High-precision flow monitoring and reckoning system based on side sweeping rada

Abstract

The invention discloses a high-precision flow monitoring and reckoning system based on side-sweeping rada, and relates to the technical field of hydrological monitoring. A side-sweeping rada monitoring module is used for carrying out spatial intensive scanning on a river section, and surface flow velocity data of dozens of vertical lines are synchronously obtained; a high-resolution surface flow velocity field space sample set is constructed, then a data processing module performs median filtering and data alignment preprocessing on the sample set, and a key vertical line group which is most closely associated with the overall flow state dynamics is intelligently screened out by calculating a correlation coefficient between the flow velocity of each vertical line and the average flow velocity of an ADCP actually measured section; on the basis of a multiple linear regression model, the surface flow velocities of the key vertical lines serve as independent variables, a calculation equation of the average flow velocity of the section is constructed, and the equation quantifies the contribution degree and the influence mode of each key vertical line to the average flow velocity of the section; according to the mechanism, the complex synergistic effect of different flow zones such as a main flow zone and a shoreside attenuation zone in the section on the overall average flow velocity can be carefully captured.

Description

Technical Field

[0001] This invention relates to the field of hydrological monitoring technology, specifically a high-precision flow monitoring and estimation system based on side-scan radar. Background Technology

[0002] Hydrological flow monitoring is an indispensable foundational task for resource management, flood control and disaster reduction, and water conservancy project planning and scheduling. Traditional mainstream monitoring methods, such as rotor current meters and mobile acoustic Doppler current profilers (ADCPs), are all contact-based measurements. These methods are highly dependent on manual operation, difficult to implement under adverse weather conditions, and pose serious challenges such as equipment damage, data loss, or even inoperability in special hydrological scenarios. They are insufficient to meet the urgent needs of modern smart hydrology for real-time, continuous, and automated data acquisition.

[0003] To overcome the limitations of contact measurements, non-contact radar flow measurement technology has emerged. However, existing technologies have revealed several key shortcomings in practice: First, in terms of measurement methods, most devices rely solely on the surface velocity along a single vertical line or very few vertical lines to estimate the total cross-sectional flow. This approach is extremely sensitive to uneven velocity distribution across the cross-section, ignoring the complex differences in the main channel, slow-flowing areas, and bank flow patterns, resulting in insufficient representativeness and limited accuracy in the estimated results. Second, in terms of system deployment, there is a lack of universally applicable installation parameter guidance. The equipment installation location and angle are poorly adapted to the specific topography of the cross-section, making it susceptible to the influence of bank buildings, vegetation, or river curvature, causing signal obstruction and multipath interference, severely restricting the stability and reliability of the data. Furthermore, it cannot adapt to dynamic hydrological conditions such as water level fluctuations and cross-sectional scouring and deposition changes.

[0004] In summary, existing radar flow measurement technology suffers from systemic problems such as insufficient monitoring accuracy and poor long-term stability in complex real-world environments due to its one-sided measurement methods and haphazard installation and deployment. Therefore, developing a technology that can integrate multi-dimensional spatial flow velocity information and features intelligent installation and configuration schemes is an inevitable direction for breaking through current industry bottlenecks and achieving high-precision automated flow monitoring. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a high-precision flow monitoring and estimation system based on side-scan radar. It uses a side-scan radar monitoring module to perform spatially dense scanning of the river cross-section, simultaneously acquiring surface velocity data along dozens of vertical lines, constructing a high-resolution spatial sample set of surface velocity fields. The data processing module then performs median filtering and data alignment preprocessing on this sample set. By calculating the correlation coefficient between the flow velocity along each vertical line and the average flow velocity measured at the ADCP cross-section, it intelligently selects the key vertical line group most closely related to the overall hemodynamics. Based on multiple... The metalinear regression model uses the surface velocity of these key vertical lines as independent variables to construct an equation for calculating the cross-sectional average velocity. This equation quantifies the contribution and influence pattern of each key vertical line to the cross-sectional average velocity. This mechanism can precisely capture the complex synergistic effect of different flow zones, such as the mainstream region and the bank attenuation region, on the overall average velocity within the cross-section. It achieves data-driven reconstruction of the dynamic characteristics of the two-dimensional flow field of the entire cross-section, thereby transforming the local information measured by a single vertical line into an average velocity value that can represent the entire cross-section, fundamentally overcoming the deficiency of insufficient representativeness of single-point measurements.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a high-precision flow monitoring and estimation system based on side-scan radar, the system comprising a side-scan radar monitoring module, a data processing module, a flow estimation module, a model dynamic optimization module, and a results display module;

