Resonance adjustable inhibition method for floor plate

Abstract

The invention belongs to the technical field of shock absorption of building structure engineering, and provides an adjustable resonance suppression method for a floorboard, and the method comprises the steps: deploying a vibration sensor on the floorboard, obtaining a vibration signal in real time, and carrying out the analysis, so as to precisely recognize a dominant excitation frequency, a transmission path of vibration energy, and the contribution degree of the transmission path; the contribution degree of a transmission path and the vibration amplitude of a floor plate are taken as optimization targets, meanwhile, the parameter constraint space of the adjustable steel support is taken into consideration, and it is ensured that the generated tuning parameter combination meets the suppression requirement and meanwhile adapts to the structural bearing capacity of the adjustable steel support; driving the adjustable steel support to perform grading adjustment, obtaining feedback data after grading adjustment, and recalculating the contribution variation of the transmission path and the amplitude attenuation rate of the floor plate; and when the regulation and control effect does not reach the standard, the solution parameter of the optimization function and the analysis parameter of the vibration signal are correspondingly adjusted, so that the suppression method is ensured to have good dynamic adaptability, and the stable resonance suppression effect can be continuously maintained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building structure engineering shock absorption, and in particular to a resonance adjustable suppression method for a floor slab. BACKGROUND

[0002] As a key load-bearing and use component in building structures, the resonance problem of a floor slab directly affects the structural safety, use comfort and durability of a building. In industrial buildings and high-rise buildings, factors such as equipment operation, personnel activity and external environmental excitation can all cause the floor slab to vibrate. When the excitation frequency is close to or coincides with the natural frequency of the floor slab, resonance occurs, which causes the vibration amplitude to be amplified. Long-term effects can cause structural fatigue damage and even affect the normal operation of equipment and the health of personnel.

[0003] To solve the resonance problem of the floor slab, existing technologies have developed from passive suppression to active control. Early passive suppression technologies mainly change the natural vibration characteristics of the floor slab by increasing the structural stiffness, adding dampers and optimizing the structural arrangement, or weaken the resonance effect by consuming vibration energy. However, these technologies have inherent limitations in control and cannot adapt to dynamically changing excitation frequencies and vibration transmission. When the excitation conditions change, the suppression effect will be significantly reduced, and the flexibility and adaptability are insufficient.

[0004] With the development of active control technology, resonance is suppressed by real-time monitoring of vibration state and adjusting parameters. However, existing active control technologies still have obvious deficiencies. Most existing technologies are based on the overall vibration state of the floor slab for control and do not deeply analyze the transmission of vibration energy, resulting in a lack of targeted allocation of control resources and insufficient control of core vibration energy transmission. Existing technologies only focus on the initial generation of adjustment parameters and do not consider the attenuation of control effect caused by changes in working conditions and initial parameter deviations during the control process, making it difficult to continuously ensure that the control effect meets the standards. In existing technologies, parameter adjustment lacks gradient and targeting, which can cause strain of adjustable steel supports to exceed the standard due to excessive adjustment amplitude in a single adjustment, posing a risk to structural safety.

[0005] Based on the deficiencies of the above existing technologies, the technical problem to be solved by the present application is how to achieve precise resonance suppression, dynamic adaptation and closed-loop optimization of the floor slab in a complex environment. SUMMARY

[0006] The purpose of the present application is to overcome the deficiencies of the above existing technologies and provide a resonance adjustable suppression method for a floor slab. The method comprises: deploying vibration sensors and adjustable steel supports on the floor slab, real-time acquisition of vibration signals of the floor slab, analysis of the vibration signals to identify the dominant excitation frequency and the transmission path and contribution of the vibration energy, and adjustment of the adjustable steel supports according to the analysis results to suppress the resonance of the floor slab. When the ratio of the dominant excitation frequency and the natural frequency of the floor panel falls into the preset resonance interval, a contribution degree of the transmission path and a vibration amplitude of the floor panel are taken as optimization objectives, and an optimization function is constructed, and the optimization function is solved in a parameter constraint space of the adjustable steel support to generate a tuning parameter combination; The adjustable steel support is driven to perform hierarchical adjustment according to the tuning parameter combination, and vibration displacement of the floor panel and strain increment of the adjustable steel support are synchronously acquired as feedback data after hierarchical adjustment, and a contribution change amount of the transmission path and an amplitude attenuation rate of the floor panel are recalculated after adjustment; When the contribution change amount and the amplitude attenuation rate reach a target threshold value in continuous multiple sampling periods, the tuning parameter combination is locked, otherwise, the feedback data is analyzed to adjust a solving parameter of the optimization function and an analysis parameter of the vibration signal.

[0007] Optionally, the dominant excitation frequency comprises: A vibration sensor and an adjustable steel support are deployed on the floor panel to acquire vibration signals of the floor panel in real time, and effective vibration signals are obtained after noise reduction processing of the vibration signals; Time-frequency processing parameters are adaptively adjusted according to amplitude fluctuation characteristics of the effective vibration signals, and the effective vibration signals are subjected to time-frequency joint decomposition to form a frequency candidate set through screening; A dynamic weight is constructed by fusing energy stability of the effective vibration signals and response sensitivity of the floor panel, evaluation indexes of different frequencies in the frequency candidate set are iteratively calculated, and a frequency with the largest evaluation index in continuous multiple sampling periods is locked as the dominant excitation frequency.

[0008] Optionally, the transmission path and the contribution degree of the vibration energy comprise: Based on the locked dominant excitation frequency, target frequency signals corresponding to the dominant excitation frequency and located at positions where the vibration sensor and the adjustable steel support are deployed are extracted; A coherence coefficient of the target frequency signals between the vibration sensor and the adjustable steel support is calculated, an association link with a coherence coefficient greater than an association threshold is screened, and a candidate transmission path is identified; Energy proportions of the target frequency signals in the candidate transmission paths, vibration amplitudes of the floor panel and strain sensitivities of the adjustable steel support are fused to calculate contribution degrees of different candidate transmission paths; The contribution degrees are sorted in descending order, and the transmission path and the contribution degree of the vibration energy are locked by verifying stability of the coherence coefficient and fluctuation degree of the contribution degrees in continuous multiple sampling periods.

[0009] Optionally, the optimization function comprises: When the ratio of the dominant excitation frequency and the natural frequency of the floor panel falls into the preset resonance interval, the locked transmission path and the contribution degree thereof are extracted; The optimization objectives are the contribution of the transmission path and the vibration amplitude of the floor slab, and the weighting coefficients are determined based on the contribution ratio of different transmission paths and the degree of deviation of the vibration amplitude. Based on the correlation between the contribution of the transmission path and the strain sensitivity of the adjustable steel brace, the fit coefficient between the transmission path and the adjustable steel brace is determined, and a parameter constraint space is formed by combining the physical structure of the adjustable steel brace. Based on the stability of the dominant excitation frequency, a coordination factor is set to embed the conflict coordination term of the optimization objective, and the optimization objective, weight coefficients, parameter constraint space and conflict coordination term are integrated to construct the optimization function.

[0010] Optionally, the generated tuning parameter combination includes: When the stability of the dominant excitation frequency is not greater than the fluctuation threshold, the optimization function is solved by gradient descent. When the stability of the dominant excitation frequency is greater than the fluctuation threshold, the optimization function is solved piecewise. During the solution process, for the adjustable steel supports corresponding to the transmission paths that are determined to have high contribution, parameters are set to solve for the bias factor, and cross-fitting constraints are embedded according to the adaptation relationship of the tuning parameters. Multiple candidate tuning parameters are generated based on the solution results of the optimization function. The candidate tuning parameters are then filtered based on the coordination factor to generate a combination of tuning parameters.

[0011] Optionally, the hierarchical adjustment includes: Based on the ranking results of the contribution of the transmission path, the tuning parameter combination is decomposed into adjustment nodes corresponding to the transmission path, and the parameter adjustment amount of the adjustment node is allocated according to the proportion of contribution. The adjustable steel support is driven to perform graded adjustments according to the ranking of contribution. Before each adjustment, the parameter adjustment amount of the corresponding adjustment node is corrected based on the adaptation coefficient between the transmission path and the adjustable steel support. During each adjustment process, the strain increment rate of the adjustable steel support is monitored in real time. When the strain increment rate exceeds the safety threshold, the parameter adjustment amount of the adjustment node is adjusted to compensate.

[0012] Optionally, the calculated change in the contribution of the adjusted transmission path and the amplitude attenuation rate of the floor slab include: After graded adjustment, the vibration displacement of the floor slab and the strain increment of the adjustable steel support are acquired synchronously as feedback data, and the target frequency signals of the vibration sensor and the adjustable steel support after graded adjustment are extracted. The coupling influence coefficient is determined based on the correlation between the coherence coefficients of the transmission paths. Combined with the change in the energy proportion of the target frequency signal, the basic change in the contribution of the transmission path is calculated. The basic change is corrected by the coupling influence coefficient to obtain the adjusted change in the contribution of the transmission path. The proportion of parameter adjustment at the adjustment node is mapped to a weighting coefficient, and the weighted vibration amplitude after graded adjustment is calculated by combining the vibration displacement increment of the floor slab after each adjustment. The initial amplitude attenuation rate is obtained by dividing the difference between the vibration amplitude before adjustment and the weighted vibration amplitude by the vibration amplitude before adjustment. The initial amplitude attenuation rate is then corrected based on the adaptation coefficient of the transmission path and the adjustable steel support to obtain the amplitude attenuation rate of the floor slab after adjustment.

