CN114216815A - Real-time monitoring system and method for rock-fill dam compacted density based on rolling wave velocity - Google Patents
Real-time monitoring system and method for rock-fill dam compacted density based on rolling wave velocity Download PDFInfo
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Abstract
The invention discloses a real-time monitoring system and a real-time monitoring method for compaction density of a rock-fill dam based on rolling wave velocity, which realize the visualization of the rolling wave velocity and the compaction density in filling and rolling engineering.
Description
Technical Field
The invention relates to the technical field of detection of compaction quality of earth and rockfill filling, in particular to a real-time monitoring system and method for compaction density of a rock-fill dam based on rolling wave velocity.
Background
According to the accident data of the earth and rockfill dam, the accident rate caused by poor design construction quality accounts for 38.5 percent. The control of the compaction quality of dam materials is a key concern in the dam filling construction process and is also a key for the whole safe and stable operation of the dam. If the compaction quality does not reach the standard, the dam body may have the accidents of seepage damage, cracks, landslide and the like, and the dam breaking accident of the earth-rock dam can also occur seriously. According to the current regulation of the industry, the dam filling construction quality is mainly monitored by a double control method, namely, rolling parameters such as rolling times, driving speed, compaction thickness, exciting force state and the like in the construction process and compaction density detected by pit test sampling after construction meet the design requirements are strictly controlled. However, the pit-testing sampling detection method has large discreteness, so that the compaction condition of the whole working area is reflected to have large errors, the detection result cannot be obtained quickly, the quality problem cannot be fed back in real time when being found, and the construction period is easy to delay. With the enlargement of filling scale and the improvement of the overall level of information of construction, the traditional compaction quality control method cannot meet the requirements of modern mechanized construction. Therefore, there is a need to develop a device capable of monitoring the construction quality condition of earth and rockfill dams in real time, so as to predict the compaction quality of the whole working area and feed back the construction quality condition in time.
In order to improve the compaction quality and efficiency, a plurality of scholars continuously provide a rolling construction parameter real-time monitoring system, a road roller integrated compaction monitoring technology and other indirect detection methods by researching the correlation between factors influencing the compaction effect and the compaction quality. Although the research results are widely applied to actual engineering, the rolling construction parameters are only a part of main factors influencing the dam material compaction quality, and the compaction state of the dam material cannot be directly represented only by monitoring the rolling construction parameters. In addition, continuous compaction indexes in the road roller integrated compaction monitoring technology are adopted to evaluate the compaction quality of the rock-fill dam material, and the defects of low evaluation precision, complex representation compaction effect, easiness in influence of properties of compacted materials and the like still exist. There is therefore a need to develop effective non-destructive testing methods to reliably assess the compaction quality of fill materials, particularly for stone-fill and sand-gravel materials.
In the nondestructive testing method, a steady-state surface wave method is one of the commonly used physical earth methods for nondestructive testing of the dynamic characteristics of rock-soil materials, and the method can achieve the purposes of eliminating interference and improving the accuracy of calculating the wave velocity of the surface wave by using a cross-correlation function theory under various external interference conditions. In addition, the elastic wave velocity is closely related to the density of the rock-soil material, and the elastic wave velocity in the rockfill material is quite sensitive to the variables such as ground stress, density, water content and the like, but is not directly influenced by the shape of the particles. In view of the above, in order to more efficiently monitor and quickly evaluate the compaction quality of the rockfill dam material in real time, the method provided by the invention tries to acquire two vertical vibration signals at a certain distance, then indirectly acquires the rolling wave speed by adopting a data continuation type related phase difference solving method, and takes the rolling wave speed monitored in real time as a characterization index of the compaction state of the rockfill material.
