Dynamic test early warning device and method for steel structural member for hoisting

Abstract

The invention relates to the related technical field of hoisting of steel structural parts, in particular to a dynamic testing and early warning device and method for a hoisting steel structural part, and the method comprises the steps that a data acquisition module monitors hoisting dynamic parameters, and an operation parameter collection module collects equipment information; the traction vector setting module sets a traction vector, and the feature evaluation module evaluates risk and stability; the resonance analysis module identifies potential resonance points, and the risk early warning module optimizes traction and establishes a resonance risk early warning mechanism, so that the technical problem that real-time and comprehensive structural health state evaluation is difficult to realize through regular inspection is solved, and continuous and real-time monitoring on dynamic parameters such as stress, strain and vibration in the hoisting process and the state of hoisting equipment is realized; potential resonance distribution points of the structure are identified through an advanced algorithm, a resonance risk early warning mechanism is established, and structural damage caused by resonance is prevented.

Description

Technical Field

[0001] The present invention relates to the technical field related to the hoisting of steel structural parts, and in particular to a dynamic testing early warning device and method for hoisting steel structural parts. Background Art

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

[0003] During the hoisting process, steel structures used for hoisting are subjected to abnormal stress, deformation or vibration due to improper operation, environmental factors or their own defects, which in turn causes structural damage or even safety accidents. The conventional detection method is regular inspection, which is difficult to achieve real-time and comprehensive structural health status assessment, especially in the complex and changeable construction site environment.

[0004] In summary, there is a technical problem in the prior art that regular inspections are difficult to achieve real-time and comprehensive structural health status assessment. Summary of the invention

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

[0006] In view of the above problems, the technical solution to implement this application is: On the one hand, the present application provides a dynamic test and early warning device for steel structural parts for hoisting, wherein the device includes: a data acquisition module, which is used to collect dynamic parameters of the hoisting process based on the steel structural parts for hoisting, and the dynamic parameters include stress data, strain data, and vibration data; an operation parameter collection module, which is used to connect the hoisting equipment corresponding to the steel structural parts for hoisting, and collect the hoisting operation parameter set, and the hoisting operation indicators corresponding to the hoisting operation parameter set include equipment operation status, hoisting path, and hoisting point force information; a traction vector setting module, which is used to collect the hoisting operation speed in the equipment operation status, the hoisting position in the hoisting path, and the hoisting speed of the equipment in the operation status. shift, set the lifting traction vector; a feature evaluation module, used to perform risk assessment through the lifting operation parameter set in combination with the dynamic parameters, and extract the lifting risk factor; through the lifting traction vector, combined with the dynamic parameters, perform stability assessment, and extract the lifting stability factor; a resonance analysis module, used to introduce a resonance analysis model based on the lifting risk factor and the lifting stability factor, and identify the potential resonance distribution point set corresponding to the lifting steel structure; a risk warning module, used to perform compensation optimization for the potential resonance distribution point set corresponding to the lifting steel structure in combination with the lifting traction vector, and establish a resonance risk warning mechanism.

[0007] On the other hand, the present application provides a dynamic test and early warning method for steel structures for lifting, wherein the method comprises: based on the steel structures for lifting, collecting dynamic parameters of the lifting process, the dynamic parameters including stress data, strain data, and vibration data; connecting the lifting equipment corresponding to the steel structures for lifting, collecting a lifting operation parameter set, the lifting operation parameter set including equipment operating status, lifting path, and lifting point force information; setting a lifting traction vector according to the lifting operation speed in the equipment operating status and the lifting displacement in the lifting path; performing risk assessment through the lifting operation parameter set in combination with the dynamic parameters, and extracting lifting risk factors; performing stability assessment through the lifting traction vector in combination with the dynamic parameters, and extracting lifting stability factors; introducing a resonance analysis model based on the lifting risk factor and the lifting stability factor to identify a potential resonance distribution point set corresponding to the steel structures for lifting; performing compensation optimization for the potential resonance distribution point set corresponding to the steel structures for lifting in combination with the lifting 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 regular inspections, and realize continuous and real-time monitoring of dynamic parameters such as stress, strain, vibration and the status of lifting equipment during the lifting process. It identifies the potential resonance distribution points of the structure through advanced algorithms, and establishes a resonance risk warning mechanism, thereby achieving the technical effect of preventing structural damage caused by resonance. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A structural diagram of a dynamic test warning device for hoisting steel structural parts is provided for this application Figure 2 A schematic diagram of a flow chart of determining a timing-synchronized hoisting operation parameter set of an operation parameter collection module in a dynamic test and early warning device for hoisting steel structural parts is provided for this application; Figure 3 A flow chart of a dynamic testing and early warning method for a steel structure for hoisting is provided for this application.

