CN120846605A - A long-distance water pipeline flow monitoring method integrating terrain dynamic compensation - Google Patents
A long-distance water pipeline flow monitoring method integrating terrain dynamic compensationInfo
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Abstract
The invention belongs to the technical field of pipeline flow monitoring, in particular to a method for monitoring the flow of a long-distance water conveying pipeline by integrating dynamic compensation of topography, which comprises the following steps of selecting a datum point of the long-distance water conveying pipeline and synchronously collecting the datum flow and static pressure; the method comprises the steps of constructing a pipeline terrain elevation parameter model based on a multi-source data fusion technology, generating a three-dimensional terrain matrix, calculating elevation difference, gradient and curvature radius, constructing a terrain correction matrix, correcting head loss, inhibiting potential energy conversion errors and eliminating water hammer effect interference based on Darcy formula correction, downhill section kinetic energy inhibition and downhill section pressure compensation algorithm, correcting flow in a pipeline by adopting edge calculation, performing global flow balance verification, and positioning a pipe burst or a leakage point based on an integrated visual platform in an auxiliary mode. The method effectively improves the measurement accuracy of the pipeline flow under the complex terrain, and provides a high-accuracy and all-terrain self-adaptive intelligent monitoring and early warning platform for the long-distance water conveying pipeline.
Description
Technical Field
The invention mainly relates to the technical field of pipeline flow monitoring, in particular to a method for monitoring the flow of a long-distance water conveying pipeline by integrating terrain dynamic compensation, which is particularly suitable for eliminating the problem of flow measurement distortion caused by pipeline gradient, elevation difference and terrain fluctuation and realizing the all-line flow consistency monitoring of a water conveying pipeline network under complex terrain conditions.
Background
The long-distance water delivery pipeline is used as a core facility of hydraulic engineering, and the flow monitoring precision directly influences the water resource scheduling efficiency and the operation safety. However, the prior flow measurement technology (traditional ultrasonic wave, electromagnetic flowmeter and the like) does not consider the influence of the longitudinal gradient change of the pipeline on the energy state of the fluid when in installation, namely, in a downhill pipeline section, continuous conversion of the potential energy of the fluid to kinetic energy leads to virtual high flow velocity and errors of even more than 15 percent.
The existing correction algorithm mostly adopts a static pressure compensation model, and a dynamic correlation equation of a topography elevation-fluid energy state is not established, and local turbulence energy loss caused by pipeline fluctuation is not quantitatively analyzed. Particularly in a complex terrain area, the mechanical energy loss is aggravated by vortex generated by turbulent boundary layer separation, and the constant friction coefficient method adopted in the current standard cannot reflect the transient hydraulic characteristics, so that the pressure compensation value deviates from the actual working condition, and the flow data deviation is further enlarged. Therefore, it is needed to construct a dynamic correction system integrating terrain elevation compensation and turbulence energy loss so as to solve the problem of systematic measurement errors caused by terrain fluctuation of a long-distance water delivery pipe network.
Disclosure of Invention
In order to solve the defects of the prior art, the invention combines the prior art, and provides the method for monitoring the flow of the long-distance water conveying pipeline by integrating the dynamic compensation of the terrain from the practical application, so that the measurement precision of the flow of the pipeline under the complex terrain is effectively improved, and a high-precision and all-terrain self-adaptive intelligent monitoring and early warning platform is provided for the long-distance water conveying pipeline.
The technical scheme of the invention is as follows:
the method for monitoring the flow of the long-distance water conveying pipeline by integrating the dynamic compensation of the terrain comprises the following steps:
S1, selecting a datum point of a long-distance water conveying pipeline, and synchronously acquiring a datum flow Q 0 and a static pressure P 0 by a pressure sensor and a multichannel ultrasonic flowmeter;
s2, constructing a pipeline terrain elevation parameter model through GIS space analysis and LiDAR point cloud processing based on a multi-source data fusion technology, generating a three-dimensional terrain matrix, calculating an elevation difference delta H, a gradient theta and a curvature radius R, and constructing a terrain correction matrix K terrain;
s3, correcting, downhill segment kinetic energy inhibition and downhill segment pressure compensation algorithm correction head loss, potential energy conversion error inhibition and water hammer effect interference elimination based on Darcy formula;
s4, correcting the flow in the pipeline by adopting edge calculation, and uniformly integrating the corrected flow on a platform to perform global flow balance verification;
S5, displaying the corrected flow, the corrected topographic parameters and the corrected pressure gradients of the nodes in real time based on the integrated visual platform, generating a pressure gradient distribution map and a pressure gradient thermodynamic diagram, and positioning a pipe explosion or a leakage point in an auxiliary mode.
