Far steady-state filtering flow velocity and flow direction detection method

Abstract

The invention discloses a clar steady-state filtering flow velocity and flow direction detection method, and belongs to the technical field of target detection, and the method comprises the steps: collecting and processing the real-time data of a fluid radar signal, and determining the target data of the fluid radar signal; analyzing and identifying the fluid radar signal target data, and determining a clar steady-state filtering flow velocity and flow direction detection result; and visually displaying the clar steady-state filtering flow velocity and flow direction detection result, and timely managing and controlling the fluid according to the clar steady-state filtering flow velocity and flow direction detection result. According to the invention, the problems of low flow velocity and flow direction detection precision and poor effect caused by the fact that the flow velocity and flow direction of the fluid cannot be effectively detected according to cfu steady-state filtering in the prior art are solved. The flow velocity and the flow direction of the fluid can be effectively detected according to cfu steady-state filtering, so that reliable flow velocity and flow direction detection results can be provided under various environmental conditions, and the flow velocity and flow direction detection precision and the flow velocity and flow direction detection effect can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of target detection, in particular to a CFAR steady-state filtering flow velocity and direction detection method. Background Art

[0002] Groundwater is one of the important freshwater resources on the earth. Accurately measuring the flow rate and direction of groundwater is of great significance to water resource management, pollution control, agricultural irrigation and other aspects.

[0003] Chinese patent publication number CN105652034B discloses an intelligent sensor for detecting groundwater velocity and direction for landslide monitoring. The sensor is suitable for measuring groundwater velocity and direction, a parameter used in landslide deformation calculations. It can be connected to an inclinometer, offering easy operation, convenient data processing, and high measurement accuracy. The tracer, extruded through a nozzle, is pollution-free, has a good aqueous solution, a specific gravity comparable to water, and good fluidity. However, the patent suffers from the following drawbacks:

[0004] Existing technology cannot effectively detect the flow velocity and direction of the fluid based on CFAR steady-state filtering, making it unable to provide reliable flow velocity and direction detection results under various environmental conditions, resulting in low flow velocity and direction detection accuracy and poor flow velocity and direction detection effect. Summary of the Invention

[0005] The purpose of the present invention is to provide a CFAR steady-state filtering flow velocity and direction detection method, which can effectively detect the flow velocity and direction of the fluid based on the CFAR steady-state filtering, so that it can provide reliable flow velocity and direction detection results under various environmental conditions, improve the flow velocity and direction detection accuracy and flow velocity and direction detection effect, and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A CFAR steady-state filtering flow velocity and direction detection method, comprising:

[0008] Collect and process real-time data of fluid radar signals to determine target data of fluid radar signals;

[0009] Establish a CFAR steady-state filter flow velocity and direction detection model, analyze and identify the fluid radar signal target data, predict the fluid flow velocity and direction, and determine the CFAR steady-state filter flow velocity and direction detection results;

[0010] Visually display the CFAR steady-state filtering flow velocity and direction detection results, and timely control the fluid based on the CFAR steady-state filtering flow velocity and direction detection results.

[0011] Preferably, collecting real-time data of fluid radar signals includes:

[0012] Install the radar sensor next to the fluid to be detected in an open area with no obstructions.

[0013] Real-time monitoring of the fluid to be detected is performed based on a radar sensor, in which the radar sensor transmits microwave or millimeter wave signals through the fluid and interacts with scattering objects in the fluid. The radar waves reflected back by the scattering objects in the fluid to be detected are received by the radar sensor, and the received radar signal is subjected to frequency analysis to extract the frequency information of the scattering objects and determine the real-time data of the fluid radar signal.

[0014] Preferably, processing the real-time data of the fluid radar signal includes:

[0015] Based on Kalman filtering, steady-state filtering is performed on the real-time data of fluid radar signals to filter out specific band frequencies in the real-time data of fluid radar signals that are worthless for CFAR steady-state filtering flow velocity and direction detection, thereby eliminating noise and interference in the real-time data of fluid radar signals.

[0016] Preferably, processing the real-time data of the fluid radar signal further includes:

[0017] Target detection is performed on real-time data of fluid radar signals based on constant false alarm rate (CFAR);

[0018] The window selection is performed based on the real-time data of the fluid radar signal. A detection area is selected in the real-time data of the fluid radar signal to determine whether a target signal exists. A plurality of reference areas are selected around the detection area to estimate the statistical characteristics of the background noise. A protection area is set between the detection area and the reference area to prevent the target signal energy from leaking into the reference area.

