Method and system for improving the accuracy of sewer network flow monitoring

Abstract

This invention discloses a method and system for improving the accuracy of flow monitoring in drainage pipe networks. The method includes: installing a flow velocity sensor and a liquid level sensor at the monitoring section to collect point flow velocity and water depth data; constructing vertical and horizontal flow velocity distribution models based on logarithmic distribution functions and quadratic functions respectively, and converting the point flow velocity into vertical average flow velocity and surface average flow velocity sequentially; introducing multiple correction coefficients such as water depth nonlinearity, Reynolds number, Froude number, and roughness to correct the surface average flow velocity; and calculating the monitored flow rate based on the corrected flow velocity or Manning's formula, depending on whether the water depth is below the sensor blind zone threshold. This invention solves the problem that single-point flow velocity measurement is difficult to reflect complex flow field distributions by constructing a three-dimensional flow velocity distribution model and a multi-coefficient comprehensive correction strategy, significantly improving the accuracy and adaptability of flow monitoring in drainage pipe networks under different water depth conditions.

Description

Technical Field

[0001] This invention relates to the field of drainage network flow monitoring technology, and specifically to a method and system for improving the accuracy of drainage network flow monitoring. Background Technology

[0002] Drainage network flow monitoring technology is widely used in urban drainage systems, municipal drainage networks, and open channel flow measurement, providing crucial technical support for smart urban drainage, flood control, and water environment management. Existing drainage network flow monitoring systems typically consist of core components such as Doppler velocity sensors, liquid level measurement devices, data acquisition and processing units, and communication transmission modules. Their basic working principle involves installing a Doppler velocity meter at a fixed position at the bottom or middle of the pipe to measure the point velocity of the fluid. Simultaneously, a pressure-type, ultrasonic, or radar-type liquid level gauge is used to obtain the actual water depth within the pipe. Finally, the flow rate is calculated using the area velocity method.

[0003] However, this traditional method based on single-point velocity measurement has significant shortcomings in practical applications. Limited by sensor structure and measurement principles, Doppler velocity meters can typically only be installed at the bottom or middle of the pipe, making it impossible to measure the velocity at multiple characteristic points across the cross-section. Furthermore, the velocity distribution in actual pipes is significantly non-uniform due to factors such as wall effects, gravity, and pipe slope. A single-point velocity measurement cannot accurately reflect the velocity distribution across the entire cross-section, leading to systematic biases in the measurement results. To compensate for this deficiency, existing technologies attempt simple correction methods, but most lack velocity distribution modeling based on fluid mechanics theory. They fail to consider the logarithmic law characteristics of vertical velocity distribution and the influence of the boundary layer, and also fail to establish a mathematical model for lateral velocity distribution. This makes them unsuitable for adapting to changes in velocity distribution under different operating conditions, thus limiting measurement accuracy.

[0004] Furthermore, existing technologies lack systematic corrections for environmental factors such as changes in pipe roughness and flow regime. The impact of long-term pipe roughness changes (such as wear and deposition) on velocity distribution is not effectively compensated for, nor are the velocity distribution characteristics of different flow regimes such as laminar, transitional, and turbulent flow differentiated, leading to a significant decrease in measurement accuracy under non-ideal conditions. Particularly in shallow water conditions, the boundary layers at the bottom and sidewalls overlap, causing significant changes in velocity distribution characteristics. Existing correction models often fail, and sensors in extremely shallow water may be located within the boundary layer or near the water surface, resulting in unrepresentative measurement data and errors exceeding 40%. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for improving the accuracy of flow monitoring in drainage pipe networks, in order to solve the technical problems such as the inability to accurately reflect the complex three-dimensional flow velocity distribution of drainage pipe networks by using single-point flow velocity measurement, resulting in low flow monitoring accuracy and poor adaptability under different operating conditions (especially in shallow water areas).

