A dynamic testing and early warning device and method for hoisting steel structural members

Abstract

The present application relates to steel structure hoisting related technical field, specifically including a kind of hoisting steel structural member's dynamic test early warning device and method, comprising: data acquisition module monitors hoisting dynamic parameter, operating parameter collection module collects equipment information;Traction vector setting module sets traction vector, characteristic evaluation module assesses risk and stability;Resonance analysis module identifies potential resonance point, risk early warning module optimizes traction and establishes resonance risk early warning mechanism, solves the technical problem that periodic inspection is difficult to realize real-time, comprehensive structure health state assessment, realizes the continuous, real-time monitoring of stress, strain, vibration and other dynamic parameters and hoisting equipment state in hoisting process, the potential resonance distribution point of structure is identified by advanced algorithm, and resonance risk early warning mechanism is established, the technical effect that prevents the structural damage caused by resonance.

Description

Technical Field

[0001] This invention relates to the technical field of steel structure hoisting, specifically to a dynamic testing and early warning device and method for hoisting steel structure components. Background Technology

[0002] With the rapid development of the construction industry, steel structures have been widely used in large-scale projects such as high-rise buildings, bridges, and stadiums due to their unique advantages, such as fast construction speed, good seismic performance, and environmental friendliness. However, steel structures face many challenges in the design, construction, operation and maintenance stages, especially the safety issues during the hoisting process.

[0003] During hoisting, steel structural components used for hoisting may be subjected to abnormal stress, deformation, or vibration due to improper operation, environmental factors, or inherent defects, which may lead to structural damage or even safety accidents. Conventional testing methods, such as periodic inspections, are insufficient to achieve real-time and comprehensive assessment of structural health status, especially in complex and ever-changing construction site environments.

[0004] In summary, existing technologies suffer from the technical problem that regular inspections make it difficult to achieve real-time and comprehensive structural health status assessment. Summary of the Invention

[0005] This application provides a dynamic testing and early warning device and method for hoisting steel structural components, aiming to solve the technical problem that regular inspections in the prior art are difficult to achieve in real-time and comprehensive assessment of structural health status.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: This application provides a dynamic testing and early warning device for hoisting steel structural components. The device includes: a data acquisition module for acquiring dynamic parameters of the hoisting process based on the hoisting steel structural component, the dynamic parameters including stress data, strain data, and vibration data; an operation parameter collection module for connecting to the hoisting equipment corresponding to the hoisting steel structural component and collecting a set of hoisting operation parameters, the hoisting operation indicators corresponding to the hoisting operation parameter set including equipment operating status, hoisting path, and force information at the hoisting point; and a traction vector setting module for setting the traction vector based on the hoisting speed in the equipment operating status and the hoisting position in the hoisting path. The system includes: a hoisting traction vector setting; a feature evaluation module for risk assessment using the hoisting operation parameter set and dynamic parameters, and extracting hoisting risk factors; a stability assessment using the hoisting traction vector and dynamic parameters, and extracting hoisting stability factors; a resonance analysis module for identifying potential resonance distribution point sets corresponding to the hoisting steel structural components by introducing a resonance analysis model based on the hoisting risk factors and hoisting stability factors; and a risk warning module for compensating and optimizing the potential resonance distribution point sets corresponding to the hoisting steel structural components using the hoisting traction vector, and establishing a resonance risk warning mechanism.

[0007] In another aspect, this application provides a dynamic testing and early warning method for hoisting steel structural components. The method includes: collecting dynamic parameters of the hoisting process based on the hoisting steel structural component, the dynamic parameters including stress data, strain data, and vibration data; connecting the hoisting equipment corresponding to the hoisting steel structural component and collecting a set of hoisting operation parameters, the set including equipment operating status, hoisting path, and force information at the hoisting points; setting a hoisting traction vector based on the hoisting speed in the equipment operating status and the hoisting displacement in the hoisting path; performing a risk assessment based on the hoisting operation parameter set and the dynamic parameters, and extracting a hoisting risk factor; performing a stability assessment based on the hoisting traction vector and the dynamic parameters, and extracting a hoisting stability factor; introducing a resonance analysis model based on the hoisting risk factor and the hoisting stability factor to identify a set of potential resonance distribution points corresponding to the hoisting steel structural component; and performing compensation optimization for the set of potential resonance distribution points corresponding to the hoisting steel structural component based on the hoisting traction vector, and establishing a resonance risk early warning mechanism.