[0019] Based on Gaussian distribution, the statistical characteristics of background noise are estimated according to the signal data in the reference area, and the noise estimation is dynamically adjusted according to real-time environmental changes to adapt to different noise levels;

[0020] The detection threshold is calculated based on the noise estimation value and the predetermined false alarm rate, and the range of the protection area is dynamically adjusted according to the signal strength of the detection area to avoid interference with the target signal;

[0021] Compare the signal in the detection area with the calculated threshold. If the signal strength in the detection area is higher than the threshold, it is determined to be a target signal; otherwise, it is determined to be noise or clutter.

[0022] The detection results are fed back to the constant false alarm rate (CFAR) in real time to update noise estimation and threshold calculation. The detection threshold is dynamically adjusted according to real-time data and environmental changes to ensure stable detection performance in different clutter environments, thereby determining the fluid radar signal target data.

[0023] Preferably, the data acquisition window is compensated and adjusted according to the real-time data of the fluid radar signal, including:

[0024] Extract real-time data of fluid radar signals after steady-state filtering;

[0025] Comparing the real-time data of the fluid radar signal after the steady-state filtering process with the real-time data of the fluid radar signal before the steady-state filtering process to obtain a signal-to-noise ratio corresponding to the real-time data of the fluid radar signal;

[0026] Comparing the signal-to-noise ratio corresponding to the real-time data of the fluid radar signal with a preset signal-to-noise ratio threshold;

[0027] When the signal-to-noise ratio corresponding to the real-time data of the fluid radar signal is lower than a preset signal-to-noise ratio threshold, the real-time data of the fluid radar signal corresponding to the signal-to-noise ratio lower than the preset signal-to-noise ratio threshold is retrieved as the target real-time data;

[0028] Obtaining, for each target real-time data, a signal vector norm corresponding to the target real-time data;

[0029] The signal vector norm corresponding to each target real-time data is used to obtain the signal vector norm standard deviation;

[0030] Comparing the signal vector norm standard deviation with a preset standard deviation threshold;

[0031] When the standard deviation of the signal vector norm exceeds a preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal.

[0032] Preferably, when the standard deviation of the signal vector norm exceeds a preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal, including:

[0033] When the signal vector norm standard deviation exceeds a preset standard deviation threshold, obtaining an excess ratio (percentage) of the signal vector norm standard deviation exceeding the standard deviation threshold by using the signal vector norm standard deviation exceeding the preset standard deviation threshold;

[0034] Retrieve the signal-to-noise ratio corresponding to each target's real-time data;

[0035] The signal-to-noise ratio corresponding to each target real-time data and the preset signal-to-noise ratio threshold are used to obtain the ratio (percentage) of the signal-to-noise ratio corresponding to each target real-time data that is lower than the preset signal-to-noise ratio threshold;

[0036] Obtaining an average value of ratios of signal-to-noise ratios of all target real-time data below the preset signal-to-noise ratio threshold by using the ratio of the signal-to-noise ratio corresponding to each target real-time data below the preset signal-to-noise ratio threshold;

[0037] The data acquisition window is compensated and adjusted by utilizing the excess ratio in combination with the ratio average value to obtain an adjusted data acquisition window.

[0038] Preferably, a CFAR steady-state filtering flow velocity and direction detection model is established, including:

[0039] Collecting historical data of fluid radar signals and dividing the collected historical data of fluid radar signals into a training set and a test set;

[0040] Based on deep learning technology, a training set is used to train the deep learning model, so that the deep learning model can autonomously learn the CFAR steady-state filtering flow velocity and direction detection behavior from the training set, and predict the flow velocity and direction of the fluid, and determine the CFAR steady-state filtering flow velocity and direction detection model based on deep learning;

[0041] The test set is used to test the CFAR steady-state filtering flow velocity and direction detection model based on deep learning, and the performance of the CFAR steady-state filtering flow velocity and direction detection model based on deep learning is evaluated, so as to determine the optimal CFAR steady-state filtering flow velocity and direction detection model.

[0042] Preferably, determining the optimal CFAR steady-state filtered flow velocity and direction detection model includes:

[0043] The test set is input into the CFAR steady-state filtering flow velocity and direction detection model based on deep learning;

[0044] The deep learning-based CFAR steady-state filtering flow velocity and direction detection model was tested based on the test set to evaluate its performance and determine whether it can achieve the expected effect of predicting the flow velocity and direction of the fluid.

