CN109813269A - Structure monitoring sensor on-line calibration data sequence matching process - Google Patents
Structure monitoring sensor on-line calibration data sequence matching process Download PDFInfo
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Abstract
The present invention relates to pick up calibration fields, disclose a kind of bridge deformation monitoring sensor on-line calibration data sequence matching process, which comprises step 1, obtain the measurement value sequence of the sensor and reference sensor to be calibrated under same incentive action；Step 2, the measurement value sequence of the sensor to be calibrated and the reference sensor is segmented；Step 3, the measured value of the sensor to be calibrated and the reference sensor is matched in each piecewise interval；Step 4, the corresponding relationship of the sensor to be calibrated and the reference sensor between the measurement value sequence under the same incentive action is obtained.The out of step conditions such as the advanced, lag that the measurement value sequence of sensor to be calibrated and reference sensor occurs in technical solution provided by the invention have good adaptability, are remarkably improved the efficiency and accuracy of the on-line calibration of bridge deformation monitoring sensor.
Description
Technical Field
The invention relates to the field of calibration of sensors, in particular to an online calibration data sequence matching method for a bridge deformation monitoring sensor.
Background
Bridge deformation monitoring is a key link for evaluating bridge safety performance, and in order to ensure bridge safety, a sensor for bridge deformation monitoring has high monitoring accuracy. In order to ensure the accuracy of the bridge deformation monitoring sensor, the bridge deformation monitoring sensor needs to be calibrated.
Taking a laser displacement meter in a bridge deformation monitoring sensor as an example, the laser displacement meter designed according to the laser linear propagation characteristic has the advantages that the transmitting module and the receiving module can be separately installed at a structure monitoring point and a reference elevation point and used for monitoring the relative deformation quantity perpendicular to the laser direction, the practicability is strong, the accuracy is high, and therefore the laser displacement meter is widely applied to the actual bridge monitoring process. However, in the use process, the sensitivity and accuracy are reduced due to the influence of the performance of the sensor and the complex environmental conditions, and the calibration is required to be performed regularly.
In a traditional calibration mode, professional measurement equipment (such as a laser interferometer and the like) with higher precision is used as a reference instrument, and within an allowable error range, if an indication value of a laser displacement meter to be calibrated is consistent with a reference value, the metering performance of the laser displacement meter is considered to meet the use requirement. The method is limited to the special requirement that the bridge deformation monitoring system operates all day long, and the laser displacement meter in use is usually not allowed to be disassembled to be calibrated in a laboratory, so the method is not universal.
In engineering practice, the combined loading effect of a running vehicle is generally used as an excitation source to perform online calibration on a bridge health monitoring sensor, and the quantity value comparison and calibration of a sensor to be calibrated (hereinafter referred to as SBC) and a reference sensor (hereinafter referred to as RS) under the same excitation source condition are realized in a mode that the reference sensors are installed side by side in the same direction at the same monitoring point. Due to the difference between the SBC and the RS in the aspects of sampling frequency, response characteristics and the like, the matching of the two-magnitude sequence is poor, and the problem of influencing on-line calibration is solved.
The time sequence matching problem is widely applied to the fields of positioning systems, environment monitoring, Internet of things, data mining, informatics, human psychology and the like, and gradually becomes a research hotspot in various fields in recent years, wherein Dynamic Time Warping (DTW) is an important method for analyzing time sequences, provides distance measurement insensitive to local compression and extension for the sequences, and is widely applied to the fields of data analysis and mining.
Aiming at the problem that a sensor magnitude sequence and a reference value sequence show unstable phase difference in bridge sensor online calibration, the matching effect under the traditional Euclidean distance measurement is poor, the dynamic time normalization by using DTW is long, if a distance lower bound function is introduced to accelerate the comparison process based on DTW, a new requirement is put forward for determining the distance lower bound function.
Disclosure of Invention
The invention aims to overcome the defects in the prior art at least to a certain extent and provides an online calibration data sequence matching method of a bridge deformation monitoring sensor, which has strong adaptability and high accuracy.