[0010] Explanation of reference numerals: data acquisition module M100, operation parameter collection module M200, traction vector setting module M300, characteristic evaluation module M400, resonance analysis module M500, risk warning module M600. DETAILED DESCRIPTION

[0011] Embodiment 1 The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the embodiment of the present application provides a dynamic test warning device for a steel structure for hoisting, wherein the device comprises: The data acquisition module M100 is used to collect dynamic parameters of the lifting process based on the steel structural parts for lifting, and the dynamic parameters include stress data, strain data, and vibration data; the operation parameter collection module M200 is used to connect the lifting equipment corresponding to the steel structural parts for lifting, and collect the lifting operation parameter set. The lifting operation indicators corresponding to the lifting operation parameter set include equipment operation status, lifting path, and lifting point force information.

[0012] Common, regular inspections of steel structures for lifting are performed, for example, through physical measurement, for example, using tools such as vernier calipers and ultrasonic thickness gauges to measure the size and thickness of steel, and theodolites and total stations to measure the geometric deviations of structures, such as verticality and deflection; and conducting comparative analysis in combination with historical data and maintenance records.

[0013] In actual application, it was found that regular inspection of steel structures for lifting is difficult to effectively reduce or eliminate risks without affecting the construction progress. Based on this, this application integrates dynamic parameter collection, lifting equipment monitoring and data analysis technology to monitor the health status of steel structures for lifting in real time, issue early warnings in time, and significantly improve operation safety.

[0014] Select appropriate stress sensors, strain sensors, and vibration sensors to measure the stress data, strain data, and vibration data of steel structures for hoisting; install the above sensors at key locations of steel structures for hoisting to ensure that the sensors can accurately capture dynamic changes during the hoisting process; connect the sensors to the data acquisition module via wired or wireless means to ensure that data can be received and transmitted in real time; calibrate the sensors before hoisting begins to ensure the accuracy of the measurement data.

[0015] Before the lifting begins, the data acquisition module is started to receive and store the data sent by the sensor in real time; during the lifting process, the sensor measures the stress data, strain data and vibration data of the steel structure used for lifting in real time, and transmits the data to the data acquisition module; the data acquisition module records all received data, including timestamp, sensor number, measurement value, etc., for subsequent analysis.

[0016] The lifting equipment corresponding to the lifting steel structure is connected by wired or wireless means to ensure that the operating status, lifting path and force information of the lifting point of the lifting equipment can be received in real time; the lifting equipment sends its operating status data in real time, such as start, stop, acceleration, deceleration, etc.; the GPS module is used to record the path information during the lifting process, including the lifting point, path length, direction, etc.; during the lifting process, the force sensor or other measuring equipment is used to measure the force condition of the lifting point; the operating status, lifting path and force information of the lifting point of the lifting equipment are integrated into a lifting 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 through the hoisting operation speed in the equipment operation state and the hoisting displacement in the hoisting path; the feature evaluation module M400 is used to perform risk evaluation through the hoisting operation parameter set in combination with the dynamic parameters, and extract the hoisting risk factor; through the hoisting traction vector in combination with the dynamic parameters, perform stability evaluation and extract the hoisting stability factor.