[0008] In summary, one or more technical solutions provided in this application solve the technical problem that it is difficult to achieve real-time and comprehensive structural health status assessment through periodic inspections. They enable continuous and real-time monitoring of dynamic parameters such as stress, strain, and vibration during hoisting, as well as the status of hoisting equipment. Through advanced algorithms, they identify potential resonance distribution points of the structure and establish a resonance risk early warning mechanism to prevent structural damage caused by resonance. Attached Figure Description

[0009] Figure 1 This application provides a structural schematic diagram of a dynamic testing and early warning device for hoisting steel structural components. Figure 2 This application provides a flowchart illustrating the determination of the timing synchronization of the hoisting operation parameter set in the operating parameter collection module of a dynamic testing and early warning device for hoisting steel structural components. Figure 3 This application provides a flowchart illustrating a dynamic testing and early warning method for hoisting steel structural components.

[0010] Explanation of reference numerals in the attached diagram: Data acquisition module M100, operating parameter collection module M200, traction vector setting module M300, feature evaluation module M400, resonance analysis module M500, risk warning module M600. Detailed Implementation Example

[0011] The present application will now be described in detail with reference to the accompanying drawings, such as... Figure 1 As shown in the figure, this application embodiment provides a dynamic testing and early warning device for hoisting steel structural components, wherein the device includes: The data acquisition module M100 is used to collect dynamic parameters of the hoisting process based on the steel structural components used for hoisting. The dynamic parameters include stress data, strain data, and vibration data. The operation parameter collection module M200 is used to connect to the hoisting equipment corresponding to the steel structural components used for hoisting and collect a set of hoisting operation parameters. The hoisting operation indicators corresponding to the set of hoisting operation parameters include equipment operating status, hoisting path, and force information at the hoisting point.

[0012] Commonly, regular inspections of steel structural components used for hoisting are conducted, for example, through physical measurements, such as using calipers, ultrasonic thickness gauges, etc., to measure the dimensions and thickness of the steel, and theodolites and total stations to measure the geometric deviations of the structure, such as verticality and deflection; and then comparing and analyzing the data with historical data and maintenance records.

[0013] In practical applications, it has been found that regular inspections of steel structural components used for hoisting are difficult to effectively mitigate or eliminate risks without affecting the construction progress. Based on this, this application integrates dynamic parameter acquisition, hoisting equipment monitoring, and data analysis technologies to monitor the health status of steel structural components used for hoisting in real time, issue timely warnings, and significantly improve operational safety.

[0014] Select appropriate stress sensors, strain sensors, and vibration sensors to measure the stress, strain, and vibration data of the steel structural components used for hoisting; install these sensors at key locations on the steel structural components to ensure they can accurately capture dynamic changes during the hoisting process; connect the sensors to the data acquisition module via wired or wireless means to ensure real-time data reception and transmission; calibrate the sensors before hoisting begins to ensure the accuracy of the measurement data.

[0015] Before the hoisting begins, the data acquisition module is activated to receive and store the data sent by the sensors in real time. During the hoisting process, the sensors measure the stress, strain, and vibration data of the steel structural components used for hoisting in real time and transmit the data to the data acquisition module. The data acquisition module records all received data, including timestamps, sensor numbers, and measured values, for subsequent analysis.

[0016] The hoisting equipment corresponding to the steel structural components is connected via wired or wireless means to ensure real-time reception of the equipment's operating status, hoisting path, and stress information at the hoisting points. The hoisting equipment transmits its operating status data in real time, such as start-up, stop, acceleration, and deceleration. A GPS module records the path information during the hoisting process, including the hoisting point, path length, and direction. During the hoisting process, force sensors or other measuring devices are used to measure the stress at the hoisting points. The operating status of the hoisting equipment, the hoisting path, and the stress information at the hoisting points are integrated into a hoisting operation parameter set to provide data support for subsequent analysis.

[0017] The traction vector setting module M300 is used to set the hoisting traction vector based on the hoisting speed and the hoisting displacement in the hoisting path during the equipment's operation. The feature evaluation module M400 is used to perform risk assessment and extract hoisting risk factors based on the hoisting operation parameter set and the dynamic parameters; and to perform stability assessment and extract hoisting stability factors based on the hoisting traction vector and the dynamic parameters.

[0018] The hoisting operation status, including hoisting speed, is acquired in real time from the hoisting equipment; hoisting path information, including hoisting displacement, is obtained from the hoisting equipment's positioning system or GPS module; furthermore, based on the hoisting speed and displacement, and combined with the hoisting equipment's design parameters (such as maximum load capacity, lifting gear type, etc.), a hoisting traction vector is calculated; the hoisting traction vector includes elements such as direction, magnitude, and speed, and is used to guide the movement direction and speed of the steel structural components during the hoisting process; in practical applications, it also includes making necessary adjustments to the hoisting traction vector according to actual hoisting conditions, such as wind force and the center of gravity position of the steel structural components.