[0013] Optionally, the solution parameters of the adjustment optimization function include: When the contribution change and amplitude attenuation rate do not reach the target threshold, the feedback data is quantified and decomposed into feedback characteristics including vibration displacement deviation, excessive strain increment and vibration strain coordination imbalance. Adjust the bias factor for solving the parameters corresponding to the vibration displacement deviation, adjust the cross-fit constraint corresponding to the strain increment exceeding the standard, and adjust the coordination factor corresponding to the vibration strain synergistic imbalance, and lock the solution parameters to be adjusted. The adjustment ratio of the solution parameters is set according to the degree of deviation of the feedback characteristics. If the solution is obtained by gradient descent, the step size coefficient is adjusted according to the degree of deviation of vibration displacement. If the solution is obtained by segmentation, the interval of segmentation is adjusted according to the degree of deviation of strain increment exceeding the standard, so as to adjust the solution parameters of the optimization function.

[0014] Optionally, adjusting the analysis parameters of the vibration signal includes: When the contribution change and amplitude decay rate do not reach the target threshold, the failure scenarios of dominant excitation frequency identification deviation, transmission path misjudgment and contribution degree calculation distortion are matched by combining feedback data, contribution change and amplitude decay rate. Adjust the time-frequency processing parameters corresponding to the identification deviation of the dominant excitation frequency, adjust the correlation threshold of the coherence coefficient corresponding to the misjudgment of the transmission path, and adjust the fusion logic in the contribution calculation corresponding to the distortion of the contribution calculation, and lock the analysis parameters of the vibration signal to be adjusted. The adjustment ratio is set according to the degree of deviation of the failure scenario. If the stability of the dominant excitation frequency is not greater than the fluctuation threshold, the number of sampling periods is increased. If the stability of the dominant excitation frequency is greater than the fluctuation threshold, the adjustment step size of the time-frequency processing parameters is increased to adjust the analysis parameters of the vibration signal.

[0015] Compared with existing technologies, the beneficial effects of this application are as follows: By deploying vibration sensors on the floor slab, vibration signals are acquired and analyzed in real time to accurately identify the dominant excitation frequency, the transmission path of vibration energy and its contribution, clarify the root cause of resonance, and avoid the blind control caused by the ambiguity of resonance identification in traditional technologies. This provides a precise basis for subsequent optimization function construction and control resource allocation. With the contribution of the transmission path and the vibration amplitude of the floor slab as optimization targets, this application focuses on both the transmission of resonance energy and the overall vibration suppression effect. At the same time, it takes into account the parameter constraint space of the adjustable steel support to ensure that the generated tuning parameter combination meets the suppression requirements while adapting to the structural bearing capacity of the adjustable steel support. This avoids structural safety risks caused by parameter over-limitation from the source and achieves dual protection of suppression effect and structural safety.

[0016] The adjustable steel supports are driven for tiered adjustments, avoiding the waste of resources caused by a one-size-fits-all approach. This significantly improves the efficiency of resource utilization, ensuring that the adjustments directly target the root cause of resonance, rapidly weakening vibration energy. Feedback data after tiered adjustments is obtained, and the contribution changes of the transmission path and the amplitude attenuation rate of the floor slab are recalculated, constructing a closed-loop optimization process of identification, adjustment, feedback, and correction. When the adjustment effect is not up to standard, the solution parameters of the optimization function and the analysis parameters of the vibration signal are adjusted accordingly to promptly correct the adjustment imbalance caused by initial parameter deviations and changes in operating conditions. This ensures that the suppression method has good dynamic adaptability and can continuously maintain a stable resonance suppression effect. The entire method process incorporates structural safety assurance logic, effectively avoiding risks such as excessive structural strain caused by improper parameters during the adjustment process. At the same time, through the full-process design of precise identification, targeted adjustment, and dynamic correction, the reliability and stability of floor slab resonance suppression are greatly improved. Attached Figure Description

[0017] Figure 1 A flowchart illustrating an adjustable method for suppressing resonance in a floor slab, as provided in this application embodiment; Figure 2 A flowchart illustrating the construction optimization function of a floor slab resonance adjustable suppression method provided in this application embodiment; Figure 3 The flowchart illustrates the calculation of the change in the contribution of the transmission path after adjustment and the amplitude attenuation rate of the floor slab in an adjustable resonance suppression method for a floor slab provided in this application embodiment. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more pairs. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0019] refer to Figure 1 This is a flowchart of a method for adjustable resonance suppression of a floor slab provided in an embodiment of this application. The method includes: S1. Deploy vibration sensors and adjustable steel supports on the floor slab to acquire vibration signals of the floor slab in real time, and analyze the vibration signals to identify the dominant excitation frequency and the transmission path of vibration energy and its contribution.

[0020] Specifically, identifying the dominant stimulus frequency includes: Vibration sensors and adjustable steel supports are deployed on the floor slab to acquire the vibration signal of the floor slab in real time. The vibration signal is then processed to reduce noise and obtain an effective vibration signal. The time-frequency processing parameters are adaptively adjusted based on the amplitude fluctuation characteristics of the effective vibration signal, and the effective vibration signal is decomposed into a time-frequency joint decomposition to form a frequency candidate set. The energy stability of the effective vibration signal and the response sensitivity of the floor slab are integrated to construct a dynamic weight. The evaluation index of different frequencies in the frequency candidate set is calculated iteratively, and the frequency with the largest evaluation index in multiple consecutive sampling periods is locked as the dominant excitation frequency.

[0021] The raw vibration signal of the floor slab is mixed with irrelevant signals such as environmental interference and equipment operating noise. If it is used directly for frequency analysis, it will lead to deviations in subsequent frequency identification. At the same time, only by properly deploying sensing devices can the vibration state of key areas of the floor slab be accurately captured, providing a reliable data foundation for frequency identification. Vibration sensors are evenly distributed in the vibration-sensitive areas of the floor slab and at the deployment nodes of the adjustable steel supports. The vibration-sensitive areas include the midpoint of the span and the load concentration points. It is ensured that the vibration sensors are rigidly connected to the floor slab surface to avoid signal distortion caused by installation gaps. The vibration sensors acquire the vibration signal of the floor slab in real time and simultaneously record the vibration response data of the deployment location of the adjustable steel supports.

[0022] The vibration signal is denoised using wavelet thresholding, and the noise distribution characteristics of the vibration signal are analyzed. The denoising threshold is adaptively selected to retain the effective components of the vibration signal related to structural vibration, while random noise and environmental interference signals are removed, ultimately yielding an effective vibration signal. This effectively eliminates irrelevant interference in the vibration signal, ensuring that the vibration signal can truly reflect the actual vibration state of the floor slab and avoiding frequency characteristic distortion caused by noise. The targeted design of the equipment deployment location allows the acquired vibration signal to focus on the vibration-sensitive area, providing high-quality and highly relevant data support for subsequent frequency analysis.

[0023] The amplitude of the effective vibration signal fluctuates dynamically with the intensity of the excitation source and the structural response state. If fixed time-frequency processing parameters are used for decomposition, it is difficult to adapt to the dynamic changes of the effective vibration signal, which may lead to the omission or misjudgment of some frequency features. Directly analyzing the full-band signal will generate a lot of redundant information and increase the complexity of subsequent calculations. Real-time monitoring of the amplitude fluctuation characteristics of the effective vibration signal, including the amplitude change rate and fluctuation period, and adaptive adjustment of time-frequency processing parameters according to the amplitude fluctuation characteristics, that is, when the amplitude fluctuation is drastic, the time window length is shortened and the frequency resolution is increased to capture rapidly changing frequency components; when the amplitude tends to be stable, the time window length is increased and the frequency resolution is reduced to improve the stability of frequency identification.

[0024] Wavelet packet decomposition is employed to perform joint time-frequency processing on the effective vibration signal, decomposing it into sub-signals of different frequency bands and extracting the frequency features of each sub-band. Based on the energy proportion and frequency stability of the sub-signals, frequencies corresponding to sub-signals with high energy proportions and obvious frequency features are selected to form a frequency candidate set, while redundant frequencies with extremely low energy and no practical physical meaning are eliminated. This achieves accurate adaptation of the effective vibration signal, avoiding the problem of incomplete frequency feature extraction caused by fixed parameters. The joint time-frequency decomposition takes into account both the time and frequency domain characteristics of the effective vibration signal, ensuring the accuracy of frequency identification. The selection of the frequency candidate set effectively simplifies the frequency range of subsequent calculations, reduces computational complexity, and improves frequency identification efficiency.

[0025] Relying solely on energy stability or structural response sensitivity to determine the dominant excitation frequency is one-sided. For example, considering only energy stability may lead to misjudging non-critical stable interference frequencies as the target frequency, and considering only response sensitivity may result in misjudgment due to transient strong response interference. Furthermore, the frequency characteristics of a single sampling period can be random, requiring multi-period verification to ensure frequency stability. A dynamic weighting is constructed by integrating the energy stability of the effective vibration signal and the response sensitivity of the floor slab. When the signal energy fluctuates significantly, the dynamic weight of response sensitivity is increased to highlight the real-time feedback of the structure to the excitation; when the signal energy tends to stabilize, the dynamic weight of energy stability is increased to emphasize the continuous influence of the frequency.

[0026] For each frequency in the candidate frequency set, its energy stability and response sensitivity are assessed. Energy stability includes whether the frequency persists without significant drift, while response sensitivity includes whether the structure's vibration feedback at that frequency is significant. The two assessment results are weighted and summed according to dynamic weights, and the evaluation index of each frequency is calculated iteratively. The evaluation index of each frequency is continuously monitored over multiple sampling periods, and the frequency with the largest evaluation index and no significant fluctuations in all sampling periods is identified as the dominant excitation frequency. This achieves a comprehensive assessment of energy and response characteristics, avoids the one-sidedness of single-index judgment, and improves the accuracy of frequency identification. Iterative verification and stability assessment over multiple sampling periods eliminate random errors caused by instantaneous interference, ensuring that the identified dominant excitation frequency is the core frequency that continuously affects the floor slab vibration.