[0018] The hoisting operation status, including the hoisting operation speed, is obtained from the hoisting equipment in real time; the hoisting path information, including the hoisting displacement, is obtained from the positioning system or GPS module of the hoisting equipment; further, the hoisting traction vector is calculated based on the hoisting speed and hoisting displacement, combined with the design parameters of the hoisting equipment (such as maximum load capacity, hoisting equipment type, etc.); the hoisting traction vector includes elements such as direction, size and speed, which are used to guide the movement direction and speed of the hoisting steel structure during the hoisting process; in the actual application process, it also includes necessary adjustments to the hoisting traction vector according to actual hoisting conditions, such as wind force, the center of gravity position of the hoisting steel structure, and other factors.

[0019] Integrate the lifting operation parameter set (including equipment operation status, lifting path, lifting point force information, etc.) with dynamic parameters (stress data, strain data, vibration data); use risk assessment models (such as models based on machine learning or statistics) to process the integrated data, and analyze the potential risks in the lifting process based on historical data and expert experience, such as overload, excessive vibration, stress concentration, etc.; in a feasible implementation method, extract the lifting risk factor from the results of the risk assessment model, which is usually related to the unsafe factors in the lifting process; the lifting risk factor can be a quantitative value (such as overload ratio, vibration amplitude) or a qualitative description (such as stress concentration area).

[0020] The hoisting process is analyzed using a stability assessment model (such as a model based on structural mechanics). Factors such as the hoisting traction vector, the stiffness of the steel structure used for hoisting, and the stress conditions at the hoisting point are considered to evaluate the stability of the hoisting process. The hoisting stability factor is extracted from the results of the stability assessment model. The hoisting stability factor reflects the stability performance of the structure during the hoisting process. The hoisting stability factor can be an indicator such as structural deformation and uniformity of stress distribution.

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

[0022] Collect the design parameters of steel structures for lifting (such as geometric shape, stiffness and mass distribution), lifting operation parameter sets (including equipment operating status, lifting path, lifting point force information, etc.) and dynamic parameters (stress data, strain data, vibration data); introduce resonance analysis models, such as modal analysis or frequency response analysis. Modal analysis can provide the natural frequency and corresponding vibration mode of the structure, and frequency response analysis can evaluate the response of the structure under different excitation frequencies; input the design parameters of steel structures for lifting, lifting operation parameter sets and dynamic parameters into the resonance analysis model.

[0023] Through the resonance analysis model, the natural frequency of the steel structure used for lifting is calculated to understand its vibration characteristics without external force; according to the lifting operation parameter set and the lifting traction vector, the external excitation frequency generated during the lifting process, such as the vibration frequency of the lifting equipment, wind force, etc., is analyzed; the natural frequency of the steel structure used for lifting is compared with the external excitation frequency to find out the frequency range that produces resonance, and determine the corresponding potential resonance distribution point set; based on the lifting risk factor and the lifting stability factor, the risks brought by the potential resonance distribution point set are evaluated, such as structural fatigue, damage, etc.; according to the evaluation results, a compensation optimization plan for the potential resonance distribution point set is formulated, and the optimization measures include adjusting the lifting traction vector, adding temporary support, changing the lifting speed, etc.; according to the optimization plan, the relevant parameters and measures of the lifting process are adjusted to reduce the resonance risk.

[0024] According to the resonance analysis model and risk assessment results, set the early warning threshold of resonance risk, such as specific frequency range, vibration amplitude, etc.; during the lifting process, monitor the vibration data of the steel structure used for lifting in real time and compare it with the early warning threshold; when the monitored vibration data exceeds the early warning threshold, trigger the resonance risk early warning mechanism and issue an alarm; according to the alarm information, take corresponding emergency response measures, such as suspending lifting, adjusting lifting parameters, etc., to reduce the resonance risk.

[0025] Based on the hoisting risk factor, hoisting stability factor and resonance analysis model, the potential resonance distribution point set of the steel structure for hoisting is effectively identified and compensation optimization is carried out, and a resonance risk early warning mechanism is established, thereby reducing the resonance risk during the hoisting process and ensuring the safe and smooth progress of the hoisting operation.