In the step S1, the datum point is preferentially arranged in a gentle region with the terrain gradient less than or equal to 1% and the straight pipe length more than or equal to 30D, wherein D is the pipe diameter;
the pressure sensor is vertically arranged on the pipe top and is orthogonal to the flow direction of the fluid, and the influence of medium temperature fluctuation on pressure measurement is eliminated through the temperature compensation module.
Further, the specific flow of step S2 includes:
s21, data preprocessing, namely generating a digital elevation model DEM by using LiDAR point cloud data, separating ground points and non-ground points by a random forest filtering algorithm, eliminating interference sources and obtaining a high-fidelity terrain surface;
s22, calculating space parameters, namely calculating an elevation difference delta H, a gradient theta and a curvature radius R;
s23, modeling a correction coefficient, namely constructing a terrain correction matrix K terrain, introducing a dynamic weight factor, performing supervised learning through multiple groups of measured data, and determining a final parameter combination by adopting a cross-validation method.
In step S22, the elevation difference delta H is calculated by adopting a Delaunay triangulation algorithm to establish a spatial topological relation between the pipeline axis and the terrain surface, and calculating the absolute elevation difference of each node relative to the datum point based on DEM data;
the gradient theta is calculated by using a gradient tool of the ArcGIS hydrologic analysis module, correcting and eliminating coordinate projection errors by combining Z factors, and calculating the gradient change rate of the projection surface of the pipeline axis, wherein the formula (1) is as follows:
θ=arctan(ΔH/ΔL)×180/π(1)
Where Δl is the length of the neighboring node.
The calculation mode of the curvature radius R is that based on a quadric surface fitting model, a Levenberg-Marquardt algorithm is adopted to iterate and optimize parameters, a differential geometric formula R=1/K is utilized to quantify geometric characteristics of a bent pipe section, and local encryption sampling is carried out on the sharp bent section with the radius less than or equal to 5D, wherein K is curvature, and D is pipe diameter.
Further, in step S23, after the dynamic weight factor is introduced, the terrain correction matrix K terrain is expressed as follows:
Kterrain=α·ΔH/H0+β·sinθ+γ·D/R(2)
Wherein alpha, beta and gamma are weight coefficients optimized by BP neural network, H 0 is reference point elevation, and D is pipe diameter.
Further, in step S3, the darcy formula is modified as follows:
Embedding a terrain correction matrix K terrain into a traditional Darcy-Weis Baha equation, correcting a head loss term, and the modified formula (3) is as follows:
hfm=（fL/D+Kterrain）·v2/(2g)(3)
Wherein h fm is the corrected head loss, f is the Darcy friction coefficient, reflects the influence of pipe wall roughness on water flow resistance, D is pipe diameter, L is pipe length, v is the water flow velocity in the pipe, and g is gravity acceleration;
The kinetic energy of the downhill section is inhibited as follows:
Aiming at abnormal conversion of fluid potential energy in a downhill section to kinetic energy, a flow correction equation based on energy conservation is provided, and a formula (4) is as follows:
Qi=Qraw[1-ηtan(θ)·ΔH/L](4)
Wherein Q i is corrected flow, Q raw is real measured flow value without terrain correction, eta is terrain attenuation factor reflecting the attenuation effect of terrain on flow speed, and is positively correlated with gradient theta and elevation difference delta H, delta H/L is the unit pipe length elevation change rate and reflects potential energy gradient strength;
Downhill section pressure fluctuation compensation is as follows:
aiming at pressure dip and water hammer effect caused by gravity acceleration in a downhill section, a pressure gradient driven flow velocity compensation model is established, and the formula (5) is as follows:
vreal=v+ζ(K/ρ)·P/t(5)
Wherein zeta is a pressure gradient response factor, the value range is 0.05-0.1, K is the water body elastic modulus; P/ t is the variation of pressure in unit time and reflects the transient fluctuation intensity, v real is the real flow velocity after multi-factor dynamic compensation, and ρ is the density of water.