[0027] Specifically, identifying the transmission path of vibrational energy and its contribution includes: Based on the locked dominant excitation frequency, the target frequency signal corresponding to the dominant excitation frequency and located at the deployment position of the vibration sensor and the adjustable steel support is extracted. Calculate the coherence coefficient of the target frequency signal between the vibration sensor and the adjustable steel support, filter the associated links with coherence coefficients greater than the association threshold, and identify candidate transmission paths; The contribution of different candidate transmission paths is calculated by integrating the energy proportion of the target frequency signal in the candidate transmission paths, the vibration amplitude of the floor slab, and the strain sensitivity of the adjustable steel support. The contribution rates are sorted in descending order, and the stability of the coherence coefficient and the degree of fluctuation of the contribution rate are verified through multiple consecutive sampling periods to pinpoint the transmission path of vibration energy and its contribution rate.

[0028] The dominant excitation frequency had been previously identified. However, the vibration signal from the floor slab contained multiple frequency components. Only the dominant excitation frequency was directly related to the resonance suppression target. Other frequency components would interfere with the identification of the energy transfer path. Meanwhile, the vibration sensor was the signal acquisition end, and the adjustable steel support was the resonance suppression execution end. Only by extracting the signals from these two key locations could the energy transfer link be established. Based on the identified dominant excitation frequency, the target frequency signal that perfectly matched the dominant excitation frequency was separated and extracted from the effective vibration signals obtained from the deployment locations of the vibration sensor and the adjustable steel support.

[0029] During the extraction process, interference components and residual noise from non-dominant excitation frequencies in the effective vibration signals are removed to ensure that the target frequency signal at each deployment location focuses only on the energy transfer characteristics related to resonance. Furthermore, the signal attribution of different vibration sensors and corresponding adjustable steel supports is clearly distinguished to avoid location confusion. This allows for the precise selection of resonance-related signals, eliminating interference from irrelevant frequency components. It ensures that subsequent analysis revolves solely around the energy transfer link, with the signal source focused on the location of the acquisition and execution ends. This provides a precise and targeted data source for establishing energy transfer correlations, avoiding link identification deviations caused by signals from non-critical locations.

[0030] The correlation of target frequency signals directly reflects the possibility of energy transfer. The stronger the correlation between the target frequency signals of two locations, the higher the probability of effective energy transfer between them. If the correlation coefficient is not used for screening, combinations of locations with no energy transfer relationship will be misjudged as transfer paths, leading to redundancy and deviation from the target in subsequent analysis. For each combination of deployment locations of vibration sensors and each adjustable steel support, the correlation coefficient between the corresponding target frequency signals of the two is calculated to quantify the degree of linear correlation between the two target frequency signals. An association threshold is set as a judgment criterion. This association threshold is determined based on the conventional transmission characteristics of structural vibration and is used to distinguish between effective transmission links and irrelevant links.

[0031] Location combinations with coherence coefficients greater than the correlation threshold are selected, and the physical links corresponding to these location combinations are identified as candidate transmission paths. At the same time, location combinations with coherence coefficients less than the correlation threshold are eliminated, removing redundant links that have no practical energy transfer significance. The correlation between target frequency signals is quantified by the coherence coefficient, providing an objective and verifiable basis for transmission path identification, avoiding misjudgments caused by subjective experience. The screening process effectively simplifies the analysis objects, retaining only candidate paths with energy transfer correlation, reducing the complexity of subsequent contribution calculations, and improving overall identification efficiency.

[0032] A single factor cannot fully assess the importance of the transmission path. Considering only the energy proportion of the target frequency signal fails to reflect the actual impact of the transmission path on structural vibration. Focusing solely on the vibration amplitude of the floor slab ignores the essence of energy transfer. Referencing only the strain sensitivity of the adjustable steel bracing is insufficient to reflect the energy transfer efficiency of the transmission path. Therefore, it is necessary to evaluate the energy proportion of the target frequency signal, the vibration amplitude of the floor slab, and the strain sensitivity of the adjustable steel bracing in each candidate transmission path. The energy proportion of the target frequency signal determines the relative proportion of the resonance-related energy transmitted by the transmission path within the total energy transmitted by all candidate transmission paths, clarifying the scale of energy transfer for that path. The vibration amplitude of the floor slab determines the intensity of vibration caused by resonance in the floor slab area corresponding to the transmission path, clarifying the actual impact of the transmission path on structural vibration. The strain sensitivity of the adjustable steel bracing determines the sensitivity of the adjustable steel bracing associated with the transmission path to the energy transmitted by the path, clarifying the potential of the adjustable steel bracing to regulate the energy of the transmission path.

[0033] The energy proportion of the target frequency signal is used as the basic evaluation item, since energy transfer is the core contribution of the transfer path. The vibration amplitude of the floor slab is used as a correction item; the stronger the vibration amplitude corresponding to the transfer path, the more critical its impact on resonance suppression, and the contribution weight of the transfer path needs to be strengthened in the basic evaluation item. Then, the strain sensitivity of the adjustable steel support is used as a secondary correction item; the higher the strain sensitivity of the adjustable steel support to the transfer path, the easier it is to control the energy of the transfer path through the adjustable steel support, further adjusting the contribution weight of the transfer path. Through the logic of basic evaluation and two corrections, the evaluation results of the three types of elements are gradually integrated to finally form the contribution of each candidate transfer path, which intuitively reflects the importance of each transfer path in the overall energy transfer. Thus, through the integration logic of basic and correction, the core role of energy transfer is highlighted, while structural influence and support adaptability are taken into account, avoiding the one-sidedness of single index evaluation. The calculation process is based on the actual transfer characteristics of the candidate transfer paths, so that the contribution can truly and accurately reflect the actual importance of the transfer path, providing a reliable basis for subsequent ranking.

[0034] The contributions of candidate transmission paths vary, necessitating a ranking process to clearly distinguish between core and secondary paths. This provides a priority basis for subsequent hierarchical adjustments. Furthermore, the coherence coefficient and contribution of a single sampling period can be affected by transient interference, exhibiting randomness. Multi-period verification is required to eliminate temporary interference and ensure that the locked transmission path is a core link for long-term stable energy transmission, avoiding misjudgments caused by short-term fluctuations. All candidate transmission paths should be ranked in descending order of contribution, prioritizing the core path with the highest contribution for targeted adjustments later. The stability of the coherence coefficient and the degree of fluctuation in contribution of each candidate transmission path should be continuously monitored over multiple sampling periods.

[0035] If the coherence coefficient of a transmission path consistently remains above the correlation threshold without significant fluctuations, and its contribution remains stable without large fluctuations, it indicates that the transmission path is a stable energy transmission link. Conversely, if the coherence coefficient or contribution fluctuates drastically, it is considered a temporary interference link and is eliminated. Finally, the transmission paths that are ranked high and have passed stability verification, along with their corresponding contributions, are retained to complete the final locking. This clarifies the priority of transmission paths, enabling subsequent suppression actions to focus on core paths, improve suppression efficiency, and eliminate random errors caused by instantaneous interference through multi-cycle verification. This ensures that the locked transmission paths and contributions have long-term stability and reliability, avoiding the development of ineffective suppression strategies due to short-term fluctuations.

[0036] S2. When the ratio of the dominant excitation frequency to the natural frequency of the floor slab falls within the preset resonance range, the contribution of the transmission path and the vibration amplitude of the floor slab are used as optimization objectives to construct an optimization function. Within the parameter constraint space of the adjustable steel support, the optimization function is solved to generate a combination of tuning parameters.

[0037] Furthermore, such as Figure 2 As shown, the construction of the optimization function includes: When the ratio of the dominant excitation frequency to the natural frequency of the floor slab falls within the preset resonance range, the locked transmission path and its contribution are extracted. The optimization objectives are the contribution of the transmission path and the vibration amplitude of the floor slab, and the weighting coefficients are determined based on the contribution ratio of different transmission paths and the degree of deviation of the vibration amplitude. Based on the correlation between the contribution of the transmission path and the strain sensitivity of the adjustable steel brace, the fit coefficient between the transmission path and the adjustable steel brace is determined, and a parameter constraint space is formed by combining the physical structure of the adjustable steel brace. Based on the stability of the dominant excitation frequency, a coordination factor is set to embed the conflict coordination term of the optimization objective, and the optimization objective, weight coefficients, parameter constraint space and conflict coordination term are integrated to construct the optimization function.

[0038] The optimization function aims to mitigate resonance risks. The floor slab will only face a substantial resonance threat when the ratio of the dominant excitation frequency to the natural frequency of the floor slab enters the preset resonance range. The previously identified transmission paths and their contributions are the core basis for identifying the optimization target. Without this information, the optimization will lose its direction. First, the identified dominant excitation frequency and the natural frequency of the floor slab are obtained. After calculating the ratio of the two, it is compared with the preset resonance range. This resonance range is the risk critical range defined by the structural vibration safety standard of the floor slab and is used to determine whether the resonance risk has manifested.

[0039] If the ratio falls within the resonance range, it indicates that resonance suppression regulation needs to be initiated. The process of constructing the optimization function is then started. From the previously identified vibration energy transmission paths, key information of all effective transmission paths is fully extracted, including the contribution of each path and its corresponding energy transmission characteristics. This clearly distinguishes the core paths requiring key regulation from the auxiliary paths that only need secondary attention. This avoids ineffective optimization in non-resonance states, ensuring that regulation actions focus on actual needs, defining a clear regulation range for the optimization function, avoiding blindly covering irrelevant paths, and significantly improving the targeting and efficiency of the optimization.

[0040] The core requirements of resonance suppression are targeted regulation of critical pathways and effective reduction of overall vibration, which together constitute the objectives of the optimization function. However, the importance of different transmission paths and the degree of exceeding the standard vibration amplitude in different areas of the floor slab vary significantly. If allocating weights equally to all objectives, optimization resources will be wasted on non-critical links, failing to solve the problem efficiently. It is necessary to clarify the optimization objectives of the optimization function, including weakening the energy transmission capacity of high-contribution paths and reducing the vibration amplitude of the floor slab, to ensure that the optimization objectives of the optimization function fully cover the dual requirements of targeted regulation of the root cause and effective suppression. When determining the weight coefficients, the contribution ratio of each transmission path should be analyzed first. The higher the contribution ratio of the core path, the higher the weight coefficient for weakening the energy transmission of the transmission path, so that the optimization function prioritizes resources towards the core path during regulation.