[0019] The hoisting operation parameter set (including equipment operating status, hoisting path, and stress information at hoisting points) is integrated with dynamic parameters (stress data, strain data, and vibration data). The integrated data is then processed using a risk assessment model (such as a machine learning or statistical model). Based on historical data and expert experience, potential risks during the hoisting process, such as overload, excessive vibration, and stress concentration, are analyzed. In one feasible implementation, hoisting risk factors are extracted from the results of the risk assessment model. These factors are typically related to unsafe factors during the hoisting process. Hoisting risk factors can be quantitative values ​​(such as overload ratio and vibration amplitude) or qualitative descriptions (such as stress concentration areas).

[0020] The hoisting process is analyzed using a stability assessment model (such as a model based on structural mechanics). Factors such as hoisting traction vector, stiffness of steel structural components used for hoisting, and stress conditions at the hoisting points are considered to assess the stability of the hoisting process. Hoisting stability factors are extracted from the results of the stability assessment model. The hoisting stability factors reflect the stability performance of the structure during the hoisting process and can be indicators such as structural deformation and stress distribution uniformity.

[0021] The resonance analysis module M500 is used to introduce a resonance analysis model based on the hoisting risk factor and the hoisting stability factor to identify the potential resonance distribution point set corresponding to the hoisting steel structural component; the risk warning module M600 is used to perform compensation optimization for the potential resonance distribution point set corresponding to the hoisting steel structural component in combination with the hoisting traction vector, and establish a resonance risk warning mechanism.

[0022] Collect design parameters (such as geometry, stiffness, and mass distribution) of the steel structural components used for hoisting, hoisting operation parameter sets (including equipment operating status, hoisting path, and stress information at hoisting points), and dynamic parameters (stress data, strain data, and vibration data). Introduce resonance analysis models, such as modal analysis or frequency response analysis. Modal analysis can provide the natural frequencies and corresponding mode shapes of the structure, while frequency response analysis can evaluate the structure's response under different excitation frequencies. Input the design parameters of the steel structural components used for hoisting, the hoisting operation parameter sets, and the dynamic parameters into the resonance analysis model.

[0023] Using a resonance analysis model, the natural frequencies of the steel structural components used for hoisting are calculated to understand their vibration characteristics under no external force. Based on the hoisting operation parameter set and hoisting traction vector, the external excitation frequencies generated during the hoisting process, such as the vibration frequency of the hoisting equipment and wind force, are analyzed. The natural frequencies of the steel structural components used for hoisting are compared with the external excitation frequencies to identify the frequency range that will generate resonance and determine the corresponding potential resonance distribution point set. Based on the hoisting risk factor and hoisting stability factor, the risks brought by the potential resonance distribution point set, such as structural fatigue and failure, are assessed. Based on the assessment results, a compensation optimization scheme is formulated for the potential resonance distribution point set. Optimization measures include adjusting the hoisting traction vector, adding temporary supports, and changing the hoisting speed. According to the optimization scheme, relevant parameters and measures in the hoisting process are adjusted to reduce the resonance risk.

[0024] Based on the resonance analysis model and risk assessment results, a warning threshold for resonance risk is set, such as a specific frequency range and vibration amplitude. During the hoisting process, the vibration data of the steel structural components used for hoisting is monitored in real time and compared with the warning threshold. When the monitored vibration data exceeds the warning threshold, the resonance risk warning mechanism is triggered and an alarm is issued. Based on the alarm information, corresponding emergency response measures are taken, such as suspending hoisting and adjusting hoisting parameters, to reduce the resonance risk.

[0025] Based on hoisting risk factors, hoisting stability factors, and resonance analysis models, the potential resonance distribution point set of steel structural components used for hoisting is effectively identified and compensated for and optimized. A resonance risk early warning mechanism is established to reduce the resonance risk during hoisting and ensure the safe and smooth progress of hoisting operations.

[0026] Furthermore, such as Figure 2 As shown, the operating parameter collection module M200 is used to perform the following methods: Using a gyroscope sensor, angular velocity data of the hoisting equipment is collected; based on the hoisting speed during the equipment's operation, real-time acceleration is calculated; using the angular velocity data and the real-time acceleration, a digital signal processing algorithm is employed to determine smooth acceleration; based on the smooth acceleration, real-time acceleration, and angular velocity data, time series analysis is performed to determine a time-synchronized set of hoisting operation parameters.

[0027] Ensure the hoisting equipment is in normal working order and prepare the gyroscope sensor and other necessary sensors; install the gyroscope sensor in a suitable position on the hoisting equipment to ensure that the sensor can accurately measure the angular velocity data of the hoisting equipment; start the gyroscope sensor installed on the hoisting equipment to begin collecting the angular velocity data of the hoisting equipment; transmit the collected angular velocity data to the data processing unit in real time.