[0007] The side-scan radar monitoring module is used to be installed on the riverbank of the monitoring section, to transmit radar signals and collect surface flow velocity data of multiple vertical lines in the section, and at the same time acquire water level data.

[0008] The data processing module is used to filter the received surface velocity data to remove outliers, and to perform time matching between the processed radar data and the measured cross-sectional average velocity data of the mobile ADCP to form a modeling dataset.

[0009] The flow estimation module, based on the modeling dataset, constructs a cross-sectional average flow velocity estimation model through multiple linear regression analysis. The average flow velocity estimation model uses a preset number of vertical flow velocity data that are most correlated with the cross-sectional average flow velocity to construct a cross-sectional average flow velocity estimation equation, and combines it with the real-time cross-sectional area to calculate the instantaneous flow rate.

[0010] The model dynamic optimization module is used to periodically acquire new ADCP data and corresponding radar data, and to execute dynamic correction strategies and cross-section adaptation strategies.

[0011] The results display module is used to display and output flow process lines, flow velocity distribution maps, and data compilation results in real time.

[0012] Furthermore, the side-scan radar monitoring module satisfies the following when monitoring the riverbank section:

[0013] The radar is 5-30 meters above the water surface in the horizontal direction and 2-20 meters above the water surface in the vertical direction.

[0014] The angle between the main beam direction of the radar transmitting antenna and the main flow direction of the river is 90°, and the angular deviation of its normal direction is controlled within ±10°.

[0015] Furthermore, the filtering process performed by the data processing module is specifically a median filtering algorithm, which is used to slide the surface velocity time series data of each vertical line according to a preset window, and take the median of all data within the window as the effective velocity value at that moment, so as to eliminate pulse-type abnormal noise.

[0016] Furthermore, the specific steps of the flow estimation module in constructing the cross-sectional average flow velocity estimation model based on multiple linear regression analysis include:

[0017] Using the modeling dataset, the correlation coefficient between the surface velocity of each vertical line and the average velocity of the ADCP measured cross section is calculated;

[0018] Based on the magnitude of the correlation coefficient, a predetermined number of perpendicular lines are selected from all perpendicular lines to form the first group of perpendicular lines.

[0019] Using the surface velocity of the first vertical line group as the independent variable and the average velocity of the ADCP measured cross section as the dependent variable, a multiple linear regression method is used to fit the data, resulting in a coefficient vector containing constant terms and coefficients of each independent variable.

[0020] The mathematical expression for the equation for calculating the average flow velocity across the cross section is: ,in, The cross-sectional average velocity is... - Selected from the first set of vertical lines Surface velocity along a characteristic vertical line to These are the model coefficients in the coefficient vector.

[0021] Furthermore, the flow estimation module receives water level data in real time and queries the water level-flow cross-sectional area relationship curve established based on the latest cross-sectional topographic data to obtain the flow cross-sectional area corresponding to the current real-time water level. The average flow velocity of the cross section Multiplying the instantaneous flow rate by the cross-sectional area A of the water passage yields the instantaneous flow rate. .

[0022] Furthermore, the screening process for the first vertical line group includes:

[0023] The first correlation coefficient is calculated based on the historical comparative measurement data set, which includes the synchronously collected ADCP measured cross-sectional average flow velocity data and the modeling dataset.

[0024] For each vertical line, the first correlation coefficient is the Pearson correlation coefficient between the surface velocity data of that vertical line and the average velocity data of the corresponding ADCP measured cross section.

