Water level detection method, device and equipment and storage medium

Abstract

The invention relates to the field of hydrographic survey, in particular to a water level detection method, device and equipment and a storage medium. The method comprises the following steps: receiving a difference frequency signal of a radar echo; decomposing the difference frequency signal of the radar echo to obtain intrinsic mode functions of different frequency bands; detecting the distribution position of an interference signal in the intrinsic mode function by adopting a self-adaptive threshold value method value; eliminating the interference signals according to the distribution positions of the interference signals, and reconstructing the difference frequency signals after the interference signals are eliminated to obtain difference frequency reconstruction signals; estimating the frequency error of the difference frequency reconstruction signal, and determining the water level of the target position according to the frequency error. According to the method, after the interference signal is eliminated by adopting the self-adaptive threshold value method, the water level of the target position is calculated by further determining the frequency error of the difference frequency reconstruction signal, so that the water level detection precision is higher, the influence of the interference signal on the detection result can be effectively reduced, and the reliability of the detection result is improved.

Description

Technical Field

[0001] The present application relates to the field of hydrological measurement, and in particular to water level detection methods, devices, equipment and storage media. Background Art

[0002] As the core link of hydrological observation, water level detection plays a vital role in many fields such as water conservancy projects, water transportation, flood prevention and control, drainage and waterlogging prevention. The real-time data it provides not only has independent value, but also is an important basis for relevant decision-making. With the continuous deepening of automation technology in the field of hydrology, society's demand for the immediacy, persistence, accuracy and credibility of hydrological information is growing.

[0003] Microwave radar water level meter has been increasingly widely used in the field of hydrological measurement due to its technical advantages. Ideally, if the installation environment is free of interference and the water surface is calm, the radar wave water level meter can provide accurate water level data.

[0004] However, in actual operation, various factors such as water surface fluctuations, foam, floating objects (such as trees and weeds), and riverbed topography may interfere with the reflection of radar waves and affect the performance of the water level meter. These interferences may cause the radar wave water level meter to be unable to receive signals, or the received signals are too weak, resulting in unstable or invalid measurement data. Especially in narrow environments such as urban pipe networks and manholes, the reflected signals of the target are often weak, and the detection depth is large. The echo signal will suffer a large attenuation during the return process, which further reduces the signal strength. In addition, the complex reflection environment may also be affected by factors such as the system interior, well wall, well fluid, etc., resulting in strong clutter interference.

[0005] Therefore, when a traditional microwave radar water level meter detects water level, the detection signal may be interfered by clutter, and the signal may not be received or the received signal is too weak, resulting in low stability and accuracy of the detected water level. Summary of the invention

[0006] In view of this, the embodiments of the present application provide a water level detection method, device, equipment and storage medium to solve the problem of low accuracy and reliability of detection results during water level detection in the prior art.

[0007] A first aspect of an embodiment of the present application provides a water level detection method, the method comprising:

[0008] Receive the difference frequency signal of radar echo;

[0009] Decomposing the difference frequency signal of the above radar echo to obtain the intrinsic mode functions of different frequency bands;

[0010] Adopting an adaptive threshold value method to detect the distribution position of the interference signal in the above intrinsic mode function;

[0011] Eliminating the interference signal according to the distribution position of the interference signal, and reconstructing the difference frequency signal after eliminating the interference signal to obtain a difference frequency reconstructed signal;

[0012] The frequency error of the difference frequency reconstructed signal is estimated, and the water level at the target location is determined based on the frequency error.

[0013] In combination with the first aspect, in a first possible implementation manner of the first aspect, before receiving the difference frequency signal of the radar echo, the method further includes:

[0014] The frequency modulation parameters are determined by the particle swarm optimization method, and the radar signal for water level detection is emitted by the above frequency modulation parameters.

[0015] In combination with the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, determining the frequency modulation parameter by a particle swarm optimization method includes:

[0016] Initialize a particle swarm, determine the fitness of particles in the particle swarm, and each particle represents a set of frequency modulation parameters;

[0017] Determine the objective function of the particle swarm, the objective function includes a signal-to-noise ratio and a complex correlation coefficient, the complex correlation coefficient is a complex correlation coefficient between a difference frequency signal of a radar echo and a difference frequency reconstruction signal;

[0018] The optimal solution of the particle swarm is determined according to the objective function, and the particle speed and position are iteratively updated according to the optimal solution until the determined optimal solution meets the preset requirements, the particles of the optimal solution are obtained, and the frequency modulation parameters corresponding to the particles are determined.