In order to achieve the purpose, the invention provides the technical scheme that:
an online calibration data sequence matching method for a bridge deformation monitoring sensor comprises the following steps:
step 1, acquiring measurement value sequences of a sensor to be calibrated and a reference sensor under the same excitation action;
step 2, segmenting the measured value sequences of the sensor to be calibrated and the reference sensor;
step 3, matching the measured values of the sensor to be calibrated and the reference sensor in each subsection interval;
and 4, acquiring the corresponding relation between the measured value sequences of the sensor to be calibrated and the reference sensor under the same excitation action.
Preferably, the measured value sequence of the sensor to be calibrated and the measured value sequence of the reference sensor are both waveform sequences; the step 2 comprises the following steps:
step B1, extracting a target peak point in the measured value sequence of the sensor to be calibrated and a target peak point in the measured value sequence of the reference sensor;
and step B2, segmenting the measured value sequences of the sensor to be calibrated and the reference sensor by taking the target peak point in the measured value sequences of the sensor to be calibrated and the reference sensor as a node respectively.
Preferably, the step B1 includes:
step S1, extracting an initial peak point in the measured value sequence of the sensor to be calibrated and an initial peak point in the measured value sequence of the reference sensor and respectively forming an initial peak point sequence;
step S2, smoothing the initial peak point sequence of the sensor to be calibrated and the initial peak point sequence of the reference sensor by the following equations (1) - (3):
P＝{p1,p2…pn} (1)
j＝(span-1)/2 (3)
wherein, P is an initial peak point sequence of the sensor to be calibrated or the reference sensor; n is the number of peak points in the initial peak point sequence; span is a smooth scale; piIs' being flatThe ith peak point in the smoothed initial peak point sequence P';
step S3, storing P in sequencei'to form a smoothed initial sequence of peak points P';
step S4, finding the initial peak point P in the original initial peak point sequence P corresponding to each peak point in the smoothed initial peak point sequence PiAnd using the initial peak point PiAs the target peak point.
Preferably, the series of measurements of the sensor to be calibrated and the series of measurements of the reference sensor are each divided into K +1 segments; wherein, the measured value sequence of the sensor to be calibrated and the measured value sequence of the reference sensor are segmented to form segmented sequences respectively { t }0,…,t1},{t1,…,t2}…{tk-1,…,tk},{tk,…,tk+1And { t'0,…,t'1},{t'1,…,t'2}…{t'k-1,…,t'k},{t'k,…,t'k+1}; wherein,
t0a starting measurement point of the measurement value sequence of the sensor to be calibrated;
tk+1a measurement point which is the end of the measurement value sequence of the sensor to be calibrated;
tnthe target peak point in the measured value sequence of the sensor to be calibrated is shown, wherein k is more than or equal to n and more than or equal to 1;
t’0a starting measurement point of a sequence of measurement values for the reference sensor;
t’k+1an end measurement point of the sequence of measurement values for the reference sensor;
t’nthe peak value is a target peak value point in the measured value sequence of the reference sensor, wherein k is more than or equal to n and more than or equal to 1.
Preferably, the step 3 comprises:sequentially selecting the measured value p to be matched in the measured value sequence of the sensor to be calibratediAnd according to said measured value p to be matchediPosition information of the position of the measured value p to be matchediSegment interval tr-1,…,tr}；
The measured value p to be matched is searched in the measured value sequence of the reference sensor by the following formula (4)iMatched measured values qjIn the position of (a) in the first,
wherein j is a measured value p to be matched in a measured value sequence of the reference sensoriMatched measured values qjThe abscissa of (a);
tr-1|xfor measuring points t in a segmented sequence of sensors to be calibratedr-1The abscissa of (a);
tr|xfor measuring points t in a segmented sequence of sensors to be calibratedrThe abscissa of (a);
t'r-1|xis a measurement point t 'in a segmented sequence of reference sensors'r-1The abscissa of (a);
t'r|xis a measurement point t 'in a segmented sequence of reference sensors'rThe abscissa of (a).
Preferably, the step 3 further comprises: output and save piAnd with said piMatched qj。
Preferably, step 1 is preceded by: and (3) setting up a calibration platform of the bridge deformation monitoring sensor to simulate the random change of the load borne by the bridge when an actual vehicle passes through the bridge.
Preferably, the calibration platform comprises a simply supported beam model bridge for simulating a bridge, an excitation source capable of acting on the simply supported beam model bridge to simulate an actual vehicle passing on the bridge, and a sensor to be calibrated and a reference sensor which are installed at the bottom of the simply supported beam model bridge to monitor the deformation of the simply supported beam model bridge.