Further, step S4 is specifically as follows:
Executing a formula (3) and a formula (4) on the actually measured flow of the flowmeter on the pipeline by utilizing edge calculation, calculating the corrected head loss through the formula (3), outputting corrected flow Q i through the formula (4), and reducing the influence of delay on transient pressure fluctuation;
and carrying out global flow balance verification on flow data integrated into the cloud platform, setting an error threshold, and when the flow deviation of a certain node exceeds the threshold, calling the topographic parameters (delta H, theta and R) of the area and the datum point data Q 0 and P 0 by the platform to recalculate the weight coefficient corresponding to the topographic correction matrix K terrain.
Further, step S5 is specifically as follows:
And integrating BIM+GIS+iot fusion architecture, realizing real-time display of flow, topographic parameters and pressure gradients after correction of each node, and generating a pressure gradient distribution map and a pressure gradient thermodynamic diagram by using the static pressure P 0 as a reference through a kriging interpolation algorithm.
The invention has the beneficial effects that:
According to the invention, by establishing a terrain elevation-fluid energy dynamic compensation system, parameters such as a pipeline elevation difference delta H, a gradient theta, a curvature radius R and the like are coupled and modeled with a fluid kinetic energy-potential energy conversion effect for the first time, a multi-mode correction algorithm is developed, and the measurement accuracy of the pipeline flow under complex terrain is effectively improved. The pressure gradient thermodynamic diagram can be generated in real time through the visual platform integrated with the BIM and the GIS, the explosion tube or the leakage point can be positioned in an auxiliary mode, and the high-precision and all-terrain self-adaptive intelligent monitoring and early warning platform is provided for long-distance water pipelines.
Drawings
Fig. 1 is a detailed flow chart of the present invention.
Detailed Description
The application will be further described with reference to the accompanying drawings and specific embodiments. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. Further, it will be understood that various changes and modifications may be made by those skilled in the art after reading the teachings of the application, and equivalents thereof fall within the scope of the application as defined by the claims.
The embodiment provides a method for monitoring the flow of a long-distance water conveying pipeline by integrating terrain dynamic compensation. In order to solve the problem of flow measurement distortion caused by pipeline gradient, elevation difference and topography fluctuation, the embodiment develops reference point selection, topography parameter modeling, researching and developing topography-flow dynamic compensation algorithm, constructing a multi-node collaborative calibration platform and establishing a visual platform. The method comprises the steps of selecting a datum point as a basis, taking subsequent flow correction as a reference, generating parameters such as elevation difference, gradient, curvature radius and the like through GIS and LiDAR data by terrain parameter modeling, and quantifying the influence of terrain on flow and pressure. The topography-flow dynamic compensation algorithm is a core, and the flow deviation is solved by correcting the Darcy formula and processing the flow of downhill and downhill sections. The multi-node cooperative check platform ensures the consistency of flow data of the whole pipe network, and the visual platform is used for real-time monitoring and fault positioning. The flow is shown in fig. 1, and the specific monitoring method is as follows.
1. Selection of fiducial points
The selection of a proper datum point in the long-distance water conveying pipeline is the basis of flow correction. The reference points are selected by comprehensively considering the topographic features, the fluid dynamic characteristics and the suitability of the measuring equipment.
The datum point should be set up in the gentle region that topography slope is less than or equal to 1% and straight tube section length is less than or equal to 30D (D is the pipe diameter) preferentially, and local turbulence interference sources such as elbow, valve can effectively be evaded to this kind of region, ensures that fluid fully develops into stable laminar flow state. In practice, the pipelines are arranged according to the relief of the terrain, the gradient of the terrain is less than or equal to 1 percent, and the gradient of the pipelines can be less than or equal to 1 percent.