[0019] In combination with the first aspect, in a third possible implementation manner of the first aspect, the difference frequency signal of the radar echo is decomposed to obtain intrinsic mode functions of different frequency bands, including:

[0020] Performing Fourier transform on the difference frequency signal of the radar echo to determine the peak frequency included in the difference frequency signal of the radar echo;

[0021] Using the peak frequency as the center frequency of each frequency band to perform band-pass filtering, a number of band-pass filtered signals are obtained;

[0022] Perform empirical mode decomposition on the above bandpass filtered signal to obtain the intrinsic mode functions of different frequency bands.

[0023] In combination with the first aspect, in a fourth possible implementation manner of the first aspect, an adaptive threshold method is used to detect the distribution position of the interference signal in the above intrinsic mode function, including:

[0024] Determine the difference between the maximum intrinsic mode function amplitude within any intrinsic mode function domain and the currently detected intrinsic mode function amplitude;

[0025] The distribution position of the interference signal is determined according to the inclusion relationship between the difference and a predetermined confidence range determined by the maximum amplitude range in the domain of the eigenmode function.

[0026] In combination with the first aspect, in a fifth possible implementation manner of the first aspect, the interference signal is removed according to the distribution position of the interference signal, and the difference frequency signal after the interference signal is removed is reconstructed to obtain the difference frequency reconstructed signal, including:

[0027] According to the distribution position of the interference signal, the amplitude of the intrinsic mode function of the non-interference position is determined;

[0028] The amplitude of the eigenmode function at the distribution position of the interference signal is updated according to the mean of the amplitude of the eigenmode function at the non-interference position, and the phase of the eigenmode function at the distribution position of the interference signal is retained.

[0029] In combination with any one of the first aspect to the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, estimating a frequency error of the difference frequency reconstructed signal, and determining a water level at a target location according to the frequency error includes:

[0030] Performing windowing and interpolation processing on the difference frequency reconstructed signal to obtain an interpolation signal;

[0031] When it is detected that the interpolation signal has main frequency interference, the frequency spectrum is refined to determine the frequency error of the difference frequency reconstructed signal;

[0032] The water level at the target position is determined based on the frequency error.

[0033] A second aspect of an embodiment of the present application provides a water level detection device, the device comprising:

[0034] A difference frequency signal receiving unit, used for receiving the difference frequency signal of the radar echo;

[0035] An intrinsic mode function determination unit is used to decompose the difference frequency signal of the radar echo to obtain intrinsic mode functions of different frequency bands;

[0036] An interference signal detection unit, used to detect the distribution position of the interference signal in the above intrinsic mode function by using an adaptive threshold value method;

[0037] A reconstruction unit, used to remove the interference signal according to the distribution position of the interference signal, and reconstruct the difference frequency signal after the interference signal is removed to obtain a difference frequency reconstructed signal;

[0038] The water level detection unit is used to estimate the frequency error of the difference frequency reconstruction signal and determine the water level at the target position according to the frequency error.

[0039] In conjunction with the second aspect, in a first possible implementation manner of the second aspect, the apparatus further includes:

[0040] The frequency modulation parameter determination unit is used to determine the frequency modulation parameters by a particle swarm optimization method, and transmit a radar signal for water level detection by using the frequency modulation parameters.

[0041] In combination with the first possible implementation manner of the second aspect, in a second possible implementation manner of the second aspect, the frequency modulation parameter determination unit includes:

[0042] An initialization subunit is used to initialize a particle swarm and determine the fitness of particles in the particle swarm. A single particle represents a set of frequency modulation parameters.

[0043] An objective function determination subunit is used to determine the objective function of the particle swarm, wherein the objective function includes a signal-to-noise ratio and a complex correlation coefficient, wherein the complex correlation coefficient is a complex correlation coefficient between a difference frequency signal and a difference frequency reconstruction signal of a radar echo;

[0044] The particle optimization subunit is used to determine the optimal solution of the above-mentioned particle group according to the above-mentioned objective function, iteratively update the particle speed and position according to the above-mentioned optimal solution, until the determined optimal solution meets the preset requirements, obtain the particles of the above-mentioned optimal solution, and determine the frequency modulation parameters corresponding to the above-mentioned particles.