[0006] To achieve the above objectives, the technical solution provided by this invention is: a method for improving the accuracy of flow monitoring in drainage pipe networks, comprising the following steps: S1: Install flow velocity sensors and liquid level sensors at the monitoring section of the pipeline to be tested, and configure the geometric characteristic parameters of the pipeline, reference roughness coefficient and liquid level gauge installation height, and collect flow velocity data and water depth data at the collection point. S2: After acquiring point velocity data and water depth data, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient and pipe geometry, a vertical velocity distribution model is established to convert the point velocity at the measurement point location into the vertical average velocity. Based on quadratic functions, combined with the water surface width and pipe material corresponding to the water depth data, a transverse velocity distribution model is established to calculate the transverse velocity distribution and convert the vertical average velocity into the surface average velocity at the detection section. S3: Introduce correction coefficients to correct the surface average velocity, and obtain the corrected surface average velocity; the correction coefficients include water depth nonlinearity correction coefficient, Reynolds number correction coefficient, Froude number correction coefficient, and roughness correction coefficient; S4: Obtain the water depth data at the current moment. If the water depth data at the current moment is greater than or equal to the preset sensor blind zone threshold, calculate the monitored flow rate value based on the corrected surface average flow velocity and the cross-sectional area of ​​the water passage and output it. If the current water depth data is less than the sensor blind zone threshold, the flow meter data is determined to be invalid, and the system automatically switches to standby mode. The flow velocity is estimated based on the Manning formula, and the monitored flow rate is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage.

[0007] To optimize the above technical solution, the specific measures also include: In step S1, the geometric feature parameters of the pipeline include the pipeline shape, pipeline size, and pipeline slope; the reference roughness coefficient is preset based on the pipeline material type or dynamically adjusted based on historical calibration data.

[0008] Furthermore, in step S2, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient, and pipe and canal geometric characteristics, a vertical velocity distribution model is established, the specific formula of which is: Based on the logarithmic law distribution function, the point velocity at the measurement point is converted into the vertical velocity:

[0009] Roughness length The calculation is performed using the following formula:

[0010] Submersion correction factor The calculation is performed using the following formula:

[0011] Furthermore, by integrating the vertical linear velocity along the water depth direction, a vertical velocity distribution model is established, with the specific formula as follows:

[0012] in, The velocity at the measurement point at a height z above the bottom of the pipe / channel; u The frictional flow velocity; k is the von Kármán constant (taken as 0.41); The roughness length; This is the diving correction factor; The roughness is for reference and is related to the pipe material; g is the acceleration due to gravity, which is 9.81 m / s². 2； B is the roughness of the current pipe channel; for a regular rectangular channel, B is the bottom width of the channel; for a circular pipe, B is the width of the water surface corresponding to the current water depth. The vertical average velocity is given.

[0013] In step S2, the calculation of the transverse velocity distribution involves converting the vertical average velocity into the surface average velocity at the detection section, specifically as follows: The formula for calculating the transverse velocity distribution is:

[0014] Furthermore, by integrating the formula for calculating the transverse velocity distribution in the width direction, the average transverse velocity is obtained. :

[0015] Surface average velocity for:

[0016] in, Let y be the flow velocity at a distance y from the side wall; The velocity is the centerline velocity. B is the shape parameter; B is the channel bottom width; These are the corrected shape parameters; The vertical average velocity; This is the centerline velocity correction factor.

[0017] In step S4, the process of estimating the flow velocity based on Manning's formula, calculating and outputting the monitored flow rate value based on the estimated flow velocity and the cross-sectional area of ​​the water passage, specifically involves:

[0018]

[0019] in, To estimate flow velocity; This represents the actual roughness. R The hydraulic radius; I For hydraulic gradient; To monitor flow rate values; This refers to the cross-sectional area of ​​the water passage.

[0020] Further, in step 3, the correction coefficients include water depth nonlinearity correction coefficients, Reynolds number correction coefficients, Froude number correction coefficients, and roughness correction coefficients, specifically: The water depth nonlinear correction coefficient is determined by a preset nonlinear function relationship based on the ratio of the current water depth to the full well water depth or the historical characteristic water depth, and is used to compensate for the boundary layer overlap effect under shallow water conditions. The Reynolds number correction factor is calculated based on the hydraulic radius, surface average velocity and kinematic viscosity, and is used to compensate for the differences in velocity distribution under different flow regimes. The Froude number correction factor is calculated based on the surface average velocity, gravitational acceleration, and hydraulic depth, and is used to compensate for the influence of velocity head on velocity distribution. Furthermore, the roughness correction coefficient is obtained by dynamically calibrating by comparing the actual hydraulic gradient with the theoretical hydraulic gradient under the current working conditions, or by comparing the difference between the calculated flow velocity and the measured flow velocity, and is used to compensate for the influence of pipe wall roughness changes on the flow velocity.

