Auxiliary low flow velocity measurement method based on water level change

Abstract

The invention relates to a water level change-based auxiliary low flow velocity measurement method, which comprises the following steps of: judging whether a water surface is in a mirror surface state or not through a threshold value of a water level change standard deviation, judging an abnormal value through a box plot method when the water surface is judged to be in the mirror surface state, and filtering the abnormal value if the abnormal value is detected. According to the invention, the defects of a traditional radar flowmeter in low flow velocity measurement are overcome, the measurement precision and the data reliability are remarkably improved, and the radar flowmeter has important practical application value.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and particularly to an auxiliary low flow velocity measurement method based on water level change. Background Art

[0002] As Figure 1 shown, a radar flow velocity meter mainly measures the flow velocity by causing Bragg scattering on the water surface, and Bragg scattering depends on minute disturbances on the water surface to generate an echo signal with sufficient intensity. When the flow velocity is relatively high, the water surface fluctuates significantly, capable of generating a strong echo signal, and the flow velocity measurement is accurate. However, when the flow velocity decreases, the water surface tends to be smooth, the Bragg scattering effect weakens, the specular reflection effect strengthens, and the intensity of the echo signal drops significantly, resulting in a large number of abnormal values in the low flow velocity measurement. Traditional radar flow velocity meters are insensitive to the measurement of flow velocities in the range of 0.1 m / s - 0.5 m / s, with large errors, which is extremely inconvenient for hydrological assessments with high precision requirements. Summary of the Invention

[0003] Aiming at the problems existing in the prior art, the purpose of the present invention is to provide an auxiliary low flow velocity measurement method based on water level change, which can effectively correct the measurement data under low flow velocity conditions.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is: An auxiliary low flow velocity measurement method based on water level change, which determines whether the water surface is in a specular state through the threshold of the standard deviation of water level change. When it is determined to be in a specular state, the box plot method is used to judge abnormal values. If abnormal values are detected, the abnormal values are filtered out.

[0005] The method specifically includes the following steps: Step 1: The radar emits a radar wave signal. After being reflected by the water surface, the echo signal is received; 2D-FFT processing or Doppler analysis is performed on each frame of the received echo signal, and the flow velocity estimation value is calculated ; meanwhile, 1D-FFT processing is performed on each frame of the received echo signal to measure the water level height data ; Step 2: Calculate the standard deviation of the water level height data collected for each frame:

[0006] wherein, is the serial number of the frame, and the water level height data includes n water level heights.

[0007] Step 3: Determine the specular state according to the standard deviation of the water level data, and the determination criteria are as follows: If , then it is determined that the The frame water surface is in a mirror state; otherwise, it is determined to be in a non-mirror state. Here, A is the threshold value. Step 4: For the flow velocity estimation values determined to be in a non-mirror state, directly output them; for the flow velocity estimation values determined to be in a mirror state , use the box plot method to judge outliers.

[0008] The specific outlier judgment is as follows: Step 4.1: Judge whether M frame flow velocity estimation values have been output. If not, directly output the flow velocity estimation value ; if so, collect the first M frame flow velocity estimation values output, and combine them with the frame flow velocity estimation value to perform ascending sorting to obtain ; Step 4.2: Calculate the lower quartile , the median , and the upper quartile

[0009] Lower quartile Calculation formula:

[0010] If is not an integer, round it and take the average of the two adjacent values as ; Median Calculation formula:

[0011] Indicates that when M is even, the flow velocity estimation value at the th position after sorting is used as , otherwise take the average of the flow velocity estimation values at the th position and the th position after sorting as ; Upper quartile Calculation formula:

[0012] If is not an integer, round it and take the average of the two adjacent values as ; Step 4.3: Calculate the interquartile range :

[0013] Among them, represents the interquartile range, which is used to measure the dispersion degree of data; Step 4.4: Determine the outlier range: Lower limit =

[0014] Upper limit =

[0015] Step 4.5: Compare the estimated flow velocity with the outlier range. If it is lower than the lower limit or higher than the upper limit, it is regarded as an outlier. Filter out the outlier, and use the estimated flow velocity output in the previous frame as the estimated flow velocity of the current frame and output it; if the estimated flow velocity is within the outlier range, output the estimated flow velocity .

