Intelligent monitoring and early warning system for building supporting structure based on ultrahigh scaffold of steel tower

Abstract

The invention relates to the technical field of building support monitoring, and discloses a building support structure intelligent monitoring and early warning system based on a steel tower ultrahigh scaffold. The system comprises a data acquisition module, a risk prediction module, a dynamic adjustment module and a behavior analysis module. The data acquisition module acquires structure monitoring data, analyzes a structure change frequency and a sensor data access frequency, compares and judges an abnormal state risk and generates an abnormal state index; the risk prediction module locates an abnormal monitoring node, analyzes a matching relationship between the abnormal monitoring node and a risk event, evaluates a risk level, calculates a correlation degree between a monitoring frequency and the risk event, predicts a structural vulnerability and generates a risk evaluation result; the dynamic adjustment module identifies risk nodes, analyzes data distribution, calculates an adjustment priority, plans a path, and adjusts a support to a low-risk node to obtain adjustment configuration; and the behavior analysis module compares the data monitoring frequencies before and after adjustment, identifies abnormal structure activities, judges behavior pattern abnormal features, locates vulnerability sources, and obtains a behavior feature analysis result.

Description

Technical Field

[0001] This invention relates to the field of building support monitoring technology, specifically to an intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding. Background Technology

[0002] In steel tower construction projects, ultra-high scaffolding serves as a crucial building support structure, and its stability directly impacts construction safety and project quality. As building heights increase, the structural complexity of scaffolding significantly rises, rendering traditional manual monitoring methods inadequate for practical needs. Manual monitoring relies primarily on regular inspections by staff, using visual observation or simple tools to measure structural deformation and connection points. This method is not only inefficient but also susceptible to human error, leading to inaccurate monitoring results. Especially under severe weather conditions, such as strong winds and heavy rain, manual inspections are difficult to conduct effectively, failing to capture dynamic changes in the structure in a timely manner and increasing safety hazards.

[0003] Traditional monitoring methods lack the ability to collect and analyze structural data in real time. During use, ultra-high steel tower scaffolding is subjected to various factors such as construction loads and environmental loads, resulting in a dynamic change in its structural state. Traditional methods cannot acquire this changing data in real time, nor can they perform in-depth data analysis, making it difficult to determine whether the structure has any abnormal conditions or risks. When minor deformations or loose joints occur, they often go undetected until the problem escalates and leads to a safety accident. Furthermore, traditional monitoring lacks an effective risk prediction mechanism; it cannot predict potential structural vulnerabilities based on existing data, nor can it develop reasonable adjustment strategies to address risks. Once structural problems occur, only reactive remedial measures can be taken, increasing construction costs and potentially delaying the project schedule.

[0004] In terms of structural adjustments, traditional methods lack scientific basis and systematic planning. Workers typically rely on experience to determine which nodes need adjustment, and the determination of adjustment priorities and the planning of adjustment paths are often arbitrary, making it difficult to guarantee that the adjusted structure is in a low-risk state. Furthermore, after adjustment, it is impossible to effectively analyze structural behavior patterns, accurately identify the sources of abnormal structural activity, and provide a reference for subsequent monitoring and maintenance, leading to a vicious cycle in structural safety management. These problems severely restrict the level of monitoring and management of the support structures of ultra-high steel tower scaffolding buildings, necessitating a more intelligent and efficient monitoring and early warning system to solve the problem. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding, the system comprising:

[0007] The data acquisition module collects structural monitoring data of the ultra-high steel tower scaffolding, analyzes the frequency of structural changes, calculates the data access frequency of sensors, compares the frequency of structural changes with the access frequency, judges the risk of abnormal state of the structure, and generates abnormal state indicators.

[0008] Based on the abnormal state indicators, the risk prediction module locates the monitoring nodes of the abnormal structure, analyzes the matching relationship between the monitoring nodes and risk events, assesses the risk level of the nodes, calculates the correlation between the monitoring frequency and risk events, predicts the structural vulnerabilities that may occur at the monitoring nodes, and generates structural risk assessment results.

[0009] Based on the structural risk assessment results, the dynamic adjustment module identifies risky structural nodes, analyzes the distribution of structural data, calculates adjustment priorities, plans adjustment paths, and adjusts structural supports to low-risk nodes to obtain structural node adjustment configurations.

[0010] The behavior analysis module adjusts the configuration based on the structure nodes, compares the data monitoring frequency of the structure before and after the adjustment, identifies abnormal structure activities, judges the abnormal characteristics of the structure behavior patterns, locates the source of structural vulnerabilities, and obtains the behavior feature analysis results.

[0011] Preferably, the abnormal state indicators include structural change frequency, access frequency difference, and support change frequency; the structural risk assessment results include node risk level, monitoring and event matching degree, and vulnerability identification results; the structural node adjustment configuration includes adjustment priority, adjustment path, and permission update standard; and the behavioral feature analysis results include behavioral pattern change indicators, monitoring frequency comparison results, and abnormal activity identifiers.

[0012] Preferably, the data acquisition module includes:

[0013] The structural change analysis submodule collects structural monitoring data of the ultra-high steel tower scaffolding, analyzes the stress time series of the structural monitoring data, calculates the time interval between continuous monitoring, statistically analyzes the frequency change of structural changes, compares the input and output ratio of stress, identifies nodes of abnormal structural activity, and obtains structural variability indicators.

[0014] Based on the structural change index, the sensor access monitoring submodule retrieves access data of structurally abnormal nodes, analyzes the distribution of access times over different time periods, calculates the degree of access fluctuation of nodes in the short term, determines whether nodes have abnormal access behavior, and obtains the node access fluctuation index.