[0045] When the CFAR steady-state filtering flow velocity and direction detection model based on deep learning cannot achieve the expected effect of predicting the flow velocity and direction of the fluid, the parameters of the CFAR steady-state filtering flow velocity and direction detection model based on deep learning are continuously adjusted and optimized until the CFAR steady-state filtering flow velocity and direction detection model based on deep learning can achieve the expected effect of predicting the flow velocity and direction of the fluid, thereby determining the optimal CFAR steady-state filtering flow velocity and direction detection model.

[0046] Preferably, predicting the flow velocity and direction of the fluid and determining the CFAR steady-state filtered flow velocity and direction detection result includes:

[0047] Obtain the optimal CFAR steady-state filtering flow velocity and direction detection model, and deploy the optimal CFAR steady-state filtering flow velocity and direction detection model in the actual CFAR steady-state filtering flow velocity and direction detection environment;

[0048] The fluid radar signal target data is input into the CFAR steady-state filtering flow velocity and direction detection model. According to the CFAR steady-state filtering flow velocity and direction detection model, the fluid radar signal target data is analyzed and identified, and the flow velocity and direction of the fluid are predicted to determine the CFAR steady-state filtering flow velocity and direction detection results.

[0049] Preferably, the CFAR steady-state filter flow velocity and direction detection results are visually displayed, and the fluid is promptly controlled based on the CFAR steady-state filter flow velocity and direction detection results, including:

[0050] Obtain the CFAR steady-state filtered flow velocity and direction detection results, and display them to users in real time in a visual form. When there are abnormalities in the flow velocity and direction of the fluid, timely warnings are issued, allowing users to timely control the fluid based on the CFAR steady-state filtered flow velocity and direction detection results.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention collects real-time data of fluid radar signals through a radar sensor, performs steady-state filtering processing on the real-time data of the fluid radar signals through Kalman filtering, performs target detection on the real-time data of the fluid radar signals through a constant false alarm rate (CFAR), determines the target data of the fluid radar signals, analyzes and identifies the target data of the fluid radar signals by establishing a CFAR steady-state filtering flow velocity and direction detection model, predicts the flow velocity and direction of the fluid, determines the CFAR steady-state filtering flow velocity and direction detection result, and visually displays the CFAR steady-state filtering flow velocity and direction detection result. When the flow velocity and direction of the fluid are abnormal, a timely warning is issued, so that the user can timely control the fluid according to the CFAR steady-state filtering flow velocity and direction detection result. The flow velocity and direction of the fluid can be effectively detected according to the CFAR steady-state filtering, so that it can provide reliable flow velocity and direction detection results under various environmental conditions, and can improve the flow velocity and direction detection accuracy and flow velocity and direction detection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the CFAR steady-state filtering flow velocity and direction detection method of the present invention;

[0054] Figure 2 This is a flowchart for evaluating the performance of the deep learning-based CFAR steady-state filtering flow velocity and direction detection model of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] In order to solve the problem that the existing CFAR steady-state filtering cannot effectively detect the flow velocity and direction of the fluid, so that it cannot provide reliable flow velocity and direction detection results under various environmental conditions, resulting in low flow velocity and direction detection accuracy and poor flow velocity and direction detection effect, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0057] Example 1

[0058] A CFAR steady-state filtering flow velocity and direction detection method, comprising:

[0059] Collect and process real-time data of fluid radar signals to determine target data of fluid radar signals;

[0060] In this embodiment, collecting real-time data of fluid radar signals includes:

[0061] Install the radar sensor next to the fluid to be detected in an open area with no obstructions.

[0062] Real-time monitoring of the fluid to be detected is performed based on a radar sensor, in which the radar sensor transmits microwave or millimeter wave signals through the fluid and interacts with scattering objects in the fluid. The radar waves reflected back by the scattering objects in the fluid to be detected are received by the radar sensor, and the received radar signal is subjected to frequency analysis to extract the frequency information of the scattering objects and determine the real-time data of the fluid radar signal.

[0063] In this embodiment, the real-time data of the fluid radar signal is processed, including:

[0064] Based on Kalman filtering, steady-state filtering is performed on the real-time data of fluid radar signals to filter out specific band frequencies in the real-time data of fluid radar signals that are worthless for CFAR steady-state filtering flow velocity and direction detection, thereby eliminating noise and interference in the real-time data of fluid radar signals.