Preferably, the excitation source is a linear module.
Preferably, the sensor to be calibrated and the reference sensor are both laser displacement meters.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
the bridge deformation monitoring sensor online calibration data sequence matching method provided by the invention comprises the steps of segmenting the measured value sequences of the sensor to be calibrated and the reference sensor, and then matching the measured values of the sensor to be calibrated and the reference sensor in each segmented interval.
Compared with the method in the prior art, the online calibration data sequence matching method with the segmentation-prior-to-matching function provided by the invention has the advantages that the relative error is obviously reduced, the accuracy is obviously improved, and the matching accuracy can be kept above 98% in different scenes.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
figure 1 is a sequence diagram of raw measurement values of an SBC and an RS according to an embodiment of the present invention;
FIG. 2 is a graph of the raw measurement sequences of SBC and RS of FIG. 1 corrected by the conventional matching algorithm;
FIG. 3 is a flowchart of an online calibration data sequence matching method for a bridge deformation monitoring sensor according to an embodiment of the present invention;
FIG. 4 is a node map before preprocessing provided by an embodiment of the present invention;
FIG. 5 is a node map after preprocessing provided by an embodiment of the present invention;
FIG. 6 is a node diagram of an extracted SBC and RS sequence according to an embodiment of the present invention;
fig. 7 is a diagram of SBC and RS sequence nodes extracted after a large offset occurs according to an embodiment of the present invention;
FIG. 8 is a flowchart illustrating an implementation of a matching algorithm according to an embodiment of the present invention;
fig. 9 is a segmented local interval sample point diagram according to an embodiment of the present invention;
fig. 10 is a node-based segment matching graph according to an embodiment of the present invention;
fig. 11 is a diagram illustrating a reverse matching between an SBC and an RS according to an embodiment of the present invention;
fig. 12 is a diagram of DFLI and LDM sampling sequences and node location provided by an embodiment of the present invention;
fig. 13 is a local interval sample point diagram after the DFLI and LDM sample sequences are segmented according to the embodiment of the present invention;
fig. 14 is a diagram of segment matching of DFLI and LDM sampling sequences according to an embodiment of the present invention;
fig. 15 is a diagram of inverse matching of DFLI and LDM sample sequences according to an embodiment of the present invention;
FIG. 16 is a front view of a calibration platform provided by an embodiment of the present invention;
FIG. 17 is a side view of a calibration platform provided by an embodiment of the present invention;
FIG. 18 is a diagram illustrating the matching result of a reference sensor and a sensor to be calibrated under low frequency excitation according to an embodiment of the present invention;
FIG. 19 is a diagram of the matching result of the reference sensor and the sensor to be calibrated under the excitation of the intermediate frequency according to the embodiment of the present invention;
FIG. 20 is a diagram illustrating the matching result of a reference sensor and a sensor to be calibrated under high frequency excitation according to an embodiment of the present invention;
FIG. 21 is a graph of node-based segment matching results for a reference sensor and a sensor to be calibrated according to an embodiment of the present invention;
FIG. 22 is a graph of Euclidean metric based matching results for a reference sensor and a sensor to be calibrated, provided by an embodiment of the present invention;
fig. 23 is a diagram of relative errors in different matching manners according to an embodiment of the present invention.
Wherein, 5-excitation source; 6-the sensor to be calibrated and the reference sensor; 7-a sensor mounting plate; 8-simply supported beam model bridge; 9-laser displacement meter receiving end; 10-laser displacement meter transmitting end.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In the present invention, unless otherwise specified, the use of the terms of orientation such as "upper, lower, left, and right" generally means upper, lower, left, and right as viewed with reference to the drawings, and "inner and outer" means inner and outer with respect to the outline of the component body.