[0026] Furthermore, if Figure 2 As shown, the operating parameter collection module M200 is used to execute the following method: The gyroscope sensor is used to collect the angular velocity data of the hoisting equipment; the real-time acceleration is calculated based on the hoisting operation speed in the operation state of the equipment; the smooth acceleration is determined by a digital signal processing algorithm through the angular velocity data and the real-time acceleration; and a time series analysis is performed based on the smooth acceleration, the real-time acceleration and the angular velocity data to determine a time-series synchronized hoisting operation parameter set.

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

[0028] Obtain the real-time hoisting speed from the control system or sensor of the hoisting equipment; calculate the real-time acceleration through physical formulas based on the hoisting speed and the basic physical parameters of the equipment (such as mass, inertia, etc.); fuse the angular velocity data collected by the gyroscope sensor and the calculated real-time acceleration; use digital signal processing algorithms (such as Kalman filtering, sliding average filtering, etc.) to process the fused data to eliminate noise and interference and obtain smooth acceleration data.

[0029] Ensure that the smooth acceleration, real-time acceleration and angular velocity data are synchronized in time, that is, the data correspond to the same time point; perform time series analysis on the synchronized data, including trend analysis, periodicity analysis, correlation analysis, etc., to obtain the dynamic characteristics of the hoisting operation process; 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, realize the connection of the hoisting equipment corresponding to the hoisting steel structure and the collection of the hoisting operation parameter set, and provide 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 method: Based on the time series corresponding to the hoisting operation parameter set, key frequency components are extracted; modal analysis is performed on the steel structure for hoisting to determine the natural frequency and modal shape, and a modal matrix is ​​established; based on the modal matrix, combined with the key frequency components corresponding to the hoisting process, the potential resonant frequency is determined.

[0031] Based on the time series corresponding to the lifting operation parameter set, organize and prepare the data for frequency analysis; use fast Fourier transform (FFT) or other frequency domain analysis techniques to process the time series data and extract key frequency components, including the vibration frequency generated by the lifting equipment operation, external excitation frequency, etc.

[0032] According to the design drawings, material properties, boundary conditions and other information of the steel structure for hoisting, the structural model is established using finite element analysis (FEA) software or manual calculation methods; modal analysis is performed on the structural model of the steel structure for hoisting to determine its natural frequency and modal shape. The natural frequency is the frequency of the structure when it is in undamped free vibration, and the modal shape describes the vibration form of the structure at a certain natural frequency; different natural frequencies and corresponding modal shapes are integrated into a modal matrix, which will be used to determine the subsequent resonant frequency.

[0033] The key frequency components corresponding to the lifting process are compared with the natural frequencies of the steel structures used for lifting. If a key frequency component is close to or equal to a natural frequency (within a certain error range), it is considered that there is a potential resonance risk for the steel structures used for lifting at this frequency. Combined with the modal shape, determine at which positions or nodes the vibration response of the steel structures used for lifting is most significant, and the corresponding positions are the potential resonance distribution point sets. In actual application, it also includes risk assessment of the lifting process based on the determined potential resonance frequency and resonance distribution point set, and assessment of structural damage, fatigue and other problems caused by resonance. According to the risk assessment results, formulate corresponding optimization measures, such as adjusting the lifting speed, adding temporary support, changing the lifting path, etc., to reduce the resonance risk. During the lifting process, the resonance situation is monitored in real time, and the optimization measures are continuously optimized and adjusted based on the feedback data.

[0034] Preferably, the resonance analysis data, risk assessment results and optimization measures of each hoisting process are recorded 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 improve the accuracy and effectiveness of the analysis. Based on the hoisting risk factor, hoisting stability factor and resonance analysis model, the potential resonance distribution point set of hoisting steel structural parts during the hoisting process is effectively identified, and support is provided for reducing the resonance risk.

[0035] Furthermore, the resonance analysis module M500 is also used to perform the following method: Based on the lifting risk factor and in combination with the potential resonance frequency, 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 the resonance risk.

[0036] Based on various hoisting risk factors in the hoisting process, such as the performance of hoisting equipment, the characteristics of steel structures used for hoisting, environmental factors, etc.; the hoisting risk factors are combined with the potential resonance frequency to evaluate the consequences of resonance, such as structural damage, fatigue, etc.; according to the severity of the resonance risk and the consequences caused, the potential risks are divided into different levels, such as low risk, medium risk, high risk, etc.