Disclosure of Invention
Aiming at the defects in the prior art, the rock-fill dam compacted density real-time monitoring system and method based on the rolling wave velocity provided by the invention can realize more efficient real-time monitoring and rapid evaluation on the rock-fill dam material compacted quality.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a real-time monitoring system for rock-fill dam compacted density based on rolling wave velocity comprises: the device comprises a vibration rolling machine and signal acquisition and processing equipment;
the signal acquisition and processing equipment is arranged on the vibration rolling machine;
the signal acquisition and processing device comprises: the system comprises an RTK-GPS automatic control subsystem, an IEPE type acceleration sensor (3), a TCDT19 dynamic signal testing and analyzing subsystem, a data conditioning module, an analyzing and processing module and a display;
the IEPE type acceleration sensor is connected with the TCDT19 dynamic signal testing and analyzing subsystem; the data conditioning module is respectively connected with the TCDT19 dynamic signal testing and analyzing subsystem and the RTK-GPS automatic control subsystem; the analysis processing module is connected with the data conditioning module; the display is connected with the analysis processing module.
Further, the vibratory roller includes: a front frame and a rear frame.
Further, the RTK-GPS automatic control subsystem is used for acquiring position data of the vibration roller;
the RTK-GPS automatic control subsystem comprises: an RTK-GPS base station and rover station;
the RTK-GPS reference station is used for receiving GPS satellite signals and transmitting the phase difference of the carrier waves in real time;
and the mobile station is used for obtaining the position data of the vibration roller according to the received GPS satellite signal and the transmitted carrier phase difference.
Further, the IEPE type acceleration sensor is a piezoelectric voltage type acceleration sensor;
the IEPE type acceleration sensor is used for collecting vibration signals of the vibration rolling machine acting with the ground, environmental noise signals and sound signals of a motor of the vibration rolling machine.
Further, the TCDT19 dynamic signal testing and analyzing subsystem is used for processing sensing data acquired by the IEPE type acceleration sensor to obtain acceleration data.
Further, the data conditioning module is used for intercepting and filtering acceleration data obtained by processing of the TCDT19 dynamic signal test analysis subsystem to obtain standard acceleration data;
the analysis processing module is used for solving the phase difference of the standard acceleration data to obtain the rolling wave speed of the rolling machine and calculating the compaction density based on the rolling wave speed; the display is used for displaying position data, time, rolling wave speed and compaction density of the vibration rolling machine.
A real-time monitoring method for the compaction density of a rock-fill dam based on the rolling wave velocity comprises the following steps:
a1, acquiring a first path of sensing data through a first acceleration sensor, and acquiring a second path of sensing data through a second acceleration sensor;
a2, filtering the first path of sensing data and the second path of sensing data by an FIR low-pass filter to obtain a first path of acceleration data and a second path of acceleration data;
a3, estimating the frequency of the first path of acceleration data and the second path of acceleration data by Fourier transform to obtain signal frequency;
a4, calculating the number of sampling points in a complete period according to the signal frequency;
a5, judging whether the first path of acceleration data and the second path of acceleration data are full-period signals or not according to the number of sampling points in a full period, if so, calculating the rolling wave speed of the vibration rolling machine, otherwise, extending the first path of acceleration data and the second path of acceleration data into full-period signals, and then calculating the rolling wave speed of the vibration rolling machine;
and A6, calculating the compaction density of the vibration rolling machine according to the rolling wave speed of the vibration rolling machine.
Further, the formula for calculating the rolling wave speed of the vibratory rolling machine in the step a5 is as follows:
v is the rolling wave speed of the vibration rolling machine, x is the distance between the first acceleration sensor and the second acceleration sensor, x (n) is the nth sensing data of the first path of sensing data sequence, k is the sequence length, y (n) is the nth sensing data of the second path of sensing data sequence, and f is the signal frequency.
Further, the formula of the compaction density of the vibrating roller in the step a6 is as follows:
ρd＝1.34736+0.00376
where ρ isdV is the compaction density of the vibratory roller and v is the roller wave speed of the vibratory roller.
The beneficial effects of the above further scheme are: the method obtains the rolling wave speed by solving the phase difference through the same-frequency signals, and is not influenced by the attenuation of the energy of the signals in the stratum.
In conclusion, the beneficial effects of the invention are as follows:
1. the invention obtains the rolling wave velocity by utilizing two vertical vibration signals (namely acceleration data) which are separated by a certain distance, calculates the compaction density based on the rolling wave velocity, takes the rolling wave velocity as the compaction quality monitoring index of the rockfill material, provides an important analysis method for the real-time evaluation of the compaction characteristics of the dam material, can realize the comprehensive fine control of the whole construction process and reduce the human intervention.
