting data and obtaining the state expression after standardization and mapping; the reference trajectory value is the normal state value of the m-th modal dimension obtained through the historical structural state trajectory in the scenario where the construction behavior is , and is used as the baseline for deviation judgment; The deviation intensity score is the abnormal significance index of the node in the current behavior state; Use the Markov latent variable graph model for modeling, establish the state transition graph of each node, and establish a latent variable space for the response trend of each modal dimension. Under each behavior label condition, infer the node state trajectory transition probability.

[0012] In a preferred embodiment, analyze the spatial propagation characteristics of anomalies based on the structural layout diagram, and use graph clustering to extract abnormal cluster regions. The specific steps are as follows: Perform spatial aggregation analysis and propagation trend identification of structural anomalies, that is, perform spatial aggregation analysis on the deviation values of all nodes to identify local abnormal aggregation areas and propagation paths; Convert the structural layout diagram into a structural graph, where the nodes are monitoring points and the edges are component connection relationships, Define the degree of abnormal deviation on the structural layout diagram: , where 、 respectively represent the deviation intensity scores of nodes i and j, represents the weight of the connection edge between the i-th node and the j-th node in the structural layout diagram; , indicating that there is a direct connection or structural coupling relationship between node i and node j in the structure; E represents the set of edges in the structural layout diagram; Combine the graph clustering algorithm to extract abnormal clumps from the deviation area, identify the affected area, and determine high-risk abnormal clumps.

[0013] In a preferred embodiment, combine the graph clustering algorithm to extract abnormal clumps from the deviation area, identify the affected area, and determine high-risk abnormal clumps. The specific steps are as follows: Real-time calculate the degree of abnormal deviation of each node in the structural layout diagram; Integrate the node abnormal deviation degree into the structural connection diagram to form a composite structural diagram with structural connection relationships and abnormal manifestation degrees; Combine the structural connection strength and the abnormal deviation difference between each node to construct an abnormal similarity graph and perform similarity analysis; Use graph clustering technology to partition the entire abnormal similarity graph to identify a set of nodes that are adjacent to each other in physical space and highly consistent in abnormal manifestation; After clustering, count the degree of abnormal deviation of all nodes in each clustering area. For the area where the degree of abnormal deviation of the nodes shows an upward trend, mark the area as a high-risk abnormal clump.

[0014] In a preferred embodiment, it is used to obtain the structural state evolution characteristics in the high-risk abnormal clump area in the abnormal clump area, analyze the time-series coordination information between the structural response behavior and the construction process, and trigger different monitoring response strategies according to the analysis results. The specific steps are as follows: Obtain the structural state evolution characteristics in the high-risk abnormal clump area and analyze the time-series coordination information between the structural response behavior and the construction process; The time-series coordination information includes the structural intervention urgency index and the monitoring strategy evolution intensity index; The structural intervention urgency index is used to represent the mutation degree of structural perturbation, the hysteresis of response recovery, and the time correlation between perturbation and construction behavior; The monitoring strategy evolution intensity index is used to represent the urgency of whether the current monitoring system strategy needs to be adaptively adjusted; Set the monitoring strategy evolution intensity threshold and the structure intervention urgency threshold; Compare and analyze the monitoring strategy evolution intensity threshold and the structure intervention urgency threshold with the monitoring strategy evolution intensity index and the structure intervention urgency index respectively.

[0015] In a preferred embodiment, the comparison and analysis of the monitoring strategy evolution intensity threshold and the structure intervention urgency threshold with the monitoring strategy evolution intensity index and the structure intervention urgency index respectively are as follows: When the structure intervention urgency index is lower than the structure intervention urgency threshold and the monitoring strategy evolution intensity index is lower than the monitoring strategy evolution intensity threshold, the structure is in the risk acceptable range, and the current observation strategy and response silent state are maintained; When the structure intervention urgency index is higher than the structure intervention urgency threshold and the monitoring strategy evolution intensity index is lower than the monitoring strategy evolution intensity threshold, the response linkage measures are preferentially executed, but the resource reconstruction operation at the monitoring strategy level is not carried out temporarily; When the structure intervention urgency index is lower than the structure intervention urgency threshold and the monitoring strategy evolution intensity index is higher than the monitoring strategy evolution intensity threshold, the monitoring strategy evolution mechanism should be triggered to reschedule the response activation frequency; When the structure intervention urgency index is higher than the structure intervention urgency threshold and the monitoring strategy evolution intensity index is higher than the monitoring strategy evolution intensity threshold, the response linkage mechanism and the monitoring strategy reconstruction module are triggered simultaneously to perform the collaborative closed-loop response adjustment of structure protection and resource scheduling.