[0025] The vertical lines are sorted in descending order based on their first correlation coefficient values. The top 6 vertical lines are selected as feature vertical lines, and then 2 vertical lines are selected as auxiliary feature vertical lines, together forming the preset number of 8 feature vertical lines.

[0026] Furthermore, the dynamic correction strategy is as follows: periodically acquire ADCP comparison data, and when the error between the radar-estimated flow rate and the ADCP measured flow rate exceeds a first threshold, trigger the flow estimation module to recalibrate the model coefficients of the cross-sectional average flow velocity estimation equation.

[0027] The cross-section adaptation strategy is as follows: periodically acquire topographic data of the monitoring cross-section and update the water level-cross-section area relationship curve.

[0028] Furthermore, the dynamic correction strategy specifically includes:

[0029] Twenty ADCP comparison tests are conducted annually, and each test yields an ADCP measured flow value and corresponding radar data.

[0030] Calculate the relative error between the radar-estimated flow rate and the ADCP-measured flow rate for each comparison test;

[0031] When the relative error of a single comparison exceeds the preset first threshold, the flow estimation module is automatically triggered to re-execute the multiple linear regression analysis using all comparison data in order to update the model coefficients of the cross-sectional average flow velocity estimation equation.

[0032] Furthermore, the cross-section adaptation strategy specifically includes:

[0033] During the non-flood season, the large-section topographic data of the monitoring section is measured every two months using sounding equipment. During the flood season, the data is updated to be measured monthly.

[0034] Based on the new topographic data obtained from each survey, a new curve relating water level to cross-sectional area is generated, and this updated curve is provided to the flow estimation module for instantaneous flow calculation.

[0035] Compared with existing technologies, this high-precision flow monitoring and estimation system based on side-scan radar has the following advantages:

[0036] This invention utilizes a side-scan radar monitoring module to perform spatially dense scanning of a river cross-section, simultaneously acquiring surface velocity data from dozens of vertical lines. This constructs a high-resolution spatial sample set of surface velocity fields. The data processing module then performs median filtering and data alignment preprocessing on this sample set. By calculating the correlation coefficient between the velocity of each vertical line and the average velocity of the cross-section measured by ADCP, the key vertical line group most closely related to the overall flow dynamics is intelligently selected. Based on a multiple linear regression model, the surface velocity of these key vertical lines is used as independent variables to construct an equation for estimating the average velocity of the cross-section. This equation quantifies the contribution and influence pattern of each key vertical line to the average velocity of the cross-section. This mechanism can precisely capture the complex synergistic effect of different flow zones, such as the mainstream area and the bank attenuation area, on the overall average velocity within the cross-section. It achieves data-driven reconstruction of the two-dimensional flow field dynamics characteristics of the entire cross-section, thereby transforming the local information measured by a single vertical line into an average velocity value that can represent the entire cross-section, fundamentally overcoming the deficiency of insufficient representativeness of single-point measurements.

[0037] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0039] Figure 1 This is an operation flowchart of a high-precision flow monitoring and estimation system based on side-scan radar.

[0040] Figure 2 This is a block diagram of a high-precision flow monitoring and estimation system based on side-scan radar.

[0041] Figure 3 A flowchart illustrating the steps involved in constructing a cross-sectional average flow velocity estimation model in a high-precision flow monitoring and estimation system based on side-scan radar. Detailed Implementation

[0042] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a high-precision flow monitoring and estimation system based on side-scan radar,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plurality forms, unless the context clearly indicates otherwise; “plural” generally includes at least two.

[0044] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0045] To address the shortcomings of existing technologies, this invention first describes the flow monitoring scenario it addresses. This invention is primarily applied to hydrological monitoring of rivers, canals, and other water bodies, particularly in flood control, water resource management, and water conservancy project scheduling, where the real-time, continuous, and accurate requirements for flow data are extremely high. Traditional contact measurement methods are limited by manual operation and environmental conditions, making it difficult to achieve all-weather automated monitoring. Existing non-contact radar technology suffers from insufficient representativeness of single-point measurements and a lack of optimized installation parameters, resulting in long-term instability and inaccuracy. This invention aims to achieve high-precision and high-reliability flow monitoring and estimation through mechanisms such as simultaneous acquisition of surface flow velocities along multiple vertical lines, intelligent data filtering and modeling, and dynamic model optimization.

