CN116625444A - Method for self-adapting characteristic wave and flow correction of ultrasonic water meter - Google Patents
Method for self-adapting characteristic wave and flow correction of ultrasonic water meter Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/66—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
- G01F1/667—Arrangements of transducers for ultrasonic flowmeters; Circuits for operating ultrasonic flowmeters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F25/00—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume
- G01F25/10—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume of flowmeters
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
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Abstract
The invention discloses a method for self-adapting characteristic wave and flow correction of an ultrasonic water meter, which comprises the following steps: s1: and acquiring an ultrasonic echo receiving signal sequence. S1: acquiring an ultrasonic echo receiving signal sequence; s1.1: controlling an ultrasonic signal transmitting circuit, wherein an upstream transducer transmits N1 Mhz excitation pulses, and a downstream transducer receives echo signals; the ratio can be calculated and the validity can be judged: the validity of the ultrasonic echo signal sequence can be determined by calculating the ratio of the first hit to the beginning valid hit in the signal. The method can avoid metering errors caused by signal attenuation due to factors such as water quality or particulate matters in the pipeline; realize the variable detection of dynamic regulation ultrasonic wave characteristic wave: if the ratio of the signals is not in the effective threshold range, the system can automatically and dynamically adjust the ultrasonic characteristic wave so as to meet the metering accuracy requirements under different conditions. The method can be adjusted according to actual conditions so as to improve measurement accuracy.
Description
Technical Field
The invention relates to the technical field of ultrasonic water meters, in particular to a method for self-adapting characteristic waves and correcting flow of an ultrasonic water meter.
Background
The ultrasonic water meter is a full-electronic structural water meter and has the characteristics of high metering accuracy, good repeatability, no pressure loss and the like. However, in field application, water quality is hard in some places, and the surface of the transducer can scale after long-term use, so that echo receiving signals of the transducer are attenuated; in addition, impurities and particles in the water pipe can also cause signal attenuation. These factors cause spurious waves in the characteristic wave of the ultrasonic echo signal, affecting ultrasonic metrology. Meanwhile, in the flow correction process, the multipoint correction is generally carried out according to an error curve, the process of the method is repeated, the adjustment and measurement period is long, the mass production efficiency is low, and the method is not suitable for production and manufacture;
meanwhile, when the ultrasonic water meter is used, because of scaling and impurity particles caused by long-time use or water quality problems, interference and attenuation can be generated on the transmission and the reception of ultrasonic signals, so that characteristic waves in ultrasonic echo signals are wrong, and the accuracy of ultrasonic metering is further affected. The traditional correction method generally adopts multipoint correction, and needs repeated adjustment and calibration, so that the adjustment and measurement period is long, and the method is not suitable for mass production of production and manufacture.
Therefore, a method for self-adapting characteristic wave and flow correction of an ultrasonic water meter is provided.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method for adaptive characteristic wave and flow correction of an ultrasonic water meter, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial choice;
the technical scheme of the embodiment of the invention is realized as follows: a method for self-adapting and flow correction of characteristic wave of an ultrasonic water meter comprises the following steps:
s1: and acquiring an ultrasonic echo receiving signal sequence.
S1: acquiring an ultrasonic echo receiving signal sequence;
s1.1: and controlling an ultrasonic signal transmitting circuit, wherein the upstream transducer transmits N1 Mhz excitation pulses, and the downstream transducer receives echo signals.
S1.2: the analog conversion circuit samples echo signals at a frequency which is 2 times greater than that of the transducer, and sequentially marks echo amplitudes as AM1, AM2, AM3 and … AMn.
In the above embodiment, the ultrasonic echo reception signal sequence is acquired; by controlling the ultrasonic signal transmitting circuit, the upstream transducer transmits N1 MHz excitation pulses, and the downstream transducer receives echo signals; the analog conversion circuit samples echo signals at a frequency which is 2 times greater than that of the transducer, and sequentially marks echo amplitudes as AM1, AM2, AM3 and … AMn.
S2: a characteristic wave of the echo received signal is determined.
S2.1: calculating the difference between the sequence of each wave and the adjacent wave sequence, as follows:
PreDIFi= AMi +1-AMi (i=1, i < N-1) (equation 1)
In the above equation, preDIF represents the difference between each wave sequence and the precursor wave sequence.
S2.2: from S2.1, a set of sequence differences can be derived as follows:
PreDIF= { PreDIF1, preDIF2, & gt, preDTFN } (equation 2)
S2.3: the maximum sequence difference value is obtained from S2.2 as follows:
PreDIFMAX=PreDIFj (j=i+1) (equation 3)
In the above equation, preDIFMAX represents the maximum sequence difference between each wave and the precursor wave, and j represents the number of the characteristic wave of the echo receiving signal.
In the above embodiment, the characteristic wave of the echo reception signal is determined; and determining the serial number of the characteristic wave of the echo receiving signal by calculating the difference value between the sequence of each wave and the sequence of the adjacent wave and obtaining the maximum sequence difference value.
S3: the first wave threshold is adjusted so that the characteristic wave of the ultrasonic echo signal is adapted under various circumstances. The range of the head wave adjustable is expressed by ThresRange, and the range of ThresRange is known from S1 and S2:
(AMi，AMi+PreDIF1)
in the above embodiment, the amplitude range of the calculated characteristic wave and the precursor wave thereof adjusts the head wave threshold of the ultrasonic echo signal.
S4: a stable effective echo is determined to reduce interference of noise. And (3) performing secondary calculation on the sequence difference set PreDIF, and determining the wave as a stable effective echo if the sequence difference ratio WaveRate is smaller than 5%. The formula is as follows (formula 4):
WaveRate＝RreDIFi+1-PreDIFi/PreDIFi*100％(i＝1；i<N)
in the above embodiment, the sequence difference set is calculated twice, and whether the wave is a stable effective echo is determined by judging whether the sequence difference ratio WaveRate is less than 5%.
