A multi-point vibration detection system for a large-scale passive RFID tag array and a method thereof

Abstract

This invention discloses a multi-point vibration detection system and method for a large-scale passive RFID tag array, comprising a host computer, an RFID reader group, a reader antenna group, and a passive RFID tag array arranged on the surface of the structure to be detected. The signal terminal of the host computer is bidirectionally connected to one end of the RFID reader group, and the other end of the RFID reader group is bidirectionally connected to the signal terminal of the reader antenna group. The antenna terminal of the reader antenna group is bidirectionally wirelessly connected to the passive RFID tag array, and vibration sensing data of several passive RFID tags in the passive RFID tag array are simultaneously collected through the antenna terminal of the reader antenna group. The reader antenna group transmits several vibration sensing data to the RFID reader group, and the RFID reader group transmits several vibration sensing data to the host computer. The host computer sequentially performs adaptive compressed sensing reconstruction operation and adaptive random resonance operation on several vibration sensing data to obtain several enhanced vibration signals of the detected structure.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless passive sensing and vibration monitoring, and particularly relates to a multi-point vibration detection system for a large-scale passive RFID tag array and a method thereof. BACKGROUND

[0002] As a new type of non-contact sensing method, passive RFID sensing technology uses electromagnetic waves to achieve energy acquisition and information transmission without external power supply, and has the advantages of small tag size, simple structure, flexible layout, low cost, and easy large-scale deployment. The vibration sensing method based on passive RFID tags can be flexibly arranged on the surface of the measured structure through pasting, embedding, etc., and is suitable for narrow spaces, complex curved surfaces and multi-layer structure environments, and has a significant advantage in realizing three-dimensional sensing of vibration multi-measurement points, thus showing a good application prospect in the field of mechanical state monitoring.

[0003] However, when the number of passive RFID tags is small, the system can stably obtain the sensing information of each tag; when the number of tags is further increased to form a large-scale tag array, the system faces new technical challenges. On the one hand, the simultaneous operation of multiple tags will introduce electromagnetic coupling and channel interference, etc., resulting in interference between the sensing signals of each tag; on the other hand, due to the limitation of RFID communication protocol and polling mechanism, the effective sampling frequency of a single reader to each tag will significantly decrease when the number of tags increases, thereby reducing the upper limit of the frequency of the detectable vibration signal, and further limiting the monitoring of the frequency range of the vibration signal. Especially in the high-frequency vibration detection scene, insufficient sampling frequency will seriously affect the availability and detection accuracy of multi-measurement point vibration information; it is difficult to maintain the advantages of large-scale deployment of passive RFID tags while overcoming the problem of limited sampling frequency caused by the coupling effect of the tag array and the communication mechanism. At the same time, there is an urgent need for large-scale, multi-measurement point vibration detection of vibration information in complex engineering scenarios, and in actual layout, due to the complex curved surface of the measured structure, the dispersed installation position of the tags, the different orientations and polarization directions of the tags, a single antenna is difficult to simultaneously consider the coverage and reading and writing stability of each tag, resulting in unstable reading and writing or loss of part of the tag signal, further exacerbating the insufficient reliability of multi-measurement point detection. SUMMARY

[0004] The application aims to overcome the deficiencies in the prior art, and provides a multi-point vibration detection system for a large-scale passive RFID tag array and a method thereof, which maintains the advantages of large-scale deployment of passive RFID tags while overcoming the problem of limited sampling frequency caused by the coupling effect of the tag array and the communication mechanism, and realizes stable multi-point vibration detection for a large-scale passive RFID tag array.

[0005] Technical solution: To achieve the above-mentioned purpose, a multi-point vibration detection system of a large-scale passive RFID tag array of the application comprises an upper computer, an RFID reader group, a reader antenna group and a passive RFID tag array arranged on the surface of the detected structure; the signal end of the upper computer is bidirectionally connected with one end of the RFID reader group, and the other end of the RFID reader group is bidirectionally connected with the signal end of the reader antenna group; the antenna end of the reader antenna group is bidirectionally and wirelessly connected with the passive RFID tag array, and the antenna end of the reader antenna group is grouped and parallelly collects vibration sensing data of a plurality of passive RFID tags in the passive RFID tag array; the upper computer controls the RFID reader group and the reader antenna group to group and parallelly collect a plurality of passive RFID tags in the passive RFID tag array, and acquires a plurality of vibration sensing data; the reader antenna group transmits the plurality of vibration sensing data to the RFID reader group, and the RFID reader group transmits the plurality of vibration sensing data to the upper computer; the upper computer sequentially performs pretreatment, adaptive compressed sensing reconstruction operation and adaptive stochastic resonance operation on the plurality of vibration sensing data, and obtains a plurality of enhanced vibration signals of the detected structure.

[0006] Further, the RFID reader group comprises a plurality of RFID readers; the reader antenna group comprises a plurality of reader antennas; each of the plurality of RFID readers corresponds to one of the plurality of reader antennas, one end of the plurality of RFID readers is bidirectionally connected with the signal end of the upper computer, and the other end of the plurality of RFID readers is respectively bidirectionally connected with the signal end of the corresponding reader antenna.

