A Doppler flow measurement method for drainage pipe networks coupling multiple physical fields

Abstract

The present invention relates to the technical field of drainage network flow measurement, and discloses a Doppler flow measurement method for drainage network coupled with multiple physical fields. The method comprises: obtaining fluid flow data of the drainage network, performing Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results, and establishing a multi-physical field model based on the data analysis results. The multi-physical field model is obtained by coupling the acoustic field, the flow field, and the temperature field. Drainage network flow measurement is performed based on the multi-physical field model. The present invention achieves accurate flow measurement in a non-full pipe flow state through Doppler frequency shift analysis. At the same time, by comprehensively considering the coupling effects of multiple physical fields such as acoustics, fluid mechanics, and thermodynamics, a multi-physical field coupling analysis is performed to achieve high-precision measurement of fluid flow in the drainage network.

Description

Technical Field

[0001] The present invention relates to the technical field of drainage pipe network flow measurement, and in particular to a drainage pipe network Doppler flow measurement method coupled with multiple physical fields. Background Art

[0002] Drainage networks, a vital component of urban infrastructure, collect and transport rainwater, domestic sewage, and industrial wastewater. Accurate flow measurement is crucial for monitoring drainage system performance, preventing floods, rationally planning urban water resource management, and complying with environmental regulations.

[0003] The flow measurement technologies currently on the market mainly include mechanical flowmeters, electromagnetic flowmeters, ultrasonic flowmeters, etc. These technologies have demonstrated good performance in certain application scenarios. However, although ultrasonic flowmeters are non-invasive, their measurement accuracy is limited by signal processing technology and accurate detection of Doppler frequency shift under complex flow conditions. Moreover, under certain conditions, such as the non-full pipe flow state in the drainage network, their accuracy and reliability will be limited. Specifically, in the drainage network, especially in the rainy or dry season, the flow rate varies greatly, and the non-full pipe flow state often occurs. In this state, the flow characteristics of the fluid are complex and changeable, including uneven velocity distribution, turbulence, eddy currents and other phenomena, making it difficult for traditional flow measurement technology to accurately measure the actual flow rate.

[0004] Furthermore, fluid flow in drainage networks is not only influenced by fluid dynamics but is also closely related to factors such as acoustic properties and temperature distribution. Traditional technologies often overlook the coupling effects between these physical fields, resulting in inaccurate flow measurements under complex flow conditions. Summary of the Invention

[0005] In view of this, the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields to solve the flow measurement problem of non-full pipe flow under the coupling effect of multiple physical fields that traditional measurement methods fail to take into account.

[0006] In a first aspect, the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields, the method comprising:

[0007] Acquiring fluid flow data of the drainage network; the fluid flow data of the drainage network includes at least one of velocity, temperature, and pressure;

[0008] Perform Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results;

[0009] Establish a multi-physics field model based on the data analysis results; the multi-physics field model is obtained by coupling the acoustic field, flow field and temperature field;

[0010] Flow measurement in drainage networks based on multi-physics models.

[0011] An embodiment of the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields. Based on Doppler frequency shift analysis, it can accurately capture the frequency changes of reflected waves of tiny particles or bubbles in the fluid. At the same time, combined with a multi-physical field model, it not only considers fluid dynamics factors, but also considers the influence of coupling effects between multiple physical fields such as acoustic fields and temperature fields, thereby more accurately reflecting the actual flow conditions of the drainage network, and enabling the measurement results to more comprehensively and deeply reflect the operating status of the network, providing richer data support for network management, optimization and decision-making.

[0012] In an optional embodiment, performing Doppler shift analysis on fluid flow data of the drainage network to obtain data analysis results includes:

[0013] Calculate Doppler shift data based on fluid flow data of the drainage network;

[0014] Perform high-order statistical analysis on Doppler frequency shift data to obtain skewness and kurtosis data;

[0015] The Doppler frequency shift data is reconstructed and combined with the skewness and kurtosis data to obtain the data analysis results.

[0016] The present invention provides a Doppler flow measurement method for drainage networks that couples multiple physical fields. Doppler frequency shift analysis accurately captures the frequency changes of reflected waves from tiny particles or bubbles in the fluid. High-order statistical analysis is then added to obtain skewness and kurtosis data to further reveal the asymmetry and peakedness of the data distribution, helping to identify and eliminate outliers or noise, thereby improving data quality and measurement accuracy. Furthermore, by combining high-order statistics with wavelet-transformed signal reconstruction results, the non-Gaussian and multi-scale characteristics of the fluid velocity distribution are analyzed, providing deeper insights into flow measurement.

[0017] In an optional embodiment, the method further includes:

[0018] Based on the fluid flow data of the drainage network, a dynamic fluid medium model is established;

[0019] The fluid medium state is predicted based on the model to obtain the prediction results.