[0046] The high-precision flow monitoring and estimation system based on side-scan radar provided by this invention performs spatially dense scanning of river cross-sections using a side-scan radar monitoring module, simultaneously acquiring surface velocity data along multiple vertical lines. A data processing module filters and matches the raw data to form a high-quality modeling dataset. A flow estimation module constructs a cross-sectional average flow velocity estimation model based on multiple linear regression analysis and calculates instantaneous flow rate by combining it with real-time water level data. A model dynamic optimization module periodically updates model parameters and cross-sectional topographic data to ensure long-term system stability. A results display module outputs monitoring results in real time, supporting data visualization and compilation. The entire system forms a closed loop from data acquisition, processing, estimation to optimization, collectively ensuring the accuracy and reliability of flow monitoring.

[0047] Specifically, such as Figure 2 As shown, a high-precision flow monitoring and estimation system based on side-scan radar is presented. The system includes: a side-scan radar monitoring module, a data processing module, a flow estimation module, a model dynamic optimization module, and a results display module.

[0048] The side-scan radar monitoring module is used to be installed on the riverbank of the monitoring section, to transmit radar signals and collect surface flow velocity data of multiple vertical lines in the section, and at the same time acquire water level data.

[0049] The data processing module is used to filter the received surface velocity data to remove outliers, and to perform time matching between the processed radar data and the measured cross-sectional average velocity data of the mobile ADCP to form a modeling dataset.

[0050] The flow estimation module, based on the modeling dataset, constructs a cross-sectional average flow velocity estimation model through multiple linear regression analysis. The average flow velocity estimation model uses a preset number of vertical flow velocity data that are most correlated with the cross-sectional average flow velocity to construct a cross-sectional average flow velocity estimation equation, and combines it with the real-time cross-sectional area to calculate the instantaneous flow rate.

[0051] The model dynamic optimization module is used to periodically acquire new ADCP data and corresponding radar data, and to execute dynamic correction strategies and cross-section adaptation strategies.

[0052] The results display module is used to display and output flow process lines, flow velocity distribution maps, and data compilation results in real time.

[0053] In its implementation, the side-scan radar monitoring module is responsible for collecting surface flow velocity and water level data of the river cross-section, providing the raw data foundation for subsequent flow estimation. Since radar signal propagation and reception are susceptible to environmental interference, and the installation location and angle directly affect data quality, the deployment of this module must strictly adhere to the preset installation parameters.

[0054] Side-scan radar equipment is preferably K-band or X-band continuous wave radar, which has high resolution and anti-interference capabilities. During installation, the radar equipment is fixed to the riverbank on one side of the monitoring section. Its spatial position and beam direction are adjusted using brackets. Specific installation parameters must meet the following requirements: the radar should be 5-30 meters above the water surface horizontally and 2-20 meters above the water surface vertically. This distance range ensures that the radar signal can effectively cover the entire section, while avoiding signal attenuation or blind spots caused by excessive height or height. The angle between the main beam direction of the radar transmitting antenna and the main flow direction of the river should be 90°, and the angular deviation of its normal direction should be controlled within ±10°. This installation configuration minimizes multipath effects and signal blockage, ensuring that the radar beam can be perpendicularly incident on the water surface, thereby accurately capturing the surface velocity in the vertical direction.

[0055] During operation, the side-scan radar monitoring module periodically transmits radar signals and receives their echoes. The radar signals interact with the water surface, and the surface velocity along each vertical line is determined through the Doppler effect. Typically, 20-30 virtual vertical lines are set within the cross-section, with the spacing dynamically adjusted according to the cross-section width, generally 1-5 meters. The radar synchronously acquires time-series data of the surface velocity along each vertical line, with a sampling frequency set to 1-10Hz to meet real-time requirements. Simultaneously, the radar integrates a water level sensor to synchronously acquire cross-sectional water level data. The water level data sampling interval is 1-5 minutes. The acquired surface velocity and water level data are transmitted in real-time to the data processing module via wired or wireless transmission.