In the aspect of measuring equipment, a 0.1% FS precision pressure sensor with double redundancy configuration and a multichannel ultrasonic flowmeter are adopted to synchronously acquire a reference flow Q 0 and a static pressure P 0, wherein the pressure sensor is vertically arranged on the top of a pipe and is orthogonal to the flow direction of fluid, and the influence of medium temperature fluctuation on pressure measurement is eliminated through a temperature compensation module.
2. Terrain elevation parameter modeling
Based on a multisource data fusion technology, the terrain elevation parameter modeling constructs a three-dimensional terrain elevation model of the pipeline through GIS space analysis and LiDAR point cloud processing. The specific flow comprises the following steps:
(1) And (3) data preprocessing, namely generating a 1cm resolution Digital Elevation Model (DEM) by using LiDAR point cloud data, separating ground points from non-ground points by a random forest filtering algorithm, eliminating interference sources such as vegetation, buildings and the like, and obtaining the high-fidelity terrain surface.
(2) Calculating spatial parameters:
The elevation difference delta H is that a Delaunay triangulation algorithm is adopted to establish a spatial topological relation between the axis of the pipeline and the terrain surface, and the absolute elevation difference of each node relative to a datum point is calculated based on DEM data, wherein the precision is +/-1 cm;
And (2) calculating the gradient change rate of the pipeline axis projection surface by using a gradient tool of the ArcGIS hydrologic analysis module and correcting and eliminating coordinate projection errors by combining Z factors, wherein the formula (1) is as follows:
θ=arctan(ΔH/ΔL)×180/π(1)
And the curvature radius R is based on a quadric surface fitting model, a Levenberg-Marquardt (LM) algorithm is adopted to iterate and optimize parameters, a differential geometric formula R=1/K (K is curvature) is utilized to quantify the geometric characteristics of the bent pipe section, and the local encryption sampling is carried out on the sharp bent section with the radius less than or equal to 5D (pipe diameter).
(3) Modeling a correction coefficient:
When the terrain correction matrix K terrain is constructed, a dynamic weight factor is introduced, and the expression of the terrain correction matrix K terrain is shown as a formula (2). And performing supervised learning through a plurality of groups of measured data (the wave speed of the pressure of the water hammer and the dissipation rate of turbulent energy), and determining a final parameter combination by adopting a cross-validation method. For complex terrains (such as gradient abrupt change areas), a local energy loss compensation submodel is built by integrating the RANS equation and the RNGk-epsilon turbulence model, so that correction coefficient errors are reduced from +/-15% to +/-3.2% of the traditional method.
Kterrain=α·ΔH/H0+β·sinθ+γ·D/R(2)
The method is characterized in that alpha, beta and gamma are weight coefficients optimized by a BP neural network, delta H is the elevation difference of each node, the elevation difference is calculated and generated through GIS and LiDAR data, the influence (m) of terrain fluctuation on fluid potential energy is reflected, theta is gradient and represents a pipeline gradient angle (degree), R is curvature radius and is a parameter for quantifying the bending degree of a pipeline, and H 0 is datum point elevation.
The terrain correction matrix K terrain carries out supervised learning through a plurality of groups of measured data, and a final parameter combination is determined by adopting a cross verification method, and the specific method is as follows.
And data acquisition, namely selecting a plurality of groups of measured data in different terrain scenes (such as plain, mountain land and hills), and synchronously recording dynamic parameters such as flow Q, pressure P, terrain parameters (delta H, theta and R), hydraulic ram pressure wave speed and the like.
And (3) optimizing the BP neural network, namely iteratively adjusting alpha, beta and gamma initial values (the initial range is set to be 0.1-0.9) by taking the measured flow deviation as an objective function through the BP neural network, so that the Mean Square Error (MSE) of the model predicted flow and the measured flow is minimized.
Cross-verifying, namely dividing the data set into 5 parts by adopting a multi-fold cross-verifying method and taking a 5-fold cross-verifying method as an example, repeating the training for 4 parts each time and the verification for 1 part, taking the average error for 5 times, and finally determining the optimal parameter combination.
The weight coefficient alpha is the weight of the elevation difference delta H and is used for leading the influence of the elevation difference on potential energy conversion, and the larger the alpha is, the larger the flow correction amount of the elevation difference is.