[0065] In this embodiment, processing the real-time data of the fluid radar signal also includes:

[0066] Target detection is performed on real-time data of fluid radar signals based on constant false alarm rate (CFAR);

[0067] The window selection is performed based on the real-time data of the fluid radar signal. A detection area is selected in the real-time data of the fluid radar signal to determine whether a target signal exists. A plurality of reference areas are selected around the detection area to estimate the statistical characteristics of the background noise. A protection area is set between the detection area and the reference area to prevent the target signal energy from leaking into the reference area.

[0068] Based on Gaussian distribution, the statistical characteristics of background noise are estimated according to the signal data in the reference area, and the noise estimation is dynamically adjusted according to real-time environmental changes to adapt to different noise levels;

[0069] The detection threshold is calculated based on the noise estimation value and the predetermined false alarm rate, and the range of the protection area is dynamically adjusted according to the signal strength of the detection area to avoid interference with the target signal;

[0070] Compare the signal in the detection area with the calculated threshold. If the signal strength in the detection area is higher than the threshold, it is determined to be a target signal; otherwise, it is determined to be noise or clutter.

[0071] The detection results are fed back to the constant false alarm rate (CFAR) in real time to update noise estimation and threshold calculation. The detection threshold is dynamically adjusted according to real-time data and environmental changes to ensure stable detection performance in different clutter environments, thereby determining the fluid radar signal target data.

[0072] Specifically, constant false alarm rate (CFAR) is an adaptive detection technology that maximizes the probability of target detection while keeping the false alarm rate constant by dynamically adjusting the detection threshold. It utilizes the characteristics of background noise and clutter to adaptively calculate the detection threshold, thereby achieving robust signal detection in complex environments.

[0073] It should be noted that the constant false alarm rate (CFAR) is used to detect targets in real-time data of fluid radar signals. By analyzing background clutter, the detection threshold is dynamically adjusted according to the environmental noise level to ensure that targets can still be accurately detected in complex environments. It can accurately detect the speed and direction of the fluid while maintaining a constant false alarm rate, and can be used for flow velocity and direction monitoring in complex waters such as rivers and oceans.

[0074] Establish a CFAR steady-state filter flow velocity and direction detection model, analyze and identify the fluid radar signal target data, predict the fluid flow velocity and direction, and determine the CFAR steady-state filter flow velocity and direction detection results;

[0075] In this embodiment, a CFAR steady-state filtering flow velocity and direction detection model is established, including:

[0076] Collecting historical data of fluid radar signals and dividing the collected historical data of fluid radar signals into a training set and a test set;

[0077] Based on deep learning technology, a training set is used to train the deep learning model, so that the deep learning model can autonomously learn the CFAR steady-state filtering flow velocity and direction detection behavior from the training set, and predict the flow velocity and direction of the fluid, and determine the CFAR steady-state filtering flow velocity and direction detection model based on deep learning;

[0078] The test set is used to test the CFAR steady-state filtering flow velocity and direction detection model based on deep learning, and the performance of the CFAR steady-state filtering flow velocity and direction detection model based on deep learning is evaluated, so as to determine the optimal CFAR steady-state filtering flow velocity and direction detection model.

[0079] In this embodiment, determining the optimal CFAR steady-state filtering flow velocity and direction detection model includes:

[0080] The test set is input into the CFAR steady-state filtering flow velocity and direction detection model based on deep learning;

[0081] The deep learning-based CFAR steady-state filtering flow velocity and direction detection model was tested based on the test set to evaluate its performance and determine whether it can achieve the expected effect of predicting the flow velocity and direction of the fluid.

[0082] When the CFAR steady-state filtering flow velocity and direction detection model based on deep learning cannot achieve the expected effect of predicting the flow velocity and direction of the fluid, the parameters of the CFAR steady-state filtering flow velocity and direction detection model based on deep learning are continuously adjusted and optimized until the CFAR steady-state filtering flow velocity and direction detection model based on deep learning can achieve the expected effect of predicting the flow velocity and direction of the fluid, thereby determining the optimal CFAR steady-state filtering flow velocity and direction detection model.

[0083] In this embodiment, the flow velocity and direction of the fluid are predicted to determine the CFAR steady-state filtered flow velocity and direction detection result, including:

[0084] Obtain the optimal CFAR steady-state filtering flow velocity and direction detection model, and deploy the optimal CFAR steady-state filtering flow velocity and direction detection model in the actual CFAR steady-state filtering flow velocity and direction detection environment;

[0085] The fluid radar signal target data is input into the CFAR steady-state filtering flow velocity and direction detection model. According to the CFAR steady-state filtering flow velocity and direction detection model, the fluid radar signal target data is analyzed and identified, and the flow velocity and direction of the fluid are predicted to determine the CFAR steady-state filtering flow velocity and direction detection results.