2. The invention realizes the visualization of the rolling quality, calculates the rolling wave speed and the rolling density in the rolling process in real time in the rolling process, and displays the information of the rolling wave speed, the rolling density and the position on the display, so that the real-time position of the rolling machine and the rolling quality of each point in the field can be visually observed, and the rapid and accurate evaluation and the timely feedback control of the rock-fill dam material rolling quality are realized.
Drawings
FIG. 1 is a system diagram of a real-time monitoring system for the compaction density of a rock-fill dam based on the rolling wave velocity;
FIG. 2 is a flow chart of a real-time monitoring method for the compaction density of a rock-fill dam based on the rolling wave velocity;
FIG. 3 is a comparison diagram before and after filtering the first path of sensing data by using an FIR low-pass filter;
FIG. 4 is a Fourier amplitude spectrum of the first path of acceleration data;
FIG. 5 is a comparison diagram before and after filtering the second path of sensing data by using an FIR low-pass filter;
FIG. 6 is a Fourier amplitude spectrum of the second path of acceleration data;
FIG. 7 is a regression plot of crush wave velocity versus compaction density;
FIG. 8 is a cloud of compaction quality assessments;
wherein, 1, a vibration rolling machine; 2. an RTK-GPS automatic control subsystem; 3. an IEPE type acceleration sensor; 4. the TCDT19 dynamic signal test analysis subsystem; 5. a data conditioning module; 6. an analysis processing module; 7. a display; 8. a front frame; 9. a vibrating wheel; 10. a rear frame; 31. a first acceleration sensor; 32. a second acceleration sensor.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1:
as shown in fig. 1, a real-time monitoring system for rock-fill dam compaction density based on rolling wave velocity comprises: the device comprises a vibration rolling machine 1 and signal acquisition and processing equipment;
the signal acquisition and processing equipment is arranged on the vibration rolling machine 1;
the signal acquisition and processing device comprises: the system comprises an RTK-GPS automatic control subsystem 2, an IEPE type acceleration sensor 3, a TCDT19 dynamic signal testing and analyzing subsystem 4, a data conditioning module 5, an analyzing and processing module 6 and a display 7;
the IEPE type acceleration sensor 3 is connected with the TCDT19 dynamic signal testing and analyzing subsystem 4; the data conditioning module 5 is respectively connected with the TCDT19 dynamic signal testing and analyzing subsystem 4 and the RTK-GPS automatic control subsystem 2; the analysis processing module 6 is connected with the data conditioning module 5; the display 7 is connected to the analysis module 6.
The vibratory roller 1 is used for on-site rolling work, and includes: a front frame 8 and a rear frame 10. The front frame 8 not only serves to connect the vibrating wheels 9, but also is an important component of the compaction. The rear frame 10 mounts components such as an engine, a battery box, a fuel tank, a cab, and the like. The front frame and the rear frame are connected through a hinge mechanism, the hinge mechanism plays roles of hinge steering, frame swinging and the like, and the hinge steering realizes a smaller turning radius.
The RTK-GPS automatic control subsystem 2 is used for acquiring the position data of the vibration roller 1;
the RTK-GPS automatic control subsystem 2 comprises: an RTK-GPS base station and rover station;
the RTK-GPS reference station is used for receiving GPS satellite signals and transmitting the phase difference of the carrier waves in real time;
and the mobile station is used for obtaining the position data of the vibration roller 1 according to the received GPS satellite signal and the transmitted carrier phase difference.
The IEPE type acceleration sensor 3 is a piezoelectric type voltage type acceleration sensor; the piezoelectric voltage type acceleration sensor has the advantages of small volume, light weight, wide frequency response range and high reliability, and is internally provided with an IEPE circuit.
The IEPE type acceleration sensor 3 is used to collect vibration signals of the vibratory mill 1 acting on the ground, ambient noise signals and sound signals of the motor of the vibratory mill 1.