[0045] In combination with the second aspect, in a third possible implementation manner of the second aspect, the intrinsic mode function determining unit includes:

[0046] A transform subunit, configured to perform Fourier transform on the difference frequency signal of the radar echo to determine the peak frequency included in the difference frequency signal of the radar echo;

[0047] A filter subunit, used for performing bandpass filtering using the peak frequency as the center frequency of each frequency band to obtain a number of bandpass filtered signals;

[0048] The decomposition subunit is used to perform empirical mode decomposition on the above-mentioned bandpass filtered signal to obtain intrinsic mode functions of different frequency bands.

[0049] In combination with the second aspect, in a fourth possible implementation manner of the second aspect, the interference signal detection unit includes:

[0050] A difference determination subunit, used to determine the difference between the maximum intrinsic mode function amplitude in any intrinsic mode function domain and the currently detected intrinsic mode function amplitude;

[0051] The distribution position of the interference signal is determined according to the inclusion relationship between the difference and a predetermined confidence range determined by the maximum amplitude range in the domain of the eigenmode function.

[0052] In combination with the second aspect, in a fifth possible implementation manner of the second aspect, the reconstruction unit includes:

[0053] An amplitude determination subunit, used to determine the amplitude of the intrinsic mode function at the non-interference position according to the distribution position of the interference signal;

[0054] The amplitude updating subunit is used to update the amplitude of the intrinsic mode function at the distribution position of the interference signal according to the mean of the amplitude of the intrinsic mode function at the non-interfered position, and retain the phase of the intrinsic mode function at the distribution position of the interference signal.

[0055] In combination with any one of the second aspect to the fifth possible implementation manner of the second aspect, in a sixth possible implementation manner of the second aspect, the water level detection unit includes:

[0056] An interpolation subunit, used for performing windowing and interpolation processing on the difference frequency reconstruction signal to obtain an interpolation signal;

[0057] A refinement subunit is used to refine the spectrum and determine the frequency error of the difference frequency reconstructed signal when it is detected that the interpolation signal has main frequency interference;

[0058] The water level determination subunit is used to determine the water level of the target position according to the frequency error.

[0059] A third aspect of an embodiment of the present application provides a water level detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods of the first aspect when executing the computer program.

[0060] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods in the first aspect are implemented.

[0061] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application decompose the difference frequency signal of the received radar echo to obtain the intrinsic mode functions of different frequency bands, detect the distribution position of the interference signal in the intrinsic mode function by an adaptive threshold detection method, eliminate the interference signal by the distribution position of the interference signal, reconstruct the difference frequency reconstruction signal, determine the frequency error of the difference frequency reconstruction signal, and determine the water level of the target position based on the frequency error. Since the method uses an adaptive threshold method to determine the distribution position of the interference signal and eliminates the interference signal, it further determines the frequency error of the difference frequency reconstruction signal to determine the water level of the target position, so that the accuracy is higher, and the influence of the interference signal on the detection result can be effectively reduced, which is conducive to improving the reliability of the detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0063] Figure 1 It is a schematic diagram of an implementation flow of a water level detection method provided in an embodiment of the present application;

[0064] Figure 2 This is a schematic diagram of an implementation flow of determining frequency modulation parameters provided by an embodiment of the present application;

[0065] Figure 3 This is a schematic diagram comparing the water level detection effects of a fluctuating water surface provided by an embodiment of the present application;

[0066] Figure 4 It is a schematic diagram comparing the water level detection effect in a scene with sparse branches provided by an embodiment of the present application;

[0067] Figure 5 This is a schematic diagram comparing the water level detection effects of a river provided in an embodiment of the present application;

[0068] Figure 6 1 is a schematic diagram comparing the water level detection effects of a manhole provided in an embodiment of the present application;

[0069] Figure 7 This is a schematic diagram for comparing the water level detection effect under a river bridge provided in an embodiment of the present application;

[0070] Figure 8 is a schematic diagram of a water level detection device provided in an embodiment of the present application;

[0071] Fig. 9It is a schematic diagram of a water level detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0073] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0074] Water level observation is the most basic observation item in hydrological observation. The water level observation results can directly provide data with independent use value for water conservancy, water transportation, flood control and waterlogging prevention.

[0075] With the development of water conservancy and hydrological automation and system construction, society has put forward higher requirements for the timeliness, stability, authenticity and reliability of hydrological data. Microwave radar water level meters are increasingly used in hydrological testing. However, due to the complexity of the application environment, climate variability and increasingly sophisticated target detection requirements, it is necessary to further suppress interference on the detection results of microwave radar.