S5: the ratio of the characteristic wave to the stationary effective wave, and the threshold value of the characteristic wave are determined. S3, defining the time of the characteristic wave higher than a set threshold value and lower than the set threshold value as TFHL; the time of the stable effective wave above zero and below zero is defined as TSHL, and the calculation formula of the ratio of the characteristic wave to the stable effective wave is as follows:
PWR＝TFHL/TSHL
in the above embodiment, the ratio PWR of the characteristic wave to the stable effective wave is obtained by calculating the ratio of the times when the characteristic wave is higher than the set threshold value and lower than the set threshold value to the times when the stable effective wave is higher than the zero point and lower than the zero point.
S6: and if the characteristic wave self-adaptive adjustment meets PWR <0.5 or PWR >0.7, the adjustment configuration is carried out according to S1, S2, S3, S4 and S5.
In the above embodiment, the characteristic wave is adaptively adjusted. By comparing the magnitude relation between PWR and 0.5 and 0.7, the characteristic wave is adaptively adjusted according to the results of the steps 1-5.
S7: and training sample data by using an LMBP neural network to realize flow correction of different temperatures.
S7.1: and carrying out an LMBP neural temperature compensation algorithm on two flow ranges of Q1-Q2 and Q2-Q3. At the Q1,1.1Q1, Q2,1.1Q2,0.5Q3, Q3, Q4 flow points, the input training data is the temperature and calculated flow values, and the output desired parameter is the actual reference flow value. The temperature was selected to be 5 ℃,20 ℃,30 ℃.
S7.2: data normalization processing is represented by the following formula:
yj' =λ (yj-ymin/ymax-ymin) +δ (equation 5)
In the above formula, yj ' is a normalization value of input data to be trained, yj is input data to be trained, y ' min is a minimum value of data to be trained, y ' max is a maximum value of data to be trained, lambda is a proportionality coefficient, and delta is a compensation constant;
s7.3: from S7.2 the following formula can be obtained:
yj ' = (yj ' - δ) (y ' max-y ' min) +y ' min (equation 6)
In the above equation, yj 'is output predicted data, yj' is output normalized predicted data, y 'min is a minimum value of normalized expected data, and y' max is a maximum value of normalized expected data.
S7.4: and (3) carrying out solving fitting on the S7.2 and the S7.3 to obtain a relation between the actual flow Q standard calculated flow Q and the temperature T, wherein the relation is as follows:
qstandard=n0+n1q+n2t+n3qt (formula 7)
In the embodiment, the LMBP neural network is used for training the sample data to realize flow correction of different temperatures; training the training data in different flow ranges by using an LMBP neural network, and normalizing the data to obtain a normalization value of the input data to be trained; output prediction data is obtained through a neural network, and an output actual flow value is obtained through inverse normalization processing.
Compared with the prior art, the invention has the beneficial effects that:
1. the ratio can be calculated and the validity can be judged: the validity of the ultrasonic echo signal sequence can be determined by calculating the ratio of the first hit to the beginning valid hit in the signal. The method can avoid metering errors caused by signal attenuation due to factors such as water quality or particulate matters in the pipeline.
2. Realize the variable detection of dynamic regulation ultrasonic wave characteristic wave: if the ratio of the signals is not in the effective threshold range, the system can automatically and dynamically adjust the ultrasonic characteristic wave so as to meet the metering accuracy requirements under different conditions. The method can be adjusted according to actual conditions so as to improve measurement accuracy.
3. Method for providing flow correction: the technology can carry out flow correction on the original measurement data by establishing a regulation model so as to adapt to different flow field turbulence. The method can reduce the correction times of the error curve, improves the production efficiency, and is suitable for mass production and manufacturing.
Detailed Description
The foregoing objects, features and advantages of the invention will be more readily apparent from the following detailed description of the invention when taken in connection with the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below;
it should be noted that the terms "first," "second," "symmetric," "array," and the like are used merely for distinguishing between description and location descriptions, and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of features indicated. Thus, a feature defining "first," "symmetry," or the like, may explicitly or implicitly include one or more such feature; also, where certain features are not limited in number by words such as "two," "three," etc., it should be noted that the feature likewise pertains to the explicit or implicit inclusion of one or more feature quantities;
in the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature; meanwhile, all axial descriptions such as X-axis, Y-axis, Z-axis, one end of X-axis, the other end of Y-axis, or the other end of Z-axis are based on a cartesian coordinate system.
In the present invention, unless explicitly specified and limited otherwise, terms such as "mounted," "connected," "secured," and the like are to be construed broadly; for example, the connection can be fixed connection, detachable connection or integrated molding; the connection may be mechanical, direct, welded, indirect via an intermediate medium, internal communication between two elements, or interaction between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art from the specification in combination with specific cases.
In the prior art, when an ultrasonic water meter is used, because of the scaling and the existence of impurity particles caused by long-time use or water quality problems, interference and attenuation can be generated on the transmission and the reception of ultrasonic signals, so that the characteristic waves in ultrasonic echo signals are in wrong waves, and the accuracy of ultrasonic metering is further affected. The traditional correction method generally adopts multipoint correction and needs repeated adjustment and calibration, so that the adjustment and measurement period is longer, and the method is not suitable for mass production of production and manufacture; therefore, the invention provides a technical scheme for solving the technical problems: a method for self-adapting and flow correction of characteristic wave of an ultrasonic water meter comprises the following steps:
S1: and acquiring an ultrasonic echo receiving signal sequence.
S1: acquiring an ultrasonic echo receiving signal sequence;
s1.1: and controlling an ultrasonic signal transmitting circuit, wherein the upstream transducer transmits N1 Mhz excitation pulses, and the downstream transducer receives echo signals.