[0037] Analyze the hoisting stability factors, such as the stability of the hoisting equipment, the support conditions of the steel structures used for hoisting, etc. The hoisting stability factors affect the resonance risk during the hoisting process; based on the hoisting stability factors, determine the optimization variables that can be used to adjust to reduce the resonance risk, such as hoisting speed, hoisting angle, location and number of temporary supports, etc.; set a reasonable value range for each optimization variable to form an initial solution space.

[0038] Based on the potential risk level, define evaluation indicators to measure the degree of reduction of resonance risk by different adjustment schemes, including the offset of resonance frequency, the reduction of structural response, etc.; integrate the evaluation indicators into an evaluation function, which can quantify the effect of different adjustment schemes on reducing resonance risk; randomly select adjustment schemes as starting points from the initial solution space; use the evaluation function to evaluate the current adjustment scheme to obtain its degree of reduction of resonance risk; according to the evaluation results, select the optimization direction that can further reduce the resonance risk, such as adjusting the hoisting speed, changing the hoisting angle, etc.; search in the optimization direction, update the solution space, and generate a new adjustment scheme; set the iterative termination conditions, such as reaching the maximum number of iterations, the change in the solution is less than the set threshold, etc. When the termination conditions are met, stop the iteration; select the optimal solution from all the adjustment schemes obtained in the iterative optimization process, that is, the adjustment scheme that can maximize the reduction of resonance risk; based on the optimal solution, formulate a specific compensation strategy, including adjusting the hoisting parameters, adding temporary support, etc.

[0039] The actual application process also includes implementing the formulated compensation strategy during the lifting process, observing and recording its effect on reducing the resonance risk; further optimizing the compensation strategy based on the feedback data during the implementation process to improve its effectiveness and practicality; effectively determining the potential risk level based on the lifting risk factor, potential resonance frequency and lifting stability factor, and performing iterative optimization in the initial solution space to obtain a compensation strategy that minimizes the resonance risk.

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

[0041] An iterative search is performed in the initial solution space, and an evaluation function is used to evaluate each adjustment plan. The optimal solution is selected based on the evaluation results. This also includes selecting the optimal solution from all adjustment plans obtained during the iterative optimization process, that is, the adjustment plan that can minimize the risk of resonance. Based on the optimal solution, a specific compensation strategy is formulated, including adjusting the lifting speed, changing the lifting angle, adding temporary support, etc. Furthermore, a sensitivity analysis is performed on the risk factors to evaluate the impact of each factor on the resonance risk. Based on the results of the sensitivity analysis, the key risk factors that have the greatest impact on the resonance risk are determined.

[0042] Implement the formulated compensation strategy during the lifting process and continuously monitor changes in resonance risks. According to the feedback data during the implementation process, make necessary adjustments and optimizations to the compensation strategy to improve its effectiveness and practicality, systematically determine the compensation strategy that minimizes resonance risks, and identify the key factors that affect resonance risks, to provide strong protection for the safety and stability of the lifting process.

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

[0044] The vibration data based on the dynamic parameters are used to fit the vibration curve of the lifting process by using appropriate mathematical methods (such as polynomial fitting, Fourier series, etc.), in order to more accurately describe the vibration mode in the lifting process; the fitted vibration curve is analyzed in the time and frequency domains, and the time domain analysis can provide the temporal variation characteristics of the vibration signal, such as mean, variance, kurtosis, peak value, etc.; the frequency domain analysis can provide the frequency components and distribution information of the vibration signal, such as peak frequency, average frequency, energy spectrum density, etc.; from the results of the time and frequency domain analysis, the key vibration time domain characteristics and frequency domain characteristics are extracted.

