High-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation

Abstract

The invention discloses a high-frequency radar water flow velocity monitoring method with a self-adaptive environmental interference compensation function, relates to the technical field of water flow velocity monitoring, and aims to generate shoreside clutter source distribution information by determining a target water area to analyze shoreside clutter source distribution characteristics. And spatial resolution optimization is carried out based on the shore clutter source distribution information, and radar monitoring configuration parameters are generated. Scanning a target water area, obtaining a water surface echo signal in real time, and recording water surface environment parameters by using a matched sensor of a preset high-frequency radar. The water surface echo signal is compensated, and Doppler frequency shift signal features related to the water flow surface velocity are extracted. And performing water flow velocity vector distribution calculation based on the Doppler frequency shift signal characteristics to generate a water flow velocity distribution diagram. The technical problem that a high-frequency radar water flow velocity monitoring method in the prior art is easily interfered by a shore clutter source and variable environmental parameters in a complex environment, so that the water flow velocity measurement precision is not high is solved.

Description

Technical Field

[0001] The present application relates to the technical field related to water flow rate monitoring, and specifically to a high-frequency radar water flow rate monitoring method with adaptive environmental interference compensation. Background Art

[0002] With the growing demand for water resources management and environmental monitoring, accurate monitoring of water flow velocity has become an important task in the fields of water conservancy projects, shipping management, and flood prevention and early warning. Although traditional water flow velocity monitoring methods such as buoy anemometers and current meters can provide accurate data to a certain extent, they have high installation and maintenance costs, limited spatial coverage, and are difficult to achieve real-time and comprehensive monitoring in large-scale water areas. Therefore, water flow velocity monitoring methods based on high-frequency radar have gradually become a research hotspot due to their advantages of wide coverage, strong real-time performance, and non-contact measurement. However, in practical applications, high-frequency radars are often interfered by shore clutter sources when monitoring water flow velocity, resulting in a large amount of noise and clutter in the radar echo signal, affecting the accurate measurement of water flow velocity. In addition, environmental parameters such as water temperature, wind speed, humidity, and wave height will also affect the propagation and reflection of radar signals, further exacerbating the uncertainty of measurement.

[0003] Therefore, in the prior art, the high-frequency radar water flow velocity monitoring method is susceptible to interference from shore clutter sources and variable environmental parameters in a complex environment, resulting in a technical problem of low water flow velocity measurement accuracy. Summary of the invention

[0004] The present application provides a high-frequency radar water velocity monitoring method with adaptive environmental interference compensation, which solves the technical problem that the high-frequency radar water velocity monitoring method in the prior art is easily interfered by shore clutter sources and variable environmental parameters in complex environments, resulting in low water velocity measurement accuracy. The adaptive environmental interference compensation model effectively eliminates clutter and noise interference, thereby improving the accuracy and reliability of high-frequency radar water velocity monitoring.

[0005] The present application provides a high-frequency radar water velocity monitoring method with adaptive environmental interference compensation. The method comprises the following steps: determining a target water area, analyzing the distribution characteristics of shore clutter sources on the target water area within a preset area, and generating shore clutter source distribution information; determining a preset high-frequency radar for water velocity monitoring, optimizing the spatial resolution based on the shore clutter source distribution information, and generating radar monitoring configuration parameters; configuring the preset high-frequency radar based on the radar monitoring configuration parameters, scanning the target water area, acquiring a water surface echo signal in real time, and using a matching sensor of the preset high-frequency radar to record water surface environmental parameters; inputting the water surface echo signal and the water surface environmental parameters into an environmental interference compensation model for compensation, and then extracting Doppler frequency shift signal characteristics related to the surface velocity of the water flow; and calculating the water velocity vector distribution based on the Doppler frequency shift signal characteristics, and generating a water velocity distribution map.