[0015] The support change statistics submodule, based on the node access fluctuation index, calls the node's support modification records, counts the number of support changes, and calculates the degree of abnormality of support changes by combining the node's structural changes and access monitoring data, generating an abnormal status index.

[0016] Preferably, the risk prediction module includes:

[0017] The anomaly monitoring and identification submodule filters the monitoring data of anomaly nodes based on the anomaly status indicators, analyzes the correlation between monitoring time and value and node operation mode, calculates the distribution density of anomaly monitoring and classifies them, identifies anomaly monitoring nodes, and generates anomaly monitoring node set.

[0018] The monitoring node matching submodule calls the set of abnormal monitoring nodes, parses the monitoring behavior characteristics, compares the patterns of identified risk events, calculates the matching degree between nodes and risk events, assesses the risk level of monitoring nodes, and generates a monitoring risk matching index.

[0019] The vulnerability risk assessment submodule analyzes the monitoring frequency of abnormal nodes based on the monitoring risk matching index, extracts the monitoring time interval, calculates the monitoring fluctuation range within a short period, predicts the probability of structural vulnerabilities based on the risk matching degree of the nodes, and generates structural risk assessment results.

[0020] Preferably, the monitoring node matching submodule assesses the degree of matching by calculating the difference between node feature values ​​and risk event feature values, and generates a monitoring risk matching index.

[0021] Preferably, the dynamic adjustment module includes:

[0022] Based on the structural risk assessment results, the risk node identification submodule detects risk nodes in the structural data network, analyzes the type and sensitivity of the structural data stored by the nodes, filters storage nodes containing structural vulnerabilities, determines the scope of structural nodes that need to be adjusted, and obtains a list of risk nodes.

[0023] Based on the risk node list, the data adjustment and analysis submodule analyzes the data distribution among the affected nodes, calculates the degree of data correlation and interaction frequency between nodes, determines the impact scope and priority of data adjustment, and generates a data adjustment priority index.

[0024] The structural support reconfiguration submodule analyzes the data flow path between structural nodes based on the data adjustment priority index, allocates structural resources, plans the optimal data adjustment path, and adjusts the access permissions of secure structural nodes to obtain the structural node adjustment configuration.

[0025] Preferably, the behavior analysis module includes:

[0026] The behavior pattern change analysis submodule adjusts the configuration based on the structure nodes, calls the behavior records of the adjusted nodes, compares the node activity characteristics before and after the adjustment, analyzes the magnitude and frequency of node behavior changes, calculates the degree of behavior pattern shift, and obtains the behavior pattern shift index.

[0027] The data monitoring frequency comparison submodule compares the data monitoring frequency of nodes before and after adjustment based on the behavior pattern offset index, analyzes the changes in monitoring time points, monitoring duration and monitoring frequency, judges the fluctuation of monitoring frequency, and generates a monitoring frequency fluctuation index.

[0028] The abnormal behavior identification submodule identifies node activities that deviate from the normal pattern based on the monitoring frequency fluctuation index, analyzes the characteristics of abnormal node behavior patterns, matches the relationship between node activity characteristics and known vulnerabilities, locates the source of structural vulnerabilities, and generates behavioral feature analysis results.

[0029] Preferably, the system further includes:

[0030] Based on the behavioral feature analysis results, the early warning response module identifies abnormal monitoring nodes, adjusts node access permissions, allocates monitoring quota ratios, updates node monitoring verification methods, and generates security protection measures.

[0031] Preferably, the early warning response module includes:

[0032] Based on the behavioral feature analysis results, the node permission control submodule analyzes the frequency of risky operations of nodes, calculates the impact range of node permission changes, identifies nodes with frequent abnormal operations, reconfigures the access permissions of nodes, implements access restrictions on high-risk nodes, and generates node permission adjustment configurations.

[0033] The monitoring quota management submodule calls the node permission adjustment configuration, calls the monitoring records of abnormal nodes, calculates the fluctuation range of the monitoring quota, analyzes the short-term monitoring quota change trend, judges the degree of deviation between the monitoring value and the normal behavior of the node, adjusts the upper limit of the node's monitoring quota, allocates the monitoring quota ratio, and generates the optimized monitoring quota.

[0034] The verification method optimization submodule, based on the optimized monitoring quota, filters nodes with abnormal monitoring frequency, extracts the identity verification records of the nodes, analyzes the security level of the abnormal node monitoring verification, determines whether the monitoring verification matches the node risk level, optimizes the node monitoring verification method, and generates security protection measures.

[0035] Preferably, the early warning response module further includes an early warning output submodule, which generates an early warning signal based on the security protection measures.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] The data acquisition module enables real-time collection of monitoring data for ultra-high steel tower scaffolding structures. It accurately analyzes the frequency of structural changes and calculates the data access frequency of sensors. By comparing these two data points, it assesses the risk of structural anomalies and generates anomaly indicators. This process changes the traditional situation where monitoring relies on manual labor and data acquisition is untimely and inaccurate. It makes the identification of structural anomalies more timely and accurate, capturing relevant signals even when subtle anomalies appear, thus preventing the anomaly from escalating.

[0038] The risk prediction module, based on abnormal state indicators, can quickly locate monitoring nodes of abnormal structures, deeply analyze the matching relationship between monitoring nodes and risk events, and then assess the risk level of the nodes. It can also calculate the correlation between monitoring frequency and risk events, predict potential structural vulnerabilities at monitoring nodes, and generate structural risk assessment results. With this module, the risk status of each node in the structure can be grasped in advance, moving beyond the traditional approach of passively responding after a risk event occurs. Instead, risks can be proactively predicted, providing clear direction for subsequent structural adjustments and maintenance. This makes risk management more forward-looking and targeted, reducing the adverse effects of sudden risk events.