As shown in fig. 1 and fig. 2, the conventional matching algorithm usually measures the degree of acquaintance between curves by using an Euclidean metric standard, the principle is intuitive, the calculation is simple, and the coefficient can keep the Euclidean distance unchanged when the sequence is subjected to common transformation (such as fourier transform), so that the method is widely applied to the time series similarity problem. However, matching based on Euclidean distance measurement requires that two matched sequences have the same length, are sensitive to mutation points of time sequences, and are also sensitive to dislocation of the time sequences when the sequences are calculated point to point in sequence according to a time axis. For the problem of the embodiment of the present invention, the data collected by the SBC and the RS inevitably has sudden changes due to the random excitation effect emitted by the excitation source, and meanwhile, the SBC and the RS data sequences also have dislocation due to the existence of the sampling frequency difference, it can be found from a comparison of fig. 1 that the data collected by the SBC and the RS are not completely synchronous, and the data deviation gradually increases along with the increase of the sampling points, which is caused by the slight Sampling Frequency Difference (SFD) between the SBC and the RS, and the number of the sampling points of the SBC is more than that of the sampling points of the RS within the same time. Meanwhile, under the same excitation condition, the response peaks of the two sensors have a relatively obvious difference, which is the personality difference generated along with the manufacturing process of the sensor, and the personality difference is usually kept within a certain threshold value range.
The traditional matching method generally carries out preprocessing such as sampling point calibration, inertia compensation and the like on SBC and RS magnitude sequences, and then carries out data matching on the basis. The principle is as follows:
data acquisition is carried out on the SBC and the RS under the action of the same excitation source in unit time, and the SBC sampling sequence is assumed as follows:
P＝{p1,p2…pn} (1)
the RS sampling sequence is as follows:
Q＝{q1,q2…qm} (2)
wherein n is the number of SBC sampling points, m is the number of RS sampling points, and the number of SBC sampling is more than that of RS sampling, i.e. n > m.
And (3) reserving sampling points synchronous with the RS by using a contrast sampling mode, and removing redundant sampling points in the SBC, wherein the sequences of the points to be removed are as follows:
P'＝{p[n·i/d]}(i＝1,2,…d) (3)
where, d is n-m, and the value is the sampling frequency difference SFD in unit time, and if the sampling sequence set of the SBC before calibration is taken as the full set U, that is:
U＝P (4)
the post-calibration SBC sampling sequence can be expressed as the absolute complement of P' over U, i.e.:
the Inertia Compensation Value (ICV) for SBC is generally determined by:
wherein k is the number of sampling segments, and nj is the number of sampling points of the j-th segment. The calibrated SBC and RS data sequences are shown in fig. 2.
It can be found that the overall matching effect of the SBC and RS magnitude sequences after sampling correction and inertia compensation is improved compared with that before processing, but when data are matched point by point, due to the discrete type of the sampled data, as long as there is a sampling error that cannot be eliminated to cause the mismatch of the matching sequence, the Euclidean distance will be significantly increased, and meanwhile, the Euclidean distance will be disturbed by uncertain factors, and if a certain sensor has an instantaneous sampling delay, the matching mismatch phenomenon will also occur by adopting the above calibration, which shows that there is a great limitation in simply using the Euclidean distance as a measurement standard.
In view of the above problems, an embodiment of the present invention provides a new method for matching an online calibration data sequence of a bridge deformation monitoring sensor, where the method considers that different sensors are segmented at a sampling peak point under the same excitation, and then matching is performed in each segmented subinterval, where a specific process is shown in fig. 3, and includes:
step 1, acquiring measurement value sequences of a sensor to be calibrated and a reference sensor under the same excitation action;
step 2, segmenting the measured value sequences of the sensor to be calibrated and the reference sensor;
step 3, matching the measured values of the sensor to be calibrated and the reference sensor in each subsection interval;
and 4, acquiring the corresponding relation between the measured value sequences of the sensor to be calibrated and the reference sensor under the same excitation action.
As shown in fig. 1 and 2, the measurement value sequence of the sensor to be calibrated and the measurement value sequence of the reference sensor are both waveform sequences, generally irregular waveform diagrams;
in step 2, in order to segment the measured value sequences of the sensor to be calibrated and the reference sensor, the segmented nodes are firstly positioned, the inventor of the application finds that the difference between two groups of data peaks is large, which is caused by the response of different sensors to the same excitation with different intensities, at the response moment, the sampled data of the two sensors are matched, if the sampled data are taken as the nodes to preferentially match the data, then the data between the two groups of peak points are processed conveniently, and meanwhile, the problems of one-to-many matching of similar DTW and the like do not exist.