[0020] An embodiment of the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields. Through a dynamic fluid medium model, it can more accurately simulate and predict the real-time state of the fluid in the drainage network, including the changing trends of parameters such as flow velocity, flow rate, and temperature. This method not only takes into account the current fluid flow data, but also combines historical data and physical field coupling effects, and can therefore more accurately reflect the future operating conditions of the drainage network.

[0021] In an optional embodiment, the method further includes:

[0022] Identify abnormal conditions based on prediction results;

[0023] When an abnormal state is identified, an early warning message is issued.

[0024] An embodiment of the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields. By identifying abnormal conditions based on the predicted results of the fluid medium state in real time, it is possible to promptly discover possible problems in the drainage network, such as blockage, leakage, abnormal flow, etc. Once an abnormal condition is identified, an early warning message is immediately issued, which helps to quickly respond and take measures to solve the problem, thereby preventing the problem from expanding or causing greater losses.

[0025] In an optional embodiment, establishing a multi-physics field model based on the data analysis results includes:

[0026] Based on the fluid flow data of the drainage network, mathematical models of the acoustic field, flow field and temperature field are established respectively;

[0027] Based on the temperature data, the coupling relationship between the mathematical models of the acoustic field, flow field and temperature field is established to obtain the multi-physics field equations.

[0028] An embodiment of the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields. By establishing mathematical models of the acoustic field, flow field, and temperature field respectively, and constructing multi-physical field equations based on the coupling relationship between these models, the interaction between different physical fields can be considered in more detail. The coupling analysis based on multiple physical fields can more accurately reflect the complex flow state of the fluid in the drainage network, including changes in fluid properties such as density and viscosity caused by temperature changes, as well as the influence of the acoustic field on the fluid flow, thereby improving the accuracy of flow measurement.

[0029] In an optional embodiment, establishing a dynamic fluid medium model based on fluid flow data of the drainage network further includes:

[0030] Obtain environmental parameter data;

[0031] The parameters of the dynamic fluid medium model are updated based on the environmental parameter data to obtain a target dynamic fluid medium model.

[0032] The present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields. This method takes into account that the operating conditions and environmental conditions of a drainage network may vary over time and geographical location, and that environmental parameters such as rainfall, temperature, humidity, and wind speed have a significant impact on the physical properties of the fluid medium. Therefore, by acquiring these environmental parameters in real time or periodically and updating the parameters of a dynamic fluid medium model accordingly, it is possible to ensure that the model more accurately reflects the actual state of the current fluid medium. Dynamically updating the model parameters also makes the model more adaptable. Based on an accurate and adaptable dynamic fluid medium model, better predictions of the fluid medium state can be made.

[0033] In an optional embodiment, the drainage network flow measurement is performed based on a multi-physics field model, including:

[0034] The drainage network flow was measured based on the multi-physics model, and preliminary measurement results were obtained;

[0035] According to the preliminary measurement results and the adaptive algorithm, the parameters are adjusted to obtain the adjusted multi-physics field model;

[0036] Based on the adjusted multi-physics field model, the drainage network flow measurement is carried out to obtain the target measurement results.

[0037] In an embodiment of the present invention, a Doppler flow measurement method for drainage networks coupled with multiple physical fields is provided. Initial measurement results may be affected by a variety of factors, such as the initial settings of model parameters and slight changes in environmental conditions. By adjusting model parameters through an adaptive algorithm, the model can be gradually optimized to more accurately reflect actual fluid flow conditions, thereby improving flow measurement accuracy. Furthermore, the adaptive algorithm allows the model to automatically learn and adapt to these changes, maintaining high measurement accuracy under various operating conditions and enhancing the model's adaptability and robustness.

[0038] In a second aspect, the present invention provides a Doppler flow measurement device for a drainage network coupled with multiple physical fields, the device comprising:

[0039] The acquisition module is used to acquire fluid flow data of the drainage network; the fluid flow data of the drainage network includes at least one of velocity, temperature, and pressure.

[0040] The frequency shift analysis module is used to perform Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results.

[0041] A module is established to establish a multi-physics field model based on data analysis results; the multi-physics field model is obtained based on the coupling of acoustic field, flow field and temperature field.

[0042] The measurement module is used to measure the flow rate of drainage pipe networks based on multi-physics field models.

[0043] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute a Doppler flow measurement method for a drainage network coupled with multiple physical fields according to the first aspect or any corresponding embodiment thereof.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute a Doppler flow measurement method for a drainage network coupled with multiple physical fields according to the first aspect or any corresponding embodiment thereof.

