A large-span air plenum membrane chamber air pressure monitoring system

Abstract

The present invention relates to the technical field of air pressure monitoring and discloses a large-span air plenum membrane chamber air pressure monitoring system, which comprises the following steps: inflating each air plenum membrane chamber; loading a golden nested perturbation pressure spectrum; applying the golden nested perturbation pressure spectrum to each air plenum membrane chamber, and acquiring air pressure data and displacement data of each air plenum membrane chamber; extracting frequency domain energy attenuation characteristics from the air pressure data; extracting cross-rib synchronous compliance wave characteristics from the displacement data; calculating coupling characteristics based on the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics; and comparing the coupling characteristics with a preset benchmark coupling characteristics to obtain a health status level.

Description

Technical Field

[0001] The present invention relates to the field of air pressure monitoring, and more particularly to an air pressure monitoring system for a large-span air plenum membrane chamber. Background Art

[0002] Large-span air-ribbed membrane structures, thanks to their lightweight construction and rapid on-site deployment and dismantling, have been widely used in venues such as stadiums and large-scale exhibitions, where rapid construction and the ability to withstand wind and snow loads are crucial. These structures primarily consist of several air-filled ribs and a flexible membrane surface. The internal air pressure not only tensions the membrane but also directly determines the overall structural rigidity and stability.

[0003] In actual operation, to ensure that the membrane chamber does not become unstable due to insufficient internal pressure or rupture due to overload due to excessive internal pressure, the air pressure of each air-rib membrane chamber must be monitored in real time. The existing technology usually installs an independent pressure sensor in each air-rib chamber to achieve air pressure monitoring.

[0004] However, the existing technology still has the following major problems in the large-span, multi-rib connected air membrane system:

[0005] Under interference from wind loads, temperature changes, and other factors, the pressure sensor output is prone to drift, but it is impossible to determine whether it is a true loss of pressure or environmental impact through a single pressure value, resulting in false alarms in the air pressure alarm and other problems. Summary of the Invention

[0006] The present invention provides a large-span air plenum membrane chamber air pressure monitoring system to solve the technical problems raised in the background technology.

[0007] The present invention provides a large-span air plenum membrane chamber air pressure monitoring system, comprising:

[0008] The pre-processing module is used to inflate each air-pleural membrane chamber and load the golden nested perturbation spectrum;

[0009] A data acquisition module is used to apply the golden nested perturbation pressure spectrum to each air pleural membrane chamber and obtain the air pressure data and displacement data of each air pleural membrane chamber;

[0010] Feature extraction module, used to extract frequency domain energy attenuation features from pressure data; extract cross-rib synchronous compliance wave features from displacement data;

[0011] Characteristic coupling module, used to calculate the coupling characteristics based on the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics;

[0012] The status assessment module is used to compare the coupling characteristics with the preset benchmark coupling characteristics to obtain the health status level.

[0013] Furthermore, each pneumothorax chamber is inflated, including:

[0014] Inflate each pneumatic pleura chamber to the target pressure and maintain it at the target pressure for a preset time;

[0015] If the air pressure amplitude and temperature amplitude of any air plenum chamber in unit time are less than or equal to the preset air pressure threshold and the preset temperature threshold, the inflation process is completed.

[0016] Furthermore, the golden nested scrambling spectrum is loaded, including:

[0017] Based on the golden ratio relationship, carriers of multiple frequency levels are generated in sequence, and the carriers of multiple frequency levels are binary phase-encoded according to the length of the Fibonacci sequence to form a golden nested scrambling spectrum.

[0018] Furthermore, the golden nested perturbation spectrum is applied to each air plenum chamber, and the air pressure data and displacement data of each air plenum chamber are obtained, including:

[0019] Each air pleural membrane chamber is adjusted according to the golden nested perturbation pressure spectrum so that the internal air pressure of the air pleural membrane chamber is tracked with the golden nested perturbation pressure spectrum as the target, and the air pressure data and displacement data of each air pleural membrane chamber are collected with a unified clock.

[0020] Furthermore, frequency domain energy attenuation features are extracted from the air pressure data, including:

[0021] The air pressure data is divided into multiple frequency bands according to multiple preset excitation frequencies;

[0022] The power spectral density is estimated after denoising for each frequency band to obtain the frequency domain energy attenuation characteristics corresponding to each excitation frequency.