[0021] Furthermore, the present invention also includes: time synchronization of the data collected by the flow velocity sensor and the liquid level sensor, and filtering of the point flow velocity data and water depth data according to a preset outlier removal rule; the outlier removal rule is: removing data that exceeds the preset flow velocity threshold and water depth threshold, as well as data in continuously collected data with a deviation greater than the threshold.

[0022] As another important technical solution, the present invention also provides a system for improving the accuracy of flow monitoring in drainage pipe networks, comprising: The parameter configuration and data acquisition module is used to install flow velocity sensors and liquid level sensors at the monitoring section of the pipeline to be tested, and to configure the geometric characteristic parameters of the pipeline, the reference roughness coefficient and the installation height of the liquid level gauge, and to collect flow velocity data and water depth data at the acquisition point. The velocity distribution model conversion module is used to acquire point velocity data and water depth data, and then, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient, and pipe and channel geometric characteristics, establish a vertical velocity distribution model to convert the point velocity at the measurement point location into a vertical average velocity. Based on a quadratic function, combined with the water surface width corresponding to the water depth data and the pipe and channel material, a transverse velocity distribution model is established to calculate the transverse velocity distribution and convert the vertical average velocity into a surface average velocity at the detection section. A multi-coefficient velocity correction module is used to introduce correction coefficients to correct the surface average velocity, thereby obtaining the corrected surface average velocity; the correction coefficients include water depth nonlinearity correction coefficient, Reynolds number correction coefficient, Froude number correction coefficient, and roughness correction coefficient; The flow calculation and low water level mode switching module is used to acquire the water depth data at the current moment. If the water depth data at the current moment is greater than or equal to the preset sensor blind zone threshold, the monitored flow value is calculated and output based on the corrected surface average flow velocity and the cross-sectional area of ​​the water passage. If the water depth data at the current moment is less than the sensor blind zone threshold, the flow velocity is estimated based on the Manning formula, and the monitored flow value is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage.

[0023] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a method for improving the accuracy of drainage network flow monitoring as described above.

[0024] The present invention also proposes a computer-readable storage medium storing a computer program that causes a computer to execute a method for improving the accuracy of flow monitoring in drainage pipe networks as described above.

[0025] Compared with the prior art, the beneficial effects of the present invention are: This invention combines a vertical logarithmic distribution model based on Prandtl's mixed length theory with a transverse quadratic distribution model characterizing the influence of sidewalls, thus forming for the first time a complete theoretical framework for point-to-surface velocity conversion applicable to complex flow regimes in drainage pipe networks. Through the collaborative modeling and correction of vertical and transverse velocity distributions, it systematically eliminates the fundamental errors caused by neglecting the spatial distribution of velocity in traditional methods, resulting in a significant improvement in the overall flow measurement accuracy.

[0026] This invention innovatively introduces a water depth nonlinearity correction coefficient, which solves the problem of sharp drop in accuracy caused by the limited development of the boundary layer and the prominent influence of the near-bottom blind zone of the sensor in extremely shallow water areas (water depth is usually less than 0.05 meters). It significantly improves the measurement accuracy under such conditions and effectively solves the technical bottleneck of poor reliability in shallow water measurement.

[0027] This invention integrates Reynolds number correction, Froude number correction, and roughness correction to construct a multi-dimensional comprehensive correction model that covers the influence of flow regime, inertial force, and sidewall conditions. The algorithm parameters are automatically adjusted according to real-time hydraulic conditions, achieving accurate adaptation to various complex working conditions such as laminar to turbulent flow, low to high flow velocity, and changes in pipe roughness, thereby improving the robustness and measurement consistency of the system under non-ideal flow fields.

[0028] In this invention, when the liquid level drops below the effective blind zone of the sensor, the system can automatically identify and switch to the backup estimation mode based on the Manning formula, thereby avoiding data failure or erroneous output at extremely low liquid levels, ensuring the continuity of the measurement process, and significantly improving the overall reliability of the system under extreme conditions.