[0045] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute a Doppler flow measurement method for a drainage network coupled with multiple physical fields according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 1 is a flow chart of a Doppler flow measurement method for a drainage network coupled with multiple physical fields according to an embodiment of the present invention;

[0048] Figure 2 is a data flow diagram of a Doppler flow measurement method for a drainage network coupled with multiple physical fields according to an embodiment of the present invention;

[0049] Figure 3 It is a technical roadmap of a Doppler flow measurement method for a drainage network coupled with multi-physics fields according to an embodiment of the present invention;

[0050] Figure 4 1 is a structural block diagram of a Doppler flow measurement device for a drainage network coupled with multiple physical fields according to an embodiment of the present invention;

[0051] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0053] The flow measurement technologies currently on the market primarily include mechanical flowmeters, electromagnetic flowmeters, and ultrasonic flowmeters. These technologies have demonstrated good performance in certain application scenarios. However, while ultrasonic flowmeters are non-invasive, their measurement accuracy under complex flow conditions is limited by signal processing techniques and the accurate detection of Doppler shift. Mechanical flowmeters are susceptible to wear and tear, have high maintenance costs, and are inaccurate under partially filled pipe flow conditions. Electromagnetic flowmeters have requirements for fluid conductivity, and measurement results are easily affected by the presence of bubbles or suspended solid particles. While ultrasonic flowmeters are non-invasive, their measurement accuracy under complex flow conditions is limited by signal processing techniques and the accurate detection of Doppler shift.

[0054] Furthermore, their accuracy and reliability are limited under certain conditions, such as under partially filled flow conditions in drainage networks. Specifically, in drainage networks, particularly during rainy and dry seasons, flow rates vary significantly, and partially filled flow conditions often occur. Under these conditions, the fluid's flow characteristics are complex and variable, including uneven velocity distribution, turbulence, and eddies, making it difficult for traditional flow measurement technologies to accurately measure actual flow.

[0055] Furthermore, fluid flow in drainage networks is not only influenced by fluid dynamics but is also closely related to factors such as acoustic properties and temperature distribution. Traditional technologies often overlook the coupling effects between these physical fields, resulting in inaccurate flow measurements under complex flow conditions.

[0056] Therefore, an embodiment of the present invention provides a Doppler flow measurement method for a drainage network coupled with multiple physical fields, which achieves high-precision measurement of fluid flow in a drainage network by introducing high-order statistical methods to analyze Doppler frequency shift and perform multi-physical field coupling analysis.

[0057] According to an embodiment of the present invention, an embodiment of a Doppler flow measurement method for a drainage network coupled with multiple physical fields is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] In this embodiment, a Doppler flow measurement method for a drainage network coupled with multiple physical fields is provided. Figure 1 FIG. 1 is a flow chart of a Doppler flow measurement method for a drainage network coupled with multiple physical fields according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0059] Step S101: Acquire fluid flow data of the drainage network.

[0060] Specifically, ultrasonic transducers or other sensors are first used to collect signals generated by fluid flow as fluid flow data of the drainage network, including at least one of velocity, temperature, and pressure. Then, the collected data is preprocessed by denoising, filtering, and other operations to improve data quality.

[0061] Furthermore, in order to ensure the effectiveness of data acquisition, it is necessary to select appropriate locations in the drainage network to be tested for data collection to ensure that the acquired data can fully reflect the fluid flow status of the network. Therefore, it is necessary to install the ultrasonic transducer array at the key nodes of the drainage network to be tested to capture the Doppler frequency shift signal generated by the fluid flow.

[0062] Furthermore, by considering factors such as the propagation distance of sound waves in the fluid, the beam width, and the fluid velocity, the transducer spacing is optimized to determine the optimal transducer spacing. Preferably, the transducer spacing should be smaller than the beam width, where the beam width θ is calculated as θ = λ / d (λ is the wavelength of the sound wave, and d is the transducer diameter) to avoid measurement blind spots. At the same time, the layout is designed so that it can transmit and receive sound waves from different angles to achieve multi-angle measurement.

[0063] Step S102: performing Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results.

[0064] Specifically, in drainage pipe networks, tiny particles, bubbles, or impurities in the fluid often serve as reflection sources for Doppler signals. For weaker signals, signal enhancement techniques, such as amplification and wavelet transform, are employed to improve signal visibility and accuracy. The reflected wave signals generated by these reflection sources are then extracted to reflect changes in fluid velocity.

[0065] Furthermore, the Doppler effect, which uses the principle of ultrasound, indicates that when ultrasound encounters a scatterer in a moving fluid, a Doppler shift occurs. This means that the frequency of the reflected wave changes according to the speed of the scatterer, and the magnitude of the frequency shift is proportional to the speed of the scatterer. The frequency change of the reflected wave relative to the transmitted wave, known as the Doppler shift, is calculated. The frequency components of the signal are then determined by performing spectrum analysis techniques such as fast Fourier transform on the signal.