[0076] Generally, radar wave systems use interference suppression methods that are divided into system level and signal processing. System level usually uses antenna polarization design, transmission waveform frequency agility, coding encryption, and spectrum sensing technologies to improve the radar's anti-interference performance. These methods have outstanding anti-interference effects, but system-level anti-interference methods usually require changing the radar structure, with high system cost and complexity, and cannot be applied to existing antenna scheme designs. Signal processing-level anti-interference mainly uses the multi-dimensional feature differences between the target echo signal and the interference signal in the time-frequency domain and the sparse domain to filter out the interference signal energy and improve the radar's anti-interference performance. The anti-interference method based on sparse recovery uses the sparse feature differences between the target echo signal and the interference signal in the time-frequency domain, and uses constrained optimization theory to separate the target echo signal from the interference signal. This method has a significant interference separation effect, but it requires iterative optimization, is complex in calculation, and has poor real-time performance. Although there are more and more studies on millimeter-wave radars, if they are widely used in industry, it is necessary to ensure that the radar is low-cost while having more outstanding reliability and stability.

[0077] Due to the complex water level measurement environment in the water conservancy industry, radar wave water level meters can achieve accurate water level measurement when the installation and application environment is ideal, free of interference, and the water surface is calm. However, in actual applications, due to many interferences such as waves, foam, floating objects such as trees and weeds, and dry beaches, the reflected wave signal of the water level meter will be affected, causing the radar wave water level meter to not receive a signal or the received reflected wave signal to be too weak, causing the water level meter flow data to jump or the data to fail. Especially in the narrow and long environment of urban pipe networks and manholes, the target reflection is weak, or the detection depth is deep, resulting in a large attenuation of the echo signal, resulting in very weak signal strength. At the same time, due to its complex reflection environment, it is easily affected by the system, well wall, well fluid, etc., resulting in very severe clutter interference. In traditional water level measurement radar technology, the waveform parameters (such as frequency, pulse width, etc.) emitted by the radar are usually pre-set frequency modulation waveform parameters, which will not be automatically adjusted according to changes in the measurement environment. This is not conducive to effectively suppressing the interference of clutter signals, separating useful signals from complex echo signals, and obtaining real radar reflection echoes, making product applications more flexible and intelligent to adapt to complex and changeable measurement environments.

[0078] Based on the above problems, the present application embodiment proposes a water level detection method, such as Figure 1 As shown, the method includes:

[0079] In S101, a difference frequency signal of a radar echo is received.

[0080] In the embodiment of the present application, a radar is used to transmit a radar signal for water level detection, and the radar signal generates a radar echo after reaching the detected surface. The distance between the detected liquid surface and the radar is determined by the time between the time when the radar signal receiver receives the radar signal and the time when the radar signal is transmitted.

[0081] In order to improve the ranging resolution, before receiving the radar echo, the embodiment of the present application can also optimize the frequency modulation parameters of the transmitted radar signal according to the received radar echo, so that the radar signal is sent through the optimized frequency modulation parameters, and the water level detection is more reliable and effective. The process of determining the frequency modulation parameters can be as follows: Figure 2 As shown, including:

[0082] In S201, a particle swarm is initialized, wherein a single particle in the particle swarm represents a set of frequency modulation parameters.

[0083] The linear frequency modulation slope can be determined by the signal bandwidth B and the pulse width T. The higher the linear frequency modulation slope, the better the ranging resolution. Therefore, the independent variables can be set to include the compression oscillator (VCO) output frequency, signal bandwidth B and pulse width T, and the constraint conditions can be set to the maximum search space, that is, the range of variation of the VCO output frequency, signal bandwidth B and pulse width T. The constraint conditions can determine multiple sets of data within the variation range according to the radar chip manual.

[0084] When initializing the particle swarm, the total number of particles n in the particle swarm can be set, and each particle represents a combination of independent variable parameters (ie, including different VCO output frequencies, signal bandwidths, and pulse widths).

[0085] In S202, the objective function of the particle swarm is determined, and the fitness of the particles is determined according to the objective function.

[0086] The objective function includes a signal-to-noise ratio and a complex correlation coefficient, wherein the complex correlation coefficient is a complex correlation coefficient between a difference frequency signal of a radar echo and a difference frequency reconstruction signal.