[0045] Preferably, fuzzy mathematics methods are used to perform fuzzy quantization processing on the vibration time domain characteristics and frequency domain characteristics, with the aim of converting specific numerical data into more general and comparable fuzzy sets to facilitate more flexible and accurate risk assessment; risk level judgment rules are configured in combination with key risk factors (such as equipment failure risk, operational risk, environmental risk, etc.) and fuzzy quantized vibration characteristics, and the risk level judgment rules will be used to determine the potential risk level of the lifting 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 the lifting risk factor and the potential resonance frequency to determine the potential risk level of the lifting process.

[0046] In the actual application process, according to the determined potential risk level, the hoisting process is risk assessed and corresponding countermeasures are formulated. For high-risk situations, emergency shutdown is required; for medium and low risk situations, emergency measures can be formulated to prevent the occurrence of potential risks. Based on the hoisting risk factor and potential resonance frequency, combined with time-frequency domain analysis and fuzzy quantification methods, the potential risk level of the hoisting process is determined, and corresponding risk assessment and countermeasures are taken accordingly.

[0047] Furthermore, the risk warning module M600 is also used to execute the following method: For the potential resonance distribution point set corresponding to the lifting steel structure, the lifting traction vector is used as an excitation variable to establish a search space; the potential resonance frequency is used as a 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] By performing modal analysis or experimental testing on the steel structure for hoisting, the potential resonant frequency and the corresponding resonant distribution point set are determined, which usually indicates the location where the steel structure for hoisting has a large vibration at a specific frequency; the hoisting traction vector is used as the excitation variable, because the hoisting traction vector (that is, the traction force and direction of the hoisting equipment on the steel structure for hoisting) directly affects the vibration response of the steel structure for hoisting; based on the variation range of the hoisting traction vector, a search space is established, which contains multiple hoisting traction vector combinations for subsequent optimization search.

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

[0050] Based on the established search space and the constructed response penalty term, an optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.) is used for iterative search; the goal of the optimization algorithm is to find a set of optimal lifting and traction vector combinations so that the vibration response of the lifting steel structure during the lifting process is as far away from the potential resonance frequency as possible, that is, to minimize the response penalty term; during the iterative process, the algorithm will continuously evaluate the response penalty value under the current lifting and traction vector combination, and adjust 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 the actual application process, it also includes verifying the optimization results to ensure that the vibration response of the steel structure used for lifting is indeed far away from the potential resonance frequency during the actual lifting process; if the verification result is not ideal, the search space, response penalty item or optimization algorithm parameters can be adjusted, and the optimization search can be re-performed. Furthermore, the optimized lifting traction vector combination can be used as a guide for the actual lifting operation to ensure that the vibration response of the steel structure used for lifting during the lifting process is effectively controlled.

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

[0053] In summary, the beneficial effects of the embodiments of the present application are: 1. By integrating dynamic parameter collection, hoisting equipment monitoring and data analysis technology, the health status of steel structures used for hoisting can be monitored in real time, risk assessment can be carried out by combining hoisting operation parameters and dynamic parameters, and early warnings can be issued in time to significantly improve operation safety.

[0054] 2. By introducing the resonance analysis model, the potential resonance distribution points can be effectively identified, and compensation optimization measures can be taken to avoid or reduce the resonance impact and protect the structure from damage.

[0055] 3. Based on the optimization iteration of risk level and compensation strategy, the lifting plan can be dynamically adjusted to optimize the operation process.

[0056] 4. Through real-time monitoring and early warning, we can respond and adjust construction strategies more quickly, ensure construction progress while guaranteeing project quality, and improve overall construction efficiency and quality management level.

[0057] 5. By using the time series corresponding to the hoisting operation parameter set, the key frequency components are extracted; modal analysis is performed on the steel structure for hoisting to determine the natural frequency and modal shape, and the modal matrix is ​​established; based on the modal matrix, the potential resonance frequency is determined in combination with the key frequency components corresponding to the hoisting process. Based on the hoisting risk factor, hoisting stability factor and resonance analysis model, the potential resonance distribution point set of the steel structure for hoisting during the hoisting process is effectively identified, and support is provided for reducing the resonance risk.