[0023] Furthermore, the cross-rib synchronous compliance wave characteristics are extracted from the displacement data, including:

[0024] According to the layout direction of the pneumatic plenum, the displacement data of each pneumatic plenum are phase tracked and amplitude statistics are performed to form a dynamic waveform propagating along the layout direction to extract the cross-rib synchronous compliance wave characteristics.

[0025] Furthermore, the coupling characteristics are calculated based on the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics, including:

[0026] The frequency domain energy attenuation characteristics are combined according to the corresponding excitation frequency, and the cross-rib synchronous compliance wave characteristics are combined according to the layout position of the corresponding air-rib membrane chamber to construct a correlation analysis matrix;

[0027] Based on the correlation analysis matrix, the frequency domain energy attenuation characteristics are matched and compared with the cross-rib synchronous compliance wave characteristics to obtain the matching comparison results;

[0028] The matching results are fused into coupling features through a multi-channel information fusion strategy.

[0029] Furthermore, the coupling characteristics are compared with the preset benchmark coupling characteristics to obtain monitoring results, including:

[0030] Comparing the coupling signature with a pre-set benchmark coupling signature to determine a health status level;

[0031] Generate treatment plans based on health status levels.

[0032] The beneficial effects of the present invention are: by generating and injecting a golden nested disturbance pressure spectrum, the slight fluctuations of the membrane warehouse air pressure are regulated, the air pressure and multi-air rib displacement data are collected synchronously at high frequency, and the frequency domain energy attenuation feature extraction, cross-rib synchronous compliance wave feature analysis and the coupling calculation of the two are automatically completed. Compared with the existing technology, the automatic correlation analysis of wide-band energy attenuation and cross-rib phase gradient is realized, and the abnormal position of the membrane warehouse air pressure and the degree of structural damage can be identified quickly and accurately, which improves the comprehensiveness, accuracy and timeliness of air pressure monitoring, and effectively ensures the safety and stability of the operation of large-span air rib membrane warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a module diagram of a large-span air plenum membrane chamber air pressure monitoring system of the present invention. DETAILED DESCRIPTION

[0034] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0035] like Figure 1 As shown, a large-span air plenum membrane chamber air pressure monitoring system includes:

[0036] Inflate each pneumothorax compartment;

[0037] In one embodiment of the present invention, each pneumatic chamber is inflated, comprising:

[0038] Inflate each pneumatic pleura chamber to the target pressure and maintain it at the target pressure for a preset time;

[0039] If the air pressure amplitude and temperature amplitude of any air plenum chamber in unit time are less than or equal to the preset air pressure threshold and the preset temperature threshold, the inflation process is completed.

[0040] It should be noted that the purpose of inflating to the target pressure and maintaining it for the preset time includes:

[0041] During rapid inflation, the gas compresses and generates heat, causing both the temperature and pressure inside the chamber to fluctuate simultaneously. Directly determining that the pressure is stable would mask secondary pressure changes after the temperature drops. By maintaining the target pressure for a preset period of time, the gas temperature can gradually return to a state of equilibrium close to the ambient temperature, allowing the internal pressure to truly reach a steady state, rather than the false high pressure caused by thermal expansion and contraction. Only after both thermal and pressure equilibrium have reached a steady state, and only then performing subtle perturbations and data collection, can we ensure that the measured pressure changes are primarily due to structural compliance and leakage, rather than residual thermal effects.

[0042] The preset air pressure threshold and the preset temperature threshold are both custom parameters and can be manually set based on experts in this field.

[0043] Load the golden nested scrambled spectrum;

[0044] It should be noted that the purpose of loading the golden nested perturbation spectrum is to provide a wide-band, multi-modal, and highly sensitive excitation input for the large-span air-pleated membrane chamber, so as to fully stimulate the structural response of the air-pleated membrane chamber.

[0045] In one embodiment of the present invention, loading the golden nested scrambling spectrum includes:

[0046] Based on the golden ratio relationship, carriers of multiple frequency levels are generated in sequence, and the carriers of multiple frequency levels are binary phase-encoded according to the length of the Fibonacci sequence to form a golden nested scrambling spectrum.