[0007] Further, based on the preset threshold of each of the plurality of RFID readers, the passive RFID tag array is divided into a plurality of passive RFID tag sub-arrays; each of the plurality of reader antennas corresponds to one of the plurality of passive RFID tag sub-arrays, and the number of passive RFID tags in each of the plurality of passive RFID tag sub-arrays is not higher than the preset threshold of the corresponding RFID reader.

[0008] Further, the working frequency bands of each of the plurality of RFID readers are different.

[0009] Further, a multi-point vibration detection method of a large-scale passive RFID tag array comprises the following steps:

[0010] Step 1, selecting respective working frequencies by the upper computer controlling a plurality of RFID readers;

[0011] Step 2, the host computer controls a plurality of RFID readers, each of which controls its corresponding reader antenna to send radio frequency signals to its corresponding passive RFID tag subarray, and receives the backscattering signals of the corresponding passive RFID tag subarray to obtain a plurality of vibration sensing data;

[0012] Step 3, the host computer performs a preprocessing operation on the plurality of vibration sensing data to obtain a plurality of vibration signal sequences;

[0013] Step 4, the plurality of vibration signal sequences are subjected to adaptive compressive sensing reconstruction operation to obtain a plurality of reconstructed vibration signals;

[0014] Step 5, the plurality of reconstructed vibration signals are subjected to adaptive stochastic resonance operation to obtain a plurality of enhanced vibration signals.

[0015] Further, in step 4, the host computer performs adaptive compressive sensing reconstruction operation on the plurality of vibration signal sequences to obtain a plurality of reconstructed vibration signals; wherein the adaptive compressive sensing reconstruction operation on any one vibration signal sequence comprises the following steps:

[0016] Step 1-1, parameter initialization of the quantum optimization algorithm used in the adaptive compressive sensing reconstruction operation.

[0017] Step 1-2, constructing a measurement matrix and a sparse matrix based on the vibration signal sequence.

[0018] Step 1-3, constructing a solution model of adaptive compressive sensing based on the measurement matrix and the sparse matrix, and using a quantum optimization algorithm to iteratively search and update the sampling point number and sparsity in the adaptive compressive sensing reconstruction operation to obtain the reconstructed vibration signal.

[0019] Further, in step 5, the plurality of reconstructed vibration signals are subjected to adaptive stochastic resonance operation to obtain a plurality of enhanced vibration signals; wherein the adaptive stochastic resonance operation on any one reconstructed vibration signal comprises the following steps:

[0020] Step 2-1, constructing a stochastic resonance enhanced nonlinear system model based on the reconstructed vibration signal.

[0021] Step 2-2, parameter initialization of the quantum optimization algorithm used in the adaptive stochastic resonance operation, and setting the signal-to-noise ratio as the fitness function.

[0022] Step 2-3, using a quantum optimization algorithm to search and adjust the parameters of the stochastic resonance enhanced nonlinear system model, so that the stochastic resonance enhanced nonlinear system model forms a significant response peak at the target vibration characteristic frequency, to extract weak vibration features in the noise background, and obtain the enhanced vibration signal.

[0023] Furthermore, in steps 2-3, a quantum optimization algorithm is used to search and adjust the parameters of the nonlinear system model with stochastic resonance enhancement to obtain the enhanced vibration signal; this includes the following steps:

[0024] Step 3-1: Update the individual optimal position and global optimal position of each particle in the quantum optimization algorithm based on the fitness function value.

[0025] Step 3-2: Iteratively update the particle positions according to the update rules of the quantum optimization algorithm, and complete the search and adjustment of the model parameters of the stochastic resonance enhanced nonlinear system.

[0026] Step 3-3: Determine whether the maximum number of iterations has been reached or the preset convergence condition has been met; if not, return to step 3-1 to continue iterating; if yes, output the optimal system parameter combination.

[0027] Steps 3-4: Substitute the optimal system parameter combination into the nonlinear system model of stochastic resonance enhancement, and input the reconstructed vibration signal into the nonlinear system model to finally obtain the enhanced vibration signal.

[0028] Beneficial Effects: This invention provides a multi-point vibration detection system and method for large-scale passive RFID tag arrays. It supports large-scale multi-point deployment, using multiple readers to collect data in parallel and dividing the large-scale passive RFID tags into multiple passive RFID tag sub-arrays. This effectively avoids the problem of increased polling load on a single reader due to the increased number of tags, which significantly reduces the effective sampling frequency of a single passive RFID tag, thus improving the scalability and stability of large-scale multi-point vibration detection. Each RFID reader operates in a different frequency band or sub-band, achieving frequency domain isolation and reducing co-frequency interference and mutual coupling between different antennas and tag sub-arrays. Simultaneously, the antenna array arrangement improves the coverage of dispersed passive RFID tags, mitigating reading instability or data loss caused by different tag orientations and polarization directions, thereby improving the continuity and reliability of multi-point data acquisition. Through adaptive compressed sensing reconstruction and adaptive random resonance operations on the vibration sensing data, the equivalent sampling frequency is increased while enhancing noise immunity, expanding the detectable frequency range of the vibration signal, making feature recognition more stable, and enhancing and extracting weak vibration features, thus improving detection robustness in complex environments. While maintaining the advantages of passive RFID tags, such as easy deployment, no power supply required, and applicability to complex structural surfaces, it significantly improves the stability and detection accuracy of large-scale multi-point vibration detection, making it suitable for monitoring the vibration status of a large number of measurement points in complex engineering structures. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of a multi-point vibration detection system for a large-scale passive RFID tag array.