The weight coefficient beta is the weight of the gradient angle theta of the pipeline and is used for quantifying the inhibition effect of the gradient on abnormal kinetic energy conversion, and the beta is positively correlated with the flow correction intensity of the downhill section.
The weight coefficient gamma is the radius of curvature Rweight and is used for reflecting the influence of curve curvature on turbulent energy consumption, and the larger the gamma is, the larger the flow correction quantity of the sharp bending section (R is less than or equal to 5D) is.
3. Topography-flow dynamic compensation algorithm
The algorithm consists of three parts, namely Darcy formula correction, downhill section kinetic energy inhibition and downhill section pressure compensation, and is respectively used for correcting head loss, inhibiting potential energy conversion errors and eliminating water hammer effect interference, so that the problem of flow measurement distortion caused by topography fluctuation of a long-distance water pipe is solved.
(1) Darcy formula correction and turbulence energy dynamic compensation:
Embedding a terrain correction matrix K terrain into a traditional Darcy-Weis Baha equation, correcting a head loss term, and the modified formula (3) is as follows:
hfm=（fL/D+Kterrain）·v2/(2g)(3)
Wherein h fm is the corrected head loss, f is the Darcy friction coefficient, reflects the influence of pipe wall roughness on water flow resistance, D is the inner diameter (m) of a pipe, L is the length (m) of the pipe, v is the water flow velocity (m/s) of the pipe, and K terrain is used for correcting local turbulence loss and energy gradient deviation caused by topography fluctuation;
(2) Kinetic energy inhibition in downhill section:
Aiming at abnormal conversion of fluid potential energy to kinetic energy in a downhill section (theta >3 DEG), a flow correction equation based on energy conservation is provided, and the formula (4) is as follows:
Qi=Qraw[1-ηtan(θ)·ΔH/L](4)
Wherein Q i is the corrected flow (m 3/s）;Qraw is the real measured flow value (m 3/s) which is not subjected to terrain correction), eta is the terrain attenuation factor, reflects the attenuation effect of terrain on the flow speed, is positively related to the gradient theta and the elevation difference delta H, and delta H/L is the unit pipe length elevation change rate, and reflects the potential energy gradient strength.
The range of the topographic attenuation factor eta is 0.1-0.5, and is determined through fitting measured data. The specific calculation formula is eta=k1.theta+k2.delta.H, wherein k1 and k2 are fitting coefficients, and the method is obtained through regression analysis of historical data.
(3) Downhill section pressure fluctuation compensation:
aiming at pressure dip and water hammer effect caused by gravity acceleration in downhill section (theta < -2 ℃), a flow velocity compensation model driven by pressure gradient is built, and the formula (5) is as follows:
vreal=v+ζ(K/ρ)·P/t(5)
wherein ζ is a pressure gradient response factor (the value is 0.05-0.1), and represents the sensitivity degree of the pipeline system to pressure fluctuation, such as the steel pipeline can be 0.07, the plastic pipeline can be 0.09, and the sensitivity degree is determined through a water hammer effect experiment; P/ t is the variation of pressure in unit time and reflects the transient fluctuation intensity (Pa/s), v real is the real flow velocity (m/s) after multi-factor dynamic compensation, ρ is the density of water, v is the flow velocity (m/s) of water in a pipeline, and the flow velocity can be directly acquired by an ultrasonic flowmeter.
4. Multi-node collaborative verification platform
And consistency and instantaneity of pipeline flow data are ensured through an edge calculation and cloud cooperation mechanism. The flow in the pipeline is corrected by adopting edge calculation, the corrected flow can be uniformly integrated on the platform, and global flow balance verification is carried out, and the method is specifically as follows:
And (3) performing formula (3) and formula (4) on the actually measured flow of the flowmeter on the pipeline by utilizing the edge calculation, calculating the corrected head loss through the formula (3), outputting corrected flow Q i through the formula (4), and reducing the influence of delay on transient pressure fluctuation. The formula (3) is used for correcting head loss caused by topography fluctuation, ensuring the accuracy of the Darcy formula, and the formula (4) is used for inhibiting abnormal conversion of fluid potential energy of a downhill section to kinetic energy based on an energy conservation principle, and further correcting a flow value. The formula (3) and the formula (4) cooperate to realize comprehensive correction of flow from two angles of head loss and energy conversion respectively.