[0047] In one embodiment of the present invention, multiple frequency level carriers are sequentially generated based on the golden ratio relationship, and binary phase encoding is performed on the multiple frequency level carriers according to the length of the Fibonacci sequence to form a golden nested scrambling spectrum, including:

[0048] Select the initial excitation frequency , and set the excitation frequency level to ;

[0049] At the initial excitation frequency As the starting point, generate incrementally according to the golden ratio Level excitation frequency; where the golden ratio is , ;

[0050] Calculate the k-th excitation frequency ; Wherein, 1≤k≤K, k is a positive integer;

[0051] Construct the Fibonacci sequence. The dimension of the Fibonacci sequence is ; Among them, the first term in the Fibonacci sequence , the second item ; Then the nth term in the Fibonacci sequence is: ; Wherein, 3≤n≤K, n is a positive integer;

[0052] For the k-th level excitation frequency, take the Fibonacci number As the phase coding sequence length ;

[0053] The length of the generated phase encoding sequence is The binary sequence , ;in, The value of each element in is 0 or 1;

[0054] Defining Phase Offset , ;

[0055] For the k-th level excitation frequency, in the time interval Within, generate with phase offset Sinusoidal carrier wave;

[0056] Synchronize all The carrier of the level excitation frequency is superimposed with the target pressure , forming a golden nested perturbation spectrum ; represents the time variable, reflecting the time elapsed from the generation of the golden nested perturbation spectrum. Indicates the carrier amplitude corresponding to the k-th excitation frequency;

[0057] It should be noted that the initial excitation frequency is selected , and set the excitation frequency level to To determine the starting frequency of the excitation signal and the number of frequency coverage levels, it lays the foundation for the subsequent generation of broadband excitation signals. For example, , , it means from Start, generate The excitation signals of different frequencies are used.

[0058] At the initial excitation frequency As the starting point, according to the golden ratio Incremental Generation Level excitation frequency; Among them, the golden ratio is the mathematical golden ratio, ≈1.618, with non-integer frequency multiplication characteristics, it can avoid harmonic overlap between the excitation frequencies at each level, ensuring that the excitation signal can cover a wider frequency band and stimulate the multi-modal response of the structure. For example, ,but , , and so on;

[0059] Calculate the k-th excitation frequency , to determine the excitation frequency of each level, ensure that the frequencies of each level increase according to the golden ratio law, and achieve broadband coverage.

[0060] Construct the Fibonacci sequence. The increasing characteristic of the Fibonacci sequence can generate a non-repeating sequence length for subsequent phase encoding, enhance the complexity and uniqueness of the excitation signal, and avoid periodic repetition of phase encoding. For example, , then the Fibonacci sequence is .

[0061] For the k-th level excitation frequency, take the Fibonacci number As the phase coding sequence length ; thus using the Fibonacci numbers To ensure the uniqueness of the phase coding sequence, a specific length is assigned to each level of excitation frequency to match the phase coding with the frequency characteristics, thereby improving the randomness and broadband characteristics of the signal. For example, k = 3, , then the phase coding sequence length By clarifying and The corresponding relationship ensures that the phase encoding length changes with the frequency level.

[0062] The length of the generated phase encoding sequence is The binary sequence , the phase offset is controlled by a binary sequence (0 or ), which causes the excitation signal to produce phase jumps at the starting points of different cycles, thereby enhancing the signal's sensitivity to structural defects.

[0063] Defining Phase Offset , , thus converting the binary sequence Converted into a specific phase offset (0 or π), the carrier of each excitation frequency has different phases at the starting point of different cycles, highlighting the difference in the structure's response to phase changes. For example, ,but , ,but ; By converting the binary code into the actual phase offset, Ensure that the phase shift is only 0 or π, forming a clear phase jump.

[0064] For the k-th level excitation frequency, in the time interval Within, generate with phase offset In a specific time interval, a sinusoidal carrier is generated by combining the phase offset, so that the excitation signal presents periodic changes in the time domain, and the phase of the starting point of each cycle is affected by control, and finally superimpose to generate a complex disturbance pressure spectrum. For example, , then the period , For the first cycle, is the second period, and so on; in the first interval, if , then generate , in the second interval, if , then generate Time interval By the excitation frequency Determine the duration of each cycle to ensure the periodicity of the carrier in the time domain. is the formula for a sinusoidal carrier with a phase shift, Control frequency characteristics, Introducing phase jump.