[0030] Figure 2 This is a schematic diagram illustrating the collaborative working principle of RFID readers based on frequency band allocation.

[0031] Figure 3 This is a flowchart of a multi-point vibration detection method for a large-scale passive RFID tag array. Detailed Implementation

[0032] The invention will now be further described with reference to the accompanying drawings.

[0033] like Figure 1 As shown, a multi-point vibration detection system for a large-scale passive RFID tag array includes a host computer, an RFID reader group, a reader antenna group, and a passive RFID tag array arranged on the surface of the structure being tested. The signal terminal of the host computer is bidirectionally connected to one end of the RFID reader group, and the other end of the RFID reader group is bidirectionally connected to the signal terminal of the reader antenna group. The antenna terminal of the reader antenna group is bidirectionally wirelessly connected to the passive RFID tag array. The antenna terminal of the reader antenna group collects vibration sensing data of several passive RFID tags in the passive RFID tag array in parallel groups. The host computer controls the RFID reader group and the reader antenna group to collect vibration sensing data of several passive RFID tags in the passive RFID tag array in parallel groups. The reader antenna group transmits several vibration sensing data to the RFID reader group, and the RFID reader group transmits several vibration sensing data to the host computer. The host computer performs preprocessing, adaptive compressed sensing reconstruction, and adaptive random resonance operations on the several vibration sensing data in sequence to obtain several enhanced vibration signals of the structure being tested.

[0034] The RFID reader group includes several RFID readers; the reader antenna group includes several reader antennas; each RFID reader corresponds to one of the reader antennas, one end of each RFID reader is bidirectionally connected to the signal terminal of the host computer, and the other end of each RFID reader is bidirectionally connected to the signal terminal of its corresponding reader antenna. The several reader antennas are respectively arranged at different positions on the structure being detected, covering passive RFID tags with different orientations or polarization directions, to respectively handle data acquisition for their corresponding target passive RFID tag subarrays. By arranging several reader antennas at different positions on the structure being detected, covering passive RFID tags with different orientations and polarization directions, each reader antenna is primarily responsible for the passive RFID tag subarray within its coverage area, thereby improving read / write stability and data acquisition continuity under conditions of varying passive RFID tag orientations, dispersed deployment, and structural obstruction. Figure 1As shown, the RFID reader group includes 2-1 to 2-n RFID readers, and the reader antenna group includes 3-1 to 3-n reader antennas.

[0035] Based on the preset threshold of each RFID reader in a plurality of RFID readers, the passive RFID tag array is divided into several passive RFID tag subarrays, resulting in 4-1 to 4-n passive RFID tag subarrays. Each reader antenna in a plurality of reader antennas corresponds to one of the passive RFID tag subarrays. The number of passive RFID tags in each passive RFID tag subarray does not exceed the preset threshold of its corresponding RFID reader, to ensure that a single passive RFID tag has a sufficient effective sampling frequency under parallel acquisition conditions. The preset threshold of each RFID reader is determined comprehensively based on the polling period, inventory period, communication rate, and the highest frequency of the target vibration signal of each RFID reader, to ensure that a single tag obtains at least a preset number of effective sampling points within one vibration cycle, thereby avoiding a decrease in the effective sampling frequency of a single tag due to excessive load on a single reader.

[0036] like Figure 2 As shown, each RFID reader operates in a different frequency band. The operating frequency of each RFID reader is set according to the reader's communication protocol and software configuration parameters to reduce mutual interference between different reader antennas and avoid communication conflicts or signal crosstalk when multiple readers operate simultaneously. This ensures the independence and stability of data acquisition for each reader's corresponding passive RFID tag subarray. Vibration sensing data from passive RFID tags is primarily acquired using phase signals; therefore, the operating frequency setting of the RFID readers mainly serves to prevent interference and ensure stable reading and writing, rather than directly altering the vibration characteristics represented by the phase signals. A frequency division and coordination approach is used to achieve group management and parallel acquisition of the large-scale passive RFID tag array, reducing co-frequency interference when multiple RFID readers operate simultaneously and ensuring that each tag has an effective sampling frequency that meets vibration detection requirements. The multiple RFID readers operate in different frequency bands or sub-bands to overcome antenna frequency band interference. Each RFID reader communicates with a preset number of passive RFID tags through its corresponding reader antenna, thereby dividing the large-scale passive RFID tag array into multiple independent sub-tags and enabling parallel acquisition by multiple readers.