[0041] By analyzing the deviation of the floor slab vibration amplitude from the ideal suppression state, areas with more significant deviations are assigned higher weighting coefficients for reducing vibration amplitude, allowing the optimization function to focus on addressing excessive vibration. These weighting coefficients are then linked to their respective optimization objectives, forming a basic optimization function: Path Control × Path Weighting Coefficient + Vibration Amplitude × Vibration Weighting Coefficient. This clearly defines the priority allocation of different optimization objectives within the function, avoiding the one-sidedness of optimization due to a single objective. The optimization function focuses on both controlling the root cause of resonance and emphasizing actual suppression effects. The binding logic between weighting coefficients and optimization objectives ensures that optimization resources are precisely directed to core paths and areas severely affected by vibration, significantly improving the optimization efficiency and accuracy of the function.

[0042] The compatibility between the energy transfer path and the adjustable steel support directly affects the control effect. If the compatibility between the energy transfer path and the adjustable steel support is poor, it will be difficult to effectively suppress energy transfer even if the parameters are adjusted. At the same time, the adjustable steel support has objective limitations in its physical structure. If the tuning parameters allowed by the optimization function exceed these limitations, it will lead to support damage or control failure. First, we analyze the correlation between the contribution of each energy transfer path and the strain sensitivity of the corresponding adjustable steel support. The higher the contribution of the energy transfer path and the more sensitive the adjustable steel support is to its strain response, the stronger the compatibility between the two, and the higher the corresponding compatibility coefficient should be set. Then, we use this compatibility coefficient as a correction term and integrate it into the path control × path weight coefficient of the basic optimization function to form the logic of path control × path weight coefficient × compatibility coefficient. This makes the basic optimization function give a stronger control tendency to the combination of well-matched energy transfer paths and adjustable steel supports, avoiding waste of control resources.

[0043] Simultaneously, a comprehensive analysis of the physical structure of the adjustable steel support was conducted, including material strength, structural dimensions, load-bearing limit, and adjustable range. The parameter constraint range of the tuning parameters was clearly defined, and this parameter constraint range was embedded as a constraint condition into the optimization function. This ensured that the tuning parameters must fall within this parameter constraint range when solving the subsequent optimization function, guaranteeing that the tuning parameter adjustments meet the physical bearing capacity of the support and eliminating the risk of equipment damage. The integration of the adaptation coefficient enables the optimization function to accurately match the transmission path with the adjustable steel support combination, significantly improving the targeting and efficiency of the control. The setting of the parameter constraint space defines a safe and feasible value boundary for the optimization function, avoiding equipment damage due to tuning parameter exceeding limits and ensuring the actual executability of subsequent tuning parameters.

[0044] There are potential conflicts between the optimization objectives of the optimization function. For example, overemphasizing core path control can lead to excessive strain in the corresponding adjustable steel support, while excessively pursuing vibration amplitude reduction can exceed the adjustable range of the support. If these conflicts are not resolved, they can lead to disordered optimization logic. The stability of the dominant excitation frequency affects the probability and intensity of the conflict. Conflicts are more likely to occur when the frequency fluctuates greatly. First, monitor the stability of the dominant excitation frequency. If the dominant excitation frequency remains stable for a long period, it indicates that the resonance state is relatively stable and the risk of conflict is low. At this time, a balanced coordination factor is set to balance the two major optimization objectives of transmission path control and vibration amplitude reduction, avoiding over-optimization of a single objective. If the dominant excitation frequency fluctuates frequently and with large amplitude, it indicates that the resonance state is dynamically changing and the risk of conflict is high. At this time, a dynamic coordination factor is set to prioritize the optimization objective of vibration amplitude reduction while strictly considering the safety boundary of the support.

[0045] By embedding a coordination factor as a conflict coordination term into the optimization function, when conflicting control tendencies arise among the optimization objectives, the weight coefficients of the corresponding optimization objectives are dynamically adjusted through the coordination factor to ensure the consistency of the overall control logic of the optimization function. The optimization function is based on path control × path weight coefficient + vibration amplitude × vibration weight coefficient. An adaptation coefficient is incorporated to complete the first layer of correction, followed by the incorporation of the coordination factor to complete the second layer of correction. Finally, the parameter constraint space is defined as the boundary of the optimization function, forming a complete optimization function. This optimization function clearly defines the control objects and priorities, as well as the precise control methods, conflict resolution mechanisms, and safety boundaries, and can be directly used for subsequent tuning parameter solutions. This effectively resolves potential conflicts between optimization objectives, ensuring that the optimization function's control logic balances effectiveness and safety. The hierarchical integration of multiple elements gives the optimization function comprehensiveness and adaptability, meeting the core requirements of resonance suppression while also supporting physical constraints and dynamic changes in operating conditions, avoiding problems such as one-sided or unrealistic optimization logic.

[0046] Specifically, generating the tuning parameter combination includes: When the stability of the dominant excitation frequency is not greater than the fluctuation threshold, the optimization function is solved by gradient descent. When the stability of the dominant excitation frequency is greater than the fluctuation threshold, the optimization function is solved piecewise. During the solution process, for the adjustable steel supports corresponding to the transmission paths that are determined to have high contribution, parameters are set to solve for the bias factor, and cross-fitting constraints are embedded according to the adaptation relationship of the tuning parameters. Multiple candidate tuning parameters are generated based on the solution results of the optimization function. The candidate tuning parameters are then filtered based on the coordination factor to generate a combination of tuning parameters.

[0047] The stable state of the dominant excitation frequency directly determines the solution strategy for the optimization function. When the dominant excitation frequency is stable, the resonance condition does not fluctuate significantly, and the optimal solution needs to be approximated through continuous iteration. When the dominant excitation frequency fluctuates drastically, the resonance state changes dynamically, and a single continuous solution cannot meet the real-time adjustment requirements of the tuning parameters. When the stability of the dominant excitation frequency is not greater than the fluctuation threshold, it indicates that the dominant excitation frequency is in a stable state. The gradient descent method is used to solve the optimization function, with transmission path control and vibration amplitude suppression as optimization objectives. Starting from the initial values ​​of mass, stiffness, and damping ratio, the improvement trend of the optimization objective is monitored in each iteration, and the iteration direction of the tuning parameters is adaptively adjusted. If the optimization objective does not improve, it is corrected in the opposite direction, gradually reducing the deviation from the optimal solution until the improvement effect of the optimization objective tends to level off.

[0048] If the stability of the dominant excitation frequency is greater than the fluctuation threshold, it indicates that the dominant excitation frequency fluctuates violently. The optimization function is split according to the degree of direct influence of the tuning parameters on resonance suppression. That is, the control sub-logic of path control × path weight coefficient × adaptation coefficient in the optimization function is extracted first as the solution part corresponding to the stiffness parameter, because stiffness directly determines the deformation resistance of the adjustable steel support and is a key parameter affecting the energy transfer of high contribution paths. The reasonable adjustment range of stiffness is solved first. Then, based on the solved stiffness adjustment range, the correlation sub-logic of vibration amplitude × vibration weight coefficient in the optimization function is extracted as the solution part corresponding to the mass parameter, because mass affects the inertial vibration response of the floor slab and it is necessary to ensure that the mass adjustment is adapted to the stiffness.

[0049] Based on the results of the first two steps, the adaptor logic of the conflict coordination term in the optimization function is extracted as the solution part corresponding to the damping ratio. Since the damping ratio is responsible for dissipating vibration energy, it is necessary to resolve the vibration rebound caused by the combination of stiffness and mass, clarify the adjustment direction of the damping ratio to match the combination of stiffness and mass, and complete the solution of the full tuning parameters in segments. The gradient descent method accurately approximates the optimal solution through continuous iteration under steady working conditions, avoiding invalid iterations. The decomposition of the optimization function not only ensures the specificity of the solution under fluctuating working conditions, but also avoids logical conflicts with the optimization function, making the tuning parameter solution more in line with the resonance requirements and improving the adaptability of the solution results.

[0050] High-contribution paths are the carriers of resonant energy transfer. If their corresponding adjustable steel supports are not prioritized during the solution process, optimization resources will be tilted towards secondary paths, failing to directly address the root cause of resonance. The functional correlation between mass, stiffness, and damping ratio in the tuning parameters is implicitly contained in the fit coefficient and parameter constraint space. Without explicit bias and constraint operations, parameter adjustments may deviate from the core path or result in combinational contradictions. From the locked transmission paths, the high-contribution transmission paths with the highest contribution ranking are selected, and their corresponding adjustable steel supports are identified. When solving the sub-logic of each decomposed optimization function, these adjustable steel supports are considered. The parameter settings include bias factors. When solving the regulation sub-logic corresponding to stiffness, the stiffness parameters of the high-contribution path support are adjusted first. The exploration range of stiffness parameters focuses on the target range of energy reduction in the transmission path. The influence weight of stiffness on the transmission path regulation target is strengthened in the iterative evaluation. When solving the correlation sub-logic corresponding to mass, the vibration amplitude of the high-contribution path is correlated simultaneously, so that the mass adjustment is adapted to the vibration suppression requirements of this type of transmission path. When solving the adaptation sub-logic corresponding to damping ratio, the adaptability of the damping ratio of the high-contribution path support to stiffness and mass is prioritized to ensure that the energy dissipation effect is focused on the core path.

[0051] Based on the parameter constraint space and adaptation coefficients of the optimization function, the cross-adaptation constraints of the tuning parameters are clearly defined and embedded in the solution process. After stiffness adjustment, the mass is automatically checked to see if it conforms to the adaptation logic of stiffness and inertia. If mass adjustment exceeds the bearing limit when stiffness increases, the adjustment direction of mass is corrected. After mass and stiffness are determined, the damping ratio is checked to see if it conforms to the adaptation logic of energy dissipation and structural response. If the combination of stiffness and mass is too rigid and the damping ratio is not adjusted synchronously, it will cause vibration rebound. The adjustment direction of damping ratio is corrected to ensure that there is no inherent contradiction in the combination of tuning parameters. This allows the solution resources to accurately focus on the core path, improve the targeting of tuning parameters, and avoid tuning parameter conflicts through automatic verification and correction of cross-adaptation constraints, ensuring that each set of tuning parameters has practical control feasibility and reducing the risk of control failure.