[0056] The data processing module receives raw surface velocity and water level data transmitted from the side-scan radar monitoring module and performs data preprocessing to remove outliers and form a high-quality modeling dataset. Raw radar data often contains pulse-like outliers caused by environmental noise, which can lead to model bias if used directly. This module employs a median filtering algorithm for data smoothing. For the surface velocity time series data of each vertical line, a sliding calculation is performed according to a preset window. The window size is set based on the sampling frequency. Within each sliding window, all data points are sorted by numerical value, and the median value is taken as the effective velocity value at that moment.

[0057] After filtering, the data processing module performs time matching between the radar data and the measured cross-sectional average velocity data of the mobile ADCP. ADCP comparisons are typically performed periodically, with each comparison acquiring average velocity data from multiple cross-sections. The data processing module aligns the radar-acquired surface velocity data with the ADCP measured data based on timestamps, ensuring that the time synchronization error between the two is less than 1 minute. The matched data forms a modeling dataset, including surface velocities along multiple vertical lines and the corresponding ADCP cross-sectional average velocities, for subsequent model construction.

[0058] The flow estimation module, based on a modeling dataset, constructs a cross-sectional average flow velocity estimation model through multiple linear regression analysis and calculates instantaneous flow rate by combining real-time water level data. The core of this module is to extract the feature vertical lines most relevant to the cross-sectional average flow velocity from multiple vertical surface velocities using a data-driven approach, thereby constructing the cross-sectional average flow velocity estimation model. Figure 3 As shown, the specific steps are as follows:

[0059] The correlation coefficient between the surface velocity along each vertical line and the average velocity of the ADCP measured cross section was calculated using the modeling dataset. The correlation coefficient was calculated using the Pearson correlation coefficient formula. ,in, Perpendicular line The correlation coefficient, Perpendicular line Surface flow velocity at the first sampling point Perpendicular line The average surface velocity, For the first Average flow velocity of ADCP cross section after sampling. This represents the mean flow velocity across the ADCP cross section. The number of samples.

[0060] Based on the magnitude of the correlation coefficient, a predetermined number of perpendicular lines are selected from all perpendicular lines to form the first group of perpendicular lines. The selection process includes:

[0061] The first correlation coefficient is calculated based on historical comparative measurement data. For each vertical line, the first correlation coefficient is the Pearson correlation coefficient between the surface velocity data of the vertical line and the average velocity data of the corresponding ADCP measured cross section.

[0062] The vertical lines are sorted in descending order based on their first correlation coefficient values. The top six vertical lines are selected as feature vertical lines, and then two more are selected as auxiliary feature vertical lines, forming a total of eight feature vertical lines. This selection mechanism ensures that the selected vertical lines represent different flow zones within the cross-section, such as the main flow zone and the shoreline attenuation zone, thus comprehensively capturing the velocity distribution characteristics of the cross-section.

[0063] Using the surface velocity of the first vertical line group as the independent variable and the average velocity of the ADCP measured cross-section as the dependent variable, a multiple linear regression method was used for fitting, resulting in a coefficient vector containing constant terms and coefficients of each independent variable. The mathematical expression of the equation for estimating the average cross-sectional velocity is: ,in, The cross-sectional average velocity is... - Selected from the first set of vertical lines Surface velocity along a characteristic vertical line to The coefficients in the coefficient vector are the model coefficients. The regression analysis uses the least squares method to solve for the coefficients to ensure that the model residuals are minimized.

[0064] During the real-time monitoring phase, the flow estimation module receives real-time surface velocity and water level data transmitted by the side-scan radar monitoring module. The flow estimation module first extracts the surface velocity along the characteristic vertical line from the real-time data, substitutes it into the cross-sectional average velocity estimation equation, and calculates the cross-sectional average velocity. Simultaneously, the module queries the water level-cross-sectional area relationship curve established based on the latest cross-sectional topographic data to obtain the cross-sectional area A corresponding to the current real-time water level. The water level-cross-sectional area relationship curve is obtained by fitting historical topographic measurement data and is usually represented by a polynomial or piecewise linear function, with the average flow velocity of the cross-section as the factor. Multiply by the cross-sectional area A to obtain the instantaneous flow rate. The calculation results are transmitted to the results display module in real time and stored in the database for subsequent analysis.