[0037] Several passive RFID tag subarrays are deployed at different locations on the surface of the structure being tested to sense vibration information. Each passive RFID tag subarray comprises several passive RFID tags, which are UHF passive RFID tags, and each passive RFID tag has a unique EPC code. The tag size and packaging form of the passive RFID tags can be selected or adjusted according to the surface material, radius of curvature, and installation space of the structure being tested. Passive RFID tags are suitable for multi-point deployment on metal or non-metal surfaces, complex curved surfaces, irregular surfaces, and confined spaces. They can maintain good adhesion and read / write stability under complex working conditions, thereby achieving multi-point vibration detection. A flexible anti-metal substrate structure is adopted, and the tags are deployed on the surface of the structure being tested by adhesive, embedding, or magnetic attraction to meet the multi-point vibration detection needs of complex curved surfaces and confined spaces.

[0038] Based on the preset threshold of each RFID reader in a plurality of RFID readers, the passive RFID tag array is divided into several passive RFID tag sub-arrays. First, the host computer controls the antennas of the RFID readers to send radio frequency signals to the passive RFID tag array and receive the reflected backscattered signals. The backscattered signals received by any RFID reader are combined into a signal sequence, and each backscattered signal is labeled with the EPC code of the passive RFID tag corresponding to the backscattered signal. The signal strength of each backscattered signal in the signal sequence of any RFID reader is calculated, and the signal strength of each backscattered signal is compared with the set strength threshold. All backscattered signals with a signal strength higher than the strength threshold are constructed into a tag signal sequence. The effective sampling frequency of each backscattered signal in the tag signal sequence is calculated. Backscattered signals with an effective sampling frequency higher than the frequency threshold are retained, and backscattered signals with an effective sampling frequency lower than the frequency threshold are removed to obtain the effective signal sequence.

[0039] The number of backscattered signals in the valid signal sequence is compared with a preset threshold of the RFID reader. If the number of backscattered signals in the valid signal sequence is higher than the preset threshold, the backscattered signals in the valid signal sequence are arranged from high to low signal strength, and then removed one by one from low to high signal strength until the number of backscattered signals in the valid signal sequence equals the preset threshold, resulting in the final signal sequence. The passive RFID tags corresponding to the backscattered signals in the final signal sequence are assigned to the passive RFID tag subarray corresponding to the RFID reader, and the EPC code of the passive RFID tag corresponding to the backscattered signal in the final signal sequence is associated with the RFID reader. If the number of backscattered signals in the valid signal sequence is lower than the preset threshold, the passive RFID tags corresponding to the backscattered signals in the valid signal sequence are assigned to the passive RFID tag subarray corresponding to the RFID reader, and the EPC code of the passive RFID tag corresponding to the backscattered signal in the valid signal sequence is associated with the RFID reader. This achieves the initial division of several passive RFID tags in the passive RFID tag array.

[0040] At this point, based on the EPC codes of several passive RFID tags, it is determined whether any passive RFID tags have not been assigned to the corresponding passive RFID tag subarray. If not, the assignment of several passive RFID tags is completed. If so, the backscattered signals received by multiple RFID readers from the passive RFID tag are used to construct a second signal sequence, where the multiple RFID readers are all RFID readers capable of receiving the backscattered signals reflected by the passive RFID tag. It is determined whether the RFID readers corresponding to each backscattered signal in the second signal sequence are full. The backscattered signals corresponding to the full RFID readers are deleted from the second signal sequence to obtain a second tag signal sequence. The backscattered signals in the second tag signal sequence are arranged from high to low signal strength, and the RFID reader corresponding to the backscattered signal with the highest signal strength is selected as the final RFID reader. The passive RFID tag is assigned to the passive RFID tag subarray of the final RFID reader. Through the above method, all unassigned passive RFID tags are assigned to the corresponding passive RFID tag subarrays, and the assignment of several passive RFID tags in the passive RFID tag array is finally completed.

[0041] To determine whether the RFID reader corresponding to each backscatter signal in the second signal sequence is full, the number of passive RFID tags in the passive RFID tag subarray corresponding to each RFID reader is compared with a preset threshold. If the number of passive RFID tags in the passive RFID tag subarray corresponding to the RFID reader is lower than the preset threshold, the RFID reader is not full; if the number of passive RFID tags in the passive RFID tag subarray corresponding to the RFID reader is equal to the preset threshold, the RFID reader is full.

[0042] Meanwhile, the preset threshold can also be adjusted online based on the actual read / write success rate, packet loss rate, signal-to-noise ratio, and effective sampling frequency of a single tag during operation. During vibration sensing data acquisition, when a decrease in the effective sampling frequency or a drop in read / write stability of a single passive RFID tag is detected, the preset threshold of the RFID reader corresponding to that passive RFID tag is adjusted in real time. The passive RFID tag subarray corresponding to the RFID reader is updated by reducing the number of tags managed by that RFID reader or redistributing tags to other readers, ensuring that the read / write performance and effective sampling frequency of each RFID reader remain within the preset range, thus achieving dynamic assurance of sampling frequency and read / write reliability. Each time the preset threshold of each RFID reader is changed, the above-described method of dividing the passive RFID tag array is used to dynamically adjust the number of passive RFID tags in the passive RFID tag subarray corresponding to each RFID reader, ensuring the stability of the sampling frequency and the reliability of read / write.