[0065] The target pressure is the baseline pressure for normal operation of the plenum chamber. This is achieved by superimposing the disturbance to ensure that it is near the normal operating pressure of the plenum chamber, avoiding deviation from the operating range.

[0066] is the sum of the K-level excitation frequency carriers, specifically:

[0067] It represents the carrier amplitude corresponding to the k-th excitation frequency, and determines the pressure fluctuation amplitude under the k-th excitation frequency; represents the phase shift, is a rounding function that is used to determine the phase encoding index corresponding to the k-th frequency according to the current time t. For example, , t = 5 seconds, then , ,Pick As a phase offset, the starting phase of each time period is switched according to a preset binary sequence, increasing signal complexity and sensitivity to structural response.

[0068] Applying the golden nested perturbation pressure spectrum to each air-pleated membrane compartment, and obtaining the air pressure data and displacement data of each air-pleated membrane compartment;

[0069] It should be noted that the golden nested perturbation spectrum is a wide-band, multi-modal, phase-jumping active excitation frequency (such as covering 6 levels of frequency from 0.05 to 0.82 Hz, and each frequency has a non-repeated phase encoding). After it is applied to the air-ribbed membrane chamber, the air pressure inside the air-ribbed membrane chamber will fluctuate slightly according to a preset rule (recorded as air pressure data), and at the same time, the air-ribbed structure will produce radial displacement due to the change in air pressure (recorded as displacement data). These two types of data together constitute the full-dimensional information of "excitation input → structural response". Under normal conditions, the correlation between air pressure fluctuations and displacement responses (phase synchronization, energy attenuation rate) has a stable rule; when the air-ribbed membrane chamber leaks or the stiffness degrades, the correlation will change;

[0070] In one embodiment of the present invention, a golden nested perturbation pressure spectrum is applied to each air-pleated membrane chamber, and air pressure data and displacement data of each air-pleated membrane chamber are obtained, including:

[0071] Each air pleural membrane chamber is adjusted according to the golden nested perturbation pressure spectrum so that the internal air pressure of the air pleural membrane chamber is tracked with the golden nested perturbation pressure spectrum as the target, and the air pressure data and displacement data of each air pleural membrane chamber are collected with a unified clock.

[0072] In one embodiment of the present invention, each pneumatic pleural diaphragm chamber is adjusted according to the golden nested perturbation pressure spectrum, so that the internal air pressure of the pneumatic pleural diaphragm chamber is tracked with the golden nested perturbation pressure spectrum as the target, and the air pressure data and displacement data of each pneumatic pleural diaphragm chamber are collected with a unified clock, including:

[0073] Based on the golden nested scrambling spectrum, the central control unit , the command is synchronously sent to the valve control unit corresponding to each pneumatic diaphragm chamber; the command contains the timestamp and the target pressure value at the corresponding moment, ensuring that the disturbance pressure spectrum received by all valve control units is completely consistent;

[0074] The valve control unit of each air-rib membrane chamber starts closed-loop control: real-time reading of the internal pressure of the membrane chamber , calculate and target pressure Tracking error , ;

[0075] Based on tracking error , adjust the opening of the proportional valve through the PID control algorithm;

[0076] The system master clock synchronizes the clocks of all collection nodes through GPS timing, ensuring that the sampling timing deviation of each node is less than or equal to the preset time error;

[0077] According to the preset sampling frequency ;in, , Represents the highest frequency of the golden nested perturbation spectrum; at the same sampling time , , M is the total number of sampling points, and the following parameters are collected: the i-th air pleura chamber at the sampling time Air pressure data , and the i-th air pleura chamber at the sampling time Displacement data .

[0078] It should be noted that To satisfy the Nyquist sampling theorem;

[0079] It should be noted that the air pressure data Based on absolute pressure sensor acquisition, displacement data Based on laser displacement meter acquisition;

[0080] In detail, all air chambers receive the same golden nested perturbation spectrum. , avoiding response deviations caused by excitation differences. For example, if two pneumatic pleura chambers are subjected to perturbations of different frequencies, their displacement responses cannot be directly compared, making it impossible to subsequently identify cross-pleura phase gradient anomalies. Unified clock calibration and fixed sampling times ensure that pressure and displacement data for all pneumatic pleura chambers are strictly aligned on the time axis.