The TCDT19 dynamic signal test analysis subsystem 4 is configured to process sensing data collected by the IEPE type acceleration sensor 3 to obtain acceleration data, i.e., a vibration signal.
The TCDT19 dynamic signal test analysis subsystem 4 comprises a collector host, a network cable and a charger. The system is a 4-channel 24-bit high-performance ADC parallel data acquisition system based on a network interface, the sampling rate is 128kHz/CH, each channel is compatible with voltage and IEPE input, and a TCDT 19Q-IEPE converter is connected to support the access of a charge type sensor. External direct current power supply, small in size, convenient to carry.
And the data conditioning module 5 is used for intercepting and filtering the acceleration data processed by the TCDT19 dynamic signal test analysis subsystem 4 to obtain standard acceleration data.
The analysis processing module 6 is used for solving the phase difference of the standard acceleration data to obtain the rolling wave speed of the rolling machine 1 and calculating the compaction density based on the rolling wave speed; the display 7 is used for displaying position data, time, rolling wave speed and compaction density of the vibrating rolling machine 1, so as to establish a spatial-temporal compaction degree distribution map of a rolling area.
The system further comprises a clock unit for providing a clock signal and time stamp converting the clock signal to obtain a time date which can be used for display by the display 7.
The formula for the timestamp conversion is: date ((+28800000)/86400000+ datenum (1970,1,1),31), where x is time in milliseconds, datestr () is a sequence value that converts a specified date into a string form, and datenum () is a sequence value that obtains the specified date.
Example 2:
when the vibration rolling machine 1 is pressed at a certain vibration frequency and rolling speed, the vibration wheel 9 transmits vibration to the rolling soil layer, the soil layer is subjected to the combined action of the dead weight and the exciting force, so that the soil layer is changed from a static state to a vibration state, and small-particle soil is filled in a gap between large-particle soil, so that the soil layer is gradually compacted. Research shows that the acceleration of the vibrating wheel 9 is closely related to the compaction condition of the pressed material, and the time course curve of the acceleration of the vibrating wheel 9 basically changes periodically along with the compaction of the soil layer from soft to compact. According to the analysis, the rolling wave velocity generated by rolling has strong correlation with the compaction degree of the soil and stone materials, so that the rolling wave velocity can be solved by two acceleration sensors which are spaced at a certain distance, and the rolling wave velocity is used as a real-time monitoring index of the compaction quality of the rock-fill dam materials.
As shown in fig. 2, a method for monitoring the compaction density of a rock-fill dam in real time based on the rolling wave velocity comprises the following steps:
a1, acquiring a first path of sensing data through a first acceleration sensor 31, and acquiring a second path of sensing data through a second acceleration sensor 32;
in step a1, the first acceleration sensor 31 and the second acceleration sensor 32 are mounted in the manner shown in fig. 1, and the first acceleration sensor 31 and the second acceleration sensor 32 are mounted on the frame near the vibrating wheel 9 of the vibrating roller 1 and under the driving rear axle near the rear rubber wheel, respectively, and both the first acceleration sensor 31 and the second acceleration sensor 32 are IEPE acceleration sensors.
A2, filtering the first path of sensing data and the second path of sensing data by an FIR low-pass filter to obtain a first path of acceleration data and a second path of acceleration data;
the sensing data collected in step a1 includes: the vibration signals generated when the vibration roller 1 interacts with soil, the environmental noise signals and the sound signals emitted by the vibration motor are all interference signals, so that the environmental noise signals and the sound signals need to be filtered, only the vibration signals are reserved, and the vibration signals are the required acceleration data.
A3, estimating the frequency of the first path of acceleration data and the second path of acceleration data by Fourier transform to obtain signal frequency;
a4, calculating the number of sampling points in a complete period according to the signal frequency;
a5, judging whether the first path of acceleration data and the second path of acceleration data are full-period signals or not according to the number of sampling points in a full period, if so, calculating the rolling wave speed of the vibration rolling machine 1, otherwise, extending the first path of acceleration data and the second path of acceleration data into full-period signals, and then calculating the rolling wave speed of the vibration rolling machine 1;
the formula for calculating the rolling wave speed of the vibratory rolling machine 1 in the step a5 is as follows:
wherein v is the rolling wave speed of the vibrating rolling machine 1, x is the distance between the first acceleration sensor 31 and the second acceleration sensor 32, x (n) is the nth sensing data of the first sensing data sequence, k is the sequence length, y (n) is the nth sensing data of the second sensing data sequence, and f is the signal frequency.