[0029] This invention achieves full coverage from extremely shallow water and shallow water to conventional water depths through the organic combination of theoretical modeling and multiple correction techniques. It adopts targeted correction strategies in different water depth ranges to ensure that high measurement accuracy is maintained throughout the entire measurement range, meeting the broad monitoring needs of urban drainage pipe networks from dry days to rainy days and from small flow rates to large flow rates.

[0030] The entire methodology of this invention is based on classical fluid mechanics theory. All the correction coefficients involved have clear physical meanings rather than being black-box parameters, which ensures the scientific nature of the technology. This provides clear guidance for model debugging, optimization, and application in different scenarios, reducing the difficulty of engineering implementation.

[0031] This invention significantly improves measurement accuracy while reducing the stringent requirements for installation environment and regular manual calibration, thus helping to reduce operation and maintenance costs and extend the effective service life of equipment. Its high reliability, strong adaptability, and full-condition coverage make it a promising candidate for widespread application in fields such as smart urban drainage and open channel flow monitoring. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the process of this invention.

[0033] Figure 2 This is a comparison curve of the channel surface velocity distribution after modification in an embodiment of the present invention.

[0034] Figure 3 This is a modified three-dimensional flow velocity distribution heat map of the channel in this embodiment of the invention. Detailed Implementation

[0035] The present invention will be further described in detail below through specific embodiments, but it should not be construed as limiting the scope of the subject matter of the present invention to the following embodiments. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0036] In some implementations, such as Figure 1 As shown, the present invention provides a method for improving the accuracy of flow monitoring in drainage pipe networks, comprising the following steps: S1: Install flow velocity sensors and liquid level sensors at the monitoring section of the pipeline to be tested, and configure the geometric characteristic parameters of the pipeline, reference roughness coefficient and liquid level gauge installation height, and collect flow velocity data and water depth data at the collection point. Install the Doppler current meter at the bottom of the pipe or channel to be measured. During installation, ensure that the probe is flush with the bottom of the pipe or channel, and record the vertical distance from the center of the probe to the bottom of the pipe or channel, which is denoted as the sensor height. .

[0037] In some implementations, a radar level gauge is vertically installed at the center of the top of the pipe channel, approximately 0.5 to 1 meter upstream or downstream of the Doppler current meter at the same monitoring section. During installation, ensure the radar wave transmitting antenna is perpendicular to the water surface and that there are no obstructions within the beam range. Accurately measure and record the vertical distance from the radar level gauge reference point (e.g., the bottom of the antenna) to the bottom of the pipe channel; this distance is recorded as the installation height. H install .

[0038] The geometric characteristic parameters of the pipeline include the pipeline shape, pipeline size, and pipeline slope; the reference roughness coefficient is preset based on the pipeline material type or dynamically adjusted based on historical calibration data.

[0039] The Doppler current meter and radar level gauge are synchronously triggered at a set frequency to collect data. The level gauge measures the distance from the sensor to the water surface. According to the installation height of the radar level gauge H install The calculated water depth data was obtained. h= H install - The point velocity measured by the Doppler current meter is obtained. .

[0040] S2: After acquiring point velocity data and water depth data, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient and pipe geometry, a vertical velocity distribution model is established to convert the point velocity at the measurement point location into the vertical average velocity. Based on quadratic functions, combined with the water surface width and pipe material corresponding to the water depth data, a transverse velocity distribution model is established to calculate the transverse velocity distribution and convert the vertical average velocity into the surface average velocity at the detection section. Based on the logarithmic law distribution function, the point velocity at the measurement point is converted into the vertical velocity:

[0041] Roughness length The calculation is performed using the following formula:

[0042] Submersion correction factor The calculation is performed using the following formula:

[0043] In some implementations, the vertical linear velocity is integrated along the water depth direction to establish a vertical velocity distribution model, the specific formula of which is:

[0044] in, The velocity at the measurement point at a height z above the bottom of the pipe / channel; u The frictional flow velocity; k is the von Kármán constant (taken as 0.41); The roughness length; This is the diving correction factor; The roughness is for reference and is related to the pipe material; g is the acceleration due to gravity, which is 9.81 m / s². 2； B is the roughness of the current pipe channel; for a regular rectangular channel, B is the bottom width of the channel; for a circular pipe, B is the width of the water surface corresponding to the current water depth. The vertical average velocity is given.