And (3) performing global flow balance verification, namely performing global flow balance verification on flow data integrated into the cloud platform, setting an error threshold value to be +/-2%, and triggering curvature parameter recalibration if the deviation of adjacent nodes is more than 3%, namely dynamically correcting the formula (2). Specifically, when the flow deviation of a certain node exceeds a threshold value, the platform calls the terrain parameters (Δh, θ, R) and the reference point data Q 0 and P 0 of the region, and recalculates α, β, γ by the least square method, so that the flow deviation of the region is minimized, that is, the weight coefficients α, β, γ corresponding to the terrain correction matrix K terrain are recalculated.
5. Visual platform
And integrating BIM+GIS+iot fusion architecture to realize real-time display of flow, topographic parameters and pressure gradient after correction of each node. And (3) generating a pressure gradient distribution map by using P 0 as a reference through a Kriging interpolation algorithm, generating a pressure gradient thermodynamic diagram, and assisting in positioning a pipe explosion or a leakage point.
Claims (8)
1. The method for monitoring the flow of the long-distance water conveying pipeline by integrating the dynamic compensation of the terrain is characterized by comprising the following steps of:
S1, selecting a datum point of a long-distance water conveying pipeline, and synchronously acquiring a datum flow Q 0 and a static pressure P 0 by a pressure sensor and a multichannel ultrasonic flowmeter;
s2, constructing a pipeline terrain elevation parameter model through GIS space analysis and LiDAR point cloud processing based on a multi-source data fusion technology, generating a three-dimensional terrain matrix, calculating an elevation difference delta H, a gradient theta and a curvature radius R, and constructing a terrain correction matrix K terrain;
s3, correcting, downhill segment kinetic energy inhibition and downhill segment pressure compensation algorithm correction head loss, potential energy conversion error inhibition and water hammer effect interference elimination based on Darcy formula;
s4, correcting the flow in the pipeline by adopting edge calculation, and uniformly integrating the corrected flow on a platform to perform global flow balance verification;
S5, displaying the corrected flow, the corrected topographic parameters and the corrected pressure gradients of the nodes in real time based on the integrated visual platform, generating a pressure gradient distribution map and a pressure gradient thermodynamic diagram, and positioning a pipe explosion or a leakage point in an auxiliary mode.
2. The method for monitoring the flow of the long-distance water conveying pipeline by integrating the dynamic compensation of the terrain according to claim 1, wherein in the step S1, a datum point is preferentially arranged in a gentle region with the terrain gradient being less than or equal to 1% and the length of a straight pipe segment being more than or equal to 30D, wherein D is the pipe diameter;
the pressure sensor is vertically arranged on the pipe top and is orthogonal to the flow direction of the fluid, and the influence of medium temperature fluctuation on pressure measurement is eliminated through the temperature compensation module.
3. The method for monitoring the flow of the long-distance water conveying pipeline by integrating terrain dynamic compensation according to claim 1, wherein the specific flow of the step S2 comprises the following steps:
s21, data preprocessing, namely generating a digital elevation model DEM by using LiDAR point cloud data, separating ground points and non-ground points by a random forest filtering algorithm, eliminating interference sources and obtaining a high-fidelity terrain surface;
s22, calculating space parameters, namely calculating an elevation difference delta H, a gradient theta and a curvature radius R;
s23, modeling a correction coefficient, namely constructing a terrain correction matrix K terrain, introducing a dynamic weight factor, performing supervised learning through multiple groups of measured data, and determining a final parameter combination by adopting a cross-validation method.