A6, calculating the compaction density of the vibration rolling machine 1 according to the rolling wave speed of the vibration rolling machine 1.
The formula for the compaction density of the vibratory roller 1 in step a6 is:
ρd＝1.34736+0.00376v (2)
where ρ isdV is the compaction density of the vibrating roller 1 and v is the roller compaction wave speed of the vibrating roller 1.
In the embodiment, an FIR low-pass filter is adopted to filter sensing data, then Fourier transform is adopted to carry out preliminary estimation on the frequency of acceleration data, the number of sampling points in a complete period is known to be n according to the frequency estimation result, then the whole period judgment is carried out on the collected signal with the relevant length of k, the quotient of dividing k by n is set as m remainder, p is set, if p is equal to 0, the collected signal is a whole period signal, and the solution can be directly carried out by using a formula (1); if p is not zero, the correlation length exceeds m periods but does not reach m +1 periods, and then p data [ x ] outside the whole period are respectively removed from the two paths of same-frequency signalsmn+1,xmn+2,…,xk]And [ ymn+1,ymn+2,…,yk]Obtaining a complete whole-cycle signal sequence x' ═ x1,x2,…,xmn]And y ═ y1,y2,…,ymn]. Then searching n-p data [ x ] from the whole period signal sequence x' and y(m-1)n+p+1,…,xmn]And [ y(m-1)n+p+1,…,ymn]After the data extension is combined into the original signal sequence, the data extension of the non-whole period part signal is adjusted into a whole period signal, and the signal sequence after the data extension is as follows: x is the number oft＝[x1,x2,…,xk,x(m-1)n+p+1,…,xmn]And yt＝[y1,y2,…,yk,y(m-1)n+p+1,…,ymn]. And finally, solving the wave velocity of the whole period signal after data extension by adopting the formula (1).
The principle and the process of the method are as follows:
assuming that a signal received by the first acceleration sensor 31 is x (t), a signal received by the second acceleration sensor 32 is y (t), and expressions of two same-frequency time-domain continuous signals are respectively:
where A and B are the amplitudes of the signals, ω is the angular frequency, t is the time,
and
as the initial phase of the signal, e1(t) and e2(t) is the noise of the signal. Because the signal and the noise are not correlated, the digitized signal is subjected to correlation operation to obtain:
wherein R isxy(0) The cross-correlation function value when the time delay between the first sensing data sequence x (n) and the second sensing data sequence y (n) is zero is obtained; rxx(0) The self-correlation function value is the self-correlation function value when the time delay of the first sensing data sequence x (n) is zero; ryy(0) And the autocorrelation function value is the autocorrelation function value when the time delay of the second sensing data sequence y (n) is zero.
The expected values for each term are:
when the correlation length k is the whole period, the second part in the formula (5) is zero, and the phase difference of the two paths of signals obtained at this time is:
when the correlation length k is a non-whole period, the second part in the formula (5) is not zero, and a phase difference obtained by continuously solving the formula (6) by using the correlation method has a large error, which also indicates that the measurement accuracy of the correlation method is greatly influenced by whether the correlation length is a whole period or not.