[0066] Furthermore, the Doppler frequency shift is converted to fluid velocity using the Doppler equation based on the known fluid properties, such as fluid density and sound speed. The resulting fluid velocity, temperature, pressure, and other parameters are then organized to facilitate subsequent multi-physics model development and flow measurement.

[0067] Optionally, cross-validation can be performed with measurement results from other measurement methods, such as flow meters, speed meters, etc., to ensure the reliability of the Doppler shift analysis results.

[0068] In some optional implementations, the above step S102 includes:

[0069] Calculate Doppler shift data based on fluid flow data of the drainage network;

[0070] Perform high-order statistical analysis on Doppler frequency shift data to obtain skewness and kurtosis data;

[0071] The Doppler frequency shift data is reconstructed and combined with the skewness and kurtosis data to obtain the data analysis results.

[0072] Specifically, high-order statistical methods are introduced to analyze Doppler shifts to capture subtle changes and turbulent characteristics in the fluid, thereby improving measurement accuracy and resolution. Specifically, high-order statistical analysis is performed on the Doppler shift data to calculate skewness and kurtosis to identify the characteristics of the fluid velocity distribution. At the same time, appropriate wavelet basis functions are selected, and Daubechies wavelets and Haar wavelets are used to perform multi-scale decomposition of the Doppler shift signal to obtain velocity variation information at different time scales. The signal is then reconstructed based on the wavelet transform coefficients to extract key features of the fluid velocity variation. Combining high-order statistics with wavelet transform results, the non-Gaussian and multi-scale characteristics of the fluid velocity distribution are analyzed to obtain data analysis results.

[0073] Step S103: establishing a multi-physics field model based on the data analysis results.

[0074] Specifically, the multi-physics field model is obtained based on the coupling of acoustic field, flow field and temperature field.

[0075] In some optional implementations, the above step S103 includes:

[0076] Step a1: Based on the fluid flow data of the drainage network, mathematical models of the acoustic field, flow field and temperature field are established respectively;

[0077] Step a2: establishing a coupling relationship between the mathematical models of the acoustic field, flow field, and temperature field based on the temperature data to obtain a multi-physics field equation.

[0078] Mathematical models of the acoustic field, flow field, and temperature field are established respectively, and equations corresponding to each physical field are established. Specifically, the mathematical equations of each physical field are as follows:

[0079] 1. Acoustic field equation (wave equation):

[0080]

[0081] Where p is the sound pressure; c is the speed of sound; is the Laplace operator.

[0082] 2. Flow field equations (Navier-Stokes equations):

[0083]

[0084] Where u is the fluid velocity vector; ρ is the fluid density; μ is the dynamic viscosity of the fluid; and f is the body force.

[0085] 3. Temperature field equation (heat conduction equation):

[0086]

[0087] Where T is the temperature; c p is the specific heat capacity; k is the thermal conductivity; and Q is the heat source term.

[0088] According to the equations of each physical field, it can be seen that the speed of sound in the acoustic field, the fluid density in the flow field, and the fluid viscosity all have corresponding mapping relationships with temperature. Therefore, temperature data is used as the coupling condition for establishing multiple physical fields. Based on the principles of fluid mechanics, acoustics, and thermodynamics, mathematical equations or sets of equations describing the coupling effects between multiple physical fields are established. That is, the three field equations are combined to establish the coupling equations of multiple physical fields, and appropriate numerical methods, such as the finite difference method, the finite element method, the finite volume method, etc., are used to solve the multi-physical field coupling equations.

[0089] Optionally, the accuracy and reliability of the multi-physics model can be verified by comparing actual measurement data with the model output results, and the model parameters can be adjusted and optimized based on the verification results.

[0090] Step S104: measuring the flow rate of the drainage network based on the multi-physics field model.

[0091] Specifically, the data analysis results obtained through Doppler shift analysis are used as input parameters into the established multi-physics model. Based on the actual conditions of the drainage network, the model's boundary conditions, such as inlet velocity, outlet pressure, and ambient temperature, are set. Then, the coupling calculations between the acoustic field, flow field, and temperature field are performed. Based on the input fluid flow data and boundary conditions, the model uses numerical methods (such as finite difference and finite element methods) to solve the multi-physics coupling equations and simulate the flow process of fluid in the drainage network.

[0092] Furthermore, during the calculation process, the actual flow of the drainage network is calculated by monitoring the fluid flow rate and cross-sectional area at a specific position or section in the network, according to the flow calculation formula such as Q=AV (where Q is the flow rate, A is the cross-sectional area, and V is the flow rate).