[0081] Extract frequency domain energy attenuation features from air pressure data;

[0082] It should be noted that the energy attenuation at each excitation frequency is converted from the time-domain pressure response to the energy attenuation at each excitation frequency by extracting the frequency-domain energy attenuation feature from the pressure data;

[0083] In one embodiment of the present invention, extracting frequency domain energy attenuation features from air pressure data includes:

[0084] The air pressure data is divided into multiple frequency bands according to multiple preset excitation frequencies;

[0085] The power spectral density is estimated after denoising for each frequency band to obtain the frequency domain energy attenuation characteristics corresponding to each excitation frequency.

[0086] In one embodiment of the present invention, the pressure data is divided into multiple frequency bands according to a plurality of preset excitation frequencies; and after denoising each frequency band, the power spectrum density is estimated to obtain the frequency domain energy attenuation characteristics corresponding to each excitation frequency, including:

[0087] Time series air pressure data Perform sliding average filtering to obtain standard air pressure data B ;

[0088] Calculate standard atmospheric pressure data B and target air pressure The difference between ;

[0089] The preprocessed time series disturbance data Case incentive frequency Cycle Divide the perturbation data into multiple segments, each segment includes: sampling points;

[0090] Performing discrete Fourier transform on each disturbance data set to obtain a frequency domain representation of each disturbance data set;

[0091] Based on the frequency domain representation, each disturbance data set is calculated at the excitation frequency The power spectral density of

[0092] Calculate the adjacent perturbation data set at the excitation frequency The difference in power spectral density is taken as the excitation frequency. The frequency domain energy attenuation characteristics.

[0093] It should be noted that the time series air pressure data Perform sliding average filtering to obtain standard air pressure data B , wherein the sliding average filter effectively suppresses high-frequency noise (for example, sensor electronic noise, ambient airflow transient disturbance) by averaging the pressure data of multiple consecutive sampling points. Preferably, the size of the sliding average filter window is the period of the highest excitation frequency. ,For example is 0.82Hz, then the window size ;

[0094] Calculate standard atmospheric pressure data B and target air pressure The difference between , where the target pressure It is the reference pressure when the air chamber is working normally. After that, we get Only the pressure fluctuations caused by the gold nested perturbation spectrum are included, excluding the interference of the reference pressure;

[0095] The preprocessed time series disturbance data Case incentive frequency Cycle Divide the perturbation data into multiple segments, each segment includes: sampling points, where each level of excitation frequency With a specific cycle , according to this cycle Split the data to ensure that each disturbance data segment contains a complete fluctuation cycle. For example, if ,but s. The length of each perturbation data set By sampling frequency The above segmented processing method makes the energy of each disturbance data set more concentrated on the excitation frequency during frequency domain analysis. , reducing spectrum leakage.

[0096] Calculate the adjacent perturbation data set at the excitation frequency The difference in power spectral density is taken as the excitation frequency. The frequency domain energy attenuation characteristic is analyzed. The difference in power spectral density between adjacent segments reflects the change in energy over time. If the pneumothorax is structurally healthy, the energy attenuation is small and stable. However, if there is leakage or stiffness degradation, the energy at specific frequencies will decay more rapidly (with significant differences). Taking an average value can reduce the influence of random factors and obtain a stable frequency domain energy attenuation characteristic.

[0097] Extract cross-rib synchronous compliance wave characteristics from displacement data;

[0098] It is important to note that by analyzing the phase tracking and amplitude statistics of the displacement data of each air rib membrane compartment, a dynamic waveform propagating along the deployment direction is generated, which effectively reflects the displacement synchronization between air ribs. Under normal conditions, the displacement between air ribs should conform to a specific synchronization law. However, if a rib experiences local damage, its displacement response will deviate from the normal phase and amplitude characteristics, causing the cross-rib synchronous compliance wave characteristics to change.

[0099] In one embodiment of the present invention, extracting cross-rib synchronous compliance wave characteristics from displacement data includes:

[0100] According to the layout direction of the pneumatic plenum, the displacement data of each pneumatic plenum are phase tracked and amplitude statistics are performed to form a dynamic waveform propagating along the layout direction to extract the cross-rib synchronous compliance wave characteristics.