[0016] The technical effects and advantages of a safety monitoring system for the steel truss column of the climbing formwork wall attachment support in the overhead floor integrating big data analysis according to the present invention: The present invention realizes the efficient acquisition and analysis of multi-modal response data in the areas of the steel truss column and the wall attachment support during the floor construction process by constructing an intelligent monitoring system with multi-module collaboration; First, behavior tags are extracted based on construction behavior control signals, a semantic mapping relationship between construction behaviors and structural responses is constructed, and a behavior-driven dataset structure is generated. Subsequently, the perturbation change trend is extracted through the node response tensor and time difference analysis, and the behavior tags are fused to construct a structural state vector to realize the dynamic division and state evolution characterization of the construction stage. On this basis, the system establishes a structural behavior distribution model, analyzes the deviation degree between the current state and the model response, and combines the structural layout diagram to identify the spatial propagation path of anomalies. The graph clustering method is used to extract high-risk abnormal cluster regions. Finally, through the time-series collaborative analysis of the structural state changes and construction behaviors in the abnormal regions, different monitoring response strategies are triggered to realize the active identification and dynamic control of structural risks during the construction process, and greatly improve the structural safety monitoring efficiency and response accuracy during the floor construction stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 FIG. is a schematic structural diagram of a safety monitoring system for the steel truss columns of the climbing formwork wall attachment supports in the overhead floor integrating big data analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] To achieve the above object, Figure 1 FIG. shows a schematic structural diagram of a safety monitoring system for the steel truss columns of the climbing formwork wall attachment supports in the overhead floor integrating big data analysis according to the present invention, which specifically includes an overhead floor monitoring and data acquisition module, a structural state change module, a structural area anomaly identification module, and a response strategy module. The modules are connected by signals; The overhead floor monitoring and data acquisition module is used to collect multi-modal response information in the areas of the steel truss columns and the wall attachment supports, extract behavior tags from the collected data according to the action type and state switching points, establish a mapping relationship between the construction behavior and the structural response, and generate structural response data with behavior-driven semantics; The structural state change module is used to construct a node response tensor based on the structural response data, extract the perturbation change trend of the structural nodes in combination with the time difference, and inject behavior tags during the expression of the structural node state to generate a node activation function, and construct a structural state vector of the structural nodes; The structural area anomaly recognition module is used to establish a structural behavior distribution model according to the construction behavior conditions, identify the degree of abnormal deviation based on the response difference between the current state and the model, analyze the spatial propagation characteristics of the anomaly based on the structural layout diagram, and extract the abnormal cluster area using graph clustering; The response strategy module is used to obtain the structural state evolution characteristics in the high-risk abnormal clusters in the abnormal cluster area, analyze the time-series coordination information between the structural response behavior and the construction process, and trigger different monitoring response strategies according to the analysis results.

[0020] Step 1: Collect and uniformly model multi-source and multi-modal information in the area of the climbing frame wall attachment support and the steel truss column in the overhead floor construction scenario to form a data set with time synchronization, semantic recognition ability, and behavior response mapping relationship. The specific steps are as follows: In the construction scenario, the steel truss columns in the overhead floor area are the main load-bearing members, and the wall attachment supports are the connection and force-bearing nodes of the climbing frame system. Therefore, combined with the structural characteristics of the overhead floor construction, the core monitoring units are determined as follows: The component response unit includes the upper, middle, and lower three sections of the steel truss column and its connection with the node; the connection response unit includes the rigid contact surface and the possible slip area of the wall attachment support; the construction behavior unit includes the lifting operation process of the climbing frame and its control state; Deploy multiple types of sensors at the key structural parts (the collected units) to obtain multi-modal response information. That is, strain gauges are arranged on the upper, middle, and lower three sections of the steel truss column to obtain the strain response of the column under vertical load and horizontal disturbance, which is used to reflect the force change of the truss column; displacement sensors are arranged between the wall attachment support and the structural wall to monitor the micro-slip, loosening, and relative displacement of the wall attachment point, which is used to identify the slip trend of the wall attachment support; high-frequency accelerometers are arranged at the node connection to capture the rapid impact and vibration response during the lifting process of the climbing frame; the behavior acquisition device is connected to the climbing frame electric control system to collect the lifting execution command, action start / end signal, and lifting displacement change data. Optionally, a vision module can be deployed to assist in identifying the climbing frame actions; Obtain the full-element raw data covering structural deformation, local disturbance, and construction behavior through the above devices. The sampling frequency should be at least 50Hz or more to meet the data timeliness requirements in the construction dynamic scenario, thereby forming a raw data set. The raw data set includes the joint information of structural response and construction behavior; Set a unified global sampling time series, and resample or interpolate all data based on the least common multiple sampling step size to ensure that the structural response and behavior state information of all types of data are completely aligned at any time point; After parsing the collected data, extract the action type and state switching points to form behavior tags, which are used to indicate whether the current state is in the lifting, steady state, unlocking, or stopping state.

[0021] Construct the response mapping between construction behaviors and structural nodes. During the actual construction process, construction behaviors do not affect all structural nodes simultaneously. The jacking-up process mainly affects the columns within a certain height range below the attachment points, while the unlocking behavior mainly affects the connection nodes and the adjacent upper and lower structures. Establish the mapping relationship between construction behaviors and structural response units. The mapping relationship can be defined based on the structural layout diagram, construction equipment layout, and on-site experience, and is represented in the system in the form of a response influence diagram, that is, the behavior state is mapped to a set of structural response node sets , for example: the jacking-up behavior is mapped to ; the locking behavior is mapped to the connection nodes ; Fuse the collected data after time synchronization, behavior labels, and the set of structural response nodes (response mapping set) to form a unified structured data output unit, which includes the multi-modal response values (strain, displacement, vibration) of all controlled nodes at the current time point, the current behavior label (for model judgment), the active response area corresponding to the behavior (for local calculation optimization), and the data of the node number and physical location (for structural mapping). The final structure is: , where is the structural response vector, is the behavior label, is the set of structural nodes mapped by the current behavior, is the structural response data.