[0052] A single solution cannot cover the potential variations in complex resonance conditions. Multiple sets of candidate tuning parameters need to be generated based on the sub-logic of the decomposed optimization function. The coordination factor is the basis for conflict coordination in the optimization function. Screening based on it can ensure that the final combination of tuning parameters meets the optimization objective and avoids subjectivity in the screening. Based on the solution results of the sub-logic of the decomposed optimization function, multiple sets of candidate tuning parameters are generated in two ways. First, the initial iteration values ​​of the sub-logic of each optimization function are adjusted within the parameter constraint space to generate solution paths with different starting points, resulting in multiple combinations of stiffness, mass, and damping ratio, each corresponding to the sub-logic objective of the optimization function. Second, the parameter exploration boundaries of the sub-logic of each optimization function are fine-tuned without exceeding the parameter constraint space to supplement multiple sets of tuning parameters, ensuring that the tuning parameters cover different resonance response scenarios.

[0053] During the screening process, a coordination factor is invoked. If the coordination factor is balanced, the standard is to balance the path control effect and vibration suppression effect. The degree to which each group of candidate tuning parameters meets the sub-logic objectives of the three types of optimization functions is evaluated, and tuning parameter groups that overemphasize a single sub-logic are eliminated. If the coordination factor is dynamic, the standard is to prioritize vibration suppression effect while considering the feasibility of path control. The dynamic adaptability of stiffness and damping ratio is evaluated, and tuning parameter groups that can quickly respond to frequency changes are screened. Finally, multiple groups of tuning parameters that meet the coordination factor, have no tuning parameter conflicts, and satisfy the overall objective of the optimization function are retained and integrated to form a tuning parameter combination. The generation of multiple groups of candidate tuning parameters is based on the sub-logic of the decomposed optimization function. This ensures the diversity of tuning parameters without deviating from the core constraints of the optimization function, providing flexible adaptation space for subsequent control. The screening logic based on the coordination factor ensures that the final tuning parameter combination is consistent with the overall objective of the optimization function, balancing control effect and safety boundary, and improving the reliability and adaptability of the tuning parameter combination.

[0054] S3. Drive the adjustable steel support to perform graded adjustments according to the combination of tuning parameters. After graded adjustments, synchronously acquire the vibration displacement of the floor slab and the strain increment of the adjustable steel support as feedback data, and recalculate the contribution change of the transmission path and the amplitude attenuation rate of the floor slab after adjustment.

[0055] Furthermore, the tiered adjustments include: Based on the ranking results of the contribution of the transmission path, the tuning parameter combination is decomposed into adjustment nodes corresponding to the transmission path, and the parameter adjustment amount of the adjustment node is allocated according to the proportion of contribution. The adjustable steel support is driven to perform graded adjustments according to the ranking of contribution. Before each adjustment, the parameter adjustment amount of the corresponding adjustment node is corrected based on the adaptation coefficient between the transmission path and the adjustable steel support. During each adjustment process, the strain increment rate of the adjustable steel support is monitored in real time. When the strain increment rate exceeds the safety threshold, the parameter adjustment amount of the adjustment node is adjusted to compensate.

[0056] The tuning parameter combination is a set of solutions for overall resonance suppression. Different transmission paths have significantly different degrees of influence on resonance. If the parameter adjustment amount is distributed equally, it will lead to insufficient control of high contribution paths or waste of resources in secondary paths, and may even cause new vibration disturbances. The core of gradient allocation is to allocate control resources according to the priority of contribution, so that the core path can be targeted for control, while secondary paths only need to be coordinated and adapted. The contribution ranking results of the locked transmission paths are retrieved, and the tuning parameter combination is decomposed for each transmission path. Adjustment nodes are set for each transmission path, and the control object of each adjustment node is clearly defined as the adjustable steel support associated with the transmission path. The specific operation of gradient allocation is to determine the adjustment priority and focus of the tuning parameters based on the contribution ratio. For the core path with the highest contribution ratio, the parameter adjustment amount is preferentially tilted towards stiffness and damping ratio.

[0057] Stiffness adjustment directly weakens the energy transfer capability of the transmission path, damping ratio adjustment quickly dissipates vibration energy, and mass adjustment is only used as an auxiliary measure, such as fine-tuning the inertial response of the adjustable steel support structure, to ensure that the core path receives the most effective control resources. For transmission paths with a moderate contribution, parameter adjustments are evenly distributed to tuning parameters, allowing stiffness and damping ratio to work together to weaken energy transfer, while mass adjustment adapts to the vibration response, balancing control effect and structural stability. For secondary paths with the lowest contribution, parameter adjustments are only allocated to basic adaptation amounts, fine-tuning damping ratio or mass parameters without interfering with the control of the core path, to ensure synergy with the control effect of the core path and avoid new vibration coupling caused by individual adjustments. Gradient allocation allows control resources to accurately match the importance of the path, avoiding resource waste caused by a one-size-fits-all approach. The core path is given priority in stiffness and damping ratio control, which can quickly target the root cause of resonance, while the synergistic adaptation of secondary paths ensures the vibration stability of the overall structure, improving the targeting and overall effect of the adjustments.

[0058] Adjusting parameters based on contribution priority can address core issues first, avoiding delayed suppression effects caused by adjusting secondary paths first. The fit coefficient reflects the matching degree between the transmission path and the adjustable steel support. Even with gradient parameter adjustments, if the adjustable steel support is not sensitive to the control response of a certain type of tuning parameter, the control will still fail. By sequentially initiating the control process of each adjustment node in descending order of the transmission path's contribution, the fit coefficient between the transmission path and the corresponding adjustable steel support is retrieved before each adjustment is executed. If the fit coefficient is high, it indicates that the adjustable steel support is sensitive to the control response of the transmission path. In this case, the fit coefficient specifically means keeping the gradient parameter adjustment amount unchanged and only fine-tuning the coordination rhythm of the tuning parameter adjustment. For example, stiffness adjustment is initiated first, and damping ratio adjustment is initiated synchronously after the adjustable steel support response stabilizes. Mass adjustment follows with a lag, avoiding short-term vibration fluctuations caused by synchronous adjustment of tuning parameters.

[0059] If the fit coefficient is low, it indicates that the adjustable steel brace is slow to respond to the control of the transmission path. In this case, the focus should be adjusted first, that is, the adjustment amount of the gradient allocation parameters should be changed. For example, instead of prioritizing stiffness adjustment, the damping ratio should be adjusted first to reduce the response threshold of the adjustable steel brace by dissipating energy, or the mass should be fine-tuned first to optimize the inertial response basis of the adjustable steel brace. After the sensitivity of the adjustable steel brace to control is improved, the adjustment amount of the gradient allocation parameters should be returned to normal. The synergistic logic of parameter adjustment is to correct the adjustment order and coordination of parameter adjustment amounts. For example, when the fit coefficient is low, a synergistic logic of damping ratio first, stiffness following, and mass adaptation should be adopted. Instead of prioritizing stiffness in gradient allocation, the adjustable steel bracing is gradually adapted to the control through sequential optimization, avoiding ineffective consumption caused by forcibly adjusting a single parameter. After correction, the adjustable steel bracing is driven to perform control actions according to the corrected parameter adjustment amount, which includes the adjustment sequence and emphasis dimension. The adaptation coefficient adjustment makes the control of high-adaptability combinations more stable, avoiding vibration interference from synchronous adjustments. The correction of emphasis dimension and coordination logic allows low-adaptability combinations to overcome the bottleneck of sluggish response, ensuring that the parameter adjustment amount can play a role, and significantly improving the adaptability and control efficiency of different transmission paths and adjustable steel bracing combinations.

[0060] During the graded adjustment process, the core risk of the adjustable steel brace's strain increment rate exceeding the safety threshold lies in the mismatch between the parameter adjustment intensity and the brace's bearing capacity, or in the conflict of parameter coordination logic. Simply pausing the adjustment would lead to interruption of control and a lag in the suppression effect. The strain increment rate of the adjustable steel brace is captured in real time and compared with the safety threshold determined based on the brace material strength and structural bearing limit. If the strain increment rate is detected to be rapidly increasing and exceeding the safety threshold, it indicates that the current adjustment intensity exceeds the adjustable steel brace's bearing capacity. Immediately, a compensation adjustment is initiated, first pausing the current stiffness parameter adjustment and then increasing the damping ratio adjustment intensity to rapidly dissipate vibration energy and reduce the brace's strain load. Once the strain increment rate falls back to the safe range, the stiffness adjustment amplitude is reduced, while simultaneously fine-tuning the mass parameter to optimize the inertial load distribution of the adjustable steel brace. A coordinated logic of damping ratio stabilization, gradual stiffness adjustment, and mass adaptation is adopted to avoid strain anomalies caused by excessive adjustment of a single parameter.

[0061] If the rate of change of strain increment is frequently monitored and exceeds the safety threshold, it indicates a conflict in the coordination logic of parameter adjustment. Compensation adjustment focuses on correcting the coordination logic. For example, if the original synchronous adjustment of stiffness and mass caused strain fluctuations in the adjustable steel support, it can be changed to stiffness adjustment with mass adjustment lagging by one response cycle, or the parameter adjustment amount of mass adjustment can be reduced to strengthen the intermediate buffering effect of damping ratio. By adjusting the coordination rhythm and parameter adjustment amount of tuning parameters, coordination conflicts can be resolved, and the rate of change of strain increment can be stabilized within the safety threshold. After the compensation adjustment is completed, the adjustment node is continued to be adjusted according to the corrected coordination logic until the adjustment at this level is fully implemented. Precise compensation for different risk causes avoids a one-size-fits-all suspension of adjustment, ensuring the structural safety of the adjustable steel support without interrupting the control process. The dynamic correction of the coordination logic ensures that the parameter adjustment amount always matches the real-time bearing state of the support, taking into account both resonance suppression effect and structural safety and stability.