[0006] In the implementation method, a preset high-frequency radar for water flow velocity monitoring is determined, and spatial resolution optimization is performed based on the shore clutter source distribution information to generate radar monitoring configuration parameters, including: collecting the geographical distribution characteristics of the target water area; performing a step-by-step division of flow velocity based on the geographical distribution characteristics to generate a flow velocity ladder; setting a plurality of monitoring points based on the flow velocity ladder; identifying the relative position information and relative distance information of the plurality of monitoring points and the shore clutter source based on the shore clutter source distribution information; and optimizing the spatial resolution of the plurality of monitoring points based on the relative position information and the relative distance information to generate the radar monitoring configuration parameters.

[0007] In an implementation manner, the spatial resolution of the multiple monitoring points is optimized based on the relative position information and the relative distance information to generate the radar monitoring configuration parameters, including: obtaining position distance samples relative to shore clutter and corresponding radar transmission parameter samples obtained through a resolution optimization test, training a resolution optimization model with the position distance samples and the radar transmission parameter samples, wherein the radar transmission parameter samples include a transmission direction, a transmission wavelength, and an operating frequency; analyzing the relative position information and the relative distance information through the resolution optimization model, outputting the transmission directions, radar transmission wavelengths, and operating frequencies corresponding to the multiple monitoring points, and generating the radar monitoring configuration parameters.

[0008] In the implementation method, the flow velocity is divided into steps based on the geographical distribution characteristics to generate a flow velocity ladder, including: collecting multiple historical geographical distribution characteristic data, and corresponding multiple historical water flow velocity distribution data; identifying water flow velocity mutation points of different geographical characteristics based on the multiple historical geographical distribution characteristic data and the multiple historical water flow velocity distribution data, using the water flow velocity mutation points as step division points, and establishing a geographical feature-water flow velocity ladder relationship; identifying the geographical distribution characteristics based on the geographical feature-water flow velocity ladder relationship to generate the flow velocity ladder, wherein each step in the flow velocity ladder corresponds to an area of ​​the target water area.

[0009] In the implementation method, the water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for compensation, and then the Doppler frequency shift signal characteristics related to the surface velocity of the water flow are extracted, including: training the environmental interference compensation model, wherein the environmental interference compensation model is constructed based on an autoencoder; inputting the water surface echo signal and the water surface environmental parameters into the environmental interference compensation model for signal reconstruction to generate a reconstructed echo signal; and extracting the Doppler frequency shift signal characteristics of the reconstructed echo signal.

[0010] In the implementation method, the environmental interference compensation model is trained, wherein the environmental interference compensation model is constructed based on an autoencoder, including: obtaining multiple groups of training data, wherein any group of training data includes historical echo signal samples and historical water surface environment parameter samples, as well as corresponding target signal samples and interference signal samples; configuring the network structure of the environmental interference compensation model, wherein the network structure includes an encoder and a decoder; constructing a reconstruction loss function, training the encoder and the decoder in combination with historical echo signal samples, historical water surface environment parameter samples, target signal samples and interference signal samples, and generating the environmental interference compensation model.

[0011] In an implementation, the reconstruction loss function is:

[0012]

[0013] Among them, L is the signal reconstruction loss value, x i is the target signal sample corresponding to the i-th group of training data, y i is a reconstructed signal output after the encoder and the decoder reconstruct the original signal of the historical echo signal samples in the i-th group of training data, and N is the total number of samples corresponding to the multiple groups of training data.

[0014] In the implementation method, historical water flow rate monitoring data of the target water area is collected; the historical water flow rate monitoring data is statistically analyzed to establish a normal water flow rate threshold; and an abnormal water flow rate warning is issued based on a water flow rate distribution diagram and the normal water flow rate threshold.

[0015] The high-frequency radar water velocity monitoring method with adaptive environmental interference compensation proposed in this application is proposed. By determining the target water area, the distribution characteristics of the shore clutter source of the target water area are analyzed within the preset area to generate the shore clutter source distribution information; the preset high-frequency radar for water velocity monitoring is determined, and the spatial resolution is optimized based on the shore clutter source distribution information to generate radar monitoring configuration parameters; the preset high-frequency radar is configured based on the radar monitoring configuration parameters, and then the target water area is scanned to obtain the water surface echo signal in real time, and the supporting sensor of the preset high-frequency radar is used to record the water surface environmental parameters; the water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for compensation, and then the Doppler frequency shift signal characteristics related to the surface velocity of the water flow are extracted; the water velocity vector distribution is calculated based on the Doppler frequency shift signal characteristics to generate a water velocity distribution map. The technical problem that the high-frequency radar water velocity monitoring method in the prior art is easily interfered by the shore clutter source and the variable environmental parameters in a complex environment, resulting in low water velocity measurement accuracy is solved. Through the adaptive environmental interference compensation model, clutter and noise interference are effectively eliminated, improving the accuracy and reliability of high-frequency radar water flow velocity monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0017] Figure 1 A schematic flow chart of a high-frequency radar water velocity monitoring method with adaptive environmental interference compensation provided in an embodiment of the present application;