[0093] In summary, the embodiments of the present invention achieve more accurate and comprehensive measurement of drainage network flow through the combined application of Doppler shift analysis and multi-physics field modeling technology. First, a pre-arranged ultrasonic transducer array is used to capture fluid flow data in the drainage network, including key parameters such as flow velocity, temperature, and pressure. Subsequently, Doppler shift analysis technology is used to calculate the frequency difference between the transmitted and received waves to obtain Doppler shift data. The specific formula is as follows:

[0094]

[0095] Among them, f d is the Doppler shift; v is the velocity of the scatterer in the fluid; f t is the frequency of the transmitted wave; c is the speed of sound.

[0096] Perform high-order statistical analysis on the Doppler frequency shift data to calculate the skewness and kurtosis. The specific formula for skewness calculation is:

[0097]

[0098] Where S is the skewness; x i is a data point; is the mean of the data points; n is the total number of data points.

[0099] The specific formula for kurtosis calculation is:

[0100]

[0101] Where K is the kurtosis; x i is a data point; is the mean of the data points; n is the total number of data points.

[0102] At the same time, appropriate wavelet basis functions are selected, and Daubechies wavelet and Haar wavelet are used to perform multi-scale decomposition on the Doppler frequency shift signal to obtain velocity change information on different time scales. The specific formula is as follows:

[0103]

[0104] Where W(f,s) is the wavelet transform coefficient; f(t) is the original signal; ψ * is the complex conjugate of the wavelet function; a is the scaling factor; and b is the translation factor.

[0105] Finally, the signal is reconstructed according to the wavelet transform coefficients to extract the key features of the fluid velocity change. The non-Gaussian and multi-scale characteristics of the fluid velocity distribution are analyzed by combining high-order statistics and wavelet transform results to obtain the data analysis results.

[0106] Optionally, nonlinear dynamics theory can be applied to analyze complex behaviors in fluid flow, such as turbulence, eddies, and chaos. Specifically, Lyapunov exponents can be calculated based on the acquired fluid flow data and nonlinear dynamics equations to detect chaotic behavior:

[0107] 1. Nonlinear dynamic equations (such as the Lorentz equation):

[0108]

[0109] Among them, x, y, z are system variables; σ, ρ, β are control parameters.

[0110] 2. Lyapunov exponent:

[0111]

[0112] Used to quantify the exponential growth or decay rate of the system state, where v(t) is the offset vector of the system state.

[0113] Based on the time series data, phase space reconstruction techniques, such as delayed coordinate embedding, are used to recover the dynamic behavior of the system. The specific steps are as follows:

[0114] 3. Phase space reconstruction:

[0115] X recon ={(y(t),y(t-τ),…,y(t-(m-1)τ))∣t=τ,2τ,…}

[0116] Among them, X recon is the reconstructed phase space; y(t) is the time series data; τ is the time delay; and m is the embedding dimension.

[0117] Based on the results of phase space reconstruction, a nonlinear dynamic model that can describe the fluid flow behavior is established, and the unknown parameters in the model are estimated, such as σ, ρ, and β in the Lorentz equation. Then, the established nonlinear model is used to simulate the complex behavior of fluid flow, including predicting future flow states to analyze the stability of fluid flow and determine whether it is prone to entering a chaotic state or undergoing other types of flow transitions.

[0118] On this basis, a multi-physics model was further constructed, integrating the complex coupling relationships among acoustic, flow, and temperature fields. This model not only considers the basic laws of fluid dynamics but also deeply analyzes the multiple effects of acoustic field fluctuations and temperature changes on fluid flow, thereby achieving high-precision simulation of actual flow conditions in the drainage network.

[0119] The specific flow calculation is based on the following formula:

[0120] Q = ∫∫ A v(x,y)dA

[0121] Where Q is the flow rate (volume flow rate or mass flow rate); v(x,y) is the fluid velocity at any point (x,y) on the drainage network section A; dA is a small area element.

[0122] The fluid velocity v(x,y) can be determined by the Doppler shift f d calculate:

[0123]

[0124] Where c is the propagation speed of sound waves in the fluid, which is affected by temperature; f d is the Doppler shift; f t is the frequency of the emitted ultrasonic wave; θ is the angle between the direction of sound wave propagation and the direction of fluid velocity.

[0125] In some optional embodiments, the method further comprises:

[0126] Based on the fluid flow data of the drainage network, a dynamic fluid medium model is established;

[0127] The fluid medium state is predicted based on the model to obtain the prediction results.

[0128] By building a dynamic model of the fluid medium and updating the fluid characteristic parameters in real time, theoretical support is provided for the prediction and control of flow characteristics. Specifically, a pre-arranged sensor network is used to collect real-time fluid flow data from the drainage network, including the velocity, density, temperature, etc. of the fluid medium. Based on the principles of fluid mechanics, a mathematical model describing the dynamic behavior of the fluid medium is constructed. The model parameters are continuously updated according to new data to reflect the real-time status of the fluid medium. The specific model and formula are as follows:

[0129] 1. Fluid velocity model:

[0130]

[0131] Where v(t) is the velocity of the fluid at time t; v0 is the initial velocity; and a(τ) is the acceleration at time τ.