S1.2: the analog conversion circuit adopts a frequency which is 2 times greater than that of the transducer to sample echo signals, and the echo amplitude values are recorded as AM1, AM2, AM3 and … AMn in sequence;
in the scheme, an ultrasonic echo receiving signal sequence is obtained; by controlling the ultrasonic signal transmitting circuit, the upstream transducer transmits N1 MHz excitation pulses, and the downstream transducer receives echo signals; the analog conversion circuit samples echo signals at a frequency which is 2 times greater than that of the transducer, and sequentially marks echo amplitudes as AM1, AM2, AM3 and … AMn.
S2: a characteristic wave of the echo received signal is determined.
S2.1: calculating the difference between the sequence of each wave and the adjacent wave sequence, as follows:
PreDIFi= AMi +1-AMi (i=1, i < N-1) (equation 1)
In the above equation, preDIF represents the difference between each wave sequence and the precursor wave sequence.
S2.2: from S2.1, a set of sequence differences can be derived as follows:
PreDIF= { PreDIF1, preDIF2, & gt, preDTFN } (equation 2)
S2.3: the maximum sequence difference value is obtained from S2.2 as follows:
PreDIFMAX=PreDIFj (j=i+1) (equation 3)
In the above equation, preDIFMAX represents the maximum sequence difference between each wave and the precursor wave, and j represents the number of the characteristic wave of the echo receiving signal.
In the scheme, the characteristic wave of an echo receiving signal is determined; and determining the serial number of the characteristic wave of the echo receiving signal by calculating the difference value between the sequence of each wave and the sequence of the adjacent wave and obtaining the maximum sequence difference value.
It should be noted that in the present embodiment, in ultrasonic nondestructive inspection, an ultrasonic sensor is generally used as a probe. In S1.1, the ultrasonic signal transmitting circuit controls the ultrasonic sensor to transmit N excitation pulses of 1MHz, which are transmitted to the surface of the object to be measured. As these ultrasonic beams pass through the object they are reflected back. In S1.1, the downstream transducer receives echo signals, which are sent to an analog conversion circuit.
In S1.2, the analog conversion circuit samples echo signals at a frequency greater than 2 times that of the transducer, and these signals are converted to digital signals and stored in a computer. Each sample value represents the amplitude of the echo signal received at that point in time. The sample values are stored in sequence as an echo receive signal sequence, AM1, AM2, AM 3.
S2: a characteristic wave of the echo received signal is determined.
In S2.1, the difference between the previous and the next sampling point for each sampling point is calculated as shown in equation 1. These differences are referred to as sequence differences. The sequence difference reflects the rate of change of the echo receive signal on the time axis.
In S2.2, all sequence differences are stored as a set, as shown in equation 2. This set reflects the rate of change of the echo receive signal over the entire time axis.
In S2.3, the largest sequence difference is found from the set, as shown in equation 3. This sequence difference corresponds to a characteristic wave. The characteristic wave generally represents a defect or a specific location of the material structure within the object under test.
For example, it is assumed that an ultrasonic water meter flow path is to be detected, and an ultrasonic wave of 1MHz is used as a detection signal. The sampling rates of the ultrasonic sensor and the analog conversion circuit are both 2MHz. It is assumed that a sequence of echo received signals of length 1000 has been acquired, wherein the amplitude of each sample point has been converted into a digital signal and stored in a computer. According to equation 1, the difference between the previous and the next sampling points of each sampling point can be calculated, thereby obtaining a sequential differential set. Assume that a sequence difference diversity of length 999 is obtained; next, a characteristic wave of an echo reception signal needs to be determined therefrom.
In S2.3, the difference between each sample point and the previous sample point is calculated using equation 3, resulting in a set of sequential differences. Then, it is necessary to find the maximum value in the set, which corresponds to the characteristic wave of one echo received signal. For convenience, the number j of this maximum is defined herein.
Now, the characteristic wave of the echo received signal has been determined, and then the properties of the object to be measured can be further analyzed and judged on the basis of some characteristic parameters of the characteristic wave. For example, the size of the object may be determined by the amplitude magnitude of the characteristic wave, the distance between the object and the detector may be determined by the time delay of the characteristic wave, and so on.
It can be understood that the detection and identification of the object to be detected are realized by the analysis of the ultrasonic echo signals. The characteristic wave of the echo receiving signal can be determined through sampling, differentiating and the like of the echo signal, and the characteristic parameter is further extracted, so that the property and the state of the measured object are judged.
In some embodiments of the application, the method further comprises:
s3: the first wave threshold is adjusted so that the characteristic wave of the ultrasonic echo signal is adapted under various circumstances. The range of the head wave adjustable is expressed by ThresRange, and the range of ThresRange is known from S1 and S2:
(AMi，AMi+PreDIF1)
In the scheme, the amplitude range of the characteristic wave and the precursor wave obtained through calculation is used for adjusting the head wave threshold value of the ultrasonic echo signal.
S4: a stable effective echo is determined to reduce interference of noise. And (3) performing secondary calculation on the sequence difference set PreDIF, and determining the wave as a stable effective echo if the sequence difference ratio WaveRate is smaller than 5%. The formula is as follows (formula 4):
WaveRate＝RreDIFi+1-PreDIFi/PreDIFi*100％(i＝1；i<N)
in the scheme, the sequence difference value set is subjected to secondary calculation, and whether the wave is a stable effective echo is determined by judging whether the sequence difference value ratio WaveRate is smaller than 5 percent.
S3: the first wave threshold is adjusted so that the characteristic wave of the ultrasonic echo signal is adapted under various circumstances.
In this scheme, the threshold value of the first wave needs to be determined to adapt to the characteristic waves in different environments. First, an adjustable head wave range needs to be calculated, which is determined by the echo signal amplitudes and the set of sequence differences obtained in S1 and S2. Suppose that a set of sequence differences, pref= { pref 1, pref 2, &..pref 999}, of length 999 is obtained. The range of the head wave adjustability is (AMi, ami+PreDIF1). This means that the amplitude of the head wave can be set to any value between AMi and AMi +PreDIF1.