[0065] The aforementioned model dynamic optimization module is responsible for periodically updating the flow estimation model and cross-sectional topographic data to address model drift caused by changes in hydrological conditions. This module implements dynamic correction and cross-sectional adaptation strategies to ensure the long-term accuracy of the system.

[0066] The dynamic correction strategy involves periodically acquiring ADCP comparison data. When the error between the radar-estimated flow rate and the ADCP measured flow rate exceeds a first threshold, the flow estimation module is triggered to recalibrate the model coefficients of the cross-sectional average velocity estimation equation. Specifically, this includes conducting 20 ADCP comparisons annually, acquiring one ADCP measured flow rate value and corresponding radar data for each comparison; calculating the relative error between the radar-estimated flow rate and the ADCP measured flow rate for each comparison; and automatically triggering the flow estimation module when the relative error of a single comparison exceeds a preset first threshold. This module then re-executes multiple linear regression analysis using all comparison data to update the model coefficients of the cross-sectional average velocity estimation equation. This strategy, through periodic comparisons and an error triggering mechanism, achieves model self-correction, avoiding accuracy degradation due to long-term operation.

[0067] The cross-section adaptation strategy is as follows: Topographic data of the monitoring cross-section is acquired periodically to update the water level-cross-sectional area relationship curve. Specifically, during the non-flood season, large-section topographic data of the monitoring cross-section is measured every two months using a depth sounding device; during the flood season, due to increased water scouring and siltation, the measurement is updated monthly. The new topographic data obtained from each measurement is transmitted to the system via a data interface to regenerate the correspondence curve between water level and cross-sectional area. The updated relationship curve is provided to the flow estimation module for instantaneous flow calculation, ensuring that the cross-sectional area matches the real-time water level.

[0068] The results display module is responsible for the real-time display and output of traffic monitoring results, providing users with an intuitive data visualization interface and compiled reports. This results display module is usually developed based on a web platform or desktop application, supporting multi-user access and remote management.

[0069] During operation, the results display module receives instantaneous flow data, cross-sectional average velocity data, and raw velocity distribution data from the flow estimation module and the side-scan radar monitoring module. The module generates a flow process line in real time, displaying flow change trends in time series format, supporting zooming and querying functions. Simultaneously, it generates a velocity distribution map, displaying the spatial distribution of surface velocity along each vertical line within the cross-section in heat map or contour line format, helping users identify main flow and backflow zones. Data compilation results include daily, monthly, and annual reports, covering statistical indicators such as maximum / minimum flow, average velocity, and water level fluctuations, and supports export in Excel, PDF, and other formats. Furthermore, the module provides an alarm function, automatically sending SMS or email notifications to management personnel when flow or velocity is abnormal.

[0070] Specifically, such as Figure 1 The flowchart shown illustrates a high-precision flow monitoring and estimation system based on side-scan radar, detailing the specific steps involved in cross-sectional flow monitoring and estimation using this system.

[0071] (1) System initialization and parameter configuration

[0072] Side-scan radar equipment was installed on the selected riverbank section, and the equipment was debugged according to the optimized parameters, including setting the radar horizontal distance and vertical height, and accurately calibrating the radar antenna direction so that its main beam was perpendicular to the flow direction.

[0073] Configure system operating parameters, including data acquisition cycle and sampling frequency.

[0074] (2) Synchronous acquisition of spatial multidimensional flow velocity data

[0075] The side-scan radar monitoring module automatically starts according to a set cycle and transmits radar signals to the river cross section.

[0076] Surface velocity data of multiple vertical lines within the cross section are collected simultaneously.

[0077] Real-time water level data of the river is acquired synchronously.

[0078] (3) Data preprocessing and quality control

[0079] The data processing module performs median filtering on the raw surface velocity data of each vertical line collected, and calculates the median using a sliding window to effectively remove pulse-like outliers caused by floating objects, surges, etc.