[0132] 2. Fluid density model:

[0133]

[0134] Where ρ(t) is the fluid density at time t; ρ0 is the reference density; α i ,ω i ,φ i are the amplitude, angular frequency, and phase of the density variation.

[0135] 3. Fluid temperature model:

[0136] T(t)=T0+ΔTsin(ωt)

[0137] Where T(t) is the fluid temperature at time t; T0 is the average temperature; and ΔT is the temperature change.

[0138] 4. Fluid flow model:

[0139] Q(t)=A(t)v(t)

[0140] Where Q(t) is the fluid flow rate at time t, and A(t) is the flow channel cross-sectional area at time t.

[0141] In some optional embodiments, the method further comprises:

[0142] Identify abnormal conditions based on prediction results;

[0143] When an abnormal state is identified, an early warning message is issued.

[0144] The embodiment of the present invention also integrates the prediction results obtained based on the dynamic fluid medium model to identify abnormal patterns and provide early warning functions. By training the model with historical data and real-time data, it can identify the regularity and abnormalities in fluid flow. When an abnormal pattern is detected or an unfavorable trend is predicted, an early warning message is issued in a timely manner, thereby realizing intelligent monitoring and early warning of the fluid flow status.

[0145] Alternatively, an abnormal pattern in fluid flow can be automatically identified by integrating a machine learning system. Taking the one-class SVM in machine learning as an example, an anomaly detection formula can be:

[0146]

[0147] Among them, w is the support vector weight; φ(x i ) is the input data x i is a nonlinear mapping; λ is the regularization parameter; ν is the ratio of support vectors; ρ is the interval parameter; ξ i is a slack variable.

[0148] Taking the autoregressive model AR as an example, a time series detection formula can be:

[0149] x t =c+φ1x t-1 +φ2x t-2 +…+φ p x t-p +∈ t

[0150] Among them, x t is the fluid flow characteristic at time point t; c is a constant term; φ i is the model parameter; p is the lag order; ∈ t is the error term.

[0151] This embodiment also includes an evaluation of the performance of the machine learning model: Accuracy; Recall; F1 score

[0152] In some optional embodiments, establishing a dynamic fluid medium model based on fluid flow data of the drainage network further includes:

[0153] Obtain environmental parameter data;

[0154] The parameters of the dynamic fluid medium model are updated based on the environmental parameter data to obtain a target dynamic fluid medium model.

[0155] Identify and screen key environmental factors that affect the state of the fluid medium, such as rainfall, temperature, humidity, wind speed, etc., use corresponding sensors or weather station data to obtain these environmental parameter data, and fuse the obtained environmental parameter data with the fluid flow data to form a more comprehensive input data set. Based on the fused data, adjust the parameters of the dynamic fluid medium model so that the model can adapt to changes in environmental parameters in real time, and obtain the target dynamic fluid medium model. Finally, use a new test data set to verify and evaluate the updated target dynamic fluid medium model to ensure that the model can still accurately predict the state of the fluid medium after the introduction of environmental parameters. The prediction results can then be applied to the management, optimization, and decision-making of the drainage network, such as scheduling water resources, preventing pipeline blockages, and optimizing pipeline layout.

[0156] In some optional implementations, drainage network flow measurement based on a multi-physics model includes:

[0157] The drainage network flow was measured based on the multi-physics model, and preliminary measurement results were obtained;

[0158] According to the preliminary measurement results and the adaptive algorithm, the parameters are adjusted to obtain the adjusted multi-physics field model;

[0159] Based on the adjusted multi-physics field model, the drainage network flow measurement is carried out to obtain the target measurement results.

[0160] Set the initial parameters of the algorithm, such as the window size, which determines the length of the signal segment considered when calculating the cross-correlation function; the window shape (e.g., rectangular window, Hamming window, etc.); and the time delay (i.e., the relative time difference between the two signals). The cross-correlation function is an important tool for measuring the similarity between two signals. Therefore, by calculating the cross-correlation function of two signals and finding the optimal time delay (i.e., the time difference that makes the two signals most similar), the cross-correlation function is:

[0161]

[0162] where R(τ) is the cross-correlation function; s1(t) and s2(t) are two signals; and τ is the time delay.

[0163] In order to improve the accuracy of flow velocity measurement, it is necessary to use optimization algorithms, such as gradient descent and genetic algorithms, to adjust the parameters of the cross-correlation algorithm. These optimization algorithms evaluate the performance of different parameter combinations based on a fitness function F(θ):

[0164] F(θ)=max[R(τ,θ)]

[0165] Among them, F(θ) is the fitness function used to evaluate the effect of parameter θ; R(τ,θ) is the cross-correlation function under parameter θ.