[0022] In summary, this step realizes the unified collection of multi-modal perception data, completes the data time alignment, behavior label generation, and semantic mapping between construction behaviors and structural responses. Through this series of structured processing, the original data is transformed into a data set with structural recognition ability and behavior-driven logic.

[0023] Step 2: Construct the structural state vector and process the feature sequence. Further organize the structural response data into a structural state vector with time continuity, spatial comparability, behavior relevance, and trend sensitivity, and construct a feature evolution sequence reflecting the structural change process. The specific steps are as follows: Integrate the modal responses of the structural response data and generate the node state tensor. That is, the data collected by the monitoring system includes multiple modes, such as the strain response of the steel truss columns, the displacement change between nodes, the vibration signal at the connection part, etc. Various sensors reflect different response mechanisms of the structure. Integrate them into a modal response tensor. Taking a certain monitoring node i as an example, at a certain time point , construct its modal response tensor: , for example, denotes the strain response of node i, denotes the axial displacement of node i, denotes the vibration energy extracted by frequency-domain transformation of node i, etc., and M is the number of modes; Encapsulate multiple dimensions of the structural response into a state body to avoid information loss or misinterpretation caused by inconsistent dimensions.

[0024] The overhead structure faced will generate large-scale disturbances during the climbing formwork lifting process. Under construction disturbances, the structural response of the nodes shows a continuous change process. Among them, mutations or non-linear trends are often important signals for identifying risks. Calculate the time difference of each structural response value in the modal response tensor, extract its speed and acceleration changing with time, and then given any modal dimension , at time and the previous moment to construct its response mutation function: , where denotes the structural response value of the m-th modal dimension of the i-th node at time ; Extract the trend feature sequence from the original collected data, so as to model the structural state evolution according to time.

[0025] Inject behavior labels into the structural node state expression , so that the state vector has the ability to interpret behavior context. The behavior state not only affects the structural response itself, but also determines whether the current node is in the controlled active area. For example, the stress fluctuation generated during the climbing formwork lifting process may be a reasonable structural reaction; however, if a similar fluctuation occurs in the locked state, it needs to be regarded as abnormal; When constructing the structural state vector, it is necessary to introduce behavior label drive for each node response, and the construction method is as follows: Obtain the behavior label at each time (such as lifting formwork, steady state, lowering formwork); Find the set of structural response nodes corresponding to this behavior label , that is, the set of nodes that may be disturbed by this behavior; If the node , then set the activation coefficient ; Otherwise, set , indicating that this node should have no significant response in the current behavior; Construct the node activation function , defined as follows: , where is the action intensity parameter of the current node relative to the behavior state, which can be given by historical disturbance statistics or on-site experience, is the response influence area corresponding to the behavior; represents the action intensity of the structural monitoring node under a specific construction behavior state, and is used to measure the sensitivity and response participation degree of the node to the structural response when the behavior occurs. The calculation expression is: , where T represents the number of time steps for which the construction behavior lasts, and are respectively the total structural response values of node i at time and ; Combining the modal response tensor, the trend perturbation function, and the node activation function, construct the structural state vector of the node at time : ; where is the modal response tensor, is the trend perturbation feature, , where each element represents the trend perturbation feature of structural node i in the corresponding modal dimension. The two together constitute the physical and dynamic joint expression of the node. The activation weight ensures its behavior correlation and avoids irrelevant perturbations from entering the analysis path, so that there is a coupling relationship between the climbing frame lifting behavior and the structural response in the overhead floor construction scenario.

[0026] Step 3: Conduct structural anomaly identification and risk assessment. The specific steps are as follows: The structural response behavior highly depends on the construction stage. For example, during the lifting process of the frame, the node strain and displacement will show a short-term increase, which is a normal perturbation. However, if the same response appears in the steady-state locking stage, it may indicate loosening of the connection or slipping of the support. It is obviously unreliable to judge only based on the response amplitude. Therefore, it is necessary to establish statistical or graphical models of the normal structural state under different construction states respectively; From the sequence of structural state vectors obtained in Step 2, to identify structural anomalies, establish a structural behavior distribution model under normal working conditions for subsequent deviation judgment. Since the structural response strongly depends on the construction behavior state, a conditional distribution model of the structural state under construction behavior conditions should be constructed. Define the state distribution function as: , where represents the state vector of the i-th structural node at time , represents the construction behavior label corresponding to this time , is a certain specific construction behavior (such as lifting the frame , steady state , etc.); represents that when the construction behavior is in the state, the state vector of node i The conditional probability distribution, in other words, this is the likelihood distribution of the node response state given that the construction state is ; Perform real-time deviation detection of the state vector and identify the abnormal intensity and deviation degree. For each structural node, at the current moment, activate the corresponding conditional distribution model according to the construction label, extract the typical response trajectory of the node in this behavior from the model, and calculate the deviation intensity score between the current state vector and the reference vector , where represents the actual state response value of the m-th modal dimension of the state vector; represents the reference trajectory value of the m-th modal dimension under the behavior condition , is the deviation mapping function; The actual state response value is obtained by collecting data through real-time sensors at and then through state expression after standardization and mapping. For example, collect the original multi-modal response data. The m-th modality may correspond to the 1st-dimensional strain, the 2nd-dimensional displacement, the 3rd-dimensional vibration energy, etc. Multiple sensors (such as strain gauges, displacement sensors, accelerometers) configured for node i will collect different types of response data in real time. Align all modal data according to the unified timestamp, and convert them into state values representing the dynamic changes of the structure through response processing functions (such as rate of change, slope, local partial derivative). Solve the response value of the m-th dimension from the state vector , which is ; The reference trajectory value is the "normal state" of the m-th modal dimension obtained through historical structural state trajectories or modeling estimation in the scenario where the construction behavior is , and is used as a reference baseline for deviation judgment; the specific acquisition method is to segment the historical data according to the behavior label (such as lifting the frame, locking), that is, all the state vector data at are grouped. For all time periods, model the data of the m-th modal dimension of node i. The methods include time window mean / median extraction (simple scenario), distribution fitting (such as Gaussian distribution, kernel density estimation), etc. At the relative time position corresponding to , extract the expected response of the current behavior state from the reference model, which is .