[0101] In one embodiment of the present invention, phase tracking and amplitude statistics are performed on the displacement data of each pneumatic pleural membrane chamber according to the layout direction of the pneumatic pleural membrane chamber to form a dynamic waveform propagating along the layout direction to extract the cross-rib synchronous compliance wave characteristics, including:

[0102] Displacement data for each pneumatic chamber Perform low-pass filtering to obtain standard displacement data ;

[0103] For standard displacement data Perform Hilbert transform to obtain the analytical signal as follows:

[0104]

[0105] in, Represents the analytical signal, Represents an imaginary unit , represents the Hilbert transform;

[0106] Based on analytical signal Calculate the instantaneous phase as follows:

[0107]

[0108] in, represents the instantaneous phase;

[0109] Calculate the displacement data corresponding to adjacent sampling moments As amplitude data ;

[0110] The horizontal axis is the layout direction of the air chamber, and the sampling time is As the vertical axis, the instantaneous phase of each air pleura membrane compartment is and amplitude data Mapped into a two-dimensional matrix, forming a dynamic waveform propagating along the layout direction ;

[0111] Based on dynamic waveform Calculate separately:

[0112] The rate of change of the instantaneous phase difference of adjacent pneumothorax capsules along the layout direction:

[0113] ;

[0114] in, Indicates the distance between adjacent pneumothorax compartments;

[0115] All pneumothorax chambers at the time of sampling The standard deviation of the amplitude is as follows:

[0116]

[0117] in, represents the standard deviation of the amplitude, Indicates the number of pneumopleural compartments, Indicates the amplitude data of each pneumothorax compartment The average value of

[0118] Propagation speed along the deployment direction , ;in, Indicates the phase difference between adjacent pneumothorax compartments, ;

[0119] Based on 、 and Combined construction of cross-rib synchronous compliant wave characteristics.

[0120] Displacement data for each pneumatic chamber Perform low-pass filtering to obtain standard displacement data , where displacement data There may be high frequency noise. Low pass filtering is done by setting the cutoff frequency (the cutoff frequency is preferably the highest frequency). 2 times of the excitation frequency) to filter out high-frequency interference and retain only the low-frequency effective signal related to the excitation frequency.

[0121] For standard displacement data Perform Hilbert transform to obtain analytical signal, where Hilbert transform generates standard displacement data The quadrature component (imaginary part) of Constructing analytical signals The analytical signal is a complex value that contains the amplitude and phase information of the displacement signal.

[0122] Based on analytical signal Calculate the instantaneous phase using the real part of the analytical signal and the imaginary part , calculate the instantaneous phase by the inverse tangent function . Instantaneous phase Reflects the displacement signal at each sampling moment phase state.

[0123] By calculating the difference in displacement between adjacent sampling moments , quantifying the rate of change of displacement and reflecting the amplitude change of displacement fluctuations. Combining amplitude data with instantaneous phase can more accurately describe the dynamic characteristics of the displacement signal.

[0124] The layout direction and sampling time of the air rib membrane chamber are used as coordinates, and the instantaneous phase and amplitude data of each air rib at different times are mapped into a two-dimensional matrix.

[0125] It reflects the spatial variation of the displacement fluctuation phase. When the structure is healthy and the coordination between the air ribs is good, the fluctuation should propagate evenly along the layout direction. If a certain air pleura compartment is damaged locally, it will interfere with the propagation of the wave, resulting in Jump.

[0126] Amplitude standard deviation Quantify the number of pneumothorax compartments at the time of sampling The degree of dispersion of displacement amplitude. When the membrane chamber structure is normal, the displacement amplitude of each air rib membrane chamber is consistent (uniformly excited). If a gas rib is damaged (e.g. leakage causing stiffness changes), its displacement amplitude will deviate from the normal range, causing Increase.

[0127] Propagation speed Phase difference between adjacent pneumothorax compartments ,spacing and excitation frequency The calculation shows that it reflects the speed of wave propagation along the direction of the air plenum chamber. When the structure is healthy, the stiffness is uniform. If the overall stiffness of the structure degrades or is partially damaged, the wave propagation characteristics will change, resulting in Decrease.

[0128] The coupling characteristics are calculated based on the energy attenuation characteristics in the frequency domain and the cross-rib synchronous compliance wave characteristics;

[0129] It should be noted that by integrating the multi-dimensional information of air pressure and displacement response, a more comprehensive and accurate structural health representation can be constructed to improve the ability to identify and determine abnormalities in large-span air pleura membrane chambers.