[0018] Figure 2 A schematic flow chart of a high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation for generating radar monitoring configuration parameters according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] The present application embodiment provides a high-frequency radar water flow rate monitoring method with adaptive environmental interference compensation, such as Figure 1 As shown, the method includes:

[0023] The target water area is determined, and the distribution characteristics of shore clutter sources are analyzed for the target water area within a preset area to generate shore clutter source distribution information; a preset high-frequency radar for water flow velocity monitoring is determined, and spatial resolution is optimized based on the shore clutter source distribution information to generate radar monitoring configuration parameters; the preset high-frequency radar is configured based on the radar monitoring configuration parameters, and then the target water area is scanned to obtain water surface echo signals in real time, and the water surface environment parameters are recorded using the matching sensors of the preset high-frequency radar.

[0024] The target water area where water velocity monitoring is required is obtained, and then the distribution characteristics of the shore clutter sources are analyzed for the target water area within the preset area, the clutter sources from the shore and the corresponding distribution positions are obtained, and the shore clutter source distribution information is generated. Among them, the clutter sources include: solid object reflection and artificial facility interference, the solid object reflection includes: fixed objects close to the shore such as rocks and vegetation, and the artificial facility interference includes: dock facilities, ship mooring, etc. Subsequently, a high-frequency radar device suitable for water velocity monitoring is selected. Based on the shore clutter source distribution information, the spatial resolution of the radar is optimized, and the radar monitoring configuration parameters are generated. Among them, the radar monitoring configuration parameters include: the radar's transmission direction, transmission wavelength, and operating frequency, etc., which are used to optimize the radar performance to adapt to the specific target water environment. Further, according to the generated radar monitoring configuration parameters, the high-frequency radar is configured, and the radar is started to scan the target water area. The water surface echo signal, that is, the signal reflected back after the electromagnetic wave emitted by the radar encounters the water surface, containing information such as water velocity, is obtained in real time, and the water surface environment parameters are recorded using the matching sensor. The supporting sensors are devices that work in conjunction with the radar and synchronously perform environmental data collection, such as temperature sensors, anemometers, etc., which are used to record environmental parameters that affect water flow speed.

[0025] like Figure 2 As shown, the method provided in the embodiment of the present application also includes: collecting the geographical distribution characteristics of the target water area; performing step-by-step division of flow velocity based on the geographical distribution characteristics to generate a flow velocity ladder; setting multiple monitoring points based on the flow velocity ladder; identifying the relative position information and relative distance information of the multiple monitoring points and the shore clutter source based on the shore clutter source distribution information; optimizing the spatial resolution of the multiple monitoring points based on the relative position information and the relative distance information to generate the radar monitoring configuration parameters.