[0039] Based on structural risk assessment results, the dynamic adjustment module accurately identifies risky structural nodes, analyzes structural data distribution, scientifically calculates adjustment priorities, and plans adjustment paths. It then adjusts structural supports to low-risk nodes, resulting in a reasonable configuration of structural nodes. This module overcomes the limitations of traditional structural adjustments that rely on experience and lack scientific basis, ensuring an orderly and efficient adjustment process. The adjusted structure is in a safer state, effectively reducing the possibility of safety accidents caused by unreasonable structural supports, while also minimizing unnecessary adjustment costs and time.

[0040] The behavior analysis module, based on structural node configuration adjustments, compares the data monitoring frequency of the structure before and after adjustments. It accurately identifies abnormal structural activity, determines the abnormal characteristics of structural behavior patterns, and pinpoints the source of structural vulnerabilities, yielding comprehensive behavioral feature analysis results. This module provides in-depth understanding of the structural state changes before and after adjustments, clarifying the root causes of abnormal structural activity. This not only helps evaluate the effectiveness of the structural adjustments but also provides detailed information for subsequent monitoring strategy optimization and structural maintenance. This creates a closed-loop management system for the entire monitoring and early warning system, continuously improving the monitoring and management level of the support structure for ultra-high steel tower scaffolding buildings and ensuring the smooth progress of steel tower construction projects. Attached Figure Description

[0041] Figure 1 This is a timing diagram of the intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding as described in this invention.

[0042] Figure 2 A flowchart for the risk prediction module sub-module;

[0043] Figure 3 A flowchart for dynamically adjusting module sub-modules;

[0044] Figure 4 A flowchart for the behavior analysis module submodule;

[0045] Figure 5 This is a flowchart of a submodule of the early warning response module. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figure 1 This invention provides an intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding, the system comprising:

[0048] Through the collaborative work of multiple modules, the system enables real-time monitoring, risk assessment, and dynamic adjustment of the structural status of ultra-high steel tower scaffolding. The data acquisition module first collects structural monitoring data, analyzes the frequency of structural changes, and calculates the data access frequency of sensors. By comparing the frequency of structural changes with the access frequency, it assesses the risk of abnormal states and generates abnormal state indicators. The risk prediction module locates abnormal monitoring nodes based on the abnormal state indicators, analyzes the matching relationship between nodes and risk events, assesses the risk level, calculates the correlation between monitoring frequency and risk events, predicts structural vulnerabilities, and generates structural risk assessment results. The dynamic adjustment module identifies risky structural nodes based on the structural risk assessment results, analyzes the distribution of structural data, calculates adjustment priorities, plans adjustment paths, and adjusts structural supports to low-risk nodes, obtaining the structural node adjustment configuration. The behavior analysis module, based on the structural node adjustment configuration, compares the data monitoring frequency of the structure before and after adjustment, identifies abnormal structural activities, judges the abnormal characteristics of structural behavior patterns, locates the source of structural vulnerabilities, and obtains behavioral characteristic analysis results. The entire system achieves intelligent early warning and response through automated processes, ensuring the safety and stability of the building's supporting structure.

[0049] Example 1: Real-time monitoring via a sensor network deployed on key scaffold nodes. At the beginning of the project, the data acquisition module began operation. The structural change analysis submodule first collected structural monitoring data using 350 stress sensors distributed on the main load-bearing nodes, horizontal links, and diagonal braces of the scaffold. These sensors recorded axial stress, bending stress, and vibration data at a frequency of 10 times per second, forming a continuous stress time series. For example, at node 78 on the southeast side of the scaffold, the sensors recorded continuous fluctuations in axial stress data between 10:00 AM and 11:00 AM during a certain construction phase. The time series showed stress values ​​varying between 120 MPa and 145 MPa, far exceeding the allowable static load stress range (80 MPa to 100 MPa) for this node. The submodule calculated the time interval between continuous monitoring sessions and found that the data acquisition interval remained stable at 0.1 seconds during this period, but the frequency of stress value changes was abnormally high. The system statistics showed that the frequency of structural changes at this node during this time period reached 45 times per minute, while under normal circumstances, the frequency of changes for this type of node should be less than 20 times per minute. Simultaneously, the submodule compared the stress input and output ratio of this node and found that the input stress mainly came from the concrete pouring load of the upper formwork system, while the stress output value transmitted to the lower structure through the node showed a mismatch, with an input-output ratio of 1.8:1, significantly deviating from the normal range of 1.2:1 to 1.5:1. Based on these analyses, the structural change analysis submodule identified this node as an abnormal structural activity node and obtained structural change indicators, including parameters such as stress fluctuation frequency, input-output deviation coefficient, and change duration.

[0050] Based on the acquired structural change indicators, the sensor access monitoring submodule begins retrieving access data from the abnormal node. The system retrieves access logs from the three stress sensors and two displacement sensors connected to the node, analyzing the distribution of access frequency across different time periods. It was found that the number of sensor accesses increased significantly during periods of abnormal stress fluctuations. Normally, these sensors upload data every 5 minutes, but during abnormal periods, the data access frequency increased to once every 30 seconds, and the access requests came not only from the central monitoring system but also from multiple temporarily added mobile monitoring terminals. The submodule calculates the node's access fluctuation level in the short term. By comparing the standard deviation of access frequency across different time windows, it was found that the node's access fluctuation index reached 4.8 within a 1-hour time window, while the system's normal threshold range is 0.5-2.0. Further analysis shows that the abnormal access behavior is mainly characterized by irregular access intervals and abnormally large amounts of data per access. Based on these analyses, the sensor access monitoring submodule obtains node access fluctuation indicators, including the access frequency variation coefficient, abnormal access period identifiers, and access source diversity index.