[0130] In one embodiment of the present invention, the coupling characteristics are calculated based on the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics, including:

[0131] The frequency domain energy attenuation characteristics are combined according to the corresponding excitation frequency, and the cross-rib synchronous compliance wave characteristics are combined according to the layout position of the corresponding air-rib membrane chamber to construct a correlation analysis matrix;

[0132] Based on the correlation analysis matrix, the frequency domain energy attenuation characteristics are matched and compared with the cross-rib synchronous compliance wave characteristics to obtain the matching comparison results;

[0133] The matching results are fused into coupling features through a multi-channel information fusion strategy.

[0134] In one embodiment of the present invention, the frequency domain energy attenuation characteristics are combined according to the corresponding excitation frequency, and the cross-rib synchronous compliance wave characteristics are combined according to the corresponding air-rib membrane compartment layout position to construct a correlation analysis matrix; based on the correlation analysis matrix, the frequency domain energy attenuation characteristics are matched and compared with the cross-rib synchronous compliance wave characteristics to obtain a matching comparison result; the matching comparison result is used to generate a coupling feature through a multi-channel information fusion strategy, including:

[0135] The frequency domain energy attenuation characteristic is excited by the frequency Arrange, get ;

[0136] Arrange the layout order of the cross-rib synchronous compliance wave characteristic case pneumothorax chamber i to form 、 and ;

[0137] Constructing a correlation analysis matrix , each element is the corresponding excitation frequency and the feature combination of layout position i, ;

[0138] For each level of excitation frequency and the layout position i, calculate the difference between the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics as follows:

[0139]

[0140] in, 、 and is the weight coefficient, 、 and The sum of is 1, and none of them is 0;

[0141] Based on the multi-channel information fusion strategy, the calculation formula of the coupling feature is as follows:

[0142]

[0143] in, represents the coupling characteristics, Represents the weight of the k-th level excitation frequency.

[0144] Preferably, The setting method is as follows:

[0145] Through simulation, we obtain the response degree of the excitation frequency k when the structure has different types of damage (such as local leakage and overall stiffness degradation), and define the sensitivity coefficient S(k). For example, the high frequency band (such as 0.82Hz) is sensitive to local leakage, and the low frequency band (such as 0.05Hz) is sensitive to overall stiffness changes. Experimental measurements show S(1) = 0.3, S(2) = 0.5, and S(3) = 0.2. Normalizing the sensitivity coefficients so that the sum of the weights is 1, we calculate: 、 and .

[0146] It should be noted that Indicates the difference between the calculated frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics; if The larger the value, the lower the frequency domain energy attenuation characteristics of the ith air-rib membrane compartment and the cross-rib synchronous compliance wave characteristics, indicating that there may be structural abnormalities. The smaller the value, the higher the matching degree of the characteristics. The response of the i-th air pleura membrane chamber at the k-th excitation frequency conforms to the normal law. Reflects the weighted average relationship between frequency and gas rib characteristic differences.

[0147] The coupling characteristics are compared with the preset benchmark coupling characteristics to obtain the health status level.

[0148] In one embodiment of the present invention, the coupling feature is compared with a preset reference coupling feature to obtain a monitoring result, including:

[0149] Comparing the coupling signature with a pre-set benchmark coupling signature to determine a health status level;

[0150] Generate treatment plans based on health status levels.

[0151] It should be noted that when the membrane chamber is installed and debugged for the first time or is confirmed to be free of damage after comprehensive inspection, each air rib membrane chamber is stimulated (applied with golden nested perturbation spectrum), and its frequency domain energy attenuation characteristics and cross-rib synchronous compliance wave characteristics are collected and calculated, thereby generating coupling characteristics. , , as a benchmark for subsequent comparisons. Coupling characteristics It represents the ideal state where the membrane structure is intact and the gas rib coordination is normal.

[0152] In subsequent monitoring, coupling characteristics are obtained , and calculate the difference ;

[0153] The difference is assigned to a preset health status level through expert preset difference thresholds;

[0154] The treatment plan is determined based on the health status level.