[0027] A specific definition form of the deviation mapping function is: , where a represents the actual state response value of structural node i at the current moment on the m-th modal dimension, such as ; b represents the construction behavior conditions The reference trajectory value of the next node i in the mth modal dimension is, e.g., ; Using asymmetric enhanced function processing can avoid misjudgment of small disturbance amplification and highlight large nonlinear anomalies; The deviation intensity score is an indicator of the abnormal significance of the node in the current behavior state.

[0028] Furthermore, the Markov latent variable graph model is used for modeling, that is, a state transition graph of each node is established; a latent variable space is established for the response trend of each modal dimension, and the node state trajectory transition probability is inferred under each behavior label condition.

[0029] Abnormal signals have spatial propagation characteristics, especially in the connection area between the climbing frame wall support and the steel truss, where small disturbances may be quickly transmitted through the structural coupling path; Perform spatial aggregation analysis and propagation trend identification of structural anomalies, that is, perform spatial aggregation analysis on all node deviation values to identify local anomaly aggregation areas and propagation paths; The structural layout diagram is converted into a structural graph G = (N, E), where nodes are monitoring points (structural response points), edges are component connection relationships, and edge weights are assigned to represent the coupling strength between nodes. The abnormal deviation propagation model is run on the graph to evaluate whether the current anomaly has spatial consistency or whether it spreads from a certain point; Define the degree of abnormal deviation on the structural layout diagram: ,in , denote the deviation strength scores of nodes i and j respectively, represents the weight of the connection edge between the i-th node and the j-th node in the structural layout diagram, which can be set according to actual needs, usually based on information such as structural layout or material stiffness; E represents the edge set in the structural layout diagram, that is, the connection relationship set between each monitoring node in the structure (for example, the connection between the steel truss column and the wall support), , indicating that there is a direct connection or structural coupling relationship between node i and node j in the structure.

[0030] The degree of anomaly deviation is used to evaluate the non-cooperativity of structural response anomalies on the connection path. If the value is high, it means that the anomaly may have started to propagate from a certain point; if it is in a local area, The concentrated rise indicates that there may be local structural instability or slip mode; Combined with the graph clustering algorithm (such as spectral clustering), the abnormal clusters in the high deviation area are extracted to identify the specific affected areas. The specific steps are as follows: The system will establish a spatial connection relationship diagram for the entire structural monitoring area based on the structural drawings, component layout information, and sensor deployment locations. In this structural diagram, each monitoring node is regarded as a structural unit, and the physical components connecting these units (such as columns, beams, supports, etc.) serve as the edges between the nodes, forming a network diagram that reflects the structural spatial layout and load transfer path; Calculate the abnormal deviation degree of each node in real-time. The deviation degree is used to measure the difference between the current structural response state of the node (such as strain, displacement, vibration, etc.) and its normal response range under the background of the current construction behavior; The system will integrate these node abnormal deviation degrees into the structural connection diagram to form a composite structural diagram with structural connection relationships and abnormal performance degrees, which means that each node in the diagram has its current abnormal degree score, and the connection lines between the nodes represent the physical connection relationship and response coupling ability; Combining the structural connection strength and the abnormal deviation differences between each node, construct an abnormal similarity map and conduct similarity analysis; In this map, the strength of the connection between adjacent nodes depends not only on whether the nodes are physically connected but also on whether the abnormal performances of the nodes are consistent. If two nodes both belong to the high deviation state and the abnormal degrees between the nodes are very close, then the connection between the nodes will be determined by the system as a high abnormal similarity connection. Thus, the system can not only identify which nodes are abnormal themselves but also identify which abnormal nodes form a consistent regional pattern; Use graph clustering technology to partition the entire abnormal similarity map, automatically identify sets of nodes that are adjacent to each other in physical space and highly consistent in abnormal performance. Each identified clustering region can be regarded as a potential structural risk region or a region affected by perturbations; After clustering, the system will count the abnormal deviation degrees of all nodes in each clustering region, calculate the overall risk level of the region. If the abnormal degrees of the nodes within a certain region are generally high, or the abnormal values of the nodes within the region show an upward trend, the system will mark this region as a high-risk abnormal cluster.