[0051] The support change statistics submodule then retrieves the support modification records for that node based on the node access fluctuation index. The system retrieves the node's historical maintenance data and finds that within 24 hours before the stress anomaly occurred, the scaffolding area where this node is located underwent three support adjustment operations: the first was the addition of two temporary diagonal braces, the second was the adjustment of the bolt preload of the node connection plate, and the third was the replacement of some worn fasteners. Statistics show that the node underwent three support changes within 24 hours, while under normal circumstances, the number of support changes does not exceed once per week. Combining the node's structural change data and access monitoring data, the submodule calculates the degree of anomaly in the support changes. The calculation method includes analyzing the trend of stress data changes and the response pattern of access frequency after each support change. For example, after the second bolt preload adjustment, the node stress fluctuation amplitude temporarily decreased, but the access frequency increased; after the third fastener replacement, the stress fluctuation frequency decreased, but the access fluctuation index remained high. Throughout the implementation process, the system achieved real-time monitoring of the ultra-high steel tower scaffolding structure through automated data stream processing. All data acquisition and analysis processes are completed collaboratively by the embedded processing unit and the cloud-based analysis platform. The analysis results are displayed to engineering managers in real time through a visual interface, providing decision support for structural safety monitoring.

[0052] Example 2: See Figure 2 The anomaly monitoring and identification submodule first receives anomaly indicators from the data acquisition module. These indicators include data such as the frequency of structural changes at the node, differences in access frequency, and frequency of support changes. The submodule then filters detailed monitoring data for the anomaly node, including continuous data streams recorded by stress sensors, changes in displacement monitor readings, and environmental data from temperature sensors. The system analyzes the correlation between monitoring time and construction progress, finding that the anomaly data for this node is concentrated during concrete pouring operations, and monitoring values ​​show a significant correlation between stress peaks and the operating cycle of the pumping equipment. The submodule calculates the distribution density of anomaly monitoring, dividing the monitoring data into multiple intervals according to the time series, and calculating the frequency and distribution characteristics of anomaly data within each interval. Through a density clustering algorithm, the system identifies three types of anomaly patterns: the first type is short-term high-frequency vibration, lasting approximately 2-3 minutes, with a frequency of 40-50 times per minute; the second type is medium- to long-term stress drift, lasting 15-20 minutes, with a slow increase in stress value; and the third type is sudden peak values, short in duration but high in intensity. Based on these analyses, the anomaly monitoring and identification submodule generates a set of anomaly monitoring nodes, which includes not only the 78th master node, but also five associated slave nodes. These nodes are marked with anomaly type, occurrence time, and severity level.

[0053] The monitoring node matching submodule then invokes the set of abnormal monitoring nodes and begins analyzing the monitoring behavior characteristics of these nodes. The system extracts the monitoring data feature values ​​of each node, including parameters such as stress fluctuation range, vibration frequency spectrum characteristics, and temperature change slope. The submodule compares these feature values ​​with known risk event patterns pre-stored in the system. These risk event patterns are derived from a historical engineering database and include feature patterns of typical accidents such as scaffolding collapse, node failure, and connection loosening. When calculating the matching degree between nodes and risk events, the system uses a multi-dimensional similarity algorithm to analyze the differences in feature values ​​across various dimensions. For example, for node number 78, its stress fluctuation characteristics match the "node fatigue failure" pattern in the database to a high degree, while its vibration characteristics are closer to the "connection loosening" pattern. Through comprehensive evaluation, the system calculates the risk matching index of this node to be 0.76 (range 0-1, higher values ​​indicate greater risk), and assesses the risk level of this node as "high risk." The monitoring node matching submodule generates a monitoring risk matching index, which includes the matching score, main risk type, and confidence index for each abnormal node.

[0054] The vulnerability risk assessment submodule further analyzes the monitoring frequency characteristics of abnormal nodes based on the monitoring risk matching index. The system extracts monitoring time interval data for node 78 and its associated nodes during the abnormal period, finding that the normal monitoring interval is 5 minutes, but a large number of intensive monitoring sessions occur during the abnormal period, with the shortest interval reaching 10 seconds. The submodule calculates the monitoring fluctuation range within a short period. Statistical analysis reveals that within one hour of the anomaly, the coefficient of variation of the monitoring frequency reaches 4.5, far exceeding the normal value of 0.8. Based on the risk matching degree of the nodes, the system predicts the probability of structural vulnerabilities. Multiple factors are considered in the prediction process: the ratio of the node's current stress level to the material's yield strength, the duration of the abnormal monitoring frequency, and the impact of environmental temperature changes on material properties. For example, for node 78, the system predicts a high probability of microcracks occurring under the current load conditions, while the probability of loosening of the connecting nodes is at a moderate level. The vulnerability risk assessment submodule ultimately generates structural risk assessment results, including the risk level of each node (node ​​78 is high risk, and related nodes are medium risk), the matching degree between monitoring data and risk events (0.76), and vulnerability identification results (potential cracks and loose connections). These results are output in a structured data format, including a risk assessment report, a visualized risk distribution map, and warning level indicators.