[0028] The hoisting equipment's control system or sensors acquire real-time hoisting speed; based on the hoisting speed and the equipment's basic physical parameters (such as mass and inertia), real-time acceleration is calculated using physical formulas; the angular velocity data collected by the gyroscope sensor and the calculated real-time acceleration are fused; digital signal processing algorithms (such as Kalman filtering and moving average filtering) are used to process the fused data to eliminate noise and interference, resulting in smooth acceleration data.

[0029] Ensure that smooth acceleration, real-time acceleration, and angular velocity data are synchronized in time, i.e., data corresponding to the same point in time; perform time series analysis on the synchronized data, including trend analysis, periodic analysis, and correlation analysis, to obtain the dynamic characteristics during the hoisting operation; integrate the smooth acceleration, real-time acceleration, angular velocity, and other parameters obtained from the time series analysis to form a time-synchronized hoisting operation parameter set; store the hoisting operation parameter set in a database or file for subsequent analysis, evaluation, and early warning, enabling the connection of hoisting equipment corresponding to the hoisting steel structure components and the collection of hoisting operation parameter sets, providing accurate data support for subsequent risk assessment, stability assessment, and resonance analysis.

[0030] Furthermore, the resonance analysis module M500 is also used to perform the following methods: Based on the time series corresponding to the hoisting operation parameter set, key frequency components are extracted; modal analysis is performed on the steel structural components used for hoisting to determine the natural frequencies and modal shapes, and a modal matrix is ​​established; based on the modal matrix and the key frequency components corresponding to the hoisting process, the potential resonant frequencies are determined.

[0031] Based on the time series corresponding to the hoisting operation parameter set, data for frequency analysis is organized and prepared; the time series data is processed using Fast Fourier Transform (FFT) or other frequency domain analysis techniques to extract key frequency components, including vibration frequencies generated by hoisting equipment operation and external excitation frequencies.

[0032] Based on the design drawings, material properties, and boundary conditions of the steel structural components used for hoisting, a structural model is established using finite element analysis (FEA) software or manual calculation methods. Modal analysis is then performed on the structural model of the steel structural components to determine their natural frequencies and modal shapes. The natural frequency is the frequency of the structure during undamped free vibration, while the modal shape describes the vibration pattern of the structure at a given natural frequency. Different natural frequencies and their corresponding modal shapes are integrated into a modal matrix, which will be used to determine the subsequent resonance frequency.

[0033] The key frequency components corresponding to the hoisting process are compared with the natural frequencies of the steel structural components used for hoisting. If a key frequency component is close to or equal to a natural frequency (within a certain error range), the steel structural components used for hoisting are considered to have a potential resonance risk at that frequency. Combined with modal shapes, the vibration response of the steel structural components used for hoisting is determined at which locations or nodes are most significant; these locations are the potential resonance distribution point set. In practical applications, this also includes conducting a risk assessment of the hoisting process based on the determined potential resonance frequencies and resonance distribution point set, evaluating the structural damage, fatigue, and other problems caused by resonance. Based on the risk assessment results, corresponding optimization measures are formulated, such as adjusting the hoisting speed, adding temporary supports, and changing the hoisting path, to reduce the resonance risk. Resonance is monitored in real time during the hoisting process, and the optimization measures are continuously optimized and adjusted based on feedback data.

[0034] Preferably, resonance analysis data, risk assessment results, and optimization measures are recorded for each hoisting process and analyzed in depth. Based on the analysis results, the resonance analysis model, modal analysis method, and risk assessment method are continuously improved and optimized to enhance the accuracy and effectiveness of the analysis. Based on hoisting risk factors, hoisting stability factors, and resonance analysis models, the potential resonance distribution point set of steel structural components used in hoisting is effectively identified during the hoisting process, providing support for reducing resonance risks.

[0035] Furthermore, the resonance analysis module M500 is also used to perform the following methods: Based on the hoisting risk factor and the potential resonance frequency, the potential risk level is determined; based on the hoisting stability factor, an initial solution space is established; based on the potential risk level, an evaluation function is set, and iterative optimization is performed in the initial solution space to obtain a compensation strategy that minimizes resonance risk.

[0036] Based on various lifting risk factors during the lifting process, such as the performance of lifting equipment, the characteristics of steel structural components used for lifting, and environmental factors, the lifting risk factors are combined with potential resonance frequencies to assess the consequences of resonance, such as structural damage and fatigue. According to the severity of resonance risk and its consequences, potential risks are classified into different levels, such as low risk, medium risk, and high risk.