Experiment:
in order to verify the effectiveness and the accuracy of the wave velocity calculation method of the embodiment, two paths of sinusoidal signals randomly added with white gaussian noise are adopted for simulation comparison analysis. Two paths of signal sequences are set as follows:
in formula (7): the signal frequency is 20Hz, and the sampling frequency is 1500 Hz; e.g. of the type1(t) and e2(t) are all additive white Gaussian noise signals generated randomly. Fig. 3 to 6 are time courses of signals with correlation length k being 750 before and after filtering and corresponding fourier amplitude spectrograms, the first path of sensing data graph of fig. 3 and the second path of sensing data graph of fig. 5 are 2 paths of original signals with signal-to-noise ratio SNR being 10dB under gaussian white noise, the first path of acceleration data graph of fig. 3 and the second path of acceleration data graph of fig. 5 are sinusoidal signals after Fir filtering, and fig. 4 and 6 are fourier amplitude spectrums of the filtered signals. Firstly, Fourier transform is adopted to carry out preliminary estimation on the period of a sampling signal, the number of sampling points in one period is 75, then the data continuation correlation method of the embodiment is adopted to solve the frequency and the phase difference of the signals with different correlation lengths, the table 1 counts the estimation error of the frequency and the phase difference when the correlation length k is even times of the length of the whole period, and the estimation accuracy of the method of the embodiment can be gradually improved along with the increase of the correlation length k. In addition, the embodiment also selects a signal with the correlation length K of 550 as a non-whole period signal for analysis, the signal is extended into a whole period signal by adopting a data extension method, the information amount of data is increased on the basis of the original data point number, and the table shows that the estimated calculation parameter error is reduced compared with the whole period signal K of 450.
TABLE 1 Absolute error of Signal frequency and phase difference at different correlation lengths
The following describes a specific implementation manner of how to monitor the vibration roller 1 in real time after obtaining the rolling wave velocity:
the compaction density data obtained by field pit test detection and the rolling wave velocity collected at the corresponding pit test position are utilized to establish a correlation equation (regression equation) of the rolling wave velocity and the compaction density value, and the correlation coefficient between the rolling wave velocity and the compaction density is 0.86488, so that the correlation is strong. By t-testing the slope and intercept in the regression equation, confidence intervals of 1.34736 ± 0.14459 and 0.00376 ± 5.83327E-4, respectively, are given for the slope and intercept with a 95% confidence. The linear regression equation between the rolling wave speed and the compacted density of the vibratory roller 1 is:
ρd＝1.34736+0.00376VR (8)
wherein, VRRolling wave speed, p, for vibrating rolling machine 1dTo compact density. The regression curve of the crushing wave speed versus the compacted density is shown in fig. 7.
After obtaining the regression equation of the rolling wave velocity and the compaction density, the compaction density of the corresponding point can be obtained according to the rolling wave velocity obtained in real time on the road roller, fig. 8 shows a certain 20m × 20m filling unit, the filling unit is divided according to a 0.5 × 0.5m grid, the rolling wave velocity of the grid point can be obtained in real time according to the method of the embodiment, then the compaction density of the corresponding grid point can be obtained according to the formula (8), and fig. 8 is a compaction density contour line cloud chart of the whole working area. The dam material design of the dam engineering requires that the full material compaction density standard-reaching rate (namely rho) of the dam materiald3) of not less than 2.18g/cm and not less than 97 percent, and as can be seen from fig. 8, the bin surface compacted density standard reaching rate is 100 percent and is more than 97 percent of the filling compacted control standard, so the bin surface compacted quality is qualified. According to the formula (8), the dam material design value rhodThe corresponding rolling wave speed is 221.45m/s when the density is not less than 2.18g/cm3, and the value can be used as the judgment of the compaction density value rho in practical applicationdQuality control of unqualified productsAnd (4) checking the points, and if necessary, performing recheck by pit excavation detection.
Claims (9)
1. A rock-fill dam compacted density real-time monitoring system based on rolling wave velocity is characterized by comprising: the device comprises a vibration rolling machine (1) and signal acquisition and processing equipment;
the signal acquisition and processing equipment is arranged on the vibration rolling machine (1);
the signal acquisition and processing device comprises: an RTK-GPS automatic control subsystem (2), an IEPE type acceleration sensor (3), a TCDT19 dynamic signal testing and analyzing subsystem (4), a data conditioning module (5), an analysis processing module (6) and a display (7);
the IEPE type acceleration sensor (3) is connected with a TCDT19 dynamic signal test analysis subsystem (4); the data conditioning module (5) is respectively connected with the TCDT19 dynamic signal testing and analyzing subsystem (4) and the RTK-GPS automatic control subsystem (2); the analysis processing module (6) is connected with the data conditioning module (5); the display (7) is connected with the analysis processing module (6).