[0055] The system employs a multi-level risk assessment algorithm to accurately predict potential structural risks. All analysis processes are completed on a distributed computing platform, utilizing real-time streaming data processing technology to ensure the timeliness and accuracy of risk assessments. Assessment results are pushed to on-site management personnel in real time through the engineering monitoring system, providing a scientific basis for taking preventative measures. The system also establishes a feedback mechanism to compare actual structural problems with the predicted results, continuously optimizing the risk assessment model and algorithm parameters.

[0056] Example 3: See Figure 3 The risk node identification submodule first receives structural risk assessment results from the risk prediction module. These results include the risk level, monitoring event matching degree, and vulnerability identification information for node 78 and its associated nodes. The submodule then detects the distribution of risk nodes throughout the entire structural data network. The system scans the digital twin model of the scaffolding structure to locate all nodes marked as high-risk and medium-risk. When analyzing the type and sensitivity of the structural data stored by the nodes, the system categorizes nodes into three types: primary load-bearing nodes store complete historical stress-strain data, connecting nodes store displacement and vibration data, and auxiliary nodes store only environmental monitoring data. By analyzing the sensitivity of the data, node 78, as a primary load-bearing node, is marked as highly sensitive for its stored stress data, while the adjacent temperature monitoring node data has lower sensitivity. The submodule then filters storage nodes containing structural vulnerabilities and uses a vulnerability propagation algorithm to analyze the correlation between risk nodes, determining the range of structural nodes requiring adjustment. The final risk node list contains 12 nodes, of which 3 are marked as high-risk nodes that urgently need to be addressed (including node 78), and 9 are medium-risk nodes. Each node is labeled with its risk type, data sensitivity, and scope of impact.

[0057] The data adjustment analysis submodule, based on the risk node list, begins by analyzing the data distribution among affected nodes. The system constructs a node data association graph to analyze the data flow process between nodes. When calculating the degree of data association between nodes, the system monitors the frequency of data exchange and dependencies between nodes. For example, node 78 synchronizes stress data with its neighboring nodes every 5 minutes and exchanges status information with a remote monitoring node every 15 minutes. Analyzing the data interaction frequency, the system finds dense data interaction patterns between high-risk nodes, with interaction frequencies exceeding three times the normal level. When determining the impact range of data adjustments, the submodule uses a network propagation model to analyze the potential chain reactions caused by data changes. By calculating the adjustment priority, the system determines the processing order: first, core nodes with frequent data interactions are processed; second, nodes with high data sensitivity are processed; and finally, peripheral nodes with a smaller impact range are processed. The generated data adjustment priority index uses a three-level classification system. Node 78 is listed as priority level one, requiring immediate processing; its five directly associated nodes are priority level two, requiring processing within 4 hours; and the remaining nodes are priority level three, requiring processing within 24 hours.

[0058] The structural support reconfiguration submodule begins planning specific adjustment schemes based on the data adjustment priority index. When analyzing data flow paths between structural nodes, the system employs a path optimization algorithm to calculate the optimal data migration path. For node 78, the system identifies three possible data flow paths: direct transmission to the central processing unit via the wireless sensor network, transmission via an intermediary node, or storage in a local buffer followed by batch transmission. When allocating structural resources, the system allocates network bandwidth and storage resources based on the importance and urgency of nodes, with priority nodes receiving the largest resource allocation. When planning the optimal data adjustment path, the system considers multiple factors: path transmission latency, data integrity assurance, and energy consumption control. The final determined adjustment path adopts a multi-path parallel scheme, with important data transmitted in real-time via the main path and auxiliary data transmitted in batches via backup paths.

[0059] When adjusting access permissions for security structure nodes, the system employs a dynamic permission management mechanism. For node 78, the access permission has been changed from fully open to restricted, allowing only monitoring terminals and the security management system to access it. Permission updates are adjusted based on the node's risk level and data sensitivity, using the following formula to calculate permission weights:

[0060] P w =α·R l +β·S d +γ·I f

[0061] Where: P w Represents the permission weight value, used to determine the level of access privileges; Rl S represents the risk level coefficient, which is determined based on the risk assessment results of the node. d The data sensitivity coefficient is determined based on the data type and its importance; I f The influence range coefficient reflects the potential impact range of a node failure; α, β, and γ are the weighting coefficients of each factor, configured according to the actual engineering situation.

[0062] The system iteratively optimizes node configuration parameters. First, it immediately adjusts the highest priority nodes, including data path redirection, permission restrictions, and resource reallocation. Then, it processes other nodes sequentially according to priority, evaluating the effectiveness and optimizing parameters at each adjustment step. The final generated structural node adjustment configuration includes a complete adjustment plan: the target configuration state for each node, the adjustment timeline, resource allocation scheme, and permission setting standards. The configuration plan details the new data flow paths, access control rules, monitoring frequency adjustments, etc., ensuring the structural support system maintains stable operation during the adjustment process.

[0063] The system employs an automated, dynamic adjustment mechanism to accurately identify and optimize the configuration of risk nodes. The adjustment plan fully considers the actual conditions of the construction site, including construction progress, environmental conditions, and resource constraints. All adjustment operations are carried out systematically under system monitoring, ensuring the stability and safety of the scaffolding support structure. The system also establishes an adjustment effect monitoring mechanism to track the status of nodes after adjustment in real time, providing data support for further optimization. Through this dynamic adjustment approach, the system can respond promptly when potential risks are detected, minimizing structural safety risks to the greatest extent possible.