[0045] A quadratic function describes the lateral velocity distribution:

[0046] in, Let y be the flow velocity at a distance y from the side wall; B is the centerline velocity; B is the channel bottom width. The shape parameter reflects the effect of sidewall friction on velocity distribution; when the pipe is made of concrete, the shape parameter... for The calculation formula is:

[0047] In some implementations, the shape factor is corrected when the pipe roughness changes:

[0048] Where D, E, X and Y are empirical coefficients related to the material of the pipes and channels. Currently, most drainage pipes are made of concrete, so we can take D=0.4, E=0.4, X=1.5 and Y=3.5. These are the corrected shape parameters.

[0049] Integrating the formula for calculating the transverse velocity distribution along the width direction yields the average transverse velocity. :

[0050] In a physical sense, the vertical average velocity is equal to the vertical average velocity along the centerline of the cross-section, i.e. Substituting into the formula for calculating the transverse average velocity, we obtain the surface average velocity. for:

[0051] In some implementations, when the flow meter is installed at locations prone to spiral flow, such as the intersection of main and branch pipes, a centerline velocity correction factor is introduced. k center Correction:

[0052] Under other conventional straight pipe section operating conditions, =1.

[0053] S3: Introduce correction coefficients to correct the surface average velocity, and obtain the corrected surface average velocity; the correction coefficients include water depth nonlinearity correction coefficient, Reynolds number correction coefficient, Froude number correction coefficient, and roughness correction coefficient; Considering the boundary layer overlap effect in extremely shallow water areas, a water depth nonlinearity correction coefficient is introduced. Correction:

[0054] Considering the boundary layer characteristics of the laminar-turbulent transition zone, a Reynolds number correction factor is introduced. Make corrections:

[0055] Considering the influence of velocity head, a Froude number correction factor is introduced. Make corrections:

[0056] in, Froude's constant is used to characterize the flow conditions of fast and slow flow, and is determined based on the flow velocity at the measurement point. and water depth h The calculation shows that: .

[0057] To compensate for flow velocity changes caused by actual pipe wall wear or deposition, a roughness correction factor is introduced. Make corrections:

[0058] in, To characterize the maximum velocity gain under extremely shallow water conditions, a value range of 0.4 to 0.6 is recommended. To characterize the rate at which the correction coefficient decays with increasing water depth, and considering its correlation with the sensor's blind zone characteristics, a value range of 10–15 is recommended; h: water depth (m). In some implementations... The Reynolds number is used to characterize the flow regime and is determined based on the flow velocity at the measurement point. and hydraulic radius R The calculation shows that:

[0059] For rectangular channels For circular pipes, R = A / P ; in: P For wet period, Let be the kinematic viscosity of water, taken as 1.01 × 10⁻⁶ at room temperature (20℃). -6 m 2 / s.

[0060] Calculate the overall correction factor : · · · .

[0061] The corrected surface average velocity is: 。

[0062] S4: Obtain the water depth data at the current moment. If the water depth data at the current moment is greater than or equal to the preset sensor blind zone threshold, calculate the monitored flow rate value based on the corrected surface average flow velocity and the cross-sectional area of ​​the water passage and output it. The monitored flow rate value is: Q corrected = A ; If the current water depth data is less than the sensor blind zone threshold, the flow meter data is determined to be invalid, and the system automatically switches to standby mode. The flow velocity is estimated based on the Manning formula, and the monitored flow rate is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage.

[0063] In some implementations, the flow velocity is estimated based on Manning's formula, and the monitored flow rate is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage. Specifically:

[0064]

[0065] in, To estimate flow velocity; I For hydraulic gradient; To monitor flow rate values; This refers to the cross-sectional area of ​​the water passage.

[0066] In some implementations, the method further includes: synchronizing the data collected by the flow velocity sensor and the liquid level sensor in time, and filtering the point flow velocity data and water depth data according to a preset outlier removal rule; the outlier removal rule is to remove data that exceeds a preset flow velocity threshold, a water depth threshold, and data in continuously collected data with a deviation greater than the threshold.