Meanwhile, S4: a stable effective echo is determined to reduce interference of noise.
S5: the ratio of the characteristic wave to the stationary effective wave, and the threshold value of the characteristic wave are determined. S3, defining the time of the characteristic wave higher than a set threshold value and lower than the set threshold value as TFHL; the time of the stable effective wave above zero and below zero is defined as TSHL, and the calculation formula of the ratio of the characteristic wave to the stable effective wave is as follows:
PWR＝TFHL/TSHL
in the scheme, the ratio PWR of the characteristic wave to the stable effective wave is obtained by calculating the ratio of the time when the characteristic wave is higher than the set threshold value and lower than the set threshold value to the time when the stable effective wave is higher than the zero point and lower than the zero point.
S6: and if the characteristic wave self-adaptive adjustment meets PWR <0.5 or PWR >0.7, the adjustment configuration is carried out according to S1, S2, S3, S4 and S5.
In this scheme, the characteristic wave is adaptively adjusted. By comparing the magnitude relation between PWR and 0.5 and 0.7, the characteristic wave is adaptively adjusted according to the results of the steps 1-5.
In this scheme, in this step, it is necessary to determine which echoes are stable effective echoes to reduce the interference of noise. Performing secondary calculation on the sequence difference set PreDIF, and calculating the difference ratio between the previous sampling point and the next sampling point of each sampling point, namely a formula 4;
If the WaveRate is less than 5%, this is an indication that the echo is a stable effective echo.
Illustratively, it is assumed that an ultrasonic meter is to be detected and that the head wave threshold needs to be adjusted according to the characteristic waves of the meter under different circumstances. The following steps are carried out:
s1: and acquiring an ultrasonic echo receiving signal sequence.
And controlling an ultrasonic signal transmitting circuit, wherein the upstream transducer transmits 10 1MHz excitation pulses, and the downstream transducer receives echo signals. The analog conversion circuit samples echo signals with a frequency which is 2 times greater than that of the transducer, and the echo amplitudes are recorded as AM1, AM2, AM3 and … AM10 in sequence.
S2: a characteristic wave of the echo received signal is determined.
The difference between each wave sequence and the adjacent wave sequence is calculated as shown in equation 1. A length 9 set of sequence differences, pref= { pref 1, pref 2, &..pref 9}, is obtained. The maximum sequence difference, i.e., the most stable characteristic wave, is obtained by calculating prefmax=prefj (j=i+1).
Through the above steps, the most stable characteristic wave has been determined, and then the time corresponding to the characteristic wave needs to be determined. The propagation time of the characteristic wave can be calculated using an ultrasonic propagation time formula. The formula is:
t＝(d*2)/c
Where t represents the propagation time, d represents the distance that the ultrasonic wave propagates in the medium, and c represents the propagation speed of the ultrasonic wave in the medium. For aqueous media, the propagation velocity is about 1500 meters/second. Thus, the propagation time of the characteristic wave can be calculated.
S6: traffic is calculated from the travel time. The time of the water flowing through the ultrasonic sensor can be obtained through the propagation time of the characteristic wave, and then the flow is calculated. Assuming a water flow velocity v, the flow Q can be calculated as:
Q＝v*A
wherein A is the sectional area of the ultrasonic sensor pipeline. Therefore, the water flow rate can be calculated according to the flow rate calculation formula.
It can be understood that, in summary, the water flow information can be obtained through a series of signal processing and calculation according to the echo receiving signal sequence obtained by the ultrasonic sensor.
It should be noted that the two exemplary descriptions above are merely used to describe their current formula application; based on the scenario to which the present embodiment is applied, the following comprehensive description is made:
s1: and acquiring an ultrasonic echo receiving signal sequence. The step is that the ultrasonic water meter receives echo signals by transmitting ultrasonic pulses and utilizing a downstream transducer, and samples the echo signals through an analog conversion circuit, and records the echo amplitude values as a sequence.
S2: a characteristic wave of the echo received signal is determined. This step is to calculate the difference between the sequence of each wave and the sequence of adjacent waves to obtain the maximum sequence difference, i.e. the most stable characteristic wave, for determining the specific water level in the ultrasonic water meter.
S3: the first wave threshold is adjusted so that the characteristic wave of the ultrasonic echo signal is adapted under various circumstances. The step refers to adjusting the threshold value of the characteristic wave according to the characteristic wave sequences obtained in S1 and S2 and the maximum sequence difference value, so that the characteristic wave sequences can adapt to different water level heights.
S4: a stable effective echo is determined to reduce interference of noise. This step refers to performing a second calculation on the sequence difference set PreDIF to determine a stable effective echo for eliminating possible noise interference.
S5: the ratio of the characteristic wave to the stationary effective wave, and the threshold value of the characteristic wave are determined. This step refers to calculating the ratio of the characteristic wave to the stable effective wave and determining a threshold value of the characteristic wave according to the ratio for further optimizing the measurement accuracy of the water level height.
S6: and (3) carrying out self-adaptive adjustment on the characteristic wave, and if PWR <0.5 or PWR >0.7 is met, carrying out adjustment and configuration according to S1-S5. The step is to perform self-adaptive adjustment according to the ratio of the characteristic wave to the stable effective wave obtained in the step S5 so as to further improve the measurement accuracy of the ultrasonic water meter.
PWR＝TFHL/TSHL
Where TFHL represents the times when the characteristic wave is above and below a set threshold and TSHL represents the times when the stationary effective wave is above and below zero.