[0026] Determine the preset high-frequency radar for water flow velocity monitoring, optimize the spatial resolution based on the shore clutter source distribution information, and generate radar monitoring configuration parameters, including: collect the geographical distribution characteristics of the target water area by remote sensing technology or on-site survey, and obtain geographical information such as the shape, area, shore structure, vegetation distribution, and water depth change of the water area. According to the collected geographical distribution characteristic data, the water flow velocity is divided into different step intervals. For example, in shallow water areas: the water flow is subject to greater bottom friction, and the flow velocity is generally slower. Deep water areas: the friction force is less affected, and the flow velocity is usually faster. Bends or tortuous areas: the water flow is faster on the outside of the bend, while the flow velocity is lower on the inside due to turbulence and backflow effects. That is, the initial division of the target water flow area is completed according to the geographical distribution characteristics, so as to facilitate the configuration of monitoring points. Each step represents a flow velocity range, which is used for subsequent setting of monitoring points and optimization of radar configuration. Further, multiple monitoring points are set based on the flow velocity step, and each flow velocity step area corresponds to a monitoring point. Further, the relative position information and relative distance information of the multiple monitoring points and the shore clutter source are identified based on the shore clutter source distribution information. The relative position information is the spatial position relationship of the monitoring point relative to the shore clutter source, including azimuth, etc. The relative distance information is the straight-line distance between the monitoring point and the clutter source, which affects the attenuation of the radar signal and the clutter intensity. The spatial resolution of the multiple monitoring points is optimized based on the relative position information and the relative distance information, and the radar monitoring configuration parameters are generated.

[0027] The method provided in an embodiment of the present application also includes: obtaining position distance samples relative to shore clutter and corresponding radar transmission parameter samples obtained through a resolution optimization test, and training a resolution optimization model with the position distance samples and the radar transmission parameter samples, wherein the radar transmission parameter samples include a transmission direction, a transmission wavelength, and an operating frequency; analyzing the relative position information and the relative distance information through the resolution optimization model, outputting the transmission directions, radar transmission wavelengths, and operating frequencies corresponding to the multiple monitoring points, and generating the radar monitoring configuration parameters.

[0028] The spatial resolution of the multiple monitoring points is optimized based on the relative position information and the relative distance information to generate the radar monitoring configuration parameters, including: obtaining the position distance samples relative to the shore clutter obtained through the resolution optimization test and the corresponding radar emission parameter samples. The position distance samples of the shore clutter include multiple clutter categories, the spatial position relationship between the monitoring point and the clutter source, and the straight-line distance. The radar emission parameter samples are radar emission parameters with the least influence of the position distance samples of the shore clutter obtained after the resolution optimization test, wherein the radar emission parameter samples include emission direction, emission wavelength, and working frequency. The resolution optimization model is trained with the position distance samples and the radar emission parameter samples, and the resolution optimization model is constructed based on the neural network model. The neural network model is supervised and trained with the position distance samples and the radar emission parameter samples until the radar emission parameters output by the model meet the preset accuracy. The trained model is obtained to obtain the resolution optimization model. The relative position information and the relative distance information are input into the resolution optimization model for analysis, and the emission directions, radar emission wavelengths, and working frequencies corresponding to the multiple monitoring points are obtained, and the radar monitoring configuration parameters are generated.

[0029] The method provided in the embodiment of the present application also includes: collecting multiple historical geographical distribution feature data, and corresponding multiple historical water flow velocity distribution data; identifying water flow velocity mutation points of different geographical features based on the multiple historical geographical distribution feature data and the multiple historical water flow velocity distribution data, and using the water flow velocity mutation points as step division points to establish a geographical feature-water flow velocity step relationship; identifying the geographical distribution features using the geographical feature-water flow velocity step relationship to generate the flow velocity step, wherein each step in the flow velocity step corresponds to an area of ​​the target water area.

[0030] Based on the geographical distribution characteristics, the flow velocity is divided into steps to generate a flow velocity step, including: collecting multiple historical geographical distribution characteristic data of different water areas and their corresponding historical water velocity distribution data. The historical water velocity distribution data is the water velocity information corresponding to the historical geographical distribution characteristic data, recording the water velocity and its changes at different times and places. Based on the collected multiple historical geographical distribution characteristic data and the corresponding historical water velocity distribution data, the water velocity changes under the geographical characteristics of each river are analyzed, and the change points in the water velocity changes that are greater than the mutation judgment threshold are obtained to obtain the water velocity mutation points. The water velocity mutation points are used as the step division points, the geographical characteristics and water velocity before and after the water velocity mutation points are obtained, and the geographical characteristics-water velocity step relationship is established. The geographical distribution characteristics are identified by the geographical characteristics-water velocity step relationship to generate the flow velocity step, wherein each step in the flow velocity step corresponds to an area of ​​the target water area. By systematically dividing the flow velocity steps of the entire river section according to geographical features, the flow velocity range in each area is clear, providing a basis for subsequent monitoring point settings and radar configuration optimization.