[0037] Analyze hoisting stability factors, such as the stability of hoisting equipment and the support conditions of steel structural components used for hoisting, as these factors affect the resonance risk during the hoisting process. Based on these hoisting stability factors, determine the optimization variables that can be adjusted to reduce the resonance risk, such as hoisting speed, hoisting angle, and the location and quantity of temporary supports. Set reasonable value ranges for each optimization variable to form an initial solution space.

[0038] Based on the potential risk level, evaluation indicators are defined to measure the degree to which different adjustment schemes reduce resonance risk. These indicators include the shift in resonance frequency and the reduction in structural response. The evaluation indicators are integrated into an evaluation function, which quantifies the effect of different adjustment schemes on reducing resonance risk. An adjustment scheme is randomly selected from the initial solution space as the starting point. The evaluation function is used to evaluate the current adjustment scheme to obtain its degree of resonance risk reduction. Based on the evaluation results, optimization directions that can further reduce resonance risk are selected, such as adjusting the hoisting speed or changing the hoisting angle. A search is performed in the optimization direction to update the solution space and generate new adjustment schemes. Iteration termination conditions are set, such as reaching the maximum number of iterations or the change in the solution being less than a set threshold. When the termination conditions are met, iteration stops. The optimal solution, i.e., the adjustment scheme that maximizes the reduction of resonance risk, is selected from all adjustment schemes obtained during the iterative optimization process. Based on the optimal solution, specific compensation strategies are formulated, including adjusting hoisting parameters and adding temporary supports.

[0039] In practical applications, this also includes implementing the established compensation strategy during hoisting, observing and recording its effect on reducing resonance risk, and further optimizing the compensation strategy based on feedback data during implementation to improve its effectiveness and practicality. Based on hoisting risk factors, potential resonance frequencies, and hoisting stability factors, the potential risk level is effectively determined, and iterative optimization is performed in the initial solution space to obtain a compensation strategy that minimizes resonance risk.

[0040] Furthermore, the resonance analysis module M500 is also used to perform the following methods: Determine a compensation strategy to minimize resonance risk; at the same time, conduct a risk level assessment and identify key risk factors.

[0041] The process involves iteratively searching the initial solution space, evaluating each adjustment scheme using an evaluation function, and selecting the optimal solution based on the evaluation results. This also includes selecting the optimal solution from all adjustment schemes obtained during the iterative optimization process—that is, the adjustment scheme that maximizes the reduction of resonance risk. Based on the optimal solution, specific compensation strategies are formulated, including adjusting the hoisting speed, changing the hoisting angle, and adding temporary supports. Furthermore, sensitivity analysis is performed on the risk factors to assess the degree of influence of each factor on the resonance risk. Based on the sensitivity analysis results, the key risk factors with the greatest impact on the resonance risk are identified.

[0042] During the hoisting process, the established compensation strategy is implemented and the changes in resonance risk are continuously monitored. Based on the feedback data during the implementation process, the compensation strategy is adjusted and optimized as necessary to improve its effectiveness and practicality. The compensation strategy that minimizes resonance risk is systematically determined, and the key factors affecting resonance risk are identified, so as to provide strong protection for the safety and stability of the hoisting process.

[0043] Furthermore, the resonance analysis module M500 is also used to perform the following methods: Based on the vibration data in the dynamic parameters, the vibration curve of the hoisting process is fitted, and the vibration time-domain characteristics and vibration frequency-domain characteristics are obtained using time-frequency domain analysis. Based on the vibration time-domain characteristics and vibration frequency-domain characteristics, fuzzy quantization is performed, and risk level judgment rules are configured in combination with the key risk factors. According to the risk level judgment rules, for abnormal peak values, the potential risk level is determined based on the hoisting risk factors and the potential resonance frequency.

[0044] Using vibration data based on the dynamic parameters, appropriate mathematical methods (such as polynomial fitting, Fourier series, etc.) are employed to fit the vibration curve of the hoisting process, aiming to more accurately describe the vibration mode during hoisting. Time-frequency domain analysis is then performed on the fitted vibration curve. Time-domain analysis provides the temporal variation characteristics of the vibration signal, such as mean, variance, kurtosis, and peak value. Frequency-domain analysis provides the frequency components and distribution information of the vibration signal, such as peak frequency, average frequency, and energy spectral density. Key vibration time-domain and frequency-domain features are extracted from the results of the time-frequency domain analysis.