[0064] Example 4: See Figure 4 The behavior pattern change analysis submodule, based on the structural node configuration adjustment, calls the behavior record data of node 78 before and after the adjustment. The system extracts node activity logs from the 72 hours before and 48 hours after the adjustment, including parameters such as stress sensor readings, displacement monitoring values, and ambient temperature records. When comparing the node activity characteristics before and after the adjustment, the system uses time series alignment technology to map monitoring data from different time periods to a unified time coordinate system. Analyzing the amplitude and frequency of node behavior changes, the system detects that after the adjustment, the stress fluctuation amplitude of the node decreased from a peak of 145 MPa to 128 MPa, and the fluctuation frequency decreased from 45 times per minute to 28 times per minute. When calculating the degree of shift in behavior pattern, the system establishes a multi-dimensional feature vector space, including dimensions such as mean stress, fluctuation range, and rate of change. By calculating the positional change of the feature vector in space, a behavior pattern shift index of 0.63 (range 0-1, with larger values ​​indicating more significant changes) is obtained. This index quantifies the overall trend of node activity characteristics.

[0065] The data monitoring frequency comparison submodule initiates a detailed analysis of the monitoring frequency based on the behavior pattern offset index. The system retrieves monitoring records for node 78 and adjacent nodes, comparing monitoring parameters before and after the adjustment. Analysis of the monitoring time point distribution reveals an irregular concentration of monitoring activity before the adjustment, while the monitoring time points show a more uniform distribution after the adjustment. Checking the monitoring duration, the duration of a single monitoring session fluctuated between 10 and 180 seconds before the adjustment, stabilizing at 30 ± 5 seconds after the adjustment. The change in monitoring frequency shows that the number of monitoring sessions per hour for this node decreased from an average of 120 before the adjustment to 72. The system generates a monitoring frequency fluctuation index, including parameters such as time distribution uniformity, duration standard deviation, and frequency change rate. Table 1 shows the comparison data of the monitoring frequency of key nodes before and after the adjustment.

[0066] Table 1: Comparison of node monitoring frequency before and after adjustment.

[0067]

[0068]

[0069] The abnormal behavior identification submodule initiates the detection of abnormal node activity based on the monitoring frequency fluctuation index. The system establishes a baseline for normal behavior patterns, trained from historical normal data using machine learning algorithms, including parameters such as stress change thresholds and vibration spectrum characteristics. When identifying node activities deviating from the normal pattern, the system detected two types of anomalies in node 78 after adjustments: first, a 20-minute-long, 15MPa-level stress gradual increase every afternoon; second, intermittent stress spikes lasting less than 5 seconds. Analyzing the abnormal behavior pattern characteristics of the nodes, the system extracts attributes such as the duration, frequency, and intensity change rate of the abnormal events. When matching node activity characteristics with known vulnerabilities, the system performs pattern comparisons between the abnormal features and the vulnerability feature database. For example, the stress gradual increase pattern has a match degree of 0.68 with the "local deformation accumulation" vulnerability feature, while the stress spike has a match degree of 0.72 with the "instantaneous overload" vulnerability feature. Through source tracing analysis, the system locates the source of the structural vulnerability: the stress gradual increase problem originates from the temperature deformation transmission of the adjacent formwork support system, while the stress spike is related to the impact load generated by the sudden start and stop of the tower crane. The final behavioral feature analysis results include detailed behavioral pattern change indicators, monitoring frequency comparison data, and abnormal activity identification reports.

[0070] The system employs a distributed computing architecture to process massive amounts of monitoring data. Node behavior data is first preprocessed on edge computing devices, and feature vectors are extracted before being transmitted to the cloud-based analysis platform. The platform uses a streaming computing engine to process the data stream in real time, and the behavior pattern analysis algorithm updates the analysis results every 5 minutes. Anomaly activity identification adopts a multi-level classification mechanism, categorized into three levels based on the severity of the anomaly: attention level, warning level, and danger level. All analysis results are displayed through a visual interface, allowing engineering managers to view real-time analysis views such as node behavior heatmaps and anomaly event distribution maps. The system also establishes a behavior analysis archive, storing complete historical behavior data and analysis results, supporting multi-dimensional searches and queries by time range, node location, anomaly type, and other dimensions.

[0071] The entire analysis process is synchronized with on-site construction activities. For example, during concrete pouring, the system detected an expected increase in stress in the node behavior pattern, but did not trigger an anomaly alarm; while when the tower crane suddenly loads, the system accurately captures the instantaneous overload characteristics and generates an anomaly identifier. Through continuous behavioral feature analysis, the system provides project managers with a complete view of the structural state evolution, helping to identify potential sources of risk. The analysis results output includes structured data reports and graphical displays, detailing the behavioral feature change trajectory of each node, the effect of monitoring frequency adjustments, and the location information of abnormal activities. This data provides a technical basis for subsequent safety decisions and is also used to optimize system analysis parameters and early warning threshold settings.

[0072] Example 5: See Figure 5 The node access control submodule, based on behavioral feature analysis results, first analyzes the frequency of risky operations on nodes. The system retrieves recent operation logs for node 78, showing that this node triggered 7 abnormal alarms in the last 24 hours, including 4 stress exceedance events and 3 vibration frequency anomalies. When calculating the impact range of node access control changes, the system scans the list of associated devices interacting with this node, identifying 15 directly associated monitoring terminals and 8 indirectly associated control units. In identifying nodes with frequent abnormal operations, the system marks node 78 and its adjacent nodes 79 and 81 as high-frequency anomaly sources. When reconfiguring node access permissions, the system changes the access mode of node 78 from fully open to a whitelist mechanism, allowing only the central monitoring server and two designated secure terminals to access it. Specific measures for restricting access to high-risk nodes include: prohibiting remote debugging commands, disabling data write permissions, and limiting real-time video stream transmission bandwidth. The generated node access control configuration includes an updated access control list, a permission activation schedule, and abnormal operation interception rules.