[0031] Step 4: Conduct hierarchical response strategy linkage and self-evolution analysis of the monitoring strategy, that is, convert the results of structural anomaly identification and risk assessment into control strategies with hierarchical response capabilities and dynamic adjustability, and further through a risk feedback mechanism, achieve the adaptive optimization of the monitoring strategy and the system evolution update. The specific steps are as follows: Obtain the structural state evolution characteristics within the high-risk abnormal cluster region, analyze the temporal coordination information between the structural response behavior and the construction process. The temporal coordination information includes the structural intervention urgency index and the monitoring strategy evolution intensity index; The structural intervention urgency index is used to indicate whether the structure is in a high-risk state that requires immediate intervention at the current moment. The structural intervention urgency index measures the mutation degree of structural disturbance, the hysteresis of response recovery, and the time correlation between the disturbance and construction behavior, and is used to judge whether to trigger a linkage response strategy, such as pausing construction or locking key structural nodes; The structural intervention urgency index supports the system's judgment of response strategies for abnormal areas. If the value of the structural intervention urgency index rises significantly, it indicates that the structural disturbance is severe, the recovery is slow, and it is closely related to construction actions. The system should immediately issue control instructions, such as pausing the lifting and lowering of the climbing formwork, locking the wall-attached supports, or issuing local / remote alarms. It is the key decision-making signal connecting the structural monitoring judgment module and the engineering linkage control module, realizing the closed-loop drive from state perception to engineering intervention; The acquisition logic of the structural intervention urgency index is as follows: Obtain the time when the disturbance response reaches the local maximum value when the disturbance occurs , obtain the first time point when the structural response recovers to the stable threshold , calculate the disturbance recovery time interval: ; Obtain the backtracking window length T, take T sampling points forward from the current moment, and calculate the first derivative sequence for the curve , which is used to represent the change rate of modal response over time, r represents the sequence of modal response values, t is the moment of the sampling point. Whenever the derivative changes from positive to negative or from negative to positive, it is recorded as a mutation, and calculate the disturbance mutation density: , where is the indicator function, which is 1 when the symbol changes, otherwise it is 0; Record the set of all construction behavior state switching time points, and find the switching point closest to the disturbance peak time , calculate the peak time interval: , the calculation expression of the structural intervention urgency index is: .

[0032] It should be noted that the structure refers to the steel truss column structure of the wall support of the overhead climbing frame. The stability threshold is set according to the safety requirements of the actual scenario. After the structure is disturbed, the response curve starts to decline from the peak value. The system detects that the response value has dropped and stabilized at the time point. The time difference is the structural recovery period. The larger the time, the slower the structure. If the disturbance peak is very close to the behavior change time), the value will be very large, indicating that the disturbance may be caused by construction behavior and should be more inclined to respond. r represents the modal response value sequence, that is, the response curve of a structural node under a specific mode. For example, if it is a strain mode, r represents the strain value; if it is a displacement mode, r represents the displacement value; if it is a vibration mode, r represents the vibration energy, etc.

[0033] The monitoring strategy evolution intensity index is used to indicate whether the current monitoring system strategy needs to be adaptively adjusted. The monitoring strategy evolution intensity index measures the complexity of the structural state, the instability of the response trend, and the integrity of the sampling data. It is used to determine whether it is necessary to adjust the sampling frequency, reconstruct the sampling node priority, or optimize the monitoring resource allocation strategy. The role of the monitoring strategy evolution intensity index is to drive the strategy adaptation mechanism of the monitoring system. When the monitoring strategy evolution intensity index value increases, it means that the structural state is becoming more complicated and the data pressure is increasing. The system will actively compress the sampling tasks of low-risk nodes and increase the monitoring density of high-risk areas. If necessary, it can also update the model input dimension or enable dynamic feature channels. The monitoring strategy evolution intensity index ensures that the system still has the ability to operate continuously with high response, high resolution and high resource utilization in scenarios with limited resources and drastic structural evolution. The logic for obtaining the monitoring strategy evolution intensity index is as follows: Get the state vector of the i-th node of the structure and the centroid of all state vectors , the envelope volume formed by this group of state vectors in the manifold space , Indicates that at the current moment, the state vectors of all structural nodes The response manifold spanned in space, the calculation expression of the complexity of the structural response manifold is: , where N is the total number of nodes in the structure; Get the total number of data that the i-th node should sample in the current time window and the number of data entries that failed to sample, were missing, or had a return error, and calculate the sampling completeness. The calculation expression is: , Indicates the total number of data that the i-th node should sample in the current time window, Represents the number of data entries that failed to be sampled by the i-th node in the time window, and calculates the monitoring strategy evolution intensity index. The calculation expression is: .

[0034] It should be noted that the centroid of the state vector refers to the central vector, and the complexity of the structural response manifold reflects the diffusion degree of the state points of multiple nodes in the high-dimensional space. The larger the coverage volume and the higher the dispersion, the more complex the structural state is, and the system needs to increase the sampling frequency.