2. The real-time monitoring system for rock-fill dam compaction density based on rolling wave speed as claimed in claim 1, characterized in that the vibrating rolling machine (1) comprises: a front frame (8) and a rear frame (10).
3. The rolling wave speed based real-time monitoring system for rock-fill dam compaction density according to claim 1 wherein the RTK-GPS automatic control subsystem (2) is used to acquire vibration roller (1) position data;
the RTK-GPS automatic control subsystem (2) comprises: an RTK-GPS base station and rover station;
the RTK-GPS reference station is used for receiving GPS satellite signals and transmitting the phase difference of the carrier waves in real time;
the rover station is used for obtaining the position data of the vibration roller (1) according to the received GPS satellite signals and the transmitted carrier phase difference.
4. The system for real-time monitoring of the compaction density of a rock-fill dam based on the rolling wave velocity according to claim 1, characterized in that the IEPE type acceleration sensor (3) is a piezoelectric voltage type acceleration sensor;
the IEPE type acceleration sensor (3) is used for collecting vibration signals of the vibration rolling machine (1) acting with the ground, environment noise signals and sound signals of a motor of the vibration rolling machine (1).
5. The system for real-time monitoring of the compaction density of the rock-fill dam based on the rolling wave velocity according to claim 1, wherein the TCDT19 dynamic signal test analysis subsystem (4) is configured to process the sensing data collected by the IEPE type acceleration sensor (3) to obtain acceleration data.
6. The real-time rockfill dam compaction density monitoring system based on roller compaction wave speed according to claim 1, wherein the data conditioning module (5) is used for performing data interception and filtering on acceleration data processed by the TCDT19 dynamic signal test analysis subsystem (4) to obtain standard acceleration data;
the analysis processing module (6) is used for solving the phase difference of the standard acceleration data to obtain the rolling wave velocity of the rolling machine (1) and calculating the compaction density based on the rolling wave velocity; the display (7) is used for displaying position data, time, rolling wave speed and compaction density of the vibration rolling machine (1).
7. A real-time monitoring method for the compaction density of a rock-fill dam based on the rolling wave velocity is characterized by comprising the following steps:
a1, collecting a first path of sensing data through a first acceleration sensor (31), and collecting a second path of sensing data through a second acceleration sensor (32);
a2, filtering the first path of sensing data and the second path of sensing data by an FIR low-pass filter to obtain a first path of acceleration data and a second path of acceleration data;
a3, estimating the frequency of the first path of acceleration data and the second path of acceleration data by Fourier transform to obtain signal frequency;
a4, calculating the number of sampling points in a complete period according to the signal frequency;
a5, judging whether the first path of acceleration data and the second path of acceleration data are full-period signals or not according to the number of sampling points in a complete period, if so, calculating the rolling wave speed of the vibration rolling machine (1), otherwise, extending the first path of acceleration data and the second path of acceleration data into the full-period signals, and then calculating the rolling wave speed of the vibration rolling machine (1);
a6, calculating the compaction density of the vibration rolling machine (1) according to the rolling wave speed of the vibration rolling machine (1).
8. The method for monitoring compaction density of rock-fill dam in real time based on rolling wave speed as claimed in claim 7, wherein the formula for calculating the rolling wave speed of the vibrating rolling machine (1) in the step A5 is as follows:
v is the rolling wave speed of the vibration rolling machine (1), x is the distance between the first acceleration sensor (31) and the second acceleration sensor (32), x (n) is the nth sensing data of the first path of sensing data sequence, k is the sequence length, y (n) is the nth sensing data of the second path of sensing data sequence, and f is the signal frequency.
9. The method for real-time monitoring of compaction density of rock-fill dam based on rolling wave velocity as claimed in claim 7, wherein the formula of compaction density of the vibrating roller (1) in step A6 is:
ρd＝1.34736+0.00376v
where ρ isdV is the compaction density of the vibrating roller (1) and v is the rolling wave speed of the vibrating roller (1).
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