[0166] The parameter combination that makes the fitness function value optimal is found to obtain the adjusted multi-physics field model, and then the flow measurement is performed based on the adjusted multi-physics field model.

[0167] Optionally, this embodiment of the invention also includes adaptive beamforming and data-driven control, which combines beamforming algorithms and data-driven control strategies to achieve real-time optimization and precise control of measurement processes and industrial processes. Adaptive beamforming technology can dynamically adjust the direction and shape of the beam to adapt to changes in the measurement environment, while data-driven control uses collected data to optimize control parameters and improve system performance. The specific steps are as follows:

[0168] 1. Beamforming weight calculation:

[0169]

[0170] Among them, w i is the weight of the i-th transducer; k is the wave number; d i is the distance from transducer i to the beam center; φ i is the phase offset of transducer i; N is the total number of transducers.

[0171] 2. Data-driven control (such as PID control)

[0172]

[0173] Where u(t) is the control input; e(t) is the error signal; K p ,K i ,K d are the proportional, integral, and differential control parameters.

[0174] 3. Adaptive algorithm (such as LMS):

[0175] w(t+1)=w(t)+μ·e(t)·x * (t)

[0176] Where w(t) is the weight vector at time t; μ is the learning rate; e(t) is the error signal; x * (t) is the conjugate of the input signal.

[0177] Please refer to Figure 2 Data flow diagram and Figure 3 Technical roadmap: The embodiment of the present invention first captures the fluid flow data of the drainage network based on a pre-arranged ultrasonic transducer array, including key parameters such as flow velocity, temperature and pressure, and performs pre-processing such as filtering on the collected data. Then, Doppler shift analysis technology is used to calculate the frequency difference between the transmitted wave and the received wave to obtain Doppler shift data, and high-order statistical analysis, wavelet transform, multi-scale fluid dynamic analysis and nonlinear dynamic analysis are performed to identify fluid dynamic characteristics and perform fluid stability analysis. Based on this, the ultrasonic transducer array layout is optimized and the beamforming algorithm is developed for adaptive beam adjustment, and then a multi-physics field coupling analysis integrating the acoustic field, flow field and temperature field is performed. Finally, adaptive beamforming and data-driven control are realized, control parameters are optimized, real-time monitoring and control are performed, and system performance is evaluated.

[0178] At the same time, if Figure 2 As shown, by collecting environmental parameters, they are used for environmental impact correction, and the environmental impact correction is used to update the fluid medium model to predict fluid flow trends and identify abnormal patterns and provide early warnings. The specific contents have been explained in the above embodiments and preferred implementation methods and will not be repeated here.

[0179] This embodiment also provides a Doppler flow measurement device for a drainage network coupled with multiple physical fields. This device is used to implement the above-mentioned embodiments and preferred embodiments, and the details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0180] This embodiment provides a Doppler flow measurement device for a drainage network coupled with multiple physical fields, such as Figure 4 Shown, including:

[0181] The acquisition module 401 is used to acquire fluid flow data of the drainage network; the fluid flow data of the drainage network includes at least one of velocity, temperature, and pressure.

[0182] The frequency shift analysis module 402 is used to perform Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results.

[0183] Establishing module 403 is used to establish a multi-physics field model based on the data analysis results; the multi-physics field model is obtained based on the coupling of the acoustic field, the flow field and the temperature field.

[0184] The measurement module 404 is used to measure the flow of the drainage network based on the multi-physics field model.

[0185] In some optional implementations, the frequency shift analysis module 402 includes:

[0186] The calculation submodule is used to calculate Doppler frequency shift data based on the fluid flow data of the drainage network.

[0187] The high-order statistics analysis submodule is used to perform high-order statistics analysis on Doppler frequency shift data to obtain skewness and kurtosis data.

[0188] The reconstruction submodule is used to reconstruct the Doppler frequency shift data and combine it with the skewness and kurtosis data to obtain data analysis results.

[0189] In some optional embodiments, the device further comprises:

[0190] A modeling module is established to establish a dynamic fluid medium model based on the fluid flow data of the drainage network.

[0191] The prediction submodule is used to predict the state of the fluid medium based on the model and obtain the prediction results.

[0192] In some optional embodiments, the device further comprises:

[0193] The anomaly recognition module is used to identify abnormal conditions based on the prediction results.

[0194] The early warning module is used to issue an early warning message when an abnormal state is identified.

[0195] In some optional implementations, the establishing module 403 includes:

[0196] A mathematical model submodule is established to establish mathematical models of the acoustic field, flow field and temperature field based on the fluid flow data of the drainage network.