[0087] The signal-to-noise ratio (SNR) is an important indicator for measuring the signal denoising performance, which can be calculated based on the signal-to-noise ratio and the complex correlation coefficient R. 2 The complex correlation coefficient is the complex correlation coefficient R between the difference frequency signal of the radar echo and the difference frequency reconstruction signal. 2 . Determine the objective function as the signal-to-noise ratio function and the complex correlation coefficient R 2 , the value of the multiple correlation coefficient is between 0 and 1. The closer the value is to 1, the higher the accuracy of the data model, that is:

[0088]

[0089] Among them, s(i) represents the acquired difference frequency signal, represents the difference frequency reconstructed signal, represents the average value of the collected difference frequency signal, n is the number of particles, and SNR(j) is the signal-to-noise ratio of the jth optimization.

[0090] In S203, the optimal solution of the particle swarm is determined according to the objective function, and the particle speed and position are iteratively updated according to the optimal solution until the determined optimal solution meets the preset requirements, the particles of the optimal solution are obtained, and the frequency modulation parameters corresponding to the particles are determined.

[0091] Substitute the particle position into the objective function to make the independent variable of the objective function take the best value within the constraints as the optimal solution, calculate the optimal solution currently searched and the optimal solution of the particle group search, and update the particle speed and position information. Repeat S203 until the number of iterations reaches the requirement, output the global optimal solution, and use the global optimal solution as the frequency modulation parameter of the next frame working waveform to obtain a better ranging resolution.

[0092] In S102, the difference frequency signal of the radar echo is decomposed to obtain the intrinsic mode functions of different frequency bands.

[0093] For the difference frequency signal of the acquired radar echo, the difference frequency signal can be processed by Fourier transform to obtain the transformation value of the frequency domain corresponding to the difference frequency signal, and the frequency range and peak frequency can be determined according to the transformation value. The peak frequency can be used as the center frequency of the bandpass filter of each frequency band, so that the signal is bandpass filtered through the bandpass filters of different frequency bands, and the difference frequency signal of the first width is decomposed into a plurality of difference frequency signals of the second width, and the first width is greater than the second width.

[0094] The difference frequency signals x(n) of each second width can be decomposed by the empirical mode decomposition (EMD) method to obtain the intrinsic mode functions (IMFs) of different frequency bands, and the complex eigenmode function (IMF(n)) can be obtained by the Hilbert transform principle.

[0095] Assume the frequency domain transfer function is:

[0096] The positive frequency component x of the difference frequency signal x(n) of the second width + (n) and negative frequency components x - (n) can be expressed as:

[0097] x + (n) = ifft[X(Ω)*H(Ω)]

[0098] x - (n) = ifft[X * (-Ω)*H(Ω)]

[0099] Where X(Ω) is the inverse Fourier transform result of x(n), * represents the conjugate operation, and the real parts of the positive and negative frequency components of x(n) are subjected to EMD decomposition and Hilbert transform respectively, and the complex eigenmode function IMF(n) is obtained by the two, and ifft is the inverse fast Fourier transform.

[0100] In S103, an adaptive threshold method is used to detect the distribution position of the interference signal in the intrinsic mode function.

[0101] During the propagation of the electromagnetic wave signal emitted by the microwave radar, the elevator wave signal will be affected by factors such as air, rain, fog, and obstacles, causing the energy of the electromagnetic wave signal to attenuate as the distance increases. Therefore, for targets with the same radar scattering cross-sectional area, the farther the distance, the smaller the power amplitude value, and the closer the distance, the larger the power amplitude value.

[0102] Due to the influence of environmental factors, the energy of the interference signal is usually much greater than the energy of the difference frequency signal of the secondary radiated radar echo.

[0103] Due to the existence of signal attenuation and interference, the echo signal of a distant target may be very weak and difficult to detect. Therefore, within the intrinsic mode function domain of the output difference frequency signal, the difference between the maximum intrinsic mode function amplitude in any intrinsic mode function domain and the currently detected intrinsic mode function amplitude can be determined, and the distribution position of the interference signal is determined according to the inclusion relationship between the difference and the predetermined confidence range determined by the maximum amplitude range in the intrinsic mode function domain. That is, when the difference falls within the confidence range, the signal is determined to be a valid difference frequency signal, otherwise the signal is determined to be an interference signal.

[0104] A dynamic adaptive threshold can be used for each IMF amplitude to detect the distribution position of the interference signal. For example, the detection judgment condition can be:

[0105] (max{|IMF[j]|}-|IMF[i]|)≤(max{|IMF[j]|}-min{|IMF[j]|})*p

[0106] Wherein, j∈[0,m-1], where m represents the number of intrinsic mode functions in the intrinsic mode function domain, |IMF[i]| represents the current detected IMF amplitude, and p represents the confidence interval value, for example, the confidence interval value can be [0.2, 0.8]. The detection result obtained according to the detection decision condition generates the vector a of the interference position. When the vector a is 1, it indicates that the position includes an interference signal, and when the vector a is 0, it indicates that the position is a normal signal.