[0043] The host computer controls the operating status of each RFID reader among several RFID readers, and performs preprocessing, adaptive compressed sensing reconstruction, and adaptive random resonance operations on vibration sensing data collected from several passive RFID tags to achieve multi-point vibration feature detection. To address noise interference caused by communication, channel contention, and electromagnetic coupling among multiple passive RFID tags managed by a single RFID reader, the host computer first uses an adaptive compressed sensing reconstruction operation to reconstruct the vibration sensing data of each passive RFID tag. This increases the equivalent sampling frequency of a single passive RFID tag while suppressing noise, obtaining a preliminary recovered vibration signal. To overcome interference caused by multi-tag coupling, multipath effects, and environmental noise, and when accurate extraction of vibration characteristic frequencies is still difficult after adaptive compressed sensing reconstruction, the host computer inputs the reconstructed vibration signal into a nonlinear system. By adjusting system parameters, the noise and the nonlinear system work together to excite a random resonance effect, generating a significant resonance response at the target vibration characteristic frequency, thereby enhancing weak vibration characteristics and achieving effective extraction of the vibration characteristic frequency.

[0044] like Figure 3 As shown, a multi-point vibration detection method for a large-scale passive RFID tag array includes the following steps:

[0045] Step 1: Control several RFID readers through the host computer to select their respective operating frequencies.

[0046] Step 2: The host computer controls several RFID readers. Each RFID reader controls its corresponding reader antenna to send radio frequency signals to its corresponding passive RFID tag subarray and receives the backscattered signals from the corresponding passive RFID tag subarray to obtain several vibration sensing data.

[0047] Step 3: The host computer performs preprocessing operations on several vibration sensing data to obtain several vibration signal sequences. Preprocessing is designed to address the characteristics of EPC mixing, non-uniform timestamps, phase jumps, and abnormal readings in the vibration sensing data of several passive RFID tags. Specifically, the host computer first sorts and classifies the collected data based on the EPC code and timestamp of the passive RFID tag; then, it identifies and removes outliers for data with missing readings or phase jumps exceeding a threshold between adjacent sampling points; subsequently, it performs baseline drift suppression and tag-based data reconstruction on the remaining phase data to construct non-uniform vibration signal sequences corresponding to each passive RFID tag, providing input for subsequent compressed sensing reconstruction and feature enhancement.

[0048] Step 4: Perform adaptive compressed sensing reconstruction on several vibration signal sequences to obtain several reconstructed vibration signals.

[0049] Step 5: Perform adaptive random resonance operation on several reconstructed vibration signals to obtain several enhanced vibration signals.

[0050] Based on the sparsity of the collected vibration sensing data in the frequency domain, an adaptive compressed sensing reconstruction method for non-uniformly sampled phase sequences is used to reconstruct vibration signals with sparse sampling points and limited sampling frequency. Unlike conventional compressed sensing methods, the adaptive compressed sensing reconstruction operation constructs a measurement matrix based on the reading timestamps of the collected phase signals and uses an optimization algorithm to iteratively search and update the number of sampling points and sparsity to adapt to the non-uniform sampling characteristics during the passive RFID tag reading and writing process. This improves the equivalent sampling frequency of a single tag while suppressing noise interference introduced by multi-tag communication, channel contention, and coupling. In step 4, several vibration signal sequences are subjected to adaptive compressed sensing reconstruction operations in the host computer to obtain several reconstructed vibration signals. The adaptive compressed sensing reconstruction operation for any vibration signal sequence includes the following steps:

[0051] Step 1-1: Initialize the parameters of the quantum optimization algorithm used in the adaptive compressed sensing reconstruction operation. The quantum optimization algorithm is a quantum particle swarm optimization algorithm. The parameters of the quantum particle swarm optimization algorithm include the particle swarm size, the maximum number of iterations, and the search boundary. Based on these parameters, an initial parameter set for compressed sensing processing is set. The search boundary includes at least the range of values ​​for the number of reconstruction sampling points and the sparsity. First, multiple initial particles are generated according to the preset particle swarm size. The position of each particle is defined as a set of parameters to be optimized, including the number of sampling points and the sparsity. Then, the initial position and initial velocity of each particle are randomly initialized within the corresponding search boundary. Next, the initial parameter set required for compressed sensing reconstruction is constructed based on the initial positions of each particle, and the initial fitness of each particle is calculated to determine the individual optimal position and the global optimal position, which serve as the initial conditions for subsequent iterative search and update.