[0080] The processed, high-quality radar surface velocity data is stored in a real-time database.

[0081] (4) Construction of core traffic model

[0082] Collect the average flow velocity data of the ADCP measured cross section synchronously over a period of time and the corresponding radar surface flow velocity data to form a modeling dataset.

[0083] The flow estimation module analyzes and models the dataset, and calculates the correlation coefficient between the flow velocity on each vertical surface and the average flow velocity of the ADCP section.

[0084] Based on the correlation coefficient, a preset number of key vertical lines are intelligently selected to form a core vertical line group.

[0085] Based on the surface velocity data of the vertical line group and the average velocity data of the ADCP section, the model coefficients of the equation for estimating the average velocity of the section are calibrated and generated using a multiple linear regression algorithm.

[0086] (5) Real-time cross-sectional average velocity estimation

[0087] The flow estimation module reads the surface velocity data of key vertical groups from the real-time database in each data acquisition cycle.

[0088] Substitute the above data into the constructed cross-sectional average flow velocity calculation equation to calculate the cross-sectional average flow velocity value at the current moment in real time.

[0089] (6) Instantaneous flow calculation and output

[0090] The flow estimation module synchronously reads real-time water level data.

[0091] Query the water level-cross-sectional area relationship curve generated based on the latest cross-sectional topographic data to obtain the cross-sectional area corresponding to the current water level.

[0092] Multiply the calculated average flow velocity of the cross section by the cross-sectional area of ​​the water flow to obtain the instantaneous flow rate at that moment.

[0093] This instantaneous flow rate value is output to the results display module and stored in the database.

[0094] (7) Visualization and remote transmission of monitoring results

[0095] The results display module receives flow rate, flow velocity, and water level data.

[0096] Automatically generate and update visualization charts such as flow process lines and velocity distribution heatmaps.

[0097] Compile data reports as required, display all results on the local interface, and simultaneously transmit them remotely to the superior hydrological information platform via the communication network.

[0098] (8) System dynamic optimization and closed-loop calibration

[0099] Model dynamic optimization (dynamic correction strategy): Regularly perform ADCP comparison tests, compare the measured ADCP flow rate obtained from the comparison test with the system's estimated flow rate. If the error exceeds the set threshold, the model recalibration process is automatically triggered, and the coefficients of the cross-sectional average flow velocity estimation equation are updated using all historical and new data.

[0100] Cross-section basic maintenance (cross-section adaptation strategy): Regularly conduct topographic surveys of the monitoring cross-sections, update large cross-section data and water level-area relationship curves, and ensure the accuracy of area parameters in flow calculation.

[0101] In summary, this invention, through the collaborative work of the aforementioned modules, constructs a high-precision flow monitoring system encompassing data acquisition, filtering, model estimation, and dynamic optimization. These modules are tightly integrated via data flow: the side-scan radar monitoring module provides a high-resolution surface velocity field; the data processing module ensures data quality; the flow estimation module achieves accurate flow calculation; the model dynamic optimization module guarantees long-term stability; and the results display module provides a user-friendly interface. The entire system achieves non-contact, all-weather, and automated flow monitoring, effectively overcoming the limitations of traditional methods and providing reliable technical support for hydrological and water resources management.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A high accuracy flow monitoring and extrapolation system based on side scan radar, characterized in that, The system comprises a side-looking radar monitoring module, a data processing module, a flow calculation module, a model dynamic optimization module and an achievement display module. The side-looking radar monitoring module is arranged on the bank of the monitoring section, emits radar signals and collects surface flow velocity data of multiple vertical lines in the section, and simultaneously acquires water level data. The data processing module is configured to filter the received surface flow velocity data to remove abnormal values, and perform time matching between the processed radar data and the measured section average flow velocity data of the boat-mounted ADCP to form a modeling data set. The flow calculation module is configured to construct a section average flow velocity calculation model based on the modeling data set, and construct a section average flow velocity calculation equation by using the preset number of vertical line flow velocity data with the highest correlation with the section average flow velocity, and calculate the instantaneous flow by combining the real-time water area of the section. The model dynamic optimization module is configured to periodically acquire new ADCP data and corresponding radar data, and execute a dynamic correction strategy and a section adaptation strategy. The achievement display module is configured to display and output the flow hydrograph, the flow velocity distribution diagram and the data compilation results in real time.

2. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 1, characterized in that, When the side-looking radar monitoring module monitors the river bank, the following conditions are met: The radar is 5-30 meters away from the water surface in the horizontal direction and 2-20 meters away from the water surface in the vertical direction. The angle between the main beam direction of the radar transmitting antenna and the main flow direction of the river is 90°, and the angle deviation of the normal direction is controlled within ±10°.

3. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 1, characterized in that, The filtering process performed by the data processing module is a median filtering algorithm, which is used to slide the surface flow velocity time series data of each vertical line according to a preset window, and take the median of all data in the window as the effective flow velocity value at that time, so as to remove the pulse type abnormal noise.

4. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 1, characterized in that, The specific steps of constructing the section average flow velocity calculation model based on the multiple linear regression analysis of the flow calculation module include: Using the modeling data set, the correlation coefficient between the surface flow velocity of each vertical line and the ADCP measured section average flow velocity is calculated. According to the size of the correlation coefficient, a preset number of vertical lines are selected from all vertical lines to form a first vertical line group. The surface flow velocity of the first vertical line group is taken as the independent variable, and the ADCP measured section average flow velocity is taken as the dependent variable, and a multiple linear regression method is used for fitting to obtain a coefficient vector containing a constant term and the coefficients of each independent variable. The mathematical expression of the cross-section average flow velocity estimation equation is: wherein, is the cross-section average flow velocity, - is the surface flow velocity of the filtered characteristic vertical line in the first vertical line group, to is the model coefficient in the coefficient vector.

5. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 4, characterized in that, The flow calculation module receives water level data in real time, and queries a water level-cross section area relation curve established according to the latest cross section terrain data to obtain a cross section area corresponding to the current real-time water level , and the cross section average flow rate is multiplied by the cross section area A to obtain the instantaneous flow .

6. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 4, characterized in that, The selection process of the first vertical line group includes: Calculate the first correlation coefficient based on the historical measurement data set, which contains the ADCP measured section average flow velocity data and the modeling data set collected synchronously. For each vertical line, the first correlation coefficient is the Pearson correlation coefficient between the surface flow velocity data of the vertical line and the corresponding ADCP measured section average flow velocity data. According to the descending order of the first correlation coefficient values of each vertical line, the first 6 vertical lines are selected as characteristic vertical lines, and then 2 vertical lines are selected as auxiliary characteristic vertical lines to form the preset number of 8 characteristic vertical lines.

7. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 1, characterized in that, The dynamic correction strategy is: periodically acquiring ADCP comparison data, when the error between the radar calculated flow and the ADCP measured flow exceeds a first threshold, triggering the flow calculation module to recalibrate the model coefficients of the cross-section average flow velocity calculation equation; The cross-section adaptation strategy is: periodically acquiring topographic data of the monitoring cross-section, updating the water level-cross-sectional area relationship curve.

8. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 7, characterized in that, The dynamic correction strategy specifically includes: 20 ADCP comparisons are performed every year, and an ADCP measured flow value and corresponding radar data are obtained each time; The relative error between the radar calculated flow and the ADCP measured flow at each comparison is calculated; When the relative error of a single comparison exceeds a preset first threshold, the flow calculation module is automatically triggered, and all comparison data is used to perform multiple linear regression analysis again to update the model coefficients of the cross-section average flow velocity calculation equation.

9. The high-precision flow monitoring and extrapolating system based on side-scanning radar according to claim 7, characterized in that, The cross-section adaptation strategy specifically includes: In the non-flood season, the large cross-section topographic data of the monitoring cross-section is measured once every 2 months by using a depth measuring device, and in the flood season, the measurement is updated to once a month; According to the new topographic data obtained each time, the corresponding relationship curve between the water level and the cross-sectional area is regenerated, and the updated relationship curve is provided to the flow calculation module for instantaneous flow calculation.

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