[0107] In S104, the interference signal is removed according to the distribution position of the interference signal, and the difference frequency signal after the interference signal is removed is reconstructed to obtain a difference frequency reconstructed signal.

[0108] After determining the distribution position of the interference signal included in the vector a according to the adaptive threshold method, the IMF amplitude of the distribution position of the interference signal can be set to zero, and then the mean of the IMF amplitude of the non-zero position can be calculated. The non-zero IMF amplitude can be selected within the predetermined range of the zero position to calculate the mean, and the IMF amplitude of the zero position within the predetermined range can be updated according to the calculated mean. The non-zero IMF amplitude can also be selected within the entire range to calculate the mean, which is used to update the IMF amplitude of all zero positions.

[0109] After the amplitude of the zero position is updated, the phase of the zero position before zeroing is still retained. According to the phase before zeroing and the updated amplitude, a difference frequency reconstruction signal, that is, a reconstructed difference frequency signal, is obtained.

[0110] In S105, the frequency error of the difference frequency reconstructed signal is estimated, and the water level at the target position is determined according to the frequency error.

[0111] Get the difference frequency reconstruction signal After that, the difference frequency reconstruction signal can be subjected to fast Fourier transform (FFT transform) to obtain the transformation sequence S(l), where (l∈[0,N / 2-1]), N represents the number of sampling points. Find |s(l)| 2 The peak frequency k of the main lobe spectrum is analyzed for main frequency interference after windowing and interpolation. If main frequency interference exists, the signal is refined using a refinement algorithm, including a refinement algorithm such as a CTZ algorithm or a ZoomFFT algorithm, and the frequency error is determined based on the difference frequency reconstructed signal after refinement. That is, the frequency error is determined based on the difference between the reconstructed signal and the standard signal.

[0112] Among them, through windowing and interpolation processing, the peak frequency point k of the transformed sequence after FFT transformation can be used to determine the two spectral lines k1 and k2 with the largest amplitude on the left and right sides of the peak frequency point k. The amplitude of each spectral line can be expressed as: i =|S(k i *Δf)|, (i=1,2), then the expression of the discrete Fourier transform of the signal after windowing is:

[0113]

[0114] Δf=f s / N, peak frequency f0=k0*f s / N, fs is the sampling frequency, N is the number of sampling points, S is the Fourier transform of the reconstructed signal after windowing, θ is the phase, f0 is the peak frequency, j is the imaginary unit, A is the amplitude, K0 is the spectrum peak, and W is the continuous spectrum of the windowing function.

[0115]

[0116] The initial phase correction expression of the measured signal is obtained as follows:

[0117]

[0118] The amplitude correction formula is:

[0119] A i =2y i / W(2π(k i -k0) / N)

[0120] in, represents the corrected initial phase, Δf represents the discrete frequency interval (Δf = f s / N), k0 represents the peak frequency, and the value range of i is [0, N-1].

[0121] After correcting the initial phase and amplitude, the main frequency interference can be further analyzed. When the interference signal is close to the main frequency signal, the spectrum is aliased, there is only one main lobe, and main lobe interference occurs on this main lobe, which causes the spectral line amplitude of the interfered frequency to be distorted. If the measured signal is analyzed and processed using a windowed interpolation algorithm, the accuracy of the obtained data will be deviated. Therefore, it is necessary to make an interference judgment on the signal in advance, that is, to determine whether main lobe interference has occurred. When there is no main lobe interference and it is not affected by signal noise, the phase and amplitude between the spectral lines in the main lobe are related, and the absolute value of the phase difference between adjacent spectral lines in the main lobe is equal to a predetermined value, such as π. Therefore, when determining the main lobe interference, the absolute value of the phase difference can be used as the basis for the main lobe interference, that is:

[0122] Δθ=||δ1-δ2|-π|

[0123] Among them, δ1 and δ2 are the phases of adjacent spectral lines. By selecting a suitable parameter Δθ, it is determined whether the current main frequency is interfering. This parameter can be combined with actual application data to select a suitable empirical value.