[0052] Steps 1-2: Construct a measurement matrix and a sparse matrix based on the vibration signal sequence to meet the solution requirements of compressed sensing reconstruction. The measurement matrix is ​​constructed based on the acquired phase signal and its corresponding non-uniform timestamp, and the sparse matrix is ​​constructed using a Fourier basis or a basis matrix suitable for sparse representation of the vibration signal. Specifically, the phase sequence acquired by a single passive RFID tag within a preset time window is considered as a vibration signal sequence. If the passive RFID tag is read M times within a time window of length N milliseconds, then the vibration signal sequence contains M actual sampling points, and its phase values ​​are denoted as y1, y2, ..., y... M The corresponding timestamps are t1, t2, ..., t M Based on the time window, a uniform reconstruction time grid of length N is pre-constructed, which contains N reconstruction sampling times, where N>M.

[0053] The measurement matrix Φ is an M×N matrix used to represent the sampling position of the actual read timestamp in the uniformly reconstructed time grid. The measurement matrix is ​​constructed based on the acquired phase signal and the corresponding timestamp. The construction principle is that if a passive RFID tag is read M times within a time period of length N milliseconds, the timestamp t after each read is used as the basis for the measurement matrix. i (1≤i≤M) The measurement matrix values ​​are arranged as follows:

[0054]

[0055] In the formula, Φ ij This represents the data in the i-th row and j-th column of the measurement matrix.

[0056] The sparse matrix can be selected to use a normalized Fourier basis Ψ, and the calculation process is shown below:

[0057]

[0058] In the formula, Ψ represents the element in the nth row and kth column of the Fourier basis, and the kth column corresponds to the time-domain representation of the k-1th discrete frequency component at all sampling times; by constructing the measurement matrix and sparse matrix as described above, the solution requirements of compressed sensing reconstruction are satisfied.

[0059] Steps 1-3: Construct an adaptive compressed sensing solution model based on the measurement matrix and sparse matrix. Use a quantum optimization algorithm to iteratively search and update the number of sampling points and sparsity in the adaptive compressed sensing reconstruction operation to obtain the reconstructed vibration signal, improve the equivalent sampling rate of the vibration signal, and suppress noise interference. The adaptive compressed sensing solution model is shown below:

[0060]

[0061] In the formula, X represents the coefficient vector of the vibration signal in the sparse domain, y represents the phase signal acquired by the reader, and ε represents the threshold caused by noise.

[0062] In steps 1-3, an adaptive compressed sensing solution model is constructed based on the measurement matrix and sparse matrix. A quantum optimization algorithm is used to iteratively search and update the number of sampling points and sparsity in the adaptive compressed sensing reconstruction operation to obtain the reconstructed vibration signal. First, the position of each particle in the quantum particle swarm optimization algorithm is represented as a set of candidate parameters, which include at least the number of reconstruction sampling points N and the sparsity K. Then, according to the particle swarm size, maximum number of iterations, and search boundary set in the parameter initialization, the initial position and initial velocity of each particle are randomly initialized. Then, for the candidate parameters (N, K) corresponding to each particle, a corresponding compressed sensing reconstruction problem is constructed, and the reconstructed vibration signal is obtained using the orthogonal matching pursuit algorithm. Then, the fitness value is calculated based on the reconstructed vibration signal. The fitness value is preferably constructed based on at least one of the reconstruction error and signal-to-noise ratio. The fitness values ​​of each particle are compared, and the individual optimal position and global optimal position of each particle are updated. According to the velocity update rule and position update rule of the particle swarm optimization algorithm, the velocity and position of each particle are iteratively updated. Finally, when the maximum number of iterations is reached or the preset convergence condition is met, the number of reconstructed sampling points and sparsity corresponding to the globally optimal particle are output, and the final reconstructed vibration signal is obtained based on this optimal parameter combination. Through the above steps, adaptive compressed sensing reconstruction of vibration signals can be performed under conditions of limited sampling frequency and non-uniform sampling. The reconstructed signal is... .

[0063] When the vibration signal is in a strong noise background or has weak vibration characteristics, the signal reconstructed by adaptive compressed sensing is input into a nonlinear system. By adjusting the system parameters, the noise and the nonlinear system work together to excite a stochastic resonance effect, thereby forming a significant response at the target vibration characteristic frequency and realizing the enhancement and extraction of weak vibration characteristics. In step 5, several reconstructed vibration signals are subjected to adaptive stochastic resonance operation to obtain several enhanced vibration signals. The adaptive stochastic resonance operation on any one of the reconstructed vibration signals includes the following steps:

[0064] Step 2-1: Construct a nonlinear system model with stochastic resonance enhancement based on the reconstructed vibration signal; the nonlinear system model with stochastic resonance enhancement is preferably a bistable Duffing oscillator system model that can generate a stochastic resonance response with adaptive frequency.

[0065] Step 2-2: Initialize the parameters of the quantum optimization algorithm used in the adaptive stochastic resonance operation, and set the signal-to-noise ratio as the fitness function to maximize the signal-to-noise ratio of the output signal of the stochastic resonance-enhanced nonlinear system model; the quantum optimization algorithm is the quantum particle swarm optimization algorithm.