[0031] The water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for compensation, and then the Doppler frequency shift signal characteristics related to the surface velocity of the water flow are extracted; the water flow velocity vector distribution is calculated based on the Doppler frequency shift signal characteristics to generate a water flow velocity distribution map.

[0032] The water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for compensation, the Doppler frequency shift signal characteristics related to the water surface velocity are extracted, and the difference between the echo signal frequency and the transmission frequency is obtained. Finally, according to the frequency offset Δf of the peak value extracted by the feature, combined with the calculation formula: v = (Δf*x) / 2cosα, the water velocity of each step is calculated. Among them, v is the water velocity, x is the wavelength of the electromagnetic wave, and α is the angle between the radar beam and the water flow direction. According to the calculation results of the water velocity of each step, a water velocity distribution map is generated. The technical problem that the high-frequency radar water velocity monitoring method in the prior art is easily disturbed by the shore clutter source and the variable environmental parameters in a complex environment, resulting in low water velocity measurement accuracy is solved. Through the adaptive environmental interference compensation model, the clutter and noise interference are effectively eliminated, and the accuracy and reliability of high-frequency radar water velocity monitoring are improved.

[0033] The method provided in an embodiment of the present application also includes: training the environmental interference compensation model, wherein the environmental interference compensation model is constructed based on an autoencoder; inputting the water surface echo signal and the water surface environmental parameters into the environmental interference compensation model for signal reconstruction to generate a reconstructed echo signal; and extracting Doppler frequency shift signal features from the reconstructed echo signal.

[0034] The water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for compensation, and then the Doppler frequency shift signal features related to the surface velocity of the water flow are extracted, including: training the environmental interference compensation model, wherein the environmental interference compensation model is constructed based on an autoencoder. The autoencoder is a neural network for unsupervised learning, which consists of an encoder and a decoder. The encoder compresses the input data into a low-dimensional representation, and the decoder reconstructs it back to the original data form. By minimizing the reconstruction error, the autoencoder can learn the main features of the data and remove noise and interference. Further, the water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for signal reconstruction to generate a reconstructed echo signal. Finally, the Doppler frequency shift signal feature extraction is performed on the reconstructed echo signal. When performing feature extraction, the reconstructed echo signal is subjected to a fast Fourier transform (FFT) and converted to the frequency domain to obtain the spectrum of the signal. The peak position and amplitude related to the Doppler frequency shift are identified in the spectrum. The frequency offset Δf of the peak, that is, the difference between the echo signal frequency and the transmission frequency, is recorded to complete the feature extraction.

[0035] The method provided in an embodiment of the present application also includes: obtaining multiple groups of training data, wherein any group of training data includes historical echo signal samples and historical water surface environment parameter samples, as well as corresponding target signal samples and interference signal samples; configuring the network structure of the environmental interference compensation model, wherein the network structure includes an encoder and a decoder; constructing a reconstruction loss function, training the encoder and the decoder in combination with historical echo signal samples, historical water surface environment parameter samples, target signal samples and interference signal samples, and generating the environmental interference compensation model.

[0036] The environmental interference compensation model is trained, wherein the environmental interference compensation model is constructed based on an autoencoder, including: obtaining multiple sets of training data, wherein any set of training data includes historical echo signal samples and historical water surface environmental parameter samples, as well as corresponding target signal samples and interference signal samples. The historical echo signal samples are water surface echo signal data historically obtained by high-frequency radar, including water flow velocity information and environmental interference. The historical water surface environmental parameter samples are environmental parameter data corresponding to the echo signal, such as water temperature, wind speed, humidity, wave height, etc. These parameters affect the propagation and reflection of the radar signal. The target signal sample is a pure water flow velocity-related signal, representing an interference-free signal that the radar should receive under ideal conditions. The interference signal sample is a signal interference portion caused by environmental factors (such as clutter, noise, etc.), which needs to be identified and removed by the model. By collecting data under different environmental conditions for multiple times, a rich training data set is formed to ensure that the model can work effectively in various situations.