[0045] Preferably, fuzzy mathematics is used to perform fuzzy quantization processing on the vibration time-domain and frequency-domain characteristics. The purpose is to transform specific numerical data into a more general and comparable fuzzy set, so as to conduct more flexible and accurate risk assessment. Combining key risk factors (such as equipment failure risk, operational risk, environmental risk, etc.) and the fuzzily quantized vibration characteristics, risk level judgment rules are configured. These rules will be used to determine the potential risk level of the hoisting process. Abnormal peaks are detected in the vibration data, indicating potential instability or resonance risks. For the detected abnormal peaks, the risk level judgment rules are applied in combination with hoisting risk factors and potential resonance frequencies to determine the potential risk level of the hoisting process.

[0046] In practical applications, a risk assessment is conducted on the hoisting process based on the determined potential risk level, and corresponding countermeasures are formulated. For high-risk situations, an emergency shutdown is required; for medium- and low-risk situations, emergency measures can be formulated to prevent the occurrence of potential risks. Based on hoisting risk factors and potential resonant frequencies, combined with time-frequency domain analysis and fuzzy quantization methods, the potential risk level of the hoisting process is determined, and corresponding risk assessments and countermeasures are taken accordingly.

[0047] Furthermore, the risk warning module M600 is also used to perform the following methods: For the potential resonance distribution point set corresponding to the hoisting steel structural component, the hoisting traction vector is used as the excitation variable to establish a search space; the potential resonance frequency is used as the response penalty variable to construct a response penalty term; and compensation optimization is performed based on the search space and the response penalty term.

[0048] Modal analysis or experimental testing of the steel structural components used for hoisting is conducted to determine the potential resonant frequencies and corresponding resonant distribution point sets, which typically represent the locations where the steel structural components experience significant vibration at specific frequencies. The hoisting traction vector is used as the excitation variable because the hoisting traction vector (i.e., the traction force and direction exerted by the hoisting equipment on the steel structural components) directly affects the vibration response of the steel structural components. Based on the variation range of the hoisting traction vector, a search space is established, which contains multiple combinations of hoisting traction vectors for subsequent optimization searches.

[0049] The potential resonance frequency is used as a response penalty variable. When the vibration frequency of the steel structure used for hoisting approaches or reaches these potential resonance frequencies during the hoisting process, it will be subject to a greater penalty to reflect the magnitude of the resonance risk. A response penalty term is constructed to quantify the relationship between the hoisting traction vector and the potential resonance frequency. Specifically, the penalty coefficient can be set according to the degree of closeness between the vibration response of the steel structure used for hoisting caused by the hoisting traction vector and the potential resonance frequency.

[0050] Based on the established search space and the constructed response penalty term, optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) are used for iterative search. The goal of the optimization algorithm is to find an optimal combination of hoisting traction vectors so that the vibration response of the steel structural components used for hoisting is as far away from the potential resonant frequency as possible, i.e., minimizing the response penalty term. During the iteration process, the algorithm continuously evaluates the response penalty value under the current hoisting traction vector combination and adjusts the search direction and step size according to the evaluation results until the optimal solution is found or the termination condition is met.

[0051] In practical applications, the optimization results are verified to ensure that the vibration response of the steel structural components used for hoisting is indeed far from the potential resonance frequency during actual hoisting. If the verification results are not ideal, the search space, response penalty term, or optimization algorithm parameters can be adjusted, and the optimization search can be performed again. Furthermore, the optimized hoisting traction vector combination is used as a guide for actual hoisting operations to ensure that the vibration response of the steel structural components used for hoisting is effectively controlled during the hoisting process.

[0052] Preferably, for the potential resonance distribution point set corresponding to the steel structural components used for hoisting, compensation optimization is performed in combination with the hoisting traction vector to reduce the resonance risk during the hoisting process. Combining modal analysis, search space construction, response penalty term design and optimization algorithm, it has high feasibility and practicality.

[0053] In summary, the beneficial effects of the embodiments of this application are: By integrating dynamic parameter acquisition, hoisting equipment monitoring, and data analysis technologies, the health status of steel structural components used for hoisting is monitored in real time. Risk assessments are conducted based on hoisting operation parameters and dynamic parameters, and timely warnings are issued, significantly improving operational safety. 2. By introducing a resonance analysis model, potential resonance distribution points can be effectively identified, and compensation and optimization measures can be taken to avoid or mitigate the effects of resonance and protect the structure from damage.

[0054] 3. Based on risk level and compensation strategy optimization iteration, dynamically adjust the hoisting plan and optimize the operation process.

[0055] 4. By monitoring and issuing early warnings in real time, we can respond and adjust construction strategies more quickly, ensuring construction progress while guaranteeing project quality, and improving overall construction efficiency and quality management.