[0066] Steps 2-3: Use quantum optimization algorithms to search and adjust the parameters of the stochastic resonance-enhanced nonlinear system model, so that the stochastic resonance-enhanced nonlinear system model forms a significant response peak at the target vibration characteristic frequency, so as to extract weak vibration characteristics in the noise background and obtain the enhanced vibration signal.

[0067] In step 2-1, constructing a nonlinear system model with stochastic resonance enhancement based on the reconstructed vibration signal includes the following steps:

[0068] Step 2-1-1: Preprocess the reconstructed vibration signal to obtain a driving signal that meets the requirements of random resonance input; the preprocessing includes mean removal, normalization and amplitude scaling.

[0069] Step 2-1-2: Perform frequency domain analysis on the reconstructed vibration signal to extract the target vibration characteristic frequency and its corresponding amplitude information.

[0070] Step 2-1-3: Select the bistable Duffing oscillator system model as the nonlinear system model for stochastic resonance enhancement, and use the driving signal that meets the stochastic resonance input requirements as the external excitation input of the nonlinear system model;

[0071]

[0072]

[0073] In the formula, z represents the output response of the stochastic resonance system, ζ represents the damping coefficient, ω(t) represents the natural frequency of the system, and k ω denoted as the learning rate; b represents the coefficient of the cubic nonlinear term, used to characterize the nonlinear strength of the system; β is the input signal gain coefficient, used to adjust the driving strength of the external input signal on the system; x(t) is the input signal, and N(t) is the noise term; the system can adaptively learn the parameters of the input signal, thereby achieving adaptive signal enhancement.

[0074] Step 2-1-4: Based on the target vibration characteristic frequency, amplitude information and sampling time parameters, set the initial values ​​or search boundaries of the potential well parameters, damping parameters and driving gain parameters of the bistable Duffing oscillator system model to obtain the initial values ​​of each parameter.

[0075] Step 2-1-5: Based on the driving signal that meets the requirements of stochastic resonance input and the initial values ​​of each parameter, establish a nonlinear system model for subsequent stochastic resonance enhancement processing.

[0076] In step 2-2, the optimization algorithm used in the adaptive stochastic resonance operation is initialized with parameters, and the signal-to-noise ratio (SNR) is set as the fitness function. First, the parameters to be optimized in the stochastic resonance-enhanced nonlinear system model are determined, and the search boundaries for each parameter are set. The parameters to be optimized include at least one of the damping coefficient, bistable state function parameters, input gain coefficient, and noise intensity. The parameters of the quantum particle swarm optimization algorithm are set, including the particle swarm size, maximum number of iterations, and contraction / expansion coefficients. An initial particle swarm is randomly generated within the search boundaries of each parameter, and the position of each particle is represented as a combination of candidate system parameters; thus, the initialization of the quantum particle swarm optimization algorithm is achieved. The SNR of each system output signal at the target vibration characteristic frequency is set as the fitness function value.

[0077] In steps 2-3, a quantum optimization algorithm is used to search and adjust the parameters of the nonlinear system model with stochastic resonance enhancement to obtain the enhanced vibration signal; this includes the following steps:

[0078] Step 3-1: Update the individual optimal position and global optimal position of each particle in the quantum optimization algorithm based on the fitness function value.

[0079] Step 3-2: Iteratively update the particle positions according to the update rules of the quantum optimization algorithm, and complete the search and adjustment of the model parameters of the stochastic resonance enhanced nonlinear system.

[0080] Step 3-3: Determine whether the maximum number of iterations has been reached or the preset convergence condition has been met; if not, return to step 3-1 to continue iterating; if yes, output the optimal system parameter combination.

[0081] Steps 3-4: Substitute the optimal system parameter combination into the nonlinear system model of stochastic resonance enhancement, and input the reconstructed vibration signal into the nonlinear system model to finally obtain the enhanced vibration signal.

[0082] Frequency domain analysis is performed on the final enhanced vibration signal to extract the target vibration characteristic frequency and its amplitude information, and the final enhanced vibration signal and spectrum results of each detection point are output.

[0083] The above description is merely a preferred embodiment of the present invention. Those skilled in the art can make several modifications and optimizations based on the above disclosure without departing from the basic principles described above. These modifications and optimizations should be considered within the scope of protection as understood by the present invention.

Claims

1. A multi-point vibration detection system for a large-scale passive RFID tag array, characterized in that: The system includes a host computer, an RFID reader group, a reader antenna group, and a passive RFID tag array arranged on the surface of the structure being inspected. The host computer's signal terminal is bidirectionally connected to one end of the RFID reader group, and the other end of the RFID reader group is bidirectionally connected to the signal terminal of the reader antenna group. The antenna terminal of the reader antenna group is bidirectionally wirelessly connected to the passive RFID tag array. The antenna terminal of the reader antenna group collects vibration sensing data from several passive RFID tags in the passive RFID tag array in parallel groups. The host computer controls the RFID reader group and the reader antenna group to collect vibration sensing data from several passive RFID tags in the passive RFID tag array in parallel groups. The reader antenna group transmits several vibration sensing data to the RFID reader group, and the RFID reader group transmits several vibration sensing data to the host computer. The host computer performs preprocessing, adaptive compressed sensing reconstruction, and adaptive random resonance operations on the several vibration sensing data in sequence to obtain several enhanced vibration signals of the inspected structure.