[0037] Subsequently, the network structure of the environmental interference compensation model is configured. The model is based on an autoencoder and includes two parts: an encoder and a decoder. The encoder is responsible for compressing the input data into a low-dimensional representation, and the decoder reconstructs it back into a high-dimensional signal. The encoder is the first half of the autoencoder, which compresses the input data into a low-dimensional representation (i.e., a latent space representation). The decoder is the second half of the autoencoder, which reconstructs the low-dimensional representation generated by the encoder back into the high-dimensional form of the original data. Finally, a reconstruction loss function is constructed, which measures the difference between the model reconstructed signal and the target signal. By minimizing the reconstruction loss function, the training model learns how to effectively remove interference and retain the target signal. The encoder and the decoder are trained in combination with historical echo signal samples, historical water surface environmental parameter samples, target signal samples, and interference signal samples to generate the environmental interference compensation model. During training, the model parameters are adjusted to minimize the loss function through back propagation and optimization algorithms (such as gradient descent).

[0038] Wherein, the reconstruction loss function is:

[0039]

[0040] Among them, L is the signal reconstruction loss value, x i is the target signal sample corresponding to the i-th group of training data, y i is a reconstructed signal output after the encoder and the decoder reconstruct the original signal of the historical echo signal samples in the i-th group of training data, and N is the total number of samples corresponding to the multiple groups of training data.

[0041] The method provided in the embodiment of the present application also includes: collecting historical water flow velocity monitoring data of the target water area; statistically analyzing the historical water flow velocity monitoring data to establish a normal water flow velocity threshold; and issuing an abnormal water flow velocity warning based on a water flow velocity distribution map and the normal water flow velocity threshold.

[0042] The historical water flow rate monitoring data of the target water area is collected, the historical water flow rate monitoring data is counted, the water flow threshold of the normal water flow in the historical water flow rate monitoring data is obtained, and the normal water flow rate threshold is obtained. An abnormal water flow rate warning is performed according to the water flow rate distribution map and the normal water flow rate threshold, and when the water flow rate is not within the normal water flow rate threshold range, an abnormal water flow rate warning is performed.

[0043] The technical solution provided by the embodiment of the present invention determines the target water area and analyzes the distribution characteristics of the shore clutter source to generate the shore clutter source distribution information. The spatial resolution is optimized based on the shore clutter source distribution information to generate radar monitoring configuration parameters. The target water area is scanned to obtain the water surface echo signal in real time, and the water surface environmental parameters are recorded using the matching sensor of the preset high-frequency radar. The water surface echo signal is compensated, and then the Doppler frequency shift signal characteristics related to the surface velocity of the water flow are extracted. The water flow velocity vector distribution is calculated based on the Doppler frequency shift signal characteristics to generate a water flow velocity distribution map. The technical problem that the high-frequency radar water flow velocity monitoring method in the prior art is easily interfered by the shore clutter source and the variable environmental parameters in a complex environment, resulting in low water flow velocity measurement accuracy is solved. Through the adaptive environmental interference compensation model, the interference of clutter and noise is effectively eliminated, and the accuracy and reliability of high-frequency radar water flow velocity monitoring are improved.

[0044] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A high-frequency radar water velocity monitoring method with adaptive environmental interference compensation, characterized in that: include: Determine the target water area, analyze the distribution characteristics of shore clutter sources for the target water area within a preset area, and generate shore clutter source distribution information; Determine a preset high-frequency radar for water velocity monitoring, optimize the spatial resolution based on the shore clutter source distribution information, and generate radar monitoring configuration parameters; After configuring the preset high-frequency radar based on the radar monitoring configuration parameters, scan the target water area, obtain the water surface echo signal in real time, and use the matching sensor of the preset high-frequency radar to record the water surface environment parameters; Inputting the water surface echo signal and the water surface environmental parameters into an environmental interference compensation model for compensation, and then extracting Doppler frequency shift signal characteristics related to the surface velocity of the water flow; The water flow velocity vector distribution is calculated based on the Doppler frequency shift signal characteristics to generate a water flow velocity distribution diagram.