Further exemplary:
assuming an ultrasonic water meter, the water level height is measured using ultrasonic waves. In this scheme, the following variables will be used:
n length of echo signal sequence
AMi amplitude of ith echo signal
PreDIFi, the difference in amplitude between the ith echo signal and the previous echo signal
PreDIFMAX maximum amplitude difference of echo signals for determining characteristic wave
ThresRange, adjustable range of head wave threshold
WaveRate sequence difference ratio for determining stable effective wave
TFHL: time of characteristic wave above/below threshold
TSHL: time to stabilize effective wave above/below zero
PWR: ratio of characteristic wave to stationary effective wave
The following are specific steps and formula derivations of this exemplary scheme:
step 1: and acquiring an ultrasonic echo receiving signal sequence.
The upstream transducer transmits N1 Mhz excitation pulses and the downstream transducer receives echo signals, which are required by an ultrasonic signal transmitting circuit. The analog conversion circuit samples echo signals with a frequency which is 2 times greater than that of the transducer, and the echo amplitudes are respectively marked as AM1, AM2, AM3 and … AMn.
Step 2: a characteristic wave of the echo received signal is determined.
And calculating the difference value between the sequence of each wave and the adjacent wave sequence to obtain the difference value PreDIFi between each wave sequence and the precursor wave sequence. The sequence difference set PreDIF is thus derived. The method comprises the following steps of:
PreDIFMAX＝PreDIFj(j＝i+1)；
the largest sequence difference, i.e. the most stable characteristic wave, is obtained.
Step 3: the first wave threshold is adjusted so that the characteristic wave of the ultrasonic echo signal is adapted under various circumstances.
The range of the head wave adjustable is expressed by ThresRange, and as can be seen from the step 1 and the step 2, the range of ThresRange is as follows:
(AMi，AMi+PreDIF1)
step 4: a stable effective echo is determined to reduce interference of noise. And (3) performing secondary calculation on the sequence difference set PreDIF, and determining the wave as a stable effective echo if the sequence difference ratio WaveRate is smaller than 5%. The calculation formula is as follows:
WaveRate＝RreDIFi+1-PreDIFi/PreDIFi*100％(i＝1；i<N)
(equation 4)
In this step, the second calculation is performed on the sequence difference set PreDIF, mainly to further screen out stable effective echoes. The stable effective echo can reduce the interference of noise and improve the reliability and accuracy of signals.
Specifically, for each element predfi in the sequence difference set predf, the ratio between its adjacent two elements, i.e., waveRate in equation 4, is calculated. If the ratio is less than 5%, the wave is considered to be a stable effective echo.
This is because in ultrasonic measurement, the signal is affected by various factors such as the non-uniformity of the propagation medium, the quality of the transducer, noise, and the like. Therefore, by performing secondary calculation on the sequence difference value set, the interference caused by the factors can be eliminated, so that the measurement precision and accuracy are improved.
Step 5: the ratio of the characteristic wave to the stationary effective wave, and the threshold value of the characteristic wave are determined. Step 3, defining the time when the characteristic wave is higher than the set threshold value and lower than the set threshold value as TFHL; the time of the stable effective wave above zero and below zero is defined as TSHL, and the calculation formula of the ratio of the characteristic wave to the stable effective wave is as follows:
PWR＝TFHL/TSHL
in this step, it is necessary to determine the ratio of the characteristic wave to the stable effective wave, and the threshold value of the characteristic wave. By calculating TFHL and TSHL, the time ranges of the characteristic wave and the stable effective wave can be obtained, and thus the ratio PWR of them is calculated.
The time ranges of the characteristic wave and the stable effective wave are obtained by adjusting the threshold value of the characteristic wave in the step 3, and the adjustment of the threshold value can be self-adaptive according to different environmental conditions. The threshold value of the characteristic wave is determined according to the number of the characteristic wave determined in step 2.
The calculation of the PWR can be used to evaluate the quality of the characteristic wave, and if the PWR is too small or too large, adjustments to steps 1-5 are needed to achieve better measurement results.
In some embodiments of the application, the method further comprises:
s7: and training sample data by using an LMBP neural network to realize flow correction of different temperatures.
S7.1: and carrying out an LMBP neural temperature compensation algorithm on two flow ranges of Q1-Q2 and Q2-Q3. At the Q1,1.1Q1, Q2,1.1Q2,0.5Q3, Q3, Q4 flow points, the input training data is the temperature and calculated flow values, and the output desired parameter is the actual reference flow value. The temperature was selected to be 5 ℃,20 ℃,30 ℃.
S7.2: data normalization processing is represented by the following formula:
yj' =λ (yj-ymin/ymax-ymin) +δ (equation 5)
In the above formula, yj ' is a normalization value of input data to be trained, yj is input data to be trained, y ' min is a minimum value of data to be trained, y ' max is a maximum value of data to be trained, lambda is a proportionality coefficient, and delta is a compensation constant;
s7.3: from S7.2 the following formula can be obtained:
yj ' = (yj ' - δ) (y ' max-y ' min) +y ' min (equation 6)
In the above equation, yj 'is output predicted data, yj' is output normalized predicted data, y 'min is a minimum value of normalized expected data, and y' max is a maximum value of normalized expected data.
S7.4: and (3) carrying out solving fitting on the S7.2 and the S7.3 to obtain a relation between the actual flow Q standard calculated flow Q and the temperature T, wherein the relation is as follows:
qstandard=n0+n1q+n2t+n3qt (formula 7)
In the scheme, an LMBP neural network is used for training sample data, so that flow correction of different temperatures is realized; training the training data in different flow ranges by using an LMBP neural network, and normalizing the data to obtain a normalization value of the input data to be trained; output prediction data is obtained through a neural network, and an output actual flow value is obtained through inverse normalization processing.
And S7, training sample data by using an LMBP neural network according to flow correction at different temperatures to obtain a relational expression between the actual flow and the standard calculated flow. Wherein, S7.1 is to carry out algorithm to different flow ranges, taking two flow ranges of Q1 less than Q2 and Q2 less than or equal to Q3 as examples, respectively inputting temperature and calculating flow values under flow points Q1,1.1Q1, Q2,1.1Q2,0.5Q3, Q3 and Q4 for training. S7.2, carrying out normalization processing on the training data, and mapping the data to be trained into the interval of [0,1 ]. S7.3 is to restore the normalized predicted data to within the original data range. And S7.4, solving and fitting by using the normalized data to obtain a relational expression between the actual flow and the standard calculated flow.