Due to the retentivity of the excitation source signal, the conventional extreme point extraction algorithm often obtains two or more peak points with equal values, and meanwhile, in the signal retention process, the sensor is disturbed by noise and fluctuates in a small range, as shown in fig. 4, a sampling sequence needs to be correspondingly processed to find a stable peak point as a target peak point, which is specifically as follows:
taking the SBC sampling sequence as an example, the SBC sampling sequence is represented by formula (1):
P＝{p1,p2…pn}
and (3) smoothing the obtained product:
wherein: j ═ span-1)/2
Where span is a smooth scale, pi' is the smoothed quantity point; under the condition of no repetition, the extreme points in the sequence P' are sequentially stored, and the original sequence sampling data P corresponding to the extreme points is found outiWith piAs a real node, fig. 5, the pseudo code of the implementation process is as follows:
if p'i＝max(p'i-t～p'i+t)or
min(p'i-t～p'i+t)
i++
while wj-1≠pi
j++
the above operations are performed on the SBC and RS sequences in sequence, and the obtained node extraction result is shown in fig. 6 and 7. In order to adapt to the difference of sampling frequencies of different sensors, the smooth scale in the algorithm is associated with the actual frequency during extraction, so that the algorithm is more universal.
And segmenting the sequence by using the nodes, wherein the sequence segment between two continuous nodes is used as a segmentation interval. For the SBC, the total number of collected nodes is set to k, and the node sequence is:
{t0,t1,t2…tk,tk+1} (8)
in order to keep data integrity, the sequence starting point p1 is set as the sequence initial node t0, and the sequence ending point pn is set as the last node tk +1 of the sequence. The initial sequence P is divided into k +1 segments:
{t0,…,t1},{t1,…,t2}…{tk-1,…,tk},{tk,…,tk+1}
for the RS sequence, selecting the first k nodes corresponding to the SBC to form a node sequence:
{t'0,t'1,t'2…t'k,t'k+1} (9)
and dividing the RS sequence into k +1 segments:
{t'0,…,t'1},{t'1,…,t'2}…{t'k-1,…,t'k},{t'k,…,t'k+1let the input be any point p on the SBC sampling sequenceiTarget output is on RS and piMatched point qjAnd j denotes the jth sample point which is the RS sample sequence.
According to piThe position information of the point p to be matched is positioned according to the node sequenceiThe segment interval is set to be at { tr-1,…,trIn (j), i.e. located in the r-th segment of the original sample sequence, j is determined by:
wherein, tr-1|x，tr|x，t'r-1|x，t'r|xRespectively SBC sequence node tr-1，trAnd RS sequence node t'r-1，t'rAlong the abscissa, the algorithm flow is as shown in FIG. 8Shown in the figure. It should be noted that the abscissa of the sequence node refers to a position coordinate of the sequence node in the entire measurement value sequence, and the position coordinate is generally an ordinal number of the node.
As shown in fig. 9, a distribution diagram of SBC and RC measurement points in a certain segment of segment interval is shown, where node information is marked in the diagram, each point in the SBC segment interval is calculated by referring to formula (10) to obtain a corresponding j value, and a target point corresponding to a point to be matched is found by using the j value index, as shown in fig. 10.
Referring to fig. 9, the sampling frequency of the RS is lower than that of the SBC, so that the sampling data is ahead of the SBC, but since the two sequences are respectively subjected to node extraction before matching, the leading-lagging relationship between the magnitude sequences does not affect the matching of the sequences, and the point-by-point matching is performed on the point to be matched from one end node.
By referring to fig. 10, it can be found that when the SBC sequence magnitude is matched, different sampling points may correspond to the same sampling point in the RS sequence, and in the segment interval, the 14 th to-be-matched point of the SBC sequence from the left node is matched with the 14 th reference point of the RS sequence, and the 15 th to-be-matched point of the SBC sequence is also matched with the 14 th reference point of the RS sequence. This is because the number of sampling points in any corresponding segment interval between the SBC and the RS is different, and the root cause is that the sampling frequency between the SBC and the RS is not consistent. Considering whether the method has the problems of index loss and the like caused by one-to-many matching, the original SBC and RS sampling sequences are reversely matched, the RS sampling sequence is used as a sequence to be matched, the SBC sequence is used as a reference sequence, and the matching is performed again by using a formula (10), as shown in fig. 11, from the result, in the segment of the segmentation interval, the 13 th reference point of the SBC sequence is matched with the 13 th reference point of the RS sequence from the node on the left side of the RS sequence, the 14 th reference point of the SBC sequence is matched with the 15 th reference point of the SBC sequence, and the 14 th reference point of the SBC sequence does not have the corresponding point to be matched. For the RS sequence to be matched, each point in the node interval can find the corresponding matching point in the reference sequence, so that the many-to-one matching sequence to be matched can not cause the problems of missing check, repeated matching and the like.