[0086] It should be noted that by establishing a CFAR steady-state filtering flow velocity and direction detection model to analyze and identify the fluid radar signal target data, and predict the flow velocity and direction of the fluid, the CFAR steady-state filtering flow velocity and direction detection results are determined, which can facilitate users to timely control the fluid based on the CFAR steady-state filtering flow velocity and direction detection results.

[0087] Example 2

[0088] Compensate and adjust the data acquisition window based on the real-time data of the fluid radar signal, including:

[0089] Extract real-time data of fluid radar signals after steady-state filtering;

[0090] Comparing the real-time data of the fluid radar signal after the steady-state filtering process with the real-time data of the fluid radar signal before the steady-state filtering process to obtain a signal-to-noise ratio corresponding to the real-time data of the fluid radar signal;

[0091] Comparing the signal-to-noise ratio corresponding to the real-time data of the fluid radar signal with a preset signal-to-noise ratio threshold;

[0092] When the signal-to-noise ratio corresponding to the real-time data of the fluid radar signal is lower than a preset signal-to-noise ratio threshold, the real-time data of the fluid radar signal corresponding to the signal-to-noise ratio lower than the preset signal-to-noise ratio threshold is retrieved as the target real-time data;

[0093] Obtaining, for each target real-time data, a signal vector norm corresponding to the target real-time data;

[0094] The signal vector norm corresponding to each target real-time data is used to obtain the signal vector norm standard deviation;

[0095] Comparing the signal vector norm standard deviation with a preset standard deviation threshold;

[0096] When the standard deviation of the signal vector norm exceeds a preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal.

[0097] The technical effect of the above technical solution is that by extracting real-time fluid radar signal data before and after steady-state filtering and comparing the signal-to-noise ratio (SNR), signal quality can be effectively assessed. By comparing the SNR with a preset threshold, target real-time data with low SNRs can be filtered out, avoiding measurement errors caused by poor signal quality and improving data reliability and accuracy. The signal vector norm and standard deviation of the target real-time data are calculated to further characterize the signal characteristics. Compensation adjustments are made when the standard deviation of the signal vector norm exceeds a preset threshold, identifying and addressing abnormal signal fluctuations, ensuring more stable and accurate data acquisition, and reducing the impact of noise interference. This solution compensates and adjusts the data acquisition window based on the SNR and SNR, adapting to different operating scenarios and changing signal environments. For example, in complex situations such as signal interference, the acquisition window can be adjusted promptly to ensure the system continuously and stably acquires high-quality data, enhancing the system's adaptability to complex operating conditions. This signal quality assessment and processing can specifically address measurement errors caused by signal problems, making fluid radar signal measurements more accurate, ensuring the precision of measurement results such as flow velocity and flow rate, and improving the performance of the entire fluid radar measurement system.

[0098] Specifically, when the standard deviation of the signal vector norm exceeds a preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal, including:

[0099] When the signal vector norm standard deviation exceeds a preset standard deviation threshold, obtaining an excess ratio (percentage) of the signal vector norm standard deviation exceeding the standard deviation threshold by using the signal vector norm standard deviation exceeding the preset standard deviation threshold;

[0100] Retrieve the signal-to-noise ratio corresponding to each target's real-time data;

[0101] The signal-to-noise ratio corresponding to each target real-time data and the preset signal-to-noise ratio threshold are used to obtain the ratio (percentage) of the signal-to-noise ratio corresponding to each target real-time data that is lower than the preset signal-to-noise ratio threshold;

[0102] Obtaining an average value of ratios of signal-to-noise ratios of all target real-time data below the preset signal-to-noise ratio threshold by using the ratio of the signal-to-noise ratio corresponding to each target real-time data below the preset signal-to-noise ratio threshold;

[0103] The data acquisition window is compensated and adjusted by utilizing the excess ratio in combination with the ratio average value to obtain an adjusted data acquisition window.