[0058] Embodiment 2 Based on the same inventive concept as the dynamic test warning device for hoisting steel structure in the above-mentioned embodiment, Figure 3 As shown, an embodiment of the present application provides a dynamic test early warning method for a steel structure for hoisting, wherein the method comprises: Based on the steel structure for lifting, the dynamic parameters of the lifting process are collected, and the dynamic parameters include stress data, strain data, and vibration data; the lifting equipment corresponding to the steel structure for lifting is connected to collect the lifting operation parameter set, and the lifting operation parameter set includes the equipment operation status, lifting path, and force information of the lifting point; the lifting traction vector is set according to the lifting operation speed in the equipment operation status and the lifting displacement in the lifting path; through the lifting operation parameter set, risk assessment is performed in combination with the dynamic parameters, and the lifting risk factor is extracted; through the lifting traction vector, stability assessment is performed in combination with the dynamic parameters, and the lifting stability factor is 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 steel structure for lifting; for the potential resonance distribution point set corresponding to the steel structure for lifting, compensation optimization is performed in combination with the lifting traction vector, and a resonance risk early warning mechanism is established.

[0059] Furthermore, the hoisting equipment corresponding to the hoisting steel structure is connected to collect the hoisting operation parameter set. The application method includes: The gyroscope sensor is used to collect the angular velocity data of the hoisting equipment; the real-time acceleration is calculated based on the hoisting operation speed in the operation state of the equipment; the smooth acceleration is determined by a digital signal processing algorithm through the angular velocity data and the real-time acceleration; and a time series analysis is performed based on the smooth acceleration, the real-time acceleration and the angular velocity data to determine a time-series synchronized hoisting operation parameter set.

[0060] Furthermore, based on the hoisting risk factor and the hoisting stability factor, a resonance analysis model is introduced to identify a potential resonance distribution point set corresponding to the hoisting steel structure. The method of the present 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 structure for hoisting to determine the natural frequency and modal shape, and a modal matrix is ​​established; based on the modal matrix, combined with the key frequency components corresponding to the hoisting process, the potential resonant frequency is determined.

[0061] Furthermore, the present application method also includes: Based on the lifting risk factor and in combination with the potential resonance frequency, 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 the resonance risk.

[0062] Furthermore, the present application method also includes: Determine compensation strategies to minimize resonance risk; at the same time, conduct risk level assessment and identify key risk factors.

[0063] Furthermore, based on the hoisting risk factor and in combination with the potential resonance frequency, the potential risk level is determined. The method of the present application includes: Based on the vibration data in the dynamic parameters, the vibration curve of the lifting process is fitted, and the time-domain characteristics and frequency-domain characteristics of the vibration are obtained by using time-frequency domain analysis; fuzzy quantization is performed based on the vibration time-domain characteristics and frequency-domain characteristics, and risk level judgment rules are configured in combination with the key risk factors; according to the risk level judgment rules, for abnormal peaks, the potential risk level is determined based on the lifting risk factor and the potential resonance frequency.

[0064] Furthermore, for the potential resonance distribution point set corresponding to the hoisting steel structure, compensation optimization is performed in combination with the hoisting traction vector. The method of the present application includes: For the potential resonance distribution point set corresponding to the lifting steel structure, the lifting traction vector is used as an excitation variable to establish a search space; the potential resonance frequency is used as a response penalty variable to construct a response penalty term; and compensation optimization is performed based on the search space and the response penalty term.

[0065] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor, and no unnecessary restrictions are made here.

[0066] Furthermore, the above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present application. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technicians in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A dynamic test warning device for hoisting steel structure, characterized in that: The device comprises: A data acquisition module, used to collect dynamic parameters of the hoisting process based on the steel structure for hoisting, wherein the dynamic parameters include stress data, strain data, and vibration data; An operation parameter collection module is used to connect to the hoisting equipment corresponding to the hoisting steel structure and collect a hoisting operation parameter set, wherein the hoisting operation indicators corresponding to the hoisting operation parameter set include equipment operation status, hoisting path, and hoisting point force information; A traction vector setting module, used to set the hoisting traction vector according to the hoisting operation speed in the equipment operation state and the hoisting displacement in the hoisting path; A feature evaluation module is used to perform risk evaluation by combining the lifting operation parameter set with the dynamic parameters and extract the lifting risk factor; perform stability evaluation by combining the lifting traction vector with the dynamic parameters and extract the lifting stability factor; A resonance analysis module, used to introduce a resonance analysis model based on the hoisting risk factor and the hoisting stability factor, and identify a potential resonance distribution point set corresponding to the hoisting steel structure; The risk warning module is used to optimize the compensation for the potential resonance distribution point set corresponding to the hoisting steel structure in combination with the hoisting traction vector, and establish a resonance risk warning mechanism.