[0062] Specifically, such as Figure 3 As shown, the calculation of the change in contribution of the adjusted transmission path and the amplitude attenuation rate of the floor slab includes: After graded adjustment, the vibration displacement of the floor slab and the strain increment of the adjustable steel support are acquired synchronously as feedback data, and the target frequency signals of the vibration sensor and the adjustable steel support after graded adjustment are extracted. The coupling influence coefficient is determined based on the correlation between the coherence coefficients of the transmission paths. Combined with the change in the energy proportion of the target frequency signal, the basic change in the contribution of the transmission path is calculated. The basic change is corrected by the coupling influence coefficient to obtain the adjusted change in the contribution of the transmission path. The proportion of parameter adjustment at the adjustment node is mapped to a weighting coefficient, and the weighted vibration amplitude after graded adjustment is calculated by combining the vibration displacement increment of the floor slab after each adjustment. The initial amplitude attenuation rate is obtained by dividing the difference between the vibration amplitude before adjustment and the weighted vibration amplitude by the vibration amplitude before adjustment. The initial amplitude attenuation rate is then corrected based on the adaptation coefficient of the transmission path and the adjustable steel support to obtain the amplitude attenuation rate of the floor slab after adjustment.

[0063] After the graded adjustments, the suppression effect needs to be evaluated using objective data. The vibration displacement of the floor slab directly reflects the overall vibration state, and the strain increment of the adjustable steel supports reflects the actual response of the adjustment action. After all graded adjustments are completed, the vibration displacement of key areas of the floor slab and the strain increment of all adjustable steel supports are acquired simultaneously to ensure complete data synchronization and avoid evaluation errors caused by time deviations. Using the dominant excitation frequency as a benchmark, the target frequency signals at the deployment locations of vibration sensors and adjustable steel supports are extracted. During the extraction process, interference components and environmental noise from non-dominant frequencies are eliminated, and the target frequency signals corresponding to different transmission paths are clearly distinguished. The synchronously acquired vibration displacement and strain increment provide comprehensive and objective basic materials for the effect evaluation, avoiding the one-sidedness of the evaluation caused by a single data dimension. The accurate extraction of the target frequency signal and the elimination of interference from irrelevant frequency components ensure that subsequent calculations are focused solely on the core of resonance suppression, improving the targeting of the evaluation.

[0064] The tiered adjustment simultaneously affects the energy transfer status of multiple transmission paths. These paths are not independent but rather mutually coupled. Calculating only the basic change of a single transmission path ignores the coupling effect, leading to distorted results. Analyzing the correlation of coherence coefficients among all transmission paths and comparing the stability of these coefficients before and after adjustment determines the strength of their mutual influence, thus determining the coupling influence coefficient for each path. If the coherence coefficients of two transmission paths show a highly consistent trend, it indicates strong coupling, and a higher coupling influence coefficient is assigned accordingly; conversely, a lower coupling influence coefficient is assigned. Based on the change in the energy proportion of the target frequency signal after adjustment, the basic change in the contribution of each transmission path is calculated. This involves comparing the difference in the energy proportion of the target frequency signal before and after adjustment to preliminarily determine the trend and approximate magnitude of the increase or decrease in contribution.

[0065] By combining the basic change amount of each transmission path with the corresponding coupling influence coefficient, the basic change amount is corrected. If the coupling effect is strong, the amplitude of the basic change amount is appropriately adjusted to offset the error caused by mutual interference between transmission paths. This yields the adjusted contribution change amount of each transmission path, clarifying the change in energy transfer capacity of each path. This approach fully considers the interaction between transmission paths, avoids deviations caused by isolated calculations, and ensures that the combined correction of the basic change amount and the coupling influence coefficient allows the contribution change amount to truly reflect the actual state of each transmission path after overall regulation, thus improving the accuracy and reliability of the calculation results.

[0066] In graded adjustments, the parameter adjustments at different adjustment nodes vary, and their contributions to overall vibration suppression also differ. Directly using an average calculation method would ignore the influence of the proportion of parameter adjustments, leading to a one-sided calculation of amplitude attenuation rate. Furthermore, the compatibility coefficient between the transmission path and the adjustable steel support would affect the implementation of the adjustment effect. By mapping the proportion of each adjustment node's parameter adjustment to the total adjustment of all adjustment nodes directly to a corresponding weighting coefficient, the adjustment node with a higher proportion of parameter adjustments receives a larger weighting coefficient, indicating a higher weight in its impact on overall vibration suppression. Combining the floor slab vibration displacement increment after each adjustment level, all vibration displacement increments are integrated and calculated using the weighting coefficients to obtain the weighted vibration amplitude after graded adjustments. This means that the vibration displacement increment corresponding to the adjustment node with a high proportion of parameter adjustments has a higher weight in the total vibration amplitude, truly reflecting the degree of influence of different adjustment node controls on overall vibration.

[0067] By comparing the original vibration amplitude before adjustment with the weighted vibration amplitude after adjustment, the initial amplitude attenuation rate is obtained by calculating the ratio of the difference between the two to the original vibration amplitude, thus making a preliminary judgment on the overall vibration suppression effect. The adaptation coefficients of the transmission path and the adjustable steel support are retrieved to correct the initial amplitude attenuation rate. If the adaptation coefficient of a certain transmission path is high, it indicates that its adjustment effect is fully realized and no significant correction is needed. If the adaptation coefficient is low, it indicates that its adjustment effect has not been fully implemented, and the magnitude of the initial amplitude attenuation rate needs to be appropriately adjusted to offset the effect deviation caused by insufficient adaptation. Finally, the amplitude attenuation rate of the floor slab after adjustment is obtained. This fully reflects the difference in contribution of different adjustment nodes to the overall suppression effect, avoids the distortion caused by averaging calculations, and ensures that the amplitude attenuation rate truly reflects the actual implementation of the adjustment effect, taking into account both the control action and the support adaptation, thereby improving the objectivity and comprehensiveness of the evaluation results.

[0068] S4. When the contribution change and amplitude attenuation rate reach the target threshold for multiple consecutive sampling periods, lock the tuning parameter combination; otherwise, analyze the feedback data to adjust the solution parameters of the optimization function and the analysis parameters of the vibration signal.

[0069] Specifically, adjusting the solution parameters of the optimization function includes: When the contribution change and amplitude attenuation rate do not reach the target threshold, the feedback data is quantified and decomposed into feedback characteristics including vibration displacement deviation, excessive strain increment and vibration strain coordination imbalance. Adjust the bias factor for solving the parameters corresponding to the vibration displacement deviation, adjust the cross-fit constraint corresponding to the strain increment exceeding the standard, and adjust the coordination factor corresponding to the vibration strain synergistic imbalance, and lock the solution parameters to be adjusted. The adjustment ratio of the solution parameters is set according to the degree of deviation of the feedback characteristics. If the solution is obtained by gradient descent, the step size coefficient is adjusted according to the degree of deviation of vibration displacement. If the solution is obtained by segmentation, the interval of segmentation is adjusted according to the degree of deviation of strain increment exceeding the standard, so as to adjust the solution parameters of the optimization function.

[0070] When the contribution change and amplitude attenuation rate do not reach the target threshold, the feedback data represents the overall result. Without breaking it down into specific features, it is impossible to pinpoint the core issue of parameter adaptation. By retrieving the synchronized feedback data after graded adjustment and the adjusted target frequency signal, and using the preset target threshold as a reference, the feedback data is compared and analyzed one by one. If there is a difference between the actual vibration displacement of the floor slab and the target suppression state, and this difference causes the overall suppression effect to fail to meet the standard, it is judged as vibration displacement deviation. If the actual strain increment of the adjustable steel support exceeds the expected adaptation range, it is judged as strain increment exceeding the standard. If the change in vibration displacement and the change in strain increment are not synchronized, causing an imbalance in the control logic, it is judged as vibration strain coordination imbalance. Through this process, the core problem of failure is broken down into three clear feedback features. Thus, the overall failure result is broken down into specific problem features, avoiding the blind adjustment of solution parameters. The feedback features accurately correspond to the core problem dimensions, ensuring that subsequent adjustments can directly address the root cause and improve the targeting of solution parameter optimization.

[0071] Different feedback characteristics correspond to different core functions of the optimization function. Without establishing a clear mapping, the core problem and the solution parameters will be misaligned. Adjusting the bias factor for the solution of vibration displacement corresponding to the parameter is aimed at strengthening the control guidance of high contribution transmission paths. By adjusting the bias factor for parameter solution, the tuning parameter solution will focus more on the stiffness and damping ratio parameters corresponding to the core path, increasing the exploration priority of these parameters in the iteration, and making up for the problem of poor displacement suppression effect caused by insufficient control of the core path. Adjusting the cross-fitting constraint corresponding to the excessive strain increment is aimed at optimizing the fitting relationship of the tuning parameters. By adjusting the parameter constraint space, the cooperative boundary between tuning parameters is clarified, avoiding strain overshoot caused by excessive adjustment of a single parameter, and ensuring that the combination of tuning parameters is compatible with the bearing capacity of the adjustable steel support.

[0072] The adjustment of the coordination factor corresponding to the vibration-strain imbalance is specifically aimed at balancing the control rhythm of vibration suppression and support safety. By adjusting the coordination factor, the adjustment rhythm of the optimization target parameters is synchronized, avoiding the imbalance where vibration displacement decreases but strain increment still increases. Ultimately, the solution parameters to be adjusted are locked. The precise mapping between feedback characteristics and solution parameters and the clear adjustment direction avoid misalignment and ambiguity in solution parameter adjustment. The locked parameters focus on the key aspects of solving the optimization function, and the adjustment direction directly addresses the root cause of the problem, improving the pertinence and operability of solution parameter optimization.