[0016] Use the CZT spectrum refinement algorithm to process the water level height data obtained after 1D-FFT processing.

[0017] After adopting the above solution, the present invention solves the deficiencies of traditional radar flow meters in low flow velocity measurement through the threshold determination of the standard deviation of water level changes and combines the box plot method for outlier detection, significantly improving the measurement accuracy and data reliability, and having important practical application value. Brief Description of the Drawings

[0018] Figure 1 It is a schematic diagram of radar flow meter measurement; Figure 2 It is a flow chart of the solution of the present invention; Figure 3 It is an effect diagram of a specific embodiment of the present invention. Detailed Embodiment

[0019] As Figure 2 shown, the present invention discloses an auxiliary low flow velocity measurement method based on water level changes, which includes the following steps: Step 1: The radar emits radar wave signals, and after being reflected by the water surface, receives the echo signals; perform 2D-FFT processing or Doppler analysis on each frame of the received echo signals, and calculate the estimated flow velocity ; at the same time, perform 1D-FFT processing on each frame of the received echo signals to measure the water level height data .

[0020] The specific method for obtaining the estimated flow velocity can adopt the existing flow velocity estimation method, and the present invention does not elaborate on this in detail. The estimated flow velocity , is the Doppler frequency shift, is the angle between the radar and the water surface, is the radar wavelength.

[0021] To improve the accuracy of water level measurement, after traditional 1D-FFT processing, the Chirp Z-Transform (CZT) spectrum refinement algorithm can be further used to analyze the signal. Although 1D-FFT can quickly obtain frequency domain information, its frequency resolution is limited. Especially when the observation window is small, it may not be able to accurately distinguish close frequency components. In water level measurement, a small change in frequency may correspond to a slight difference in water surface height. Therefore, higher-precision spectrum analysis means are required. Compared with the traditional FFT, the CZT algorithm can perform higher-resolution spectrum analysis in any frequency range, and finely scan the target frequency band in a "zooming" manner, so as to more accurately extract the characteristic frequencies in the water level signal and achieve a more accurate water level height estimation.

[0022] Step 2: Calculate the standard deviation of the water level height data collected in each frame:

[0023] where is the frame serial number, and the water level height data contains n water level heights, represents the average value of n water level heights.

[0024] Step 3: Determine the mirror state according to the standard deviation of the water level data. The determination criteria are as follows: If , then it is determined that the water surface of the th frame is in the mirror state, otherwise it is determined to be in the non-mirror state. Where A is the threshold, which can be set according to the data characteristics of the actual application. In this embodiment, A is 0.05.

[0025] Step 4: For the flow velocity estimation values determined to be in the non-mirror state, directly output them; for the flow velocity estimation values determined to be in the mirror state , use the box plot method to judge outliers. Specifically as follows: Step 4.1: Judge whether M frame flow velocity estimation values have been output. If not, directly output the flow velocity estimation value ; if so, collect the first M frame flow velocity estimation values output, and combine them with the flow velocity estimation value of the th frame and sort them in ascending order. Collect the first M frame flow velocity estimation values output, and combine them with the flow velocity estimation value of the th frame and sort them in ascending order to obtain .

[0026] Step 4.2: Calculate the lower quartile , median , upper quartile

[0027] Lower quartile Calculation formula:

[0028] If is not an integer, round it and take the average of the two adjacent values as .

[0029] Median Calculation formula:

[0030] Indicates that when M is even, the flow velocity estimated value at the th position after sorting is used as , otherwise take the average of the flow velocity estimated values at the th and th positions after sorting as .

[0031] Upper quartile Calculation formula:

[0032] If is not an integer, round it and take the average of the two adjacent values as .