[0155] Preferably, the health status levels include: healthy, slightly abnormal, moderately abnormal and severely abnormal;

[0156] If the difference is less than 0.1, it is judged to be healthy, indicating that there is no significant change in the membrane structure, and the gas-rib synergy and energy characteristics are normal.

[0157] If the difference is between 0.1 and 0.25, it is judged as a mild abnormality; there may be slight damage (such as small leaks, slight changes in local stiffness), which requires close attention.

[0158] If the difference is between 0.25 and 0.5, it is considered a moderate abnormality, indicating that the structure is significantly damaged (such as aggravated local leakage or loose gas rib connections), and maintenance is required;

[0159] If the difference is between 0.5 and 1, it is considered a serious abnormality; it indicates that there is a serious problem with the structure (such as large-scale leakage, air rib rupture), and it is necessary to stop use and repair it immediately.

[0160] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A large-span air plenum membrane chamber air pressure monitoring system, characterized in that: include: The pre-treatment module is used to inflate each pneumothorax chamber, including: Inflate each pneumatic pleura chamber to the target pressure and maintain it at the target pressure for a preset time; If the pressure amplitude and temperature amplitude of any air plenum chamber within a unit time are both less than or equal to the preset pressure threshold and the preset temperature threshold, the inflation process is completed; Load the golden nested scrambled spectrum, including: Based on the golden ratio relationship, multiple frequency level carriers are generated in sequence, and the multiple frequency level carriers are binary phase-encoded according to the length of the Fibonacci sequence to form a golden nested scrambling spectrum; A data acquisition module is used to apply the golden nested perturbation pressure spectrum to each air pleural membrane chamber and obtain the air pressure data and displacement data of each air pleural membrane chamber; Feature extraction module, used to extract frequency domain energy attenuation features from pressure data; extract cross-rib synchronous compliance wave features from displacement data; Characteristic coupling module, used to calculate the coupling characteristics based on the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics; The status assessment module is used to compare the coupling characteristics with the preset benchmark coupling characteristics to obtain the health status level.

2. A large-span air plenum chamber air pressure monitoring system according to claim 1, characterized in that: Apply the golden nested perturbation spectrum to each air-pleated membrane chamber, and obtain the air pressure data and displacement data of each air-pleated membrane chamber, including: Each air pleural membrane chamber is adjusted according to the golden nested perturbation pressure spectrum so that the internal air pressure of the air pleural membrane chamber is tracked with the golden nested perturbation pressure spectrum as the target, and the air pressure data and displacement data of each air pleural membrane chamber are collected with a unified clock.

3. A large-span air plenum chamber air pressure monitoring system according to claim 2, characterized in that: Extract frequency domain energy attenuation features from pressure data, including: The air pressure data is divided into multiple frequency bands according to multiple preset excitation frequencies; The power spectral density is estimated after denoising for each frequency band to obtain the frequency domain energy attenuation characteristics corresponding to each excitation frequency.

4. A large-span air plenum chamber air pressure monitoring system according to claim 3, characterized in that: Extract cross-rib synchronous compliance wave characteristics from displacement data, including: According to the layout direction of the pneumatic plenum, the displacement data of each pneumatic plenum are phase tracked and amplitude statistics are performed to form a dynamic waveform propagating along the layout direction to extract the cross-rib synchronous compliance wave characteristics.

5. A large-span air plenum chamber air pressure monitoring system according to claim 4, characterized in that: The coupling characteristics are calculated based on the frequency domain energy attenuation characteristics and the cross-rib synchronous compliance wave characteristics, including: The frequency domain energy attenuation characteristics are combined according to the corresponding excitation frequency, and the cross-rib synchronous compliance wave characteristics are combined according to the layout position of the corresponding air-rib membrane chamber to construct a correlation analysis matrix; Based on the correlation analysis matrix, the frequency domain energy attenuation characteristics are matched and compared with the cross-rib synchronous compliance wave characteristics to obtain the matching comparison results; The matching results are fused into coupling features through a multi-channel information fusion strategy.

6. A large-span air plenum chamber air pressure monitoring system according to claim 5, characterized in that: Compare the coupling characteristics with the preset benchmark coupling characteristics to obtain monitoring results, including: Comparing the coupling signature with a pre-set benchmark coupling signature to determine a health status level; Generate treatment plans based on health status levels.

Images