[0035] Set the structural intervention urgency threshold to determine whether the structure is currently in a critical state that requires triggering an intervention response, and set the monitoring strategy evolution intensity threshold to determine whether the current monitoring strategy is still applicable to the trend of the structural state evolution. Compare and analyze them with the structural intervention urgency index and the monitoring strategy evolution intensity index respectively; When the structural intervention urgency index is lower than the structural intervention urgency threshold and the monitoring strategy evolution intensity index is lower than the monitoring strategy evolution intensity threshold, it indicates that the change of the current disturbance waveform of the structure is relatively stable, and the response after the disturbance can fall back to the stable interval in a short time, indicating that the structure does not show persistent mutation behavior or instability signs, and the disturbance effect of the construction behavior on the structural state is also within the controllable range. At the same time, the structural response vector in the state space maintains a low-dimensional convergence trend, without frequent reversal of the response direction or divergence of the characteristic trajectory, and the sampling data has high temporal integrity and spatial coverage rate. The system can determine that the structure is in the risk-acceptable interval and maintain the current observation strategy and response silent state; When the structural intervention urgency index is higher than the structural intervention urgency threshold and the monitoring strategy evolution intensity index is lower than the monitoring strategy evolution intensity threshold, it indicates that there is an obvious dense area of disturbance reversal in the local time window of the structure, and there is a hysteresis phenomenon in the response recovery after the disturbance peak. At the same time, the time distance between this disturbance event and the switching of the latest construction behavior is short, indicating that the current structural response has a high-sensitivity coupling with the external behavior and has the characteristics of construction-induced. The system still maintains good information coherence between the sampling nodes, the expansion degree of the state manifold in space is limited, and there is no continuous interruption or trend jump phenomenon in the sampling data. The system determines that response linkage measures should be given priority to prevent the expansion of structural anomalies, but no resource reconstruction operation at the monitoring strategy level is carried out for the time being; When the structural intervention urgency index is lower than the structural intervention urgency threshold, and the monitoring strategy evolution intensity index is higher than the monitoring strategy evolution intensity threshold, it indicates that the current disturbance response of the structure has the characteristic of rapid decline. The disturbance amplitude can be controlled within the steady-state interval in a short time, and the disturbance waveform does not show high-frequency inversion behavior in the local derivative density distribution. The response time interval between the structure and the construction behavior is in the insensitive interval, and the risk incentive is weak. At the same time, the response states of multiple nodes show a locally non-uniform distribution in the embedding space, the characteristic trajectory directions are scattered, and there are sampling information discontinuities at some nodes, and there are coverage gaps in the sensing data, indicating that there is a matching deviation between the current monitoring strategy and the structural state evolution. The system should trigger the monitoring strategy evolution mechanism accordingly to reschedule the response activation frequency to ensure the state perception accuracy; When the structural intervention urgency index is higher than the structural intervention urgency threshold, and the monitoring strategy evolution intensity index is higher than the monitoring strategy evolution intensity threshold, it indicates that the structure has entered a high-risk situation interval with coexisting disturbance enhancement and response lag. The disturbance curve shows high-frequency nonlinear jump characteristics in the near-time window, the response hysteresis interval is significantly extended, and the disturbance peak coincides highly with the construction behavior switching time, with a typical behavior-induced non-steady response mode. At the same time, the structural state vector shows a high-dimensional expansion trend in the embedding manifold space, and the characteristic expression differences between nodes are significantly enhanced. There are transmission interruptions or backhaul delays at some sampling points, and the system state perception ability is interfered. The response linkage mechanism and the monitoring strategy reconstruction module should be triggered simultaneously to perform coordinated closed-loop response adjustment and optimization of structural protection and resource scheduling.

[0036] According to the threshold comparison result, determine the current risk level (such as low risk, local concentrated risk, dispersed diffusion risk, global high risk, etc.), and map this level to the structural space to generate a structural partition identifier with a risk level label. According to the determined risk level, match the corresponding set of response strategies, including whether to trigger a warning prompt, whether to initiate local re-inspection or modal recalibration, whether to require suspension of construction or loading processes in a certain area, whether to enter the full-structure emergency response state, etc. At the same time, the system needs to convert the selected strategy into an executable plan.

[0037] In summary, the present invention realizes the efficient acquisition and analysis of multi-modal response data in the areas of steel truss columns and wall-attached supports during the floor construction process by constructing an intelligent monitoring system with multi-module collaboration; First, behavior tags are extracted based on construction behavior control signals, a semantic mapping relationship between construction behaviors and structural responses is constructed, and a behavior-driven dataset structure is generated. Subsequently, the perturbation change trend is extracted through node response tensors and time difference analysis, and the structural state vector is constructed by integrating behavior tags to achieve the dynamic division and state evolution characterization of the construction stage. On this basis, the system establishes a structural behavior distribution model, analyzes the deviation degree between the current state and the model response, and combines the structural layout diagram to identify the spatial propagation path of anomalies. The graph clustering method is used to extract high-risk abnormal cluster regions. Finally, through the temporal collaborative analysis of the structural state changes and construction behaviors in the abnormal regions, differential monitoring response strategies are triggered to achieve the active identification and dynamic control of structural risks during the construction process, greatly improving the structural safety monitoring efficiency and response accuracy during the floor construction stage.