[0033] Step 4.3, Calculate the interquartile range :

[0034] Among them, represents the interquartile range, which is used to measure the dispersion degree of data.

[0035] Step 4.4, Determine the outlier range: Lower limit =

[0036] Upper limit =

[0037] Step 4.5, Compare the flow velocity estimated value with the outlier range. If it is lower than the lower limit or higher than the upper limit, it is regarded as an outlier. Filter out the outlier, and use the flow velocity estimated value output in the previous frame as the flow velocity estimated value of the current frame and output it; if the flow velocity estimated value is within the outlier range, output the flow velocity estimated value .

[0038] For example Figure 3As shown, the blue line represents the original flow velocity estimated value data, and the red line represents the flow velocity estimated value data obtained after processing by the method of the present invention. It can be seen that the present invention solves the deficiencies of traditional radar flow meters in low flow velocity measurement by determining the threshold of the standard deviation of water level changes and combining the box plot method for outlier detection, significantly improving the measurement accuracy and data reliability, and having important practical application value.

[0039] As described above, it is only an embodiment of the present invention, and does not impose any limitation on the technical scope of the present invention. Therefore, any minor modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An auxiliary low-flow velocity measurement method based on water level changes, characterized in that: The method determines whether the water surface is in a mirror state through the threshold of the standard deviation of water level changes. When it is determined to be in a mirror state, the box plot method is used to judge outliers. If outliers are detected, the outliers are filtered out.

2. The auxiliary low-flow velocity measurement method based on water level change according to claim 1, wherein: The method specifically includes the following steps: Step 1: The radar emits radar wave signals. After being reflected by the water surface, the echo signals are received; 2D-FFT processing or Doppler analysis is performed on each frame of the received echo signals, and the flow velocity estimation value is calculated. Meanwhile, 1D-FFT processing is performed on each frame of the received echo signals to measure the water level height data. ; Step 2: Calculate the standard deviation of the water level height data collected for each frame: Among them, among them, is the sequence number of the frame, and the water level height data contains n water level heights. Step 3: Determine the mirror state according to the standard deviation of the water level data. The determination criteria are as follows: If , it is determined that the water surface of the th frame is in a mirror state; otherwise, it is determined to be in a non-mirror state. Here, A is a threshold value; Step 4: Directly output the flow velocity estimation values determined to be in a non-mirror state; for the flow velocity estimation values determined to be in a mirror state , use the box plot method to determine outliers.

3. The auxiliary low flow rate measurement method based on water level change according to claim 2, wherein: The specific judgment of the outliers is as follows: Step 4.1: Determine whether the M-frame flow rate estimation values have been output. If not, directly output the flow rate estimation value ; If so, collect the first M-frame flow rate estimation values output, and combine them with the flow rate estimation value of the frame to perform ascending order sorting to obtain ; Step 4.2, calculate the lower quartile , median , upper quartile Lower quartile Calculation formula: If is not an integer, round it and take the average of the two adjacent values as ; Median Calculation formula: Indicates that when M is an even number, the flow velocity estimation value ranked is used as , otherwise, the average value of the flow velocity estimation values ranked and ranked is used as ; Upper quartile Calculation formula: If is not an integer, round it and take the average of the two adjacent values as ; Step 4.3: Calculate the interquartile range : Among them, represents the interquartile range and is used to measure the dispersion of data; Step 4.4: Determine the outlier range: Lower limit= Upper limit= Step 4.

5. Compare the flow velocity estimated value with the outlier range. If it is lower than the lower limit or higher than the upper limit, it is regarded as an outlier. Filter out the outlier, and use the flow velocity estimated value output in the previous frame as the flow velocity estimated value of the current frame and output it. If the flow velocity estimated value is within the outlier range, output the flow velocity estimated value .

4. An auxiliary low-flow velocity measurement method based on water level change according to claim 2, characterized in that: The CZT spectrum zooming algorithm is used to process the water level height data obtained after 1D-FFT processing.

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