Equation 6 is a calculation equation for restoring normalized predicted data to the original data range, where yj ' is the output predicted data, yj is the output normalized predicted data, y ' min is the minimum value of the normalized expected data, and y ' max is the maximum value of the normalized expected data. The function of the formula is to restore normalized predicted data obtained through LMBP neural network calculation into predicted data in the range of original data.
Equation 7 is a relation between the actual flow Q and the standard calculated flow Q, where n0, n1, n2, n3 are coefficients to be solved, Q is the actual flow, and T is the temperature. The function of the formula is to obtain a relational expression between the actual flow and the standard calculated flow by fitting training data, so that flow correction is carried out in actual application.
An exemplary description will be made below in connection with the original steps S1 to 7:
in the current scenario, there is an ultrasonic water meter, which measures the water level height using ultrasonic waves, and calculates the flow value. The flow at different temperatures needs to be corrected to improve the measurement accuracy. And training sample data by adopting an LMBP neural network to realize flow correction at different temperatures.
S1: and acquiring ultrasonic echo, transmitting an ultrasonic signal into the water body by using an ultrasonic sensor, and calculating the water level height by measuring the echo time.
S2: calculating the flow value is calculated according to the water level height and the caliber of the water meter using formula 1.
S3: and recording the temperature value by using a temperature sensor, and recording the temperature value of the current water body.
S4: the training data set is constructed at the flow points of Q1,1.1Q1, Q2,1.1Q2,0.5Q3, Q3 and Q4, the training data is input as temperature and calculated flow values, and the expected parameters are output as actual reference flow values. The temperature was selected to be 5 ℃,20 ℃,30 ℃.
S5: and the data normalization processing uses a formula 5 to normalize the input data to be trained, and maps all data to the range of 0 to 1, so that the neural network is convenient to train.
S6: and (3) performing difference calculation on the normalized training data set by using a formula 4 to obtain a sequence difference set PreDIF.
S7: training the LMBP neural network by using training data and a sequence difference set PreDIF obtained in the S4 and the S6, and training the LMBP neural network to realize flow correction at different temperatures.
S7.1: the LMBP neural temperature compensation algorithm is used for carrying out the LMBP neural temperature compensation algorithm on two flow ranges of Q1 which is more than or equal to Q2 and Q2 which is more than or equal to Q3. At a temperature of 5 ℃,20 ℃ and 30 ℃, at the flow points of Q1,1.1Q1, Q2,1.1Q2,0.5Q3, Q3 and Q4, training data are input as temperature and calculated flow values, and expected parameters are output as actual reference flow values.
S7.2: the data normalization processing uses formula 5 to normalize the data to be trained.
S7.3: outputting the predicted data using equation 6 to output the neural network
Specifically, in S7.3, the normalized prediction data output by the neural network is subjected to inverse normalization processing by using the formula 6, so as to obtain a predicted value of the actual flow Q; equation 6 is as follows:
yj’＝(yj-δ)*(y’max-y’min)+y’min
where yj ' is output predicted data, yj is output normalized predicted data, y ' min is a minimum value of normalized expected data, y ' max is a maximum value of normalized expected data, and δ is a compensation constant.
And (3) reversely normalizing the normalized predicted data output by the neural network through a formula 6 to obtain a predicted value of the actual flow Q. The predicted value can be used for correcting the reading of the water meter, so that the measurement accuracy is improved;
it can be understood that through the steps, the LMBP neural network can be utilized to correct the flow at different temperatures, so that the measurement accuracy of the water meter is improved. Wherein, S7.1 obtains a relation between temperature and calculated flow value through training data, S7.2 and S7.3 are used for normalizing and inverse normalizing predicted data, and S7.4 is a predicted value for converting the normalized predicted data into actual flow Q.
In this scheme, the specific output physical components of the above method may be selected as follows:
acoustic wave sensor: for measuring the propagation speed of an ultrasonic wave in a fluid and an echo signal;
and a data acquisition system: the system comprises a sensor, a computer, a data acquisition module and a data acquisition module, wherein the sensor is used for acquiring raw data measured by the sensor and transmitting the raw data to the computer;
and (3) an embedded module: the method is used for processing and analyzing the original data, establishing a regulation model and realizing flow correction;
LMBP neural network: the method is used for training and optimizing the tuning model;
and the operational amplifier circuit: for amplifying and filtering the echo signals;
analog-to-digital converter (ADC): for converting the analog signal into a digital signal;
digital-to-analog converter (DAC): for converting digital signals into analog signals;
and (3) a filter: for filtering noise and interference;
high frequency ultrasonic sensor: for detecting high frequency echo signals;
phase Locked Loop (PLL): for locking the phase of the echo signal to ensure accurate reception of the echo signal;
a signal processor: the method is used for processing echo signals and extracting characteristic waves;
and (3) a filter: for filtering noise and interference;
digital Signal Processor (DSP): for processing and calculating the digital signal;
and (3) FPGA: for implementing digital logic circuits and algorithm acceleration;
Matrix calculation module: for accelerating matrix operations;
preferably, the embedded module is preferably an ARM Cortex series or Raspberry Pi (Raspberry Pi); the system comprises a microprocessor, a memory, an input/output device, various sensors, an actuator and other hardware devices;
specifically, the embedded single board is used as a carrier, so that sufficient computing capacity and storage capacity can be provided, various input/output interfaces and communication interfaces are provided, various sensors and actuators can be conveniently connected, and data acquisition and control are realized.