[0038] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0039] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0040] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0041] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0042] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0043] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An in-wall support steel truss column safety monitoring system for an overhead climbing formwork, integrated with big data analysis, characterized in that: It includes an overhead floor monitoring and data acquisition module, a structural state change module, a structural area anomaly identification module, and a response strategy module, which are connected by signals; The overhead floor monitoring and data acquisition module is used to collect multimodal response information in the areas of steel truss columns and wall-attached supports, extract behavior labels from the collected data according to the action type and state switching points, establish the mapping relationship between construction behaviors and structural responses, and generate structural response data with behavior-driven semantics; The structural state change module is used to construct a node response tensor based on the structural response data, extract the disturbance change trend of structural nodes by combining time difference, and inject behavior labels into the structural node state expression process to generate a node activation function, and construct the structural state vector of the structural node; The structural area anomaly identification module is used to establish a structural behavior distribution model according to the construction behavior conditions, identify the abnormal deviation degree based on the response difference between the current state and the model, analyze the spatial propagation characteristics of the anomaly based on the structural layout diagram, and use graph clustering to extract the abnormal cluster area; The response strategy module is used to obtain the structural state evolution characteristics in the high-risk abnormal clusters in the abnormal cluster area, analyze the time-series coordination information between the structural response behavior and the construction process, and trigger different monitoring response strategies according to the analysis results.

2. The safety monitoring system for the steel truss column of the climbing formwork wall attachment support in the overhead floor integrated with big data analysis according to claim 1, characterized in that: It is used to collect multimodal response information in the areas of steel truss columns and wall-attached supports, and extract behavior labels from the collected data according to the action type and state switching points. The specific steps are as follows: Collect multimodal response information in the areas of steel truss columns and wall-attached supports. The monitoring and acquisition units include the upper, middle, and lower three sections of the steel truss columns and the node connection parts, the rigid contact surface and the sliding area of the wall-attached supports, and the lifting operation process of the climbing formwork; Deploy sensors in the acquisition units, including strain gauges arranged in the upper, middle, and lower three sections of the steel truss columns to obtain the strain response of the columns under vertical loads and horizontal disturbances; Displacement sensors are arranged between the wall-attached supports and the structural wall to monitor the micro-slip, loosening, and relative displacement of the wall-attached points; High-frequency accelerometers are arranged at the node connection parts to capture the rapid impact and vibration responses during the lifting process of the climbing formwork; The behavior acquisition device is connected to the climbing formwork electric control system to collect lifting execution commands, action start and end signals, and lifting displacement change data; Set a unified global sampling time series, and resample or interpolate all the collected data based on the least common multiple sampling step; When parsing the collected data, extract the action type and state switching points to form behavior state labels, and the formed behavior state labels include whether it is currently in the state of lifting the formwork, steady state, unlocking, or stopping; 3. The safety monitoring system for the steel truss column of the attached wall support of the overhead climbing formwork according to claim 2, which is integrated with big data analysis, is characterized in that: Establish the mapping relationship between construction behaviors and structural responses, and generate structural response data with behavior-driven semantics. The specific steps include: Establish the mapping relationship between construction behaviors and structural response units. The mapping relationship is determined based on the structural layout diagram, the layout of construction equipment, and field experience, and is represented in the form of a response influence diagram in the system, mapping the behavior state to a set of structural response node sets; Fuse the collected data, behavior labels, and structural response node sets after time synchronization to form structural response data; The structural response data contains the modal response values of all controlled nodes at the current time point, the current behavior label, the active response area corresponding to the behavior, the node number and physical location data.

4. The safety monitoring system for the steel truss column of the climbing formwork attachment support in the overhead floor integrating big data analysis according to claim 3, characterized in that: It is used to construct a node response tensor based on the structural response data, extract the perturbation change trend of the structural nodes by combining time difference, and inject the behavior label into the process of expressing the structural node state to generate a node activation function, and construct the structural state vector of the structural node. The specific steps are as follows: Integrate the collected data into a modal response tensor uniformly; Calculate the time difference of each response value in the modal response tensor, extract the velocity and acceleration changing with time, and given the modal dimension of adjacent time, determine the response mutation function; Extract the trend feature sequence from the original collected data and perform structural state evolution modeling according to time; Introduce behavior label driving for each node response to construct the structural state vector of the node.

5. The safety monitoring system for the steel truss column of the attached wall support of the overhead scaffolding incorporating big data analysis according to claim 4, characterized in that: Introduce behavior label driving for each node response to construct the structural state vector of the node. The specific steps are as follows: Obtain each time behavior label ; Find the influence structure area corresponding to the behavior label , and obtain the set of nodes disturbed by the behavior; If the node , then set the node activation coefficient to , otherwise, set the node activation coefficient , where is the action intensity parameter of the current node relative to the behavior state, is the response influence area corresponding to the behavior; , where T represents the number of time steps for which the construction behavior lasts, , are the total structural response values of node i at times and respectively; Construct the structural state vector of the node at time by combining the modal response tensor, the trend perturbation function, and the node activation function: , where is the modal response tensor and is the trend perturbation feature.

6. The safety monitoring system for the steel truss column of the climbing formwork attachment support in the overhead floor integrating big data analysis according to claim 5, characterized in that: It is used to establish a structural behavior distribution model according to the construction behavior conditions, and identify the abnormal deviation degree based on the response difference between the current state and the model. The specific steps are as follows: Establish a structural behavior distribution model under normal working conditions, conduct deviation judgment, and set the state distribution function as: , where represents the state vector of the i-th structural node at time , represents the behavior label corresponding to time , is a specific construction behavior; represents the conditional probability distribution of the state vector of node i when the construction behavior is in the state; Perform real-time deviation detection of the state vector and identify the degree of abnormal deviation. For each structural node i, extract the typical response trajectory of the node under this behavior according to the behavior label at the current moment, and calculate the deviation intensity score between the current state vector and the reference vector , where represents the m-th modal dimension of the state vector; represents the behavior condition the reference trajectory value of the m-th dimension under the condition, is the deviation mapping function; Actual state response value is obtained after collecting data through real-time sensors at time, and through state expression after standardization and mapping; The reference trajectory value is the normal state value of the m-th modal dimension obtained from the historical structural state trajectory in the scenario where the construction behavior is and is used as the baseline for deviation judgment; The deviation intensity score is the abnormal significance index of this node in the current behavior state; Use the Markov latent variable graph model for modeling, establish the state transition graph of each node, and establish a latent variable space for the response trend of each modal dimension. Under each behavior label condition, infer the node state trajectory transition probability.