[0056] 5. By employing a time series analysis based on the hoisting operation parameter set, key frequency components were extracted; modal analysis was performed on the steel structural components used for hoisting to determine natural frequencies and modal shapes, and a modal matrix was established; based on the modal matrix and the key frequency components corresponding to the hoisting process, potential resonance frequencies were determined. Based on hoisting risk factors, hoisting stability factors, and resonance analysis models, the potential resonance distribution point set of the steel structural components used for hoisting during the hoisting process was effectively identified, providing support for reducing resonance risk. Example

[0057] Based on the same inventive concept as the dynamic testing and early warning device for hoisting steel structural components in the foregoing embodiments, such as Figure 3 As shown in the figure, this application provides a dynamic testing and early warning method for hoisting steel structural components, wherein the method includes: Based on the steel structural components used for hoisting, dynamic parameters of the hoisting process are collected, including stress data, strain data, and vibration data. The hoisting equipment corresponding to the steel structural components is connected, and a set of hoisting operation parameters is collected, including equipment operating status, hoisting path, and force information at the hoisting points. A hoisting traction vector is set based on the hoisting speed in the equipment operating status and the hoisting displacement in the hoisting path. A risk assessment is performed using the hoisting operation parameter set and the dynamic parameters, and a hoisting risk factor is extracted. A stability assessment is performed using the hoisting traction vector and the dynamic parameters, and a hoisting stability factor is extracted. Based on the hoisting risk factor and the hoisting stability factor, a resonance analysis model is introduced to identify the potential resonance distribution point set corresponding to the steel structural components. For the potential resonance distribution point set corresponding to the steel structural components, compensation optimization is performed using the hoisting traction vector, and a resonance risk early warning mechanism is established.

[0058] Furthermore, by connecting the hoisting equipment corresponding to the hoisting steel structure and collecting a set of hoisting operation parameters, the method of this application includes: Using a gyroscope sensor, angular velocity data of the hoisting equipment is collected; based on the hoisting speed during the equipment's operation, real-time acceleration is calculated; using the angular velocity data and the real-time acceleration, a digital signal processing algorithm is employed to determine smooth acceleration; based on the smooth acceleration, real-time acceleration, and angular velocity data, time series analysis is performed to determine a time-synchronized set of hoisting operation parameters.

[0059] Furthermore, based on the hoisting risk factor and the hoisting stability factor, a resonance analysis model is introduced to identify the potential resonance distribution point set corresponding to the hoisting steel structural components. The method of this application also includes: Based on the time series corresponding to the hoisting operation parameter set, key frequency components are extracted; modal analysis is performed on the steel structural components used for hoisting to determine the natural frequencies and modal shapes, and a modal matrix is ​​established; based on the modal matrix and the key frequency components corresponding to the hoisting process, the potential resonant frequencies are determined.

[0060] Furthermore, the method of this application also includes: Based on the hoisting risk factor and the potential resonance frequency, the potential risk level is determined; based on the hoisting stability factor, an initial solution space is established; based on the potential risk level, an evaluation function is set, and iterative optimization is performed in the initial solution space to obtain a compensation strategy that minimizes resonance risk.

[0061] Furthermore, the method of this application also includes: Determine a compensation strategy to minimize resonance risk; at the same time, conduct a risk level assessment and identify key risk factors.

[0062] Furthermore, based on the lifting risk factor and the potential resonance frequency, the potential risk level is determined. The method of this application includes: Based on the vibration data in the dynamic parameters, the vibration curve of the hoisting process is fitted, and the vibration time-domain characteristics and vibration frequency-domain characteristics are obtained using time-frequency domain analysis. Based on the vibration time-domain characteristics and vibration frequency-domain characteristics, fuzzy quantization is performed, and risk level judgment rules are configured in combination with the key risk factors. According to the risk level judgment rules, for abnormal peak values, the potential risk level is determined based on the hoisting risk factors and the potential resonance frequency.

[0063] Furthermore, for the potential resonance distribution point set corresponding to the hoisting steel structural component, compensation and optimization are performed in conjunction with the hoisting traction vector. The method of this application includes: For the potential resonance distribution point set corresponding to the hoisting steel structural component, the hoisting traction vector is used as the excitation variable to establish a search space; the potential resonance frequency is used as the response penalty variable to construct a response penalty term; and compensation optimization is performed based on the search space and the response penalty term.