[0197] The coupling submodule is used to establish the coupling relationship between the mathematical models of the acoustic field, flow field and temperature field based on temperature data to obtain the multi-physics field equations.

[0198] In some optional embodiments, building a model module includes:

[0199] The environment parameter acquisition submodule is used to obtain environment parameter data.

[0200] The updating submodule is used to update the parameters of the dynamic fluid medium model based on the environmental parameter data to obtain the target dynamic fluid medium model.

[0201] In some optional implementations, the measurement module 404 includes:

[0202] The preliminary measurement submodule is used to measure the flow of the drainage network based on the multi-physics field model and obtain preliminary measurement results.

[0203] The parameter adjustment submodule is used to adjust the parameters according to the preliminary measurement results in combination with the adaptive algorithm to obtain the adjusted multi-physics field model.

[0204] The flow measurement submodule is used to measure the flow of the drainage network based on the adjusted multi-physics field model to obtain the target measurement results.

[0205] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0206] In this embodiment, a Doppler flow measurement device for a drainage network coupled with multiple physical fields is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0207] The embodiment of the present invention also provides a computer device having the above Figure 4A Doppler flow measurement device for a drainage network coupled with multiple physical fields is shown.

[0208] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0209] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0210] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0211] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0212] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0213] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0214] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0215] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0216] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0217] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A Doppler flow measurement method for a drainage network coupled with multiple physical fields, characterized in that: The method comprises: Acquiring fluid flow data of the drainage network; the fluid flow data of the drainage network includes at least one of velocity, temperature, and pressure; Performing Doppler frequency shift analysis on the fluid flow data of the drainage pipe network to obtain data analysis results; Establishing a multi-physics field model based on the data analysis results; the multi-physics field model is obtained based on the coupling of the acoustic field, the flow field and the temperature field; Performing drainage network flow measurement based on the multi-physics field model; Wherein, performing Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results includes: Calculating Doppler shift data based on fluid flow data of the drainage network; Performing high-order statistical analysis on the Doppler frequency shift data to obtain skewness and kurtosis data; Signal reconstruction is performed on the Doppler frequency shift data, and combined with the skewness and kurtosis data to obtain a data analysis result.

2. The method according to claim 1, characterized in that The method further comprises: Establishing a dynamic fluid medium model based on the fluid flow data of the drainage pipe network; The fluid medium state is predicted based on the model to obtain a prediction result.

3. The method according to claim 2, characterized in that The method further comprises: performing abnormal state identification based on the prediction result; When the abnormal state is identified, an early warning message is issued.

4. The method according to claim 1, wherein The establishing of a multi-physics field model based on the data analysis results includes: Based on the fluid flow data of the drainage pipe network, mathematical models of the acoustic field, flow field and temperature field are established respectively; A coupling relationship between the mathematical models of the acoustic field, flow field and temperature field is established based on the temperature data to obtain a multi-physics field equation.

5. The method according to claim 2, characterized in that The method of establishing a dynamic fluid medium model based on the fluid flow data of the drainage pipe network further includes: Obtain environmental parameter data; The parameters of the dynamic fluid medium model are updated based on the environmental parameter data to obtain a target dynamic fluid medium model.

6. The method according to claim 1, characterized in that The drainage network flow measurement based on the multi-physics field model includes: Perform drainage network flow measurement based on the multi-physics field model to obtain preliminary measurement results; Adjusting parameters based on the preliminary measurement results in combination with an adaptive algorithm to obtain an adjusted multi-physics field model; Based on the adjusted multi-physics field model, drainage network flow measurement is performed to obtain target measurement results.

7. A Doppler flow measurement device for drainage pipe network coupled with multiple physical fields, characterized in that: The device comprises: An acquisition module, configured to acquire fluid flow data of a drainage network; the fluid flow data of the drainage network includes at least one of velocity, temperature, and pressure; a frequency shift analysis module, configured to perform Doppler frequency shift analysis on the fluid flow data of the drainage network to obtain data analysis results; An establishment module is used to establish a multi-physics field model based on the data analysis results; the multi-physics field model is obtained based on the coupling of the acoustic field, the flow field and the temperature field; A measurement module, configured to measure the flow rate of a drainage network based on the multi-physics field model; Wherein, the frequency shift analysis module is further used for: Calculating Doppler shift data based on fluid flow data of the drainage network; Performing high-order statistical analysis on the Doppler frequency shift data to obtain skewness and kurtosis data; Signal reconstruction is performed on the Doppler frequency shift data, and combined with the skewness and kurtosis data to obtain a data analysis result.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the Doppler flow measurement method for a drainage network coupled with multiple physical fields according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the Doppler flow measurement method for a drainage network coupled with multiple physical fields according to any one of claims 1 to 6.

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