[0104] The adjusted data acquisition window is obtained by the following formula:

[0105]

[0106] Where E represents the data collection window after adjustment; E0 represents the data collection window before adjustment; ||S|| b represents the ratio of the signal vector norm standard deviation exceeding the standard deviation threshold; η represents the expansion gain coefficient, ranging from 0.12 to 0.83; b represents the transition zone smoothing factor, ranging from 0.5 to 1.2; P represents the average ratio of the signal-to-noise ratio corresponding to all target real-time data below the preset signal-to-noise ratio threshold. is an exponential decay function. decreases with the increase of . It is converted into a function that varies from 0 to 1, which is used to quantify the adjustment ratio of the acquisition window under the combined influence of signal fluctuation and signal quality. The closer the value is to 1, the larger the window adjustment; the closer it is to 0, the smaller the adjustment. This formula can adaptively adjust the data acquisition window size based on two key indicators: the ratio of the standard deviation of the signal vector norm exceeding the threshold and the average value of the signal-to-noise ratio below the threshold. When the signal fluctuation is large (||S|| b When the signal-to-noise ratio (P) is large and the signal-to-noise ratio (SNR) is low (P), the acquisition window is increased to acquire more data, improving signal quality and measurement accuracy. Conversely, when the signal conditions are good, the window size is appropriately reduced or maintained to avoid wasting resources and enable the system to adapt to different signal environments. The smoothing factor b in the transition region ensures the smoothness of the data acquisition window adjustment process. This prevents excessive or frequent window adjustments due to small signal fluctuations or slight changes in the signal-to-noise ratio, enhancing system stability and reducing measurement errors or system instability that may be caused by sudden window changes. Combining the expansion gain factor η with a calculation method that comprehensively considers signal fluctuations and the signal-to-noise ratio enables precise control of the data acquisition window. Under varying signal conditions, the appropriate window adjustment range can be determined based on quantitative indicators, improving the quality of the acquired data and, in turn, the accuracy and reliability of subsequent analysis and measurements based on this data.

[0107] The technical solution described above quantifies signal fluctuation and signal quality by calculating the average of the signal vector norm standard deviation (SNV) and the signal-to-noise ratio (SNR) below a threshold. Compensating and adjusting the data acquisition window based on these two quantitative metrics allows for more precise optimization of signal issues, reducing errors caused by noise interference and signal fluctuations, and thus improving data accuracy. Dynamically adjusting the data acquisition window based on actual signal conditions (such as signal fluctuation and SNR) under different operating conditions allows the system to adapt to signal changes. Compared to fixed-parameter acquisition windows, this dynamic adjustment method better matches actual signal requirements, ensuring the consistency of acquired data with the real signal, and further improving data accuracy. This technical solution establishes an adaptive feedback mechanism. When the signal vector norm standard deviation and SNR show anomalies, the system automatically adjusts the data acquisition window based on these quantified metrics. This mechanism effectively addresses sudden signal changes or environmental interference, reduces measurement fluctuations or errors caused by signal issues, and enhances system stability. By comprehensively considering signal fluctuations and SNR, it can more comprehensively handle various complex signal scenarios. Whether the signal is affected by external interference, resulting in a reduced signal-to-noise ratio, or by abnormal signal fluctuations, the system can compensate by adjusting the acquisition window, improving its interference resistance and robustness in complex environments. By evaluating signal fluctuation and signal quality, and adjusting the acquisition window accordingly, the system avoids the limitations of single-metric evaluation. This multi-dimensional, comprehensive evaluation and optimization more reliably ensures the validity of the collected data, thereby improving the reliability of subsequent measurements and analysis based on this data, resulting in more trustworthy measurement results.

[0108] Example 3

[0109] Visually display the CFAR steady-state filtering flow velocity and direction detection results, and timely control the fluid based on the CFAR steady-state filtering flow velocity and direction detection results.

[0110] In this embodiment, the CFAR steady-state filtered flow velocity and direction detection results are visually displayed, and the fluid is promptly controlled based on the CFAR steady-state filtered flow velocity and direction detection results, including:

[0111] Obtain the CFAR steady-state filtered flow velocity and direction detection results, and display them to users in real time in a visual form. When there are abnormalities in the flow velocity and direction of the fluid, timely warnings are issued, allowing users to timely control the fluid based on the CFAR steady-state filtered flow velocity and direction detection results.