In fact, the method can adapt to the matching of sampling sequences with different frequencies due to the node positioning and extraction of the two groups of sampling sequences. In the embodiment of the invention, a comparison test is carried out between a magnitude sequence acquired by a double-frequency laser interferometer (DFLI) and the Laser Displacement Meter (LDM), and as the sampling frequency is completely different from that of the laser displacement meter, the sampling sequence image (figure 12) can be found visually, the sampling data of the DFLI leads the LDM by nearly half of an excitation period, and as the sampling data is accumulated continuously, the Euclidean distance between a node of the DFLI and the previous node of the LDM is minimized, and the matching dislocation is inevitably caused by using a traditional method. According to the embodiment of the invention, the node segmentation method is utilized to extract the target peak points of the two sequences respectively, then the two sequences are segmented according to the target peak points, one segment (figure 13) is selected from the two sequences for matching in an interval, and the node-based segmentation matching method has good applicability to matching of different frequency sensor magnitude sequences. The test result is shown in fig. 14, wherein the DFLI and LDM sampling points connected by the label are matched sampling points, the DFLI sampling sequence is used as a sampling sequence to be matched, the LDM sampling sequence is used as a reference sequence, 15 sampling points in the DFLI segment interval can all find the matched sampling points in the LDM node interval, and similarly, through reverse matching (fig. 15), the LDM sampling sequence is used as a sampling sequence to be matched, the DFLI sampling sequence is used as a reference sequence, and 21 sampling points in the LDM node interval can also find the matched sampling points in the DFLI node interval.
In order to implement the online calibration data sequence matching method provided by the invention, a calibration platform of the bridge deformation monitoring sensor is firstly required to be built for simulating the random change of the load borne by the bridge when an actual vehicle passes through the bridge.
As shown in fig. 16 and 17, the calibration platform comprises a simply supported beam model bridge 8 for simulating a bridge, an excitation source 5 capable of acting on the simply supported beam model bridge 8 to simulate an actual vehicle passing on the bridge, and a sensor to be calibrated and a reference sensor 6 which are installed at the bottom of the simply supported beam model bridge 8 to monitor the deformation of the simply supported beam model bridge 8, wherein the sensor 6 can be installed at the bottom of the simply supported beam model bridge 8 through a sensor installation plate 7, and monitoring data of the sensor 6 is collected by a data collection unit.
In a specific embodiment, the SBC and the RS may each employ, for example, an STP-DM-ST type laser displacement meter, Y-axis measurement range: -24mm to 24mm, X-axis measurement range: -15mm to 15 mm; referring to fig. 16, each laser displacement meter comprises a laser displacement meter receiving end 9 and a laser displacement meter transmitting end 10, the receiving end 9 is installed at a bridge monitoring point, the transmitting end 10 is installed at a reference elevation point, and the bridge deformation monitoring is realized through the mutual cooperation of the two. The data acquisition unit can be a FAROPTFAR-J-1/01 type online monitoring data acquisition instrument. The low-frequency random load excitation source is generated by a LySeiKi ZP140-300H type linear module, the control software can convert an input random number sequence into motion control parameters, the motion control parameters are sent to a USB9030 motion control card, and the linear module is controlled to generate corresponding motion.
By adjusting the excitation frequency given by the program control linear module, data sequences of two groups of laser displacement meters under three different conditions are obtained, the three groups of sequences are respectively subjected to magnitude matching, in order to visually display the matching effect, 12 (12, 27, 45, 58, 68, 84, 101, 122, 143, 166, 179, 196) sampling points are randomly selected from the SBC sequence to be matched for matching, whether the corresponding matching points can be accurately found in the RS reference sequence is observed, and the result is shown in fig. 18-20.