[0124] After judging the main lobe interference, when there is a main lobe interference component, it means that there are more than one frequency components in this frequency band, and their frequency values ​​are very different, and spectrum aliasing has occurred. The spectrum refinement algorithm can be used, including the CZT (Chirp-Z Transform) algorithm to directly refine the frequency band data near the main frequency to improve the frequency resolution, thereby determining the frequency error between the difference frequency reconstructed signal and the standard signal, and accurately estimating the frequency error. According to the frequency error, the water level information of the target can be given more accurately and efficiently. The ZoomFFT algorithm can also refine the signal, and obtain it by analyzing more sampling points when improving the frequency resolution.

[0125] Through the anti-interference method of the embodiment of the present application, most of the interference energy of the difference frequency signal is eliminated and suppressed. However, due to the insufficient interference suppression performance of the data after FFT processing, a large number of false correlation peaks will appear in the entire spectrum after FFT transformation, resulting in data jumps. The water level detection method of the embodiment of the present application is used to perform interference suppression processing on the difference frequency signal under interference to obtain the target ranging result. The detection comparison results of the two are shown as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 The water level detection method in the embodiment of the present application can effectively suppress the influence of interference and obtain the target water level distance within the allowable error range. Figure 3 A comparison of the detection results on a fluctuating water surface with a large water flow shows that the water level detection method in the embodiment of the present application is more stable. Figure 4In the detection results in the river with sparse branches, the water level detection method in the embodiment of the present application is more stable and reliable. Figure 5 As shown, when conducting water level detection in a river channel, radar waves may be reflected from both the riverbed and the water surface due to the influence of rocks, vegetation or other objects, causing a multipath effect. This will complicate the signal received by the radar and make it difficult to distinguish the actual water surface height. However, the water level detection method in the embodiment of the present application can obtain stable water level detection results. Figure 6 This is a schematic diagram comparing the detection results of a period of time hanging down a manhole. In this environment, due to the interference of the well wall and debris, the echo signal is strong, and the echo signal data will jump. The traditional detection method will have data jumps. However, the water level detection method in the embodiment of the present application can obtain stable water level detection results. Figure 7 After hanging the device under a river in Shenzhen for a period of time, the environment was affected by the refracted echo from the bridge piers and the wind and rain. The actual echo signal data of the equipment would jump, but the fluctuation of the value after algorithm filtering was significantly reduced.

[0126] This method can effectively remove the interference noise of the original data, effectively improve the anti-interference and clutter capabilities of the millimeter-wave radar, and improve the detection and parameter estimation performance of the radar. The above interference suppression method can effectively reduce the false alarm and missed alarm rate of the radar to achieve an ideal anti-interference effect.

[0127] In addition, the applicant has shown through a large number of experiments that the distance measurement accuracy of the iterative interpolation frequency algorithm is significantly better than that of the direct FFT algorithm, and its measurement can achieve millimeter-level errors, thus achieving higher distance measurement accuracy.

[0128] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0129] Figure 8 A schematic diagram of a water level detection device provided in an embodiment of the present application, the device comprising:

[0130] The difference frequency signal receiving unit 801 is used to receive the difference frequency signal of the radar echo;

[0131] An intrinsic mode function determination unit 802 is used to decompose the difference frequency signal of the radar echo to obtain intrinsic mode functions of different frequency bands;

[0132] The interference signal detection unit 803 is used to detect the distribution position of the interference signal in the above intrinsic mode function by using an adaptive threshold method;

[0133] The reconstruction unit 804 is used to remove the interference signal according to the distribution position of the interference signal, and reconstruct the difference frequency signal after the interference signal is removed to obtain a difference frequency reconstructed signal;

[0134] The water level detection unit 805 is used to estimate the frequency error of the difference frequency reconstruction signal and determine the water level at the target location according to the frequency error.

[0135] Figure 8 The water level detection device shown is Figure 1 The water level detection method shown corresponds to.

[0136] Fig. 9 Schematic diagram of a water level detection device provided in an embodiment of the present application. Fig. 9 As shown, the water level detection device 9 of this embodiment includes: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90, such as a water level detection program. When the processor 90 executes the computer program 92, the steps in the above-mentioned water level detection method embodiments are implemented. Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0137] Exemplarily, the computer program 92 may be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 92 in the water level detection device 9.

[0138] The water level detection device 9 can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The water level detection device can include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art can understand that Fig. 9 It is only an example of a water level detection device 9 and does not constitute a limitation of the water level detection device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the water level detection device may also include input and output devices, network access devices, buses, etc.

[0139] The processor 90 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0140] The memory 91 may be an internal storage unit of the water level detection device 9, such as a hard disk or memory of the water level detection device 9. The memory 91 may also be an external storage device of the water level detection device 9, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the water level detection device 9. Further, the memory 91 may also include both an internal storage unit and an external storage device of the water level detection device 9. The memory 91 is used to store the computer program and other programs and data required by the water level detection device. The memory 91 may also be used to temporarily store data that has been output or is to be output.