7. An in-situ climbing formwork attachment support steel truss column safety monitoring system integrating big data analysis according to claim 6, characterized in that: Based on the structural layout diagram, analyze the spatial propagation characteristics of the anomaly, and use graph clustering to extract the abnormal cluster area. The specific steps are as follows: Conduct spatial aggregation analysis and propagation trend identification of structural anomalies, that is, conduct spatial aggregation analysis on all node deviation values to identify local abnormal aggregation areas and propagation paths; Convert the structural layout diagram into a structural graph, where the nodes are monitoring points and the edges are component connection relationships, Define the degree of abnormal deviation on the structural layout diagram: , where 、 respectively represent the deviation intensity scores of nodes i and j, represents the weight of the connection edge between the ith node and the jth node in the structural layout diagram; , indicating that there is a direct connection or structural coupling relationship between node i and node j; E represents the set of edges in the structural layout diagram; Combine the graph clustering algorithm to extract abnormal clusters from the deviation area, identify the affected area, and determine the high-risk abnormal clusters.

8. An in-air-story climbing formwork attached wall support steel truss column safety monitoring system integrating big data analysis according to claim 7, characterized in that: Combine the graph clustering algorithm to extract abnormal clusters from the deviation area, identify the affected area, and determine the high-risk abnormal clusters. The specific steps are as follows: Calculate the abnormal deviation degree of each node in the structural layout diagram in real time; Integrate the node abnormal deviation degree into the structural connection diagram to form a composite structural diagram with structural connection relationships and abnormal performance degrees; Combine the structural connection strength and the abnormal deviation difference between each node to construct an abnormal similarity graph and conduct similarity analysis; Use graph clustering technology to partition the entire abnormal similarity graph to identify a set of nodes that are adjacent to each other in physical space and highly consistent in abnormal performance; After clustering, count the abnormal deviation degrees of all nodes in each clustering area. For the node abnormal deviation degrees of the nodes existing in the area showing an upward trend, mark the area as a high-risk abnormal cluster.

9. The safety monitoring system for the steel truss column of the attached wall support of the overhead climbing formwork according to claim 8, which is integrated with big data analysis, is characterized in that: It is used to obtain the structural state evolution characteristics in the high-risk abnormal clusters in the abnormal cluster area, analyze the time-series coordination information between the structural response behavior and the construction process, and trigger different monitoring response strategies according to the analysis results. The specific steps are as follows: Obtain the structural state evolution characteristics within the high-risk abnormal mass area, and analyze the temporal coordination information between the structural response behavior and the construction process; The temporal coordination information includes the structural intervention urgency index and the monitoring strategy evolution intensity index; The structural intervention urgency index is used to represent the mutation degree of structural perturbation, the hysteresis of response recovery, and the time correlation between perturbation and construction behavior; The monitoring strategy evolution intensity index is used to represent the urgency of whether the current monitoring system strategy needs to be adaptively adjusted; Set the monitoring strategy evolution intensity threshold and the structural intervention urgency threshold; Compare and analyze the monitoring strategy evolution intensity threshold and the structural intervention urgency threshold with the monitoring strategy evolution intensity index and the structural intervention urgency index respectively.

10. The safety monitoring system for the steel truss column of the climbing formwork attachment support in the overhead floor, which integrates big data analysis according to claim 9, is characterized in that: Compare and analyze the monitoring strategy evolution intensity threshold and the structural intervention urgency threshold with the monitoring strategy evolution intensity index and the structural intervention urgency index respectively. The specific steps are as follows: When the structural intervention urgency index is lower than the structural intervention urgency threshold and the monitoring strategy evolution intensity index is lower than the monitoring strategy evolution intensity threshold, the structure is in the risk-acceptable interval, and the current observation strategy and response silent state are maintained; When the structural intervention urgency index is higher than the structural intervention urgency threshold and the monitoring strategy evolution intensity index is lower than the monitoring strategy evolution intensity threshold, the response linkage measures are preferentially executed, but the resource reconstruction operation at the monitoring strategy level is not performed temporarily; When the structural intervention urgency index is lower than the structural intervention urgency threshold and the monitoring strategy evolution intensity index is higher than the monitoring strategy evolution intensity threshold, the monitoring strategy evolution mechanism should be triggered to reschedule the response activation frequency; When the structural intervention urgency index is higher than the structural intervention urgency threshold and the monitoring strategy evolution intensity index is higher than the monitoring strategy evolution intensity threshold, both the response linkage mechanism and the monitoring strategy reconstruction module are triggered to perform the coordinated closed-loop response adjustment of structural protection and resource scheduling.

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