And (3) respectively carrying out quantitative analysis on the three groups of sequences, wherein the matching effect is judged by using the DTW distance in the traditional analysis method, and the smaller the DTW distance is, the better the matching effect is. However, the method is more suitable for the situation of short wave matching (for example, an audio sequence of a single letter pronunciation in speech recognition), and for the experimental object of the embodiment of the invention, on one hand, the total quantity of the two groups of sampling sequences is larger, the DTW distance obtained through calculation is higher, and on the other hand, the DTW algorithm has good applicability to the time scale, but is sensitive to the quantity scale, so that the reference value of the calculated DTW distance is reduced due to the influence of inherent inertia. Considering that the sensor magnitude matching of the conventional DTW metric applied to the embodiment of the present invention has the above limitations, the embodiment of the present invention introduces a concept of magnitude mean square deviation (hereinafter abbreviated as MVD), and evaluates the algorithm matching effect by calculating the magnitude mean square deviation.
Any point p in the sequence to be matchediLet its corresponding point in the reference sequence be qjThe point p to be matched is known by an Euclidean distance formulaiWith reference point qjThe distance between the two plates is as follows:
since the matching is performed between different frequency magnitude sequences, the difference between the horizontal axes is the sampling frequency difference, and the value should not be added in the calculation, so the value is takenIn order to remove the influence of the inherent inertia of the sensor on the evaluation accuracy, the difference between the two series of quantities is subjected to inertia compensation in the calculation, and the following equations (6) and (11) are provided:
the three sets of magnitude sequence matching results under different excitation states were analyzed, and the results are shown in table 1:
TABLE 1 analysis of matching results under different frequency excitation conditions
As can be seen from the above table 1, the relative error of the SBC and RS sequence matching performed after the node segmentation can be controlled within 2%.
To verify the superiority of the node-based segment matching algorithm (FPS) over the conventional Euclidean metric based matching (EM), comparative experiments were performed in the embodiments of the present invention, and the experimental results are shown in fig. 21 to 23.
Comparing fig. 21-fig. 22, it can be seen that the node-based segment matching has good adaptability to the drift of the sampling frequency of the sensor, the corresponding relationship between the point to be matched and the reference point is basically correct, while the matching based on the Euclidean metric is affected by the sampling frequency and the inherent inertia, the matching accuracy is low, the relative error is large, and the error is also gradually increased as the sampling points are gradually increased, while the relative error based on the node-based segment matching is low and relatively stable, as shown in fig. 23. In fact, if the excitation source generates a periodic excitation, the matching error peaks with the two sequences of values leading and lagging each other by half a period. Matching accuracy of different methods is quantitatively analyzed by comparing and matching three groups of magnitude sequences under different excitation states, and experimental results are shown in table 2.
TABLE 2 matching accuracy under different matching methods
Comparing the data in table 2, it can be found that the matching accuracy of the node-based segment matching algorithm is improved compared with that of the traditional Euclidean matching algorithm, and the influence of factors such as frequency and sensor inertia is smaller.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (10)
1. An online calibration data sequence matching method for a bridge deformation monitoring sensor is characterized by comprising the following steps:
step 1, acquiring measurement value sequences of a sensor to be calibrated and a reference sensor under the same excitation action;
step 2, segmenting the measured value sequences of the sensor to be calibrated and the reference sensor;
step 3, matching the measured values of the sensor to be calibrated and the reference sensor in each subsection interval;
and 4, acquiring the corresponding relation between the measured value sequences of the sensor to be calibrated and the reference sensor under the same excitation action.
2. The on-line calibration data sequence matching method of the bridge deformation monitoring sensor according to claim 1, wherein the measured value sequence of the sensor to be calibrated and the measured value sequence of the reference sensor are both waveform sequences; the step 2 comprises the following steps:
step B1, extracting a target peak point in the measured value sequence of the sensor to be calibrated and a target peak point in the measured value sequence of the reference sensor;
and step B2, segmenting the measured value sequences of the sensor to be calibrated and the reference sensor by taking the target peak point in the measured value sequences of the sensor to be calibrated and the reference sensor as a node respectively.