In this aspect, the above-mentioned component is a main functional mechanism in the device provided in this embodiment; on the basis of the above mechanism, it is arranged on an integrated component; specifically, the integrated component is used as a reference supporting structure of the whole device, provides a foundation for the device to cooperate with the external environment, and can be matched with external staff to carry out maintenance, adjustment, assembly of related parts and other conventional mechanical maintenance operations;
specifically, through the support of the integrated component to the mechanism, the whole device can be placed and applied to an ultrasonic water meter automatic production line, so that the whole device is used as an additional procedure in the existing automatic production line, and the function of providing process test for the production and preparation of the ultrasonic water meter is realized;
In the scheme, all electric elements of the whole device are powered by mains supply; specifically, the electric elements of the whole device are in conventional electrical connection with the commercial power output port through the relay, the transformer, the button panel and other devices, so that the energy supply requirements of all the electric elements of the device are met.
Specifically, a controller is further arranged outside the integrated assembly, and the controller is used for connecting and controlling all electrical components of the whole integrated assembly to drive according to a preset program as a preset value and a drive mode; it should be noted that the driving modes correspond to the driving and operation modes corresponding to the related electrical components;
preferably, the controller is a PLC controller, and the control requirement is completed through a ladder diagram, a sequence function diagram, a function block diagram, an instruction list or a structural text and other conventional PLC control modes; it should be noted that the output parameters such as the operation start-stop time interval, the rotation speed, the power and the like of the electric element or other power elements driven by the programming are not limited; specifically, the control of the relevant drive is adjusted according to the actual use requirement.
The technical features of the above-described embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above-described embodiments may not be described, however, they should be considered as the scope of the present description as long as there is no contradiction between the combinations of the technical features.
Examples
In order to make the above-described embodiments of the present invention more comprehensible, embodiments accompanied with the present invention are described in detail by way of example. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, so that the invention is not limited to the embodiments disclosed below.
The present embodiment is based on the relevant principles described in the above detailed description, where exemplary applications are:
step 1: acquiring an ultrasonic echo receiving signal sequence;
in the step, an ultrasonic signal transmitting circuit is controlled, an upstream transducer transmits N1 Mhz excitation pulses, and a downstream transducer receives echo signals; the analog conversion circuit adopts a frequency which is 2 times greater than that of the transducer to sample echo signals, and the echo amplitude values are recorded as AM1, AM2, AM3 and … AMn in sequence;
step 2: determining a characteristic wave of an echo receiving signal;
in the step, calculating the difference value between the sequence of each wave and the adjacent wave sequence to obtain a sequence difference value set; obtaining the maximum sequence difference value from the sequence difference value set, namely the characteristic wave;
step 3: adjusting the threshold value of the first wave so that the characteristic wave of the ultrasonic echo signal is adapted in each environment;
In the step, firstly, a range of adjustable head waves, namely (AMi, ami+PreDIF1), is obtained through the step 1 and the step 2, and then, the threshold value of the head waves is adjusted according to actual conditions;
step 4: determining a stable effective echo to reduce interference of noise;
in the step, performing secondary calculation on a sequence difference set PreDIF, and if the sequence difference ratio WaveRate is smaller than 5%, determining that the wave is a stable effective echo;
step 5: determining the ratio of the characteristic wave to the stable effective wave and the threshold value of the characteristic wave;
in the step, firstly, defining the time of the characteristic wave higher than a set threshold value and lower than the set threshold value as TFHL, and defining the time of the stable effective wave higher than zero and lower than zero as TSHL; then calculating the ratio of the characteristic wave to the stable effective wave, namely PWR=TFHL/TSHL;
step 6: characteristic wave self-adaptive adjustment;
in this step, if PWR <0.5 or PWR >0.7, the configuration is adjusted according to step 1, step 2, step 3, step 4, step 5;
step 7: training sample data by using an LMBP neural network to realize flow correction of different temperatures;
in the step, LMBP neural temperature compensation is firstly carried out on two flow ranges of Q1 which is less than or equal to Q2 and Q2 which is less than or equal to Q3
In step 7, training sample data by using an LMBP neural network to realize flow correction of different temperatures; the LMBP neural network is a feedforward neural network which is trained by adopting a back propagation algorithm, and a random gradient descent algorithm is used for minimizing a loss function in the training process;
before training, the data set needs to be divided into a training set and a testing set; the training set is used for training the neural network, and the test set is used for evaluating the performance of the neural network; normalization of the data sets is also required to ensure that each feature is within a similar range, which helps to improve the training effect of the neural network;
in training a neural network, it is necessary to define the structure of the neural network, including the number of neurons of the input layer, the hidden layer, and the output layer, and to select appropriate activation functions and loss functions; the super parameters such as proper learning rate, batch size, training round number and the like are also required to be selected so as to optimize the performance of the neural network;
once the neural network is trained, it can be used for traffic correction; when new temperature data is input into the neural network, the neural network outputs a correction coefficient for correcting the flow data; therefore, accurate flow measurement at different temperatures can be realized, and the control precision and efficiency of the industrial production process are improved.
The above examples merely illustrate embodiments of the invention that are specific and detailed for the relevant practical applications, but are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. The method for self-adapting and flow correction of the characteristic wave of the ultrasonic water meter comprises the following steps:
s1, acquiring an ultrasonic echo receiving signal sequence; by controlling the ultrasonic signal transmitting circuit, the upstream transducer transmits N1 MHz excitation pulses, and the downstream transducer receives echo signals; the analog conversion circuit adopts a frequency which is 2 times greater than that of the transducer to sample echo signals, and the echo amplitude values are recorded as AM1, AM2, AM3 and … AMn in sequence;
s2, determining characteristic waves of echo receiving signals; determining the serial number of the echo receiving signal characteristic wave by calculating the difference value between the sequence of each wave and the adjacent wave sequence and obtaining the maximum sequence difference value;
S3, adjusting the head wave threshold value of the ultrasonic echo signal in the amplitude range of the characteristic wave and the precursor wave obtained through calculation;
s4, performing secondary calculation on the sequence difference value set, and determining whether the wave is a stable effective echo by judging whether the sequence difference value ratio WaveRate is smaller than 5%;
s5, calculating the ratio of the time when the characteristic wave is higher than a set threshold value and lower than the set threshold value to the time when the stable effective wave is higher than the zero point and lower than the zero point, and obtaining the ratio PWR of the characteristic wave and the stable effective wave;
s6, characteristic wave self-adaptive adjustment. By comparing the magnitude relation between PWR and 0.5 and 0.7, the characteristic wave is adaptively adjusted according to the results of the steps 1-5.