[0141] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0142] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0147] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0148] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A water level detection method, characterized in that: The method comprises: Receive the difference frequency signal of radar echo; Decomposing the difference frequency signal of the radar echo to obtain the intrinsic mode functions of different frequency bands; Adopting an adaptive threshold value method to detect the distribution position of the interference signal in the intrinsic mode function; Eliminating the interference signal according to the distribution position of the interference signal, and reconstructing the difference frequency signal after the interference signal is eliminated to obtain a difference frequency reconstructed signal; The frequency error of the difference frequency reconstruction signal is estimated, and the water level at the target position is determined according to the frequency error.

2. The method according to claim 1, characterized in that Before receiving the difference frequency signal of the radar echo, the method further includes: The frequency modulation parameters are determined by a particle swarm optimization method, and a radar signal for water level detection is emitted by the frequency modulation parameters.

3. The method according to claim 2, characterized in that The frequency modulation parameters are determined by particle swarm optimization method, including: Initializing a particle swarm, determining the fitness of particles in the particle swarm, wherein a single particle represents a set of frequency modulation parameters; Determine the objective function of the particle swarm, the objective function includes a signal-to-noise ratio and a complex correlation coefficient, the complex correlation coefficient is a complex correlation coefficient between a difference frequency signal and a difference frequency reconstruction signal of a radar echo; The optimal solution of the particle swarm is determined according to the objective function, and the particle speed and position are iteratively updated according to the optimal solution until the determined optimal solution meets the preset requirements, the particles of the optimal solution are obtained, and the frequency modulation parameters corresponding to the particles are determined.

4. The method according to claim 1, characterized in that: Decomposing the difference frequency signal of the radar echo to obtain the intrinsic mode functions of different frequency bands, including: Performing Fourier transform on the difference frequency signal of the radar echo to determine the peak frequency included in the difference frequency signal of the radar echo; Performing bandpass filtering using the peak frequency as the center frequency of each frequency band to obtain a number of bandpass filtered signals; Performing empirical mode decomposition on the bandpass filtered signal to obtain intrinsic mode functions of different frequency bands.

5. The method according to claim 1, characterized in that Adopting an adaptive threshold value method to detect the distribution position of the interference signal in the intrinsic mode function includes: Determine the difference between the maximum intrinsic mode function amplitude in any intrinsic mode function domain and the currently detected intrinsic mode function amplitude, The distribution position of the interference signal is determined according to the inclusion relationship between the difference and a predetermined confidence range determined by a maximum amplitude range in the intrinsic mode function domain.

6. The method according to claim 1, characterized in that Eliminating the interference signal according to the distribution position of the interference signal, and reconstructing the difference frequency signal after the interference signal is eliminated to obtain a difference frequency reconstructed signal, including: Determining the amplitude of the intrinsic mode function of the non-interference position according to the distribution position of the interference signal; The amplitude of the intrinsic mode function at the distribution position of the interference signal is updated according to the mean of the amplitude of the intrinsic mode function at the non-interference position, and the phase of the intrinsic mode function at the distribution position of the interference signal is retained.

7. The method according to any one of claims 1 to 6, characterized in that: Estimating a frequency error of the difference frequency reconstructed signal, and determining a water level at a target location according to the frequency error, comprising: Performing windowing and interpolation processing on the difference frequency reconstruction signal to obtain an interpolation signal; When it is detected that the interpolation signal has main frequency interference, the frequency spectrum is refined to determine the frequency error of the difference frequency reconstructed signal; The water level at the target location is determined according to the frequency error.

8. A water level detection device, characterized in that: The device comprises: A difference frequency signal receiving unit, used for receiving the difference frequency signal of the radar echo; An intrinsic mode function determination unit, used for decomposing the difference frequency signal of the radar echo to obtain intrinsic mode functions of different frequency bands; An interference signal detection unit, used for detecting the distribution position of the interference signal in the intrinsic mode function by using an adaptive threshold value method; A reconstruction unit, used to remove the interference signal according to the distribution position of the interference signal, and reconstruct the difference frequency signal after the interference signal is removed to obtain a difference frequency reconstructed signal; The water level detection unit is used to estimate the frequency error of the difference frequency reconstruction signal and determine the water level at the target position according to the frequency error.

9. A water level detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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