3. The on-line calibration data sequence matching method for bridge deformation monitoring sensors according to claim 2, wherein the step B1 comprises:
step S1, extracting an initial peak point in the measured value sequence of the sensor to be calibrated and an initial peak point in the measured value sequence of the reference sensor and respectively forming an initial peak point sequence;
step S2, smoothing the initial peak point sequence of the sensor to be calibrated and the initial peak point sequence of the reference sensor by the following equations (1) - (3):
P＝{p1，p2…pn} (1)
j＝(span-1)/2 (3)
wherein, P is an initial peak point sequence of the sensor to be calibrated or the reference sensor; n is the initial peak point sequenceThe number of peak points in; span is a smooth scale; pi'is the ith peak point in the smoothed initial peak point sequence P';
step S3, storing P in sequencei'to form a smoothed initial sequence of peak points P';
step S4, finding the initial peak point P in the original initial peak point sequence P corresponding to each peak point in the smoothed initial peak point sequence PiAnd using the initial peak point PiAs the target peak point.
4. The method for matching the online calibration data sequence of the bridge deformation monitoring sensor according to claim 1, wherein the measurement value sequence of the sensor to be calibrated and the measurement value sequence of the reference sensor are divided into K +1 segments; wherein, the measured value sequence of the sensor to be calibrated and the measured value sequence of the reference sensor are segmented to form segmented sequences respectively { t }0,…,t1},{t1,…,t2}…{tk-1,…,tk},{tk,…,tk+1And { t'0,…,t'1},{t'1,…,t'2}…{t'k-1,…,t'k},{t'k,…,t'k+1}; wherein,
t0a starting measurement point of the measurement value sequence of the sensor to be calibrated;
tk+1a measurement point which is the end of the measurement value sequence of the sensor to be calibrated;
tnthe target peak point in the measured value sequence of the sensor to be calibrated is shown, wherein k is more than or equal to n and more than or equal to 1;
t’0a starting measurement point of a sequence of measurement values for the reference sensor;
t’k+1an end measurement point of the sequence of measurement values for the reference sensor;
t’nthe peak value is a target peak value point in the measured value sequence of the reference sensor, wherein k is more than or equal to n and more than or equal to 1.
5. The on-line calibration data sequence matching method for bridge deformation monitoring sensors according to claim 4, wherein the step 3 comprises: sequentially selecting the measured value p to be matched in the measured value sequence of the sensor to be calibratediAnd according to said measured value p to be matchediPosition information of the position of the measured value p to be matchediSegment interval tr-1,…,tr}；
The measured value p to be matched is searched in the measured value sequence of the reference sensor by the following formula (4)iMatched measured values qjIn the position of (a) in the first,
wherein j is a measured value p to be matched in a measured value sequence of the reference sensoriMatched measured values qjThe abscissa of (a);
tr-1|xfor measuring points t in a segmented sequence of sensors to be calibratedr-1The abscissa of (a);
tr|xfor measuring points t in a segmented sequence of sensors to be calibratedrThe abscissa of (a);
t'r-1|xis a measurement point t 'in a segmented sequence of reference sensors'r-1The abscissa of (a);
t'r|xis a measurement point t 'in a segmented sequence of reference sensors'rThe abscissa of (a).
6. The on-line calibration data sequence matching method for bridge deformation monitoring sensors according to claim 5, wherein the step 3 further comprises: output and save piAnd with said piMatched qj。
7. The on-line calibration data sequence matching method for bridge deformation monitoring sensors according to claim 1, wherein the step 1 is preceded by: and (3) setting up a calibration platform of the bridge deformation monitoring sensor to simulate the random change of the load borne by the bridge when an actual vehicle passes through the bridge.
8. The method for matching the on-line calibration data sequence of the bridge deformation monitoring sensor according to claim 7, wherein the calibration platform comprises a simply supported beam model bridge for simulating a bridge, an excitation source capable of acting on the simply supported beam model bridge to simulate an actual vehicle passing through the bridge, and a sensor to be calibrated and a reference sensor which are installed at the bottom of the simply supported beam model bridge to monitor the deformation of the simply supported beam model bridge.
9. The on-line calibration data sequence matching method for the bridge deformation monitoring sensor according to claim 8, wherein the excitation source is a linear module.
10. The on-line calibration data sequence matching method for the bridge deformation monitoring sensor according to any one of claims 1 to 9, wherein the sensor to be calibrated and the reference sensor are both laser displacement meters.
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