[0073] Different feedback characteristics exhibit varying degrees of deviation, necessitating differentiated adjustment of the corresponding solution parameters to avoid over- or under-adjustment. The degree of deviation for each feedback characteristic should be evaluated individually, comparing the actual feedback data with the target threshold. Adjustment ratios for corresponding solution parameters should be set according to the degree of deviation; higher deviations require larger adjustment ratios, ensuring more concentrated adjustment resources for the core problem. If gradient descent is used initially, when adjusting parameters to solve for the bias factor, the control weight of the core path should be strengthened based on the degree of vibration displacement deviation. For larger deviations, the influence range of the bias factor on the core path parameters should be expanded, prioritizing the core path parameters in each iteration. Explore optimization directions for stiffness and damping ratio corresponding to the core path and shorten the iterative exploration cycle of these parameters to quickly compensate for insufficient displacement suppression. When the deviation is small, only slightly increase the iteration priority of the core path parameters to avoid over-adjustment that could trigger new vibration fluctuations. When adjusting the coordination factor, optimize the target balance rhythm based on the degree of deviation in the vibration-strain imbalance. When the deviation is large, adjust the coordination factor to give moderate priority to transmission path control, and then simultaneously promote vibration suppression after the strain increment stabilizes to ensure that both change synchronously. When the deviation is small, fine-tune the balance weight of the coordination factor to allow the parameter adjustment rhythm of the optimization target to be slightly adapted, thus resolving minor imbalances.

[0074] If a piecewise solution method is used in the early stage, when adjusting the cross-fit constraints, the fitting relationship of the tuning parameters should be optimized according to the degree of deviation of the strain increment exceeding the limit. When the deviation is large, the coordination logic between the tuning parameters in the cross-fit constraints should be refined, and the upper limit of the fitting between the damping ratio and mass after stiffness adjustment should be clarified. For example, a linkage ratio between the stiffness adjustment range and the damping ratio adjustment range should be set to avoid over-adjustment of a single parameter. At the same time, a strain prediction link should be added to the constraints to check whether the parameter adjustment will cause strain exceeding the limit before adjusting the parameters, so as to avoid risks in advance. When the deviation is small, only the adjustment in the cross-fit constraints should be fine-tuned. The adaptation boundary of the tuning parameters ensures a slight fit between the tuning parameter combination and the load-bearing capacity of the adjustable steel support, without affecting the overall solution efficiency. When adjusting the parameter solution bias factor, the priority of segmented solution of the core path is focused according to the degree of deviation of vibration displacement. When the deviation is large, the solution of the stiffness corresponding to the core path is promoted in the segmented solution, and the refinement of the stiffness solution is extended. Then, the mass and damping ratio are solved based on the stiffness results to strengthen the control of the core path. When the deviation is small, the priority of segmented solution of the core path parameters is slightly increased to maintain the overall solution rhythm.

[0075] If the deviation of the vibration-strain coordination imbalance is significant, the logic of the coordination factor is directly adjusted from a balanced type to a synchronous type. This binds the adjustment steps of vibration displacement-related parameters and strain-related parameters together. For example, after adjusting the vibration displacement, the strain increment must be stabilized before the next round of adjustment. By binding the steps, the rhythm is synchronized. When the deviation is small, only the intensity of the coordination factor is finely adjusted to shorten the response delay of the two types of parameter adjustments and resolve minor imbalances. The adjustment ratio is set according to the degree of deviation to achieve a precise match between the adjustment intensity and the severity of the problem. Combined with the solution logic, the specific adjustment operations of the solution parameters are refined to avoid ambiguity in the adjustment and greatly improve the adaptability and accuracy of the optimization function solution parameters, laying a reliable foundation for the subsequent secondary solution of the optimization function.

[0076] Specifically, adjusting the analysis parameters of the vibration signal includes: When the contribution change and amplitude decay rate do not reach the target threshold, the failure scenarios of dominant excitation frequency identification deviation, transmission path misjudgment and contribution degree calculation distortion are matched by combining feedback data, contribution change and amplitude decay rate. Adjust the time-frequency processing parameters corresponding to the identification deviation of the dominant excitation frequency, adjust the correlation threshold of the coherence coefficient corresponding to the misjudgment of the transmission path, and adjust the fusion logic in the contribution calculation corresponding to the distortion of the contribution calculation, and lock the analysis parameters of the vibration signal to be adjusted. The adjustment ratio is set according to the degree of deviation of the failure scenario. If the stability of the dominant excitation frequency is not greater than the fluctuation threshold, the number of sampling periods is increased. If the stability of the dominant excitation frequency is greater than the fluctuation threshold, the adjustment step size of the time-frequency processing parameters is increased to adjust the analysis parameters of the vibration signal.

[0077] When the contribution change and amplitude attenuation rate do not reach the target threshold, in addition to the problem of optimizing the function to solve the parameters, the deviation in the setting of the vibration signal analysis parameters will also lead to the misjudgment of the dominant excitation frequency, the transmission path, and the distortion of the contribution calculation in the early stage, thus affecting the control effect. By retrieving the feedback data and the calculated contribution change and amplitude attenuation rate for comprehensive comparison and analysis, if there is a continuous difference between the actual monitored vibration signal dominant frequency and the previously locked dominant excitation frequency, and this difference causes the control target to deviate from the resonance core, the failure scenario is determined to be a deviation in the dominant excitation frequency identification; if the coherence coefficient of the transmission path is correlated... The results do not match the actual energy transfer path, leading to high-contribution paths not being prioritized for control or secondary paths being over-controlled, which is judged as a misjudgment of the transfer path. If the calculated result of the contribution change is inconsistent with the actual energy transfer change trend reflected by the feedback data, and cannot be corrected by adjusting the optimization function parameters, it is judged as a distortion in the contribution calculation. Through this process, the failure scenario is accurately matched. By comprehensively comparing multi-dimensional data, the misjudgment of failure scenarios caused by a single data dimension is avoided. The scenario accurately corresponds to the core link of vibration signal analysis, ensuring that subsequent adjustments can directly target the root cause of the deviation of the analysis parameters and improve the targeting of the analysis parameter optimization.

[0078] Different failure scenarios stem from different functional defects in vibration signal analysis. Without establishing a clear mapping, misalignment between functional defects and analysis parameters can occur. Based on the correspondence between three types of failure scenarios and vibration signal analysis parameters: For the deviation in identifying the dominant excitation frequency, the core issue is that the time-frequency processing parameters fail to accurately capture frequency characteristics. Therefore, the adjustment of the corresponding time-frequency processing parameters should focus on optimizing the fit between frequency resolution and time window length, allowing time-frequency processing to more accurately extract the dominant frequency components in the vibration signal. For the misjudgment of the transmission path, the core issue is that the correlation threshold of the coherence coefficient is set unreasonably, leading to incorrect judgment of the transmission path correlation. Therefore, the adjustment of the correlation threshold of the corresponding coherence coefficient should focus on optimizing the judgment criteria for the correlation threshold, clarifying the minimum correlation degree that the coherence coefficients between transmission paths must meet, and avoiding misjudging weakly correlated paths as effective transmission paths or omitting strongly correlated paths.

[0079] The core issue of distorted contribution calculation lies in the failure of the fusion logic used to calculate the contribution to align with the actual energy transfer characteristics. Therefore, adjustments to the fusion logic in the contribution calculation are necessary. Specifically, the adjustment direction involves optimizing the fusion weights of energy proportion and coherence coefficient to ensure that the contribution calculation accurately reflects the energy transfer impact of each transmission path. The time-frequency processing parameters, the correlation threshold of the coherence coefficient, and the fusion logic for calculating the contribution are identified as the analytical parameters to be adjusted. A precise mapping between failure scenarios and analytical parameters, along with a clear adjustment direction, is crucial to avoid misalignment and ambiguity in parameter adjustments. The identified analytical parameters focus on key aspects of vibration signal analysis, and the adjustment direction directly addresses the root cause of failure, improving the targeting and operability of analytical parameter optimization.

[0080] Different failure scenarios exhibit varying degrees of deviation, necessitating differentiated adjustments to the corresponding analysis parameters. The degree of deviation for each of the three failure scenarios is assessed by comparing the current settings of the analysis parameters with actual requirements. Adjustments to the corresponding analysis parameters are then made according to the degree of deviation; higher deviations require larger adjustments to ensure that core failure scenarios receive more concentrated adjustment resources. Considering the relationship between the stability of the dominant excitation frequency and the fluctuation threshold, differentiated adjustments are implemented. If the stability of the dominant excitation frequency is not greater than the fluctuation threshold, it indicates that the dominant excitation frequency is stable. In this case, for the time-frequency processing parameters corresponding to the deviation in the dominant excitation frequency identification, the representativeness of the signal samples is improved by increasing the number of sampling periods. Simultaneously, the frequency resolution of the time-frequency processing parameters is optimized according to the adjustment ratio, making the identification of stable frequencies more accurate. For the correlation threshold of the coherence coefficient corresponding to misjudgment of the transmission path, the correlation threshold is fine-tuned according to the degree of deviation. When the deviation is large, the correlation threshold is appropriately lowered to avoid missing strongly correlated paths; when the deviation is small, the correlation threshold is slightly increased to eliminate weak correlation interference. For the fusion logic corresponding to the distortion in contribution calculation, the fusion weights of energy proportion and coherence coefficient are optimized according to the degree of deviation, making the calculation results more consistent with the actual energy transmission trend.