[0073] After the monitoring quota management submodule calls the node permission adjustment configuration, it initiates refined control over monitoring resources. The system calls the abnormal monitoring records of node 78 and analyzes the data flow characteristics over the past 48 hours. When calculating the fluctuation range of the monitoring quota, the system counts the number of monitoring data packets per minute for this node, finding that the peak period reaches 12 data packets per minute, while the trough period is only 3 data packets per minute. Analyzing the short-term trend of monitoring quota changes, the system identifies two high-load periods each day: 10:00 AM to 12:00 PM and 2:00 PM to 4:00 PM. When judging the degree of deviation between the monitoring values ​​and the normal behavior of the node, the system establishes a dynamic baseline model, which automatically updates the reference range based on historical normal data. When adjusting the upper limit of the node's monitoring quota, the system limits the peak monitoring frequency of node 78 to 8 data packets per minute, and maintains 4 data packets per minute during off-peak periods. The specific scheme for allocating the monitoring quota ratio is as follows: 40% for stress monitoring, 30% for vibration monitoring, 20% for displacement monitoring, and 10% for environmental monitoring. The generated optimized monitoring quota configuration includes a time-based quota allocation table, a data type priority matrix, and an over-quota monitoring and interception strategy.

[0074] The verification method optimization submodule, based on the optimized monitoring quota configuration, filters out a list of nodes with abnormal monitoring frequencies. The system extracts the identity verification records of node number 78 and adjacent high-risk nodes, analyzing login data from the past 30 days. When analyzing the security level of the abnormal node's monitoring verification, the system detected three verification methods for this node: password verification, fingerprint recognition, and work card swiping, with password verification accounting for 85% of the usage frequency. To determine whether the monitoring verification matches the node's risk level, the system assesses the current verification mechanism's security strength coefficient as medium, while the node's high-risk status requires high-strength verification. Specific measures to optimize the node's monitoring verification methods include: enabling a two-factor authentication mechanism, requiring the combined use of passwords and dynamic verification codes; increasing the frequency of biometric verification, requiring fingerprint confirmation before each data upload; and introducing behavioral feature-assisted verification, automatically triggering additional authentication for abnormal operation patterns. The generated security protection measures include tiered verification strength standards, multi-factor authentication processes, and abnormal behavior triggering rules.

[0075] The early warning output submodule generates multi-level early warning signals based on comprehensive safety protection measures. The system categorizes early warnings into three levels according to risk level and urgency: Level 1 warning corresponds to an immediate dangerous state, triggering a system-wide red alert; Level 2 warning corresponds to a potentially high risk, triggering a regional yellow alert; and Level 3 warning corresponds to an anomaly requiring attention, triggering a node-level blue alert. For the current status of node 78, the system generates a Level 2 early warning signal. The early warning signal includes a structured data message and a visual alarm interface. The data message details the risk location, risk type, and recommended handling measures; the visual interface displays the risk node location map on the large screen in the engineering command center and simultaneously pushes briefing alerts to the mobile terminals of relevant responsible personnel. The early warning output employs a multi-channel synchronization mechanism to ensure effective transmission of alarm information even when some communication links are interrupted.

[0076] The entire implementation process took place in the actual environment of the engineering site. When the tower crane was lifting large components, the system detected a momentary stress peak at node 78, triggering the monitoring quota management mechanism and automatically limiting the frequency of non-critical data collection. Simultaneously, the verification method optimization module activated enhanced authentication, requiring operators to confirm their fingerprints before adjusting monitoring parameters. During concrete pumping operations, the node access control module intercepted three unauthorized access requests. The early warning output system sent a level-two alert to the project supervisor within 90 seconds of the event, detailing the risk situation and the protective measures already taken. All response operations were completed within the system's automated workflow, forming a closed-loop management system from risk identification to proactive protection. The system continuously recorded the effectiveness of the protective measures, providing operational data support for subsequent optimization of early warning strategies.

[0077] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0078] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding, characterized in that, The system includes: The data acquisition module collects structural monitoring data of the ultra-high steel tower scaffolding, analyzes the frequency of structural changes, calculates the data access frequency of sensors, compares the frequency of structural changes with the access frequency, judges the risk of abnormal state of the structure, and generates abnormal state indicators. Based on the abnormal state indicators, the risk prediction module locates the monitoring nodes of the abnormal structure, analyzes the matching relationship between the monitoring nodes and risk events, assesses the risk level of the nodes, calculates the correlation between the monitoring frequency and risk events, predicts the structural vulnerabilities that may occur at the monitoring nodes, and generates structural risk assessment results. Based on the structural risk assessment results, the dynamic adjustment module identifies risky structural nodes, analyzes the distribution of structural data, calculates adjustment priorities, plans adjustment paths, and adjusts structural supports to low-risk nodes to obtain structural node adjustment configurations. The behavior analysis module adjusts the configuration based on the structure nodes, compares the data monitoring frequency of the structure before and after the adjustment, identifies abnormal structure activities, judges the abnormal characteristics of the structure behavior patterns, locates the source of structural vulnerabilities, and obtains the behavior feature analysis results.

2. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 1, characterized in that, The abnormal status indicators include the frequency of structural changes, the difference in access frequency, and the frequency of support changes. The structural risk assessment results include the node risk level, the matching degree between monitoring and events, and the vulnerability identification results. The structural node adjustment configuration includes the adjustment priority, the adjustment path, and the permission update standard. The behavioral feature analysis results include behavioral pattern change indicators, monitoring frequency comparison results, and abnormal activity identifiers.

3. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 1, characterized in that, The data acquisition module includes: The structural change analysis submodule collects structural monitoring data of the ultra-high steel tower scaffolding, analyzes the stress time series of the structural monitoring data, calculates the time interval between continuous monitoring, statistically analyzes the frequency change of structural changes, compares the input and output ratio of stress, identifies nodes of abnormal structural activity, and obtains structural variability indicators. Based on the structural change index, the sensor access monitoring submodule retrieves access data of structurally abnormal nodes, analyzes the distribution of access times over different time periods, calculates the degree of access fluctuation of nodes in the short term, determines whether nodes have abnormal access behavior, and obtains the node access fluctuation index. The support change statistics submodule, based on the node access fluctuation index, calls the node's support modification records, counts the number of support changes, and calculates the degree of abnormality of support changes by combining the node's structural changes and access monitoring data, generating an abnormal status index.

4. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 1, characterized in that, The risk prediction module includes: The anomaly monitoring and identification submodule filters the monitoring data of anomaly nodes based on the anomaly status indicators, analyzes the correlation between monitoring time and value and node operation mode, calculates the distribution density of anomaly monitoring and classifies them, identifies anomaly monitoring nodes, and generates anomaly monitoring node set. The monitoring node matching submodule calls the set of abnormal monitoring nodes, parses the monitoring behavior characteristics, compares the patterns of identified risk events, calculates the matching degree between nodes and risk events, assesses the risk level of monitoring nodes, and generates a monitoring risk matching index. The vulnerability risk assessment submodule analyzes the monitoring frequency of abnormal nodes based on the monitoring risk matching index, extracts the monitoring time interval, calculates the monitoring fluctuation range within a short period, predicts the probability of structural vulnerabilities based on the risk matching degree of the nodes, and generates structural risk assessment results.

5. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 4, characterized in that, The monitoring node matching submodule assesses the degree of matching by calculating the difference between node feature values ​​and risk event feature values, and generates a monitoring risk matching index.

6. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 1, characterized in that, The dynamic adjustment module includes: Based on the structural risk assessment results, the risk node identification submodule detects risk nodes in the structural data network, analyzes the type and sensitivity of the structural data stored by the nodes, filters storage nodes containing structural vulnerabilities, determines the scope of structural nodes that need to be adjusted, and obtains a list of risk nodes. Based on the risk node list, the data adjustment and analysis submodule analyzes the data distribution among the affected nodes, calculates the degree of data correlation and interaction frequency between nodes, determines the impact scope and priority of data adjustment, and generates a data adjustment priority index. The structural support reconfiguration submodule analyzes the data flow path between structural nodes based on the data adjustment priority index, allocates structural resources, plans the optimal data adjustment path, and adjusts the access permissions of secure structural nodes to obtain the structural node adjustment configuration.

7. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 1, characterized in that, The behavior analysis module includes: The behavior pattern change analysis submodule adjusts the configuration based on the structure nodes, calls the behavior records of the adjusted nodes, compares the node activity characteristics before and after the adjustment, analyzes the magnitude and frequency of node behavior changes, calculates the degree of behavior pattern shift, and obtains the behavior pattern shift index. The data monitoring frequency comparison submodule compares the data monitoring frequency of nodes before and after adjustment based on the behavior pattern offset index, analyzes the changes in monitoring time points, monitoring duration and monitoring frequency, judges the fluctuation of monitoring frequency, and generates a monitoring frequency fluctuation index. The abnormal behavior identification submodule identifies node activities that deviate from the normal pattern based on the monitoring frequency fluctuation index, analyzes the characteristics of abnormal node behavior patterns, matches the relationship between node activity characteristics and known vulnerabilities, locates the source of structural vulnerabilities, and generates behavioral feature analysis results.

8. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 1, characterized in that, The system also includes: Based on the behavioral feature analysis results, the early warning response module identifies abnormal monitoring nodes, adjusts node access permissions, allocates monitoring quota ratios, updates node monitoring verification methods, and generates security protection measures.

9. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 8, characterized in that, The early warning response module includes: Based on the behavioral feature analysis results, the node permission control submodule analyzes the frequency of risky operations of nodes, calculates the impact range of node permission changes, identifies nodes with frequent abnormal operations, reconfigures the access permissions of nodes, implements access restrictions on high-risk nodes, and generates node permission adjustment configurations. The monitoring quota management submodule calls the node permission adjustment configuration, calls the monitoring records of abnormal nodes, calculates the fluctuation range of the monitoring quota, analyzes the short-term monitoring quota change trend, judges the degree of deviation between the monitoring value and the normal behavior of the node, adjusts the upper limit of the node's monitoring quota, allocates the monitoring quota ratio, and generates the optimized monitoring quota. The verification method optimization submodule, based on the optimized monitoring quota, filters nodes with abnormal monitoring frequency, extracts the identity verification records of the nodes, analyzes the security level of the abnormal node monitoring verification, determines whether the monitoring verification matches the node risk level, optimizes the node monitoring verification method, and generates security protection measures.

10. The intelligent monitoring and early warning system for building support structures based on ultra-high steel tower scaffolding according to claim 9, characterized in that, The early warning response module also includes an early warning output submodule, which generates an early warning signal based on the security protection measures.

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