2. A dynamic test warning device for a steel structure for hoisting as claimed in claim 1, characterized in that: The operating parameter collection module includes: Using a gyro sensor to collect angular velocity data of the hoisting equipment; Calculating real-time acceleration based on the hoisting operation speed in the operation state of the equipment; Determine smooth acceleration by using a digital signal processing algorithm through the angular velocity data and the real-time acceleration; A time series analysis is performed based on the smooth acceleration and the real-time acceleration and angular velocity data to determine a time-series synchronized hoisting operation parameter set.

3. A dynamic test warning device for a steel structure for hoisting as claimed in claim 2, characterized in that: The resonance analysis module also includes: Extracting key frequency components based on the time series corresponding to the hoisting operation parameter set; Performing modal analysis on the steel structure for hoisting, determining the natural frequency and modal shape, and establishing a modal matrix; Based on the modal matrix and in combination with the key frequency components corresponding to the lifting process, the potential resonant frequency is determined.

4. A dynamic test warning device for a steel structure for hoisting as claimed in claim 3, characterized in that: The resonance analysis module also includes: Based on the hoisting risk factor and in combination with the potential resonance frequency, determining a potential risk level; Based on the hoisting stability factor, an initial solution space is established; An evaluation function is set based on the potential risk level, and iterative optimization is performed in the initial solution space to obtain a compensation strategy that minimizes the resonance risk.

5. A dynamic test warning device for a steel structure for hoisting as claimed in claim 4, characterized in that: Identify compensation strategies that minimize resonance risk; At the same time, conduct risk level assessment and identify key risk factors.

6. A dynamic test warning device for a steel structure for hoisting as claimed in claim 5, 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; Perform fuzzy quantification based on the vibration time domain characteristics and vibration frequency domain characteristics, and configure risk level judgment rules in combination with the key risk factors; According to the risk level judgment rule, for the abnormal peak value, based on the hoisting risk factor and combined with the potential resonance frequency, the potential risk level is determined.

7. A dynamic test warning device for a steel structure for hoisting as claimed in claim 3, characterized in that: The risk warning module also includes: For the potential resonance distribution point set corresponding to the hoisting steel structure, the hoisting traction vector is used as an excitation variable to establish a search space; Taking the potential resonance frequency as the response penalty variable, a response penalty term is constructed; Compensation optimization is performed based on the search space and the response penalty item.

8. A dynamic test early warning method for hoisting steel structure parts, characterized in that: The method comprises: Based on the steel structure for hoisting, dynamic parameters of the hoisting process are collected, and the dynamic parameters include stress data, strain data, and vibration data; Connecting the hoisting equipment corresponding to the hoisting steel structure to collect a hoisting operation parameter set, wherein the hoisting operation parameter set includes equipment operation status, hoisting path, and hoisting point force information; Setting a hoisting traction vector according to the hoisting operation speed in the equipment operation state and the hoisting displacement in the hoisting path; Through the hoisting operation parameter set, combined with the dynamic parameters, risk assessment is performed, and the hoisting risk factor is extracted; through the hoisting traction vector, combined with the dynamic parameters, stability assessment is performed, and the hoisting stability factor is extracted; Based on the hoisting risk factor and the hoisting stability factor, a resonance analysis model is introduced to identify a potential resonance distribution point set corresponding to the hoisting steel structure; For the potential resonance distribution point set corresponding to the hoisting steel structure, compensation optimization is performed in combination with the hoisting traction vector, and a resonance risk early warning mechanism is established.

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