2. The multi-point vibration detection system for a large-scale passive RFID tag array according to claim 1, characterized in that: The RFID reader group includes several RFID readers; the reader antenna group includes several reader antennas; each RFID reader corresponds to one of the reader antennas; one end of each RFID reader is bidirectionally connected to the signal terminal of the host computer, and the other end of each RFID reader is bidirectionally connected to the signal terminal of its corresponding reader antenna.

3. The multi-point vibration detection system for a large-scale passive RFID tag array according to claim 2, characterized in that: Based on the preset threshold of each RFID reader in the plurality of RFID readers, the passive RFID tag array is divided into several passive RFID tag sub-arrays; each reader antenna in the plurality of reader antennas corresponds to one of the several passive RFID tag sub-arrays, and the number of passive RFID tags in each passive RFID tag sub-array in the plurality of passive RFID tag sub-arrays does not exceed the preset threshold of its corresponding RFID reader.

4. The multi-point vibration detection system for a large-scale passive RFID tag array according to claim 2, characterized in that: Each of the several RFID readers operates on a different frequency band.

5. A multi-point vibration detection method for a large-scale passive RFID tag array according to claims 1-4, characterized in that: Includes the following steps: Step 1: Control several RFID readers via a host computer to select their respective operating frequencies; Step 2: The host computer controls several RFID readers. Each RFID reader controls its corresponding reader antenna to send radio frequency signals to its corresponding passive RFID tag subarray and receives the backscattered signals from the corresponding passive RFID tag subarray to obtain several vibration sensing data. Step 3: The host computer performs preprocessing operations on several vibration sensing data to obtain several vibration signal sequences; Step 4: Perform adaptive compressed sensing reconstruction on several vibration signal sequences to obtain several reconstructed vibration signals; Step 5: Perform adaptive random resonance operation on several reconstructed vibration signals to obtain several enhanced vibration signals.

6. The multi-point vibration detection method for a large-scale passive RFID tag array according to claim 5, characterized in that: In step 4, the host computer performs an adaptive compressed sensing reconstruction operation on several vibration signal sequences to obtain several reconstructed vibration signals; the adaptive compressed sensing reconstruction operation on any one vibration signal sequence includes the following steps: Step 1-1: Initialize the parameters of the quantum optimization algorithm used in the adaptive compressed sensing reconstruction operation; Steps 1-2: Construct the measurement matrix and sparse matrix based on the vibration signal sequence; Steps 1-3: Construct an adaptive compressed sensing solution model based on the measurement matrix and sparse matrix, and use a quantum optimization algorithm to iteratively search and update the number of sampling points and sparsity in the adaptive compressed sensing reconstruction operation to obtain the reconstructed vibration signal.

7. A multi-point vibration detection method for a large-scale passive RFID tag array according to claim 6, characterized in that: In step 5, several reconstructed vibration signals are subjected to adaptive random resonance operation to obtain several enhanced vibration signals; wherein performing adaptive random resonance operation on any one of the reconstructed vibration signals includes the following steps: Step 2-1: Construct a nonlinear system model for stochastic resonance enhancement based on the reconstructed vibration signal; Step 2-2: Initialize the parameters of the quantum optimization algorithm used in the adaptive stochastic resonance operation and set the signal-to-noise ratio as the fitness function; Steps 2-3: Use quantum optimization algorithms to search and adjust the parameters of the stochastic resonance-enhanced nonlinear system model, so that the stochastic resonance-enhanced nonlinear system model forms a significant response peak at the target vibration characteristic frequency, in order to extract weak vibration characteristics in the noise background and obtain the enhanced vibration signal.

8. The multi-point vibration detection method for a large-scale passive RFID tag array according to claim 7, characterized in that: In steps 2-3, a quantum optimization algorithm is used to search and adjust the parameters of the nonlinear system model with stochastic resonance enhancement to obtain the enhanced vibration signal; this includes the following steps: Step 3-1: Update the individual optimal position and global optimal position of each particle in the quantum optimization algorithm based on the fitness function value; Step 3-2: Iteratively update the particle positions according to the update rules of the quantum optimization algorithm, and complete the search and adjustment of the model parameters of the stochastic resonance enhanced nonlinear system. Step 3-3: Determine whether the maximum number of iterations has been reached or the preset convergence condition has been met; if not, return to step 3-1 to continue iterating; if yes, output the optimal system parameter combination. Steps 3-4: Substitute the optimal system parameter combination into the nonlinear system model of stochastic resonance enhancement, and input the reconstructed vibration signal into the nonlinear system model to finally obtain the enhanced vibration signal.

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