[0081] If the stability of the dominant excitation frequency is greater than the fluctuation threshold, it indicates that the frequency fluctuation is severe. In this case, the adjustment step size of the time-frequency processing parameters should be increased to accelerate the adaptation of the time-frequency processing parameters to the dynamic changes in the frequency. At the same time, the adjustment range of the time window length in the time-frequency processing parameters should be expanded proportionally to ensure that the characteristics of the fluctuating frequency can be captured quickly. For the correlation threshold of the coherence coefficient, the adjustment range should be increased according to the degree of deviation. When the degree of deviation is large, the correlation threshold should be significantly optimized to clarify the path correlation under fluctuating conditions and avoid misjudgment. For the fusion logic of contribution calculation, the adjustment pace should be increased according to the degree of deviation so that the fusion logic can quickly adapt to the changes in energy transfer caused by frequency fluctuations and reduce calculation distortion. Throughout the adjustment process, the degree of deviation of the failure scenario should always be the core basis to ensure that the adjustment actions are both in line with the frequency state and directly address the defects of the analysis parameters.

[0082] The adjustment ratio is set according to the degree of deviation to achieve a precise match between the adjustment intensity and the severity of failure, avoiding ineffective one-size-fits-all adjustments. Combined with differentiated operation based on the stability of the dominant excitation frequency, the time-frequency processing parameters can adapt to both the accurate identification requirements of stable operating conditions and the dynamic adaptation requirements of fluctuating operating conditions. At the same time, the coherence coefficient threshold and fusion logic are optimized to comprehensively improve the accuracy and reliability of vibration signal analysis, providing a precise signal analysis basis for subsequent control. The analysis parameters of the adjusted vibration signal directly determine the accuracy of subsequent dominant excitation frequency identification, transmission path misjudgment, and contribution calculation, ensuring that subsequent control actions are based on accurate signal analysis results.

Claims

1. A method for adjustable suppression of resonance in a floor slab, characterized in that, include: Vibration sensors and adjustable steel supports are deployed on the floor slab to acquire vibration signals of the floor slab in real time. The vibration signals are analyzed to identify the dominant excitation frequency and the transmission path of vibration energy and its contribution. When the ratio of the dominant excitation frequency to the natural frequency of the floor slab falls within the preset resonance range, the contribution of the transmission path and the vibration amplitude of the floor slab are used as optimization objectives to construct an optimization function. Within the parameter constraint space of the adjustable steel support, the optimization function is solved to generate a combination of tuning parameters. The adjustable steel support is driven to be adjusted in stages according to the combination of tuning parameters. After the stage adjustment, the vibration displacement of the floor slab and the strain increment of the adjustable steel support are obtained synchronously as feedback data, and the contribution change of the transmission path and the amplitude attenuation rate of the floor slab after adjustment are recalculated. When the contribution change and amplitude attenuation rate reach the target threshold for multiple consecutive sampling periods, the tuning parameter combination is locked; otherwise, the feedback data is analyzed to adjust the solution parameters of the optimization function and the analysis parameters of the vibration signal.

2. The method for adjustable resonance suppression of a floor slab as described in claim 1, characterized in that, The identified dominant excitation frequency includes: Vibration sensors and adjustable steel supports are deployed on the floor slab to acquire the vibration signal of the floor slab in real time. The vibration signal is then processed to reduce noise and obtain an effective vibration signal. The time-frequency processing parameters are adaptively adjusted based on the amplitude fluctuation characteristics of the effective vibration signal, and the effective vibration signal is subjected to joint time-frequency decomposition to form a frequency candidate set. The energy stability of the effective vibration signal and the response sensitivity of the floor slab are integrated to construct a dynamic weight. The evaluation index of different frequencies in the frequency candidate set is calculated iteratively, and the frequency with the largest evaluation index in multiple consecutive sampling periods is locked as the dominant excitation frequency.

3. The method for adjustable resonance suppression of a floor slab as described in claim 2, characterized in that, Identifying the transmission path of the vibrational energy and its contribution includes: Based on the locked dominant excitation frequency, the target frequency signal corresponding to the dominant excitation frequency and located at the deployment position of the vibration sensor and the adjustable steel support is extracted. Calculate the coherence coefficient of the target frequency signal between the vibration sensor and the adjustable steel support, filter the associated links with coherence coefficients greater than the association threshold, and identify candidate transmission paths; The contribution of different candidate transmission paths is calculated by integrating the energy proportion of the target frequency signal in the candidate transmission paths, the vibration amplitude of the floor slab, and the strain sensitivity of the adjustable steel support. The contribution rates are sorted in descending order, and the stability of the coherence coefficient and the degree of fluctuation of the contribution rate are verified through multiple consecutive sampling periods to pinpoint the transmission path of vibration energy and its contribution rate.

4. The method for adjustable resonance suppression of a floor slab as described in claim 3, characterized in that, The construction optimization function includes: When the ratio of the dominant excitation frequency to the natural frequency of the floor slab falls within the preset resonance range, the locked transmission path and its contribution are extracted. The optimization objectives are the contribution of the transmission path and the vibration amplitude of the floor slab, and the weighting coefficients are determined based on the contribution ratio of different transmission paths and the degree of deviation of the vibration amplitude. Based on the correlation between the contribution of the transmission path and the strain sensitivity of the adjustable steel brace, the fit coefficient between the transmission path and the adjustable steel brace is determined, and a parameter constraint space is formed by combining the physical structure of the adjustable steel brace. Based on the stability of the dominant excitation frequency, a coordination factor is set to embed the conflict coordination term of the optimization objective, and the optimization objective, weight coefficients, parameter constraint space and conflict coordination term are integrated to construct the optimization function.

5. The adjustable resonance suppression method for a floor slab as described in claim 4, characterized in that, The generated tuning parameter combination includes: When the stability of the dominant excitation frequency is not greater than the fluctuation threshold, the optimization function is solved by gradient descent. When the stability of the dominant excitation frequency is greater than the fluctuation threshold, the optimization function is solved piecewise. During the solution process, for the adjustable steel supports corresponding to the transmission paths that are determined to have high contribution, parameters are set to solve for the bias factor, and cross-fitting constraints are embedded according to the adaptation relationship of the tuning parameters. Multiple candidate tuning parameters are generated based on the solution results of the optimization function. The candidate tuning parameters are then screened based on the coordination factor to generate a combination of tuning parameters.

6. The method for adjustable suppression of resonance in a floor slab as described in claim 5, characterized in that, The tiered adjustment includes: Based on the ranking results of the contribution of the transmission path, the tuning parameter combination is decomposed into adjustment nodes corresponding to the transmission path, and the parameter adjustment amount of the adjustment node is allocated according to the proportion of contribution. The adjustable steel support is driven to perform graded adjustments according to the ranking of contribution. Before each adjustment, the parameter adjustment amount of the corresponding adjustment node is corrected based on the adaptation coefficient between the transmission path and the adjustable steel support. During each adjustment process, the strain increment change rate of the adjustable steel support is monitored in real time. When the strain increment change rate exceeds the safety threshold, the parameter adjustment amount of the adjustment node is compensated.

7. The method for adjustable resonance suppression of a floor slab as described in claim 6, characterized in that, The calculated change in the contribution of the adjusted transmission path and the amplitude attenuation rate of the floor slab include: After graded adjustment, the vibration displacement of the floor slab and the strain increment of the adjustable steel support are acquired synchronously as feedback data, and the target frequency signals of the vibration sensor and the adjustable steel support after graded adjustment are extracted. The coupling influence coefficient is determined based on the correlation between the coherence coefficients of the transmission paths. Combined with the change in the energy proportion of the target frequency signal, the basic change in the contribution of the transmission path is calculated. The basic change is corrected by the coupling influence coefficient to obtain the adjusted change in the contribution of the transmission path. The proportion of parameter adjustment at the adjustment node is mapped to a weighting coefficient, and the weighted vibration amplitude after graded adjustment is calculated by combining the vibration displacement increment of the floor slab after each adjustment. The initial amplitude attenuation rate is obtained by dividing the difference between the vibration amplitude before adjustment and the weighted vibration amplitude by the vibration amplitude before adjustment. The initial amplitude attenuation rate is then corrected based on the adaptation coefficient of the transmission path and the adjustable steel support to obtain the amplitude attenuation rate of the floor slab after adjustment.

8. The method for adjustable resonance suppression of a floor slab as described in claim 7, characterized in that, The solution parameters of the adjustment optimization function include: When the contribution change and amplitude attenuation rate do not reach the target threshold, the feedback data is quantified and decomposed into feedback characteristics including vibration displacement deviation, excessive strain increment and vibration strain coordination imbalance. Adjust the bias factor for solving the parameters corresponding to the vibration displacement deviation, adjust the cross-fit constraint corresponding to the strain increment exceeding the standard, and adjust the coordination factor corresponding to the vibration strain synergistic imbalance, and lock the solution parameters to be adjusted. The adjustment ratio of the solution parameters is set according to the degree of deviation of the feedback characteristics. If the solution is obtained by gradient descent, the step size coefficient is adjusted according to the degree of deviation of vibration displacement. If the solution is obtained by segmentation, the interval of segmentation is adjusted according to the degree of deviation of strain increment exceeding the standard, so as to adjust the solution parameters of the optimization function.

9. The method for adjustable suppression of resonance in a floor slab as described in claim 8, characterized in that, Adjusting the analysis parameters of the vibration signal includes: When the contribution change and amplitude decay rate do not reach the target threshold, the failure scenarios of dominant excitation frequency identification deviation, transmission path misjudgment and contribution degree calculation distortion are matched by combining feedback data, contribution change and amplitude decay rate. Adjust the time-frequency processing parameters corresponding to the identification deviation of the dominant excitation frequency, adjust the correlation threshold of the coherence coefficient corresponding to the misjudgment of the transmission path, and adjust the fusion logic in the contribution calculation corresponding to the distortion of the contribution calculation, and lock the analysis parameters of the vibration signal to be adjusted. The adjustment ratio is set according to the degree of deviation of the failure scenario. If the stability of the dominant excitation frequency is not greater than the fluctuation threshold, the number of sampling periods is increased. If the stability of the dominant excitation frequency is greater than the fluctuation threshold, the adjustment step size of the time-frequency processing parameters is increased to adjust the analysis parameters of the vibration signal.

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