4. The method for monitoring the flow of the long-distance water conveying pipeline with the dynamic compensation of the fusion terrain according to claim 3, wherein in the step S22, the calculation mode of the elevation difference delta H is that a Delaunay triangulation algorithm is adopted to establish the spatial topological relation between the pipeline axis and the terrain surface, and the absolute elevation difference of each node relative to a datum point is calculated based on DEM data;
the gradient theta is calculated by using a gradient tool of the ArcGIS hydrologic analysis module, correcting and eliminating coordinate projection errors by combining Z factors, and calculating the gradient change rate of the projection surface of the pipeline axis, wherein the formula (1) is as follows:
θ=arctan(ΔH/ΔL)×180/π(1)
wherein DeltaL is the length of the adjacent node;
The calculation mode of the curvature radius R is that based on a quadric surface fitting model, a Levenberg-Marquardt algorithm is adopted to iterate and optimize parameters, a differential geometric formula R=1/K is utilized to quantify geometric characteristics of a bent pipe section, and local encryption sampling is carried out on the sharp bent section with the radius less than or equal to 5D, wherein K is curvature, and D is pipe diameter.
5. The method for monitoring the flow of the long-distance water conveying pipeline by integrating the terrain dynamic compensation according to claim 3, wherein in the step S23, after the dynamic weight factors are introduced, the terrain correction matrix K terrain is as follows:
Kterrain=α·ΔH/H0+β·sinθ+γ·D/R(2)
Wherein alpha, beta and gamma are weight coefficients optimized by BP neural network, H 0 is reference point elevation, and D is pipe diameter.
6. The method for monitoring the flow of the long-distance water conveying pipeline by integrating the terrain dynamic compensation according to claim 1, wherein in the step S3, the Darcy formula is modified as follows:
Embedding a terrain correction matrix K terrain into a traditional Darcy-Weis Baha equation, correcting a head loss term, and the modified formula (3) is as follows:
hfm=（fL/D+Kterrain）·v2/(2g)(3)
Wherein h fm is the corrected head loss, f is the Darcy friction coefficient, reflects the influence of pipe wall roughness on water flow resistance, D is pipe diameter, L is pipe length, v is the water flow velocity in the pipe, and g is gravity acceleration;
The kinetic energy of the downhill section is inhibited as follows:
Aiming at abnormal conversion of fluid potential energy in a downhill section to kinetic energy, a flow correction equation based on energy conservation is provided, and a formula (4) is as follows:
Qi=Qraw[1-ηtan(θ)·ΔH/L](4)
Wherein Q i is corrected flow, Q raw is real measured flow value without terrain correction, eta is terrain attenuation factor reflecting the attenuation effect of terrain on flow speed, and is positively correlated with gradient theta and elevation difference delta H, delta H/L is the unit pipe length elevation change rate and reflects potential energy gradient strength;
Downhill section pressure fluctuation compensation is as follows:
aiming at pressure dip and water hammer effect caused by gravity acceleration in a downhill section, a pressure gradient driven flow velocity compensation model is established, and the formula (5) is as follows:
vreal=v+ζ(K/ρ)·P/t(5)
Wherein zeta is a pressure gradient response factor, the value range is 0.05-0.1, K is the water body elastic modulus; P/ t is the variation of pressure in unit time and reflects the transient fluctuation intensity, v real is the real flow velocity after multi-factor dynamic compensation, and ρ is the density of water.
7. The method for monitoring the flow of the long-distance water conveying pipeline by integrating terrain dynamic compensation according to claim 6, wherein the step S4 is specifically as follows:
Executing a formula (3) and a formula (4) on the actually measured flow of the flowmeter on the pipeline by utilizing edge calculation, calculating the corrected head loss through the formula (3), outputting corrected flow Q i through the formula (4), and reducing the influence of delay on transient pressure fluctuation;
and carrying out global flow balance verification on flow data integrated into the cloud platform, setting an error threshold, and when the flow deviation of a certain node exceeds the threshold, calling the topographic parameters (delta H, theta and R) of the area and the datum point data Q 0 and P 0 by the platform to recalculate the weight coefficient corresponding to the topographic correction matrix K terrain.
8. The method for monitoring the flow of the long-distance water conveying pipeline by integrating terrain dynamic compensation according to claim 1, wherein the step S5 is specifically as follows:
And integrating BIM+GIS+iot fusion architecture, realizing real-time display of flow, topographic parameters and pressure gradients after correction of each node, and generating a pressure gradient distribution map and a pressure gradient thermodynamic diagram by using the static pressure P 0 as a reference through a kriging interpolation algorithm.
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