S7, training sample data by using an LMBP neural network to realize flow correction of different temperatures; training the training data in different flow ranges by using an LMBP neural network, and normalizing the data to obtain a normalization value of the input data to be trained;
output prediction data is obtained through a neural network, and an output actual flow value is obtained through inverse normalization processing.
2. The method for adapting and correcting the flow rate of the characteristic wave of the ultrasonic water meter according to claim 1, wherein the method comprises the following steps: in the S1, further includes:
S1.1, controlling an ultrasonic signal transmitting circuit, wherein an upstream transducer transmits N1 Mhz excitation pulses, and a downstream transducer receives echo signals;
s1.2, the analog conversion circuit adopts a frequency which is 2 times greater than that of the transducer to sample the echo signals, and the echo amplitude values are recorded as AM1, AM2, AM3 and … AMn in sequence.
3. The method for adapting and correcting the flow rate of the characteristic wave of the ultrasonic water meter according to claim 2, wherein the method comprises the following steps: in the step S2, further comprising:
s2.1, calculating the difference value between the sequence of each wave and the sequence of the adjacent wave:
PreDIFi= AMi +1-AMi (i=1, i < N-1)
PreDIF represents the difference between each wave sequence and the precursor wave sequence;
s2.2, a sequence difference value set can be obtained from the S2.1:
PreDIF＝{PreDIF1，PreDIF2,...,PreDTFN}
s2.3, obtaining the maximum sequence difference value from S2.2:
PreDIFMAX＝PreDIFj(j＝i+1)
PreDIFMAX represents the maximum sequence difference between each wave and the precursor wave, and j represents the characteristic wave number of the echo receiving signal.
4. A method for adapting and flow correction of ultrasonic water meter characteristics according to any one of claims 1-3, characterized by: in the step S3, further comprising: adjusting a head wave threshold;
adapting the characteristic wave of the ultrasonic echo signal in each environment; the range of head wave adjustability is represented by ThresRange, based on the range of S1-S2:
(AMi，AMi+PreDIF1)。
5. The method for adapting and correcting the flow rate of the characteristic wave of the ultrasonic water meter according to claim 4, wherein the method comprises the following steps: in the S4, further includes: performing secondary calculation on the sequence difference set PreDIF, and if the sequence difference ratio WaveRate is smaller than 5%, determining that the wave is a stable effective echo:
WaveRate＝RreDIFi+1-PreDIFi/PreDIFi*100％(i＝1；i<N)。
6. the method for adapting and correcting the flow rate of the characteristic wave of the ultrasonic water meter according to claim 4, wherein the method comprises the following steps: in the step S5, further comprising: determining the ratio of the characteristic wave to the stable effective wave and the threshold value of the characteristic wave;
based on S3, defining the time that the characteristic wave is higher than a set threshold value and lower than the set threshold value as TFHL; defining the time of the stable effective wave higher than zero point and lower than zero point as TSHL, calculating the ratio of the characteristic wave to the stable effective wave:
PWR＝TFHL/TSHL。
7. the method for adapting and correcting the flow rate of the characteristic wave of the ultrasonic water meter according to claim 6, wherein the method comprises the following steps: in the step S6, further comprising: characteristic wave self-adaptive adjustment;
if the current S6 input satisfies PWR <0.5 or PWR >0.7, the adjustment configuration is performed according to S1-S5.
8. The method for adapting and correcting the flow rate of the characteristic wave of the ultrasonic water meter according to claim 7, wherein the method comprises the following steps: in the step S7, further comprising:
S7.1, performing an LMBP neural temperature compensation algorithm on two flow ranges of Q1 is more than or equal to Q2 and Q2 is more than or equal to Q3; at the flow points Q1,1.1Q1, Q2,1.1Q2,0.5Q3, Q3, Q4, the input training data is the temperature and calculated flow value, and the output desired parameter is the actual reference flow value; the temperature is selected to be 5 ℃, 20 ℃ or 30 ℃.
S7.2, data normalization processing:
yj’＝λ*(yj-ymin/ymax-ymin)+δ
yj ' is a normalization value of input data to be trained, yj is input data to be trained, y ' min is a minimum value of the data to be trained, y ' max is a maximum value of the data to be trained, lambda is a proportionality coefficient, and delta is a compensation constant;
s7.3, based on S7.2:
yj’＝(yj’-δ)(y’max-y’min)+y’min
yj 'is output predicted data, yj' is output normalized predicted data, y 'min is a minimum value of normalized expected data, and y' max is a maximum value of normalized expected data;
s7.4, solving and fitting the S7.2-S7.3 to obtain the actual flow Q standard calculated flow Q and a temperature T relation:
qstandard=n0+n1q+n2t+n3qt.
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| CN117807375A (en) * | 2024-02-27 | 2024-04-02 | 成都秦川物联网科技股份有限公司 | Ultrasonic water meter noise processing method, system and equipment based on Internet of Things |
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| CN117268483B (en) * | 2023-11-23 | 2024-02-23 | 青岛鼎信通讯科技有限公司 | Instantaneous flow metering method suitable for ultrasonic water meter |
| CN117807375A (en) * | 2024-02-27 | 2024-04-02 | 成都秦川物联网科技股份有限公司 | Ultrasonic water meter noise processing method, system and equipment based on Internet of Things |
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