[0064] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0065] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A dynamic testing and early warning device for steel structural components used in hoisting, characterized in that, The device includes: The data acquisition module is used to collect dynamic parameters of the hoisting process based on the steel structural components used for hoisting. The dynamic parameters include stress data, strain data, and vibration data. The operation parameter collection module is used to connect to the hoisting equipment corresponding to the hoisting steel structure component and collect the hoisting operation parameter set. The hoisting operation index corresponding to the hoisting operation parameter set includes equipment operation status, hoisting path, and hoisting point force information. The traction vector setting module is used to set the hoisting traction vector based on the hoisting speed and the hoisting displacement in the hoisting path during the equipment's operation. The feature evaluation module is used to perform risk assessment and extract lifting risk factors by combining the lifting operation parameter set with the dynamic parameters; and to perform stability assessment and extract lifting stability factors by combining the lifting traction vector with the dynamic parameters. The resonance analysis module is used to introduce a resonance analysis model based on the hoisting risk factor and the hoisting stability factor to identify the potential resonance distribution point set corresponding to the hoisting steel structural components. The resonance analysis module further includes: extracting key frequency components based on the time series corresponding to the hoisting operation parameter set; performing modal analysis on the hoisting steel structural components to determine the natural frequencies and modal shapes, and establishing a modal matrix; determining potential resonance frequencies based on the modal matrix and the key frequency components corresponding to the hoisting process; determining potential risk levels based on the hoisting risk factor and the potential resonance frequencies; establishing an initial solution space based on the hoisting stability factor; and setting an evaluation function based on the potential risk level, performing iterative optimization in the initial solution space to obtain a compensation strategy that minimizes resonance risk. The risk warning module is used to compensate and optimize the potential resonance distribution point set corresponding to the hoisting steel structural components in combination with the hoisting traction vector, and to establish a resonance risk warning mechanism.

2. The dynamic testing and early warning device for hoisting steel structural components as described in claim 1, characterized in that, The operating parameter collection module includes: The angular velocity data of the hoisting equipment is collected using a gyroscope sensor. Calculate the real-time acceleration based on the hoisting speed of the equipment in its operating state; Using the angular velocity data and the real-time acceleration, a digital signal processing algorithm is employed to determine the smooth acceleration. Based on the smooth acceleration and real-time acceleration and angular velocity data, time series analysis is performed to determine the set of hoisting operation parameters for time synchronization.

3. The dynamic testing and early warning device for hoisting steel structural components as described in claim 1, characterized in that, Determine a compensation strategy that minimizes resonance risk; At the same time, risk level assessments are conducted to identify key risk factors.

4. The dynamic testing and early warning device for hoisting steel structural components as described in claim 3, characterized in that, The resonance analysis module also includes: Based on the vibration data in the dynamic parameters, the vibration curve of the hoisting process is fitted, and the vibration time-domain characteristics and vibration frequency-domain characteristics are obtained by using time-frequency domain analysis. Based on the vibration time-domain characteristics and vibration frequency-domain characteristics, fuzzy quantization is performed, and risk level judgment rules are configured in conjunction with the key risk factors. Based on the aforementioned risk level judgment rules, for abnormal peak values, the potential risk level is determined by combining the lifting risk factor with the potential resonance frequency.

5. The dynamic testing and early warning device for hoisting steel structural components as described in claim 1, characterized in that, The risk warning module also includes: For the potential resonance distribution point set corresponding to the hoisting steel structural component, the hoisting traction vector is used as the excitation variable to establish a search space; The potential resonant frequency is used as the response penalty variable to construct the response penalty term; Compensation optimization is performed based on the search space and the response penalty term.

6. A dynamic testing and early warning method for steel structural components used in hoisting, characterized in that, The method includes: Based on the steel structural components used for hoisting, dynamic parameters of the hoisting process are collected, including stress data, strain data, and vibration data. Connect the hoisting equipment corresponding to the hoisting steel structure component, and collect a set of hoisting operation parameters, which includes equipment operating status, hoisting path, and force information at the hoisting point. The hoisting traction vector is set based on the hoisting speed during the equipment's operation and the hoisting displacement along the hoisting path. Risk assessment is performed using the set of hoisting operation parameters and the dynamic parameters, and hoisting risk factors are extracted; stability assessment is performed using the hoisting traction vector and the dynamic parameters, and hoisting stability factors are extracted. Based on the lifting risk factor and the lifting stability factor, a resonance analysis model is introduced to identify the potential resonance distribution point set corresponding to the lifting steel structural components. Based on the time series corresponding to the lifting operation parameter set, key frequency components are extracted. Modal analysis is performed on the lifting steel structural components to determine the natural frequencies and modal shapes, and a modal matrix is ​​established. Based on the modal matrix and the key frequency components corresponding to the lifting process, potential resonance frequencies are determined. Based on the lifting risk factor and the potential resonance frequencies, the potential risk level is determined. Based on the lifting stability factor, an initial solution space is established. Based on the potential risk level, an evaluation function is set, and iterative optimization is performed in the initial solution space to obtain a compensation strategy that minimizes resonance risk. For the potential resonance distribution point set corresponding to the hoisting steel structural components, compensation optimization is performed in conjunction with the hoisting traction vector, and a resonance risk early warning mechanism is established.

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