2. The high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation as claimed in claim 1, characterized in that: Determine a preset high-frequency radar for water velocity monitoring, optimize the spatial resolution based on the shore clutter source distribution information, and generate radar monitoring configuration parameters, including: Collecting geographical distribution characteristics of the target waters; Based on the geographical distribution characteristics, flow velocity is divided into steps to generate flow velocity steps; Setting a plurality of monitoring points based on the flow velocity ladder; Based on the shore clutter source distribution information, identifying relative position information and relative distance information between the plurality of monitoring points and the shore clutter source; The spatial resolution of the multiple monitoring points is optimized based on the relative position information and the relative distance information to generate the radar monitoring configuration parameters.

3. The high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation as claimed in claim 2, characterized in that: Optimizing the spatial resolution of the plurality of monitoring points based on the relative position information and the relative distance information to generate the radar monitoring configuration parameters includes: Acquire position distance samples relative to shore clutter and corresponding radar transmission parameter samples obtained through resolution optimization testing, and train a resolution optimization model with the position distance samples and the radar transmission parameter samples, wherein the radar transmission parameter samples include transmission direction, transmission wavelength, and operating frequency; The relative position information and the relative distance information are analyzed by the resolution optimization model, and the emission directions, radar emission wavelengths and operating frequencies corresponding to the multiple monitoring points are output to generate the radar monitoring configuration parameters.

4. The high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation as claimed in claim 2, characterized in that: Based on the geographical distribution characteristics, flow velocity is divided into steps to generate flow velocity steps, including: Collect multiple historical geographical distribution feature data and corresponding multiple historical water velocity distribution data; Based on the multiple historical geographical distribution feature data and the multiple historical water flow velocity distribution data, water flow velocity mutation points of different geographical features are identified, and the water flow velocity mutation points are used as step division points to establish a geographical feature-water flow velocity step relationship; The geographical distribution feature is identified based on the geographical feature-water flow velocity step relationship to generate the flow velocity step, wherein each step in the flow velocity step corresponds to an area of ​​the target water area.

5. The high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation as claimed in claim 1, characterized in that: The water surface echo signal and the water surface environmental parameters are input into the environmental interference compensation model for compensation, and then the Doppler frequency shift signal characteristics related to the water surface velocity are extracted, including: Training the environmental interference compensation model, wherein the environmental interference compensation model is constructed based on an autoencoder; Inputting the water surface echo signal and the water surface environment parameter into the environmental interference compensation model to perform signal reconstruction to generate a reconstructed echo signal; The Doppler frequency shift signal feature extraction is performed on the reconstructed echo signal.

6. The high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation as claimed in claim 5, characterized in that: Training the environmental interference compensation model, wherein the environmental interference compensation model is constructed based on an autoencoder, comprising: Acquire multiple sets of training data, wherein any set of training data includes historical echo signal samples and historical water surface environment parameter samples, as well as corresponding target signal samples and interference signal samples; Configuring a network structure of the environmental interference compensation model, wherein the network structure includes an encoder and a decoder; A reconstruction loss function is constructed, and the encoder and the decoder are trained in combination with historical echo signal samples, historical water surface environment parameter samples, target signal samples and interference signal samples to generate the environmental interference compensation model.

7. The high-frequency radar water flow velocity monitoring method with adaptive environmental interference compensation as claimed in claim 6, characterized in that: The reconstruction loss function is: Among them, L is the signal reconstruction loss value, x i is the target signal sample corresponding to the i-th group of training data, y i is a reconstructed signal output after the encoder and the decoder reconstruct the original signal of the historical echo signal samples in the i-th group of training data, and N is the total number of samples corresponding to the multiple groups of training data.

8. The high-frequency radar water velocity monitoring method with adaptive environmental interference compensation as claimed in claim 1, characterized in that: Also includes: Collecting historical water flow rate monitoring data of the target water area; Collecting statistics on the historical water flow rate monitoring data and establishing a normal threshold value for water flow rate; An abnormal water flow rate warning is performed based on the water flow rate distribution diagram and the normal water flow rate threshold.

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