[0112] In summary, the real-time data of the fluid radar signal is collected by the radar sensor, and the real-time data of the fluid radar signal is subjected to steady-state filtering processing by the Kalman filter. The real-time data of the fluid radar signal is subjected to target detection by the constant false alarm rate (CFAR), and the target data of the fluid radar signal is determined. The target data of the fluid radar signal is analyzed and identified by establishing a CFAR steady-state filtering flow velocity and direction detection model, and the flow velocity and direction of the fluid are predicted, and the flow velocity and direction of the fluid are determined. The flow velocity and direction detection results of the CFAR steady-state filtering are determined, and the flow velocity and direction detection results of the CFAR steady-state filtering are visualized. When there are abnormal conditions in the flow velocity and direction of the fluid, timely warnings are issued, so that users can timely control the fluid according to the flow velocity and direction detection results of the CFAR steady-state filtering. The flow velocity and direction of the fluid can be effectively detected according to the CFAR steady-state filtering, so that it can provide reliable flow velocity and direction detection results under various environmental conditions, which can improve the accuracy and effect of flow velocity and direction detection.

[0113] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0114] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting flow velocity and direction using a CFAR steady-state filter, characterized in that: include: Collect and process real-time data of fluid radar signals to determine target data of fluid radar signals; When the standard deviation of the signal vector norm exceeds the preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal; Establish a CFAR steady-state filter flow velocity and direction detection model, analyze and identify the fluid radar signal target data, predict the fluid flow velocity and direction, and determine the CFAR steady-state filter flow velocity and direction detection results; Visually display the CFAR steady-state filtering flow velocity and direction detection results, and timely control the fluid based on the CFAR steady-state filtering flow velocity and direction detection results.

2. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 1, characterized in that: Collect real-time data of fluid radar signals, including: Install the radar sensor next to the fluid to be detected in an open area with no obstructions. Real-time monitoring of the fluid to be detected is performed based on a radar sensor, in which the radar sensor transmits microwave or millimeter wave signals through the fluid and interacts with scattering objects in the fluid. The radar waves reflected back by the scattering objects in the fluid to be detected are received by the radar sensor, and the received radar signal is subjected to frequency analysis to extract the frequency information of the scattering objects and determine the real-time data of the fluid radar signal.

3. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 1, characterized in that: Processing of real-time data of fluid radar signals, including: Based on Kalman filtering, steady-state filtering is performed on the real-time data of fluid radar signals to filter out specific band frequencies in the real-time data of fluid radar signals that are worthless for CFAR steady-state filtering flow velocity and direction detection, thereby eliminating noise and interference in the real-time data of fluid radar signals.

4. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 1, characterized in that: Processing of real-time data of fluid radar signals also includes: Target detection is performed on real-time data of fluid radar signals based on constant false alarm rate (CFAR); The window selection is performed based on the real-time data of the fluid radar signal. A detection area is selected in the real-time data of the fluid radar signal to determine whether a target signal exists. A plurality of reference areas are selected around the detection area to estimate the statistical characteristics of the background noise. A protection area is set between the detection area and the reference area to prevent the target signal energy from leaking into the reference area. Based on Gaussian distribution, the statistical characteristics of background noise are estimated according to the signal data in the reference area, and the noise estimation is dynamically adjusted according to real-time environmental changes to adapt to different noise levels; The detection threshold is calculated based on the noise estimation value and the predetermined false alarm rate, and the range of the protection area is dynamically adjusted according to the signal strength of the detection area to avoid interference with the target signal; Compare the signal in the detection area with the calculated threshold. If the signal strength in the detection area is higher than the threshold, it is determined to be a target signal; otherwise, it is determined to be noise or clutter. The detection results are fed back to the constant false alarm rate (CFAR) in real time to update noise estimation and threshold calculation. The detection threshold is dynamically adjusted according to real-time data and environmental changes to ensure stable detection performance in different clutter environments, thereby determining the fluid radar signal target data.

5. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 4, characterized in that: Compensate and adjust the data acquisition window based on the real-time data of the fluid radar signal, including: Extract real-time data of fluid radar signals after steady-state filtering; Comparing the real-time data of the fluid radar signal after the steady-state filtering process with the real-time data of the fluid radar signal before the steady-state filtering process to obtain a signal-to-noise ratio corresponding to the real-time data of the fluid radar signal; Comparing the signal-to-noise ratio corresponding to the real-time data of the fluid radar signal with a preset signal-to-noise ratio threshold; When the signal-to-noise ratio corresponding to the real-time data of the fluid radar signal is lower than a preset signal-to-noise ratio threshold, the real-time data of the fluid radar signal corresponding to the signal-to-noise ratio lower than the preset signal-to-noise ratio threshold is retrieved as the target real-time data; Obtaining, for each target real-time data, a signal vector norm corresponding to the target real-time data; The signal vector norm corresponding to each target real-time data is used to obtain the signal vector norm standard deviation; Comparing the signal vector norm standard deviation with a preset standard deviation threshold; When the standard deviation of the signal vector norm exceeds a preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal.

6. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 5, characterized in that: When the standard deviation of the signal vector norm exceeds a preset standard deviation threshold, the data acquisition window is compensated and adjusted according to the current real-time data of the fluid radar signal, including: When the signal vector norm standard deviation exceeds a preset standard deviation threshold, obtaining an excess ratio of the signal vector norm standard deviation exceeding the standard deviation threshold by using the signal vector norm standard deviation exceeding the preset standard deviation threshold; Retrieve the signal-to-noise ratio corresponding to each target's real-time data; Using the signal-to-noise ratio corresponding to each target real-time data and a preset signal-to-noise ratio threshold, obtaining a ratio of the signal-to-noise ratio corresponding to each target real-time data that is lower than the preset signal-to-noise ratio threshold; Obtaining an average value of ratios of signal-to-noise ratios of all target real-time data below the preset signal-to-noise ratio threshold by using the ratio of the signal-to-noise ratio corresponding to each target real-time data below the preset signal-to-noise ratio threshold; The data acquisition window is compensated and adjusted using the excess ratio in combination with the ratio average value to obtain an adjusted data acquisition window.

7. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 1, characterized in that: Establish a CFAR steady-state filtering flow velocity and direction detection model, including: Collecting historical data of fluid radar signals and dividing the collected historical data of fluid radar signals into a training set and a test set; Based on deep learning technology, a training set is used to train the deep learning model, so that the deep learning model can autonomously learn the CFAR steady-state filtering flow velocity and direction detection behavior from the training set, and predict the flow velocity and direction of the fluid, and determine the CFAR steady-state filtering flow velocity and direction detection model based on deep learning; The test set is used to test the CFAR steady-state filtering flow velocity and direction detection model based on deep learning, and the performance of the CFAR steady-state filtering flow velocity and direction detection model based on deep learning is evaluated, so as to determine the optimal CFAR steady-state filtering flow velocity and direction detection model.

8. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 7, characterized in that: Determine the optimal CFAR steady-state filtering flow velocity and direction detection model, including: The test set is input into the CFAR steady-state filtering flow velocity and direction detection model based on deep learning; The deep learning-based CFAR steady-state filtering flow velocity and direction detection model was tested based on the test set to evaluate its performance and determine whether it can achieve the expected effect of predicting the flow velocity and direction of the fluid. When the CFAR steady-state filtering flow velocity and direction detection model based on deep learning cannot achieve the expected effect of predicting the flow velocity and direction of the fluid, the parameters of the CFAR steady-state filtering flow velocity and direction detection model based on deep learning are continuously adjusted and optimized until the CFAR steady-state filtering flow velocity and direction detection model based on deep learning can achieve the expected effect of predicting the flow velocity and direction of the fluid, thereby determining the optimal CFAR steady-state filtering flow velocity and direction detection model.

9. A CFAR steady-state filtering flow velocity and direction detection method as claimed in claim 8, characterized in that: Predict the flow velocity and direction of the fluid and determine the CFAR steady-state filtering flow velocity and direction detection results, including: Obtain the optimal CFAR steady-state filtering flow velocity and direction detection model, and deploy the optimal CFAR steady-state filtering flow velocity and direction detection model in the actual CFAR steady-state filtering flow velocity and direction detection environment; The fluid radar signal target data is input into the CFAR steady-state filtering flow velocity and direction detection model. According to the CFAR steady-state filtering flow velocity and direction detection model, the fluid radar signal target data is analyzed and identified, and the flow velocity and direction of the fluid are predicted to determine the CFAR steady-state filtering flow velocity and direction detection results.

10. A CFAR steady-state filtering flow velocity and direction detection method according to claim 1, characterized in that: Visually display the CFAR steady-state filter flow velocity and direction detection results, and timely control the fluid based on the CFAR steady-state filter flow velocity and direction detection results, including: Obtain the CFAR steady-state filtered flow velocity and direction detection results, and display them to users in real time in a visual form. When there are abnormalities in the flow velocity and direction of the fluid, timely warnings are issued, allowing users to timely control the fluid based on the CFAR steady-state filtered flow velocity and direction detection results.

Images