[0067] In some implementations, such as Figure 2 As shown, the comparison results of the surface average velocity corrected by the method of the present invention with the original single-point measured velocity and the actual velocity distribution of the cross section (such as the velocity converted from the true value of the Parshall flume) are presented. This verifies that the present invention can accurately convert the single-point velocity into the cross section average velocity in both deep and shallow water conditions. like Figure 3 As shown in the figure, the color intensity represents the flow velocity magnitude, demonstrating the flow velocity distribution characteristics of the monitoring section in the vertical and horizontal directions. This intuitively reflects the accurate reproduction effect of the vertical logarithmic law distribution model and the horizontal quadratic function distribution model constructed in this invention on complex flow fields.

[0068] In another embodiment of the present invention, a system for improving the accuracy of flow monitoring in drainage pipe networks is proposed, comprising: The parameter configuration and data acquisition module is used to install flow velocity sensors and liquid level sensors at the monitoring section of the pipeline to be tested, and to configure the geometric characteristic parameters of the pipeline, the reference roughness coefficient and the installation height of the liquid level gauge, and to collect flow velocity data and water depth data at the acquisition point. The velocity distribution model conversion module is used to acquire point velocity data and water depth data, and then calculate the lateral velocity distribution based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient, and pipe and channel geometric characteristics, converting the point velocity at the measurement point location into the vertical average velocity. Based on the quadratic function, combined with the water surface width corresponding to the water depth data and the pipe and channel material, a lateral velocity distribution model is established, converting the vertical average velocity into the surface average velocity at the detection section. A multi-coefficient velocity correction module is used to introduce correction coefficients to correct the surface average velocity, thereby obtaining the corrected surface average velocity; the correction coefficients include water depth nonlinearity correction coefficient, Reynolds number correction coefficient, Froude number correction coefficient, and roughness correction coefficient; The flow calculation and low water level mode switching module is used to acquire the water depth data at the current moment. If the water depth data at the current moment is greater than or equal to the preset sensor blind zone threshold, the monitored flow value is calculated and output based on the corrected surface average flow velocity and the cross-sectional area of ​​the water passage. If the water depth data at the current moment is less than the sensor blind zone threshold, the flow velocity is estimated based on the Manning formula, and the monitored flow value is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage.

[0069] In another embodiment of the present invention, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a method for improving the accuracy of drainage network flow monitoring as described above.

[0070] In another embodiment of the present invention, a computer-readable storage medium is provided storing a computer program that causes a computer to perform a method for improving the accuracy of flow monitoring in drainage networks as described above.

[0071] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0072] Example 1 This embodiment selects a square tiled drainage channel for verification: Select a regular rectangular channel, width B =0.473 meters; concrete base with tiled inner walls. Due to its relatively smooth surface, its actual Manning roughness coefficient is set. n actual =0.012; A Doppler ultrasonic velocimeter was used, with the probe mounted flush against the bottom, and the center of the probe was at a distance of [height missing] from the bottom. z =0.03 meters. A standard Barcol flute is installed upstream, with a throat width of... b The value is 0.3m, and its measurement data is used as the true value of the flow rate. Q ref For comparison:

[0073] A continuous monitoring period was selected, encompassing high water level (full pipe flow), receding water period, shallow water period, and extremely shallow water period, spanning from 13:03:00 to 14:00:00. The original measurement data were corrected using the method described in this invention.

[0074] Input channel width B =0.473m, sensor height z measured =0.03m reference roughness coefficient n ref =0.014, actual roughness coefficient n actual =0.012. Set the shallow water correction parameters α=0.5, β=12.

[0075] Level gauge reading h =0.384m, flow meter reading v measured =0.660m / s.

[0076] Calculate the water flow area: A = B × h =0.473 × 0.384 = 0.1816m 2 .

[0077] Calculate the velocity submersion coefficient: according to the formula Substituting B=0.473, h=0.384, n ref =0.014, n actual =0.012, α is calculated. dip ≈0.82. This coefficient value indicates that the point of maximum flow velocity is significantly lower than the water surface, and the phenomenon of flow velocity submerging is obvious.

[0078] Calculation of roughness length: Since the wall surface is smooth, the calculated z0 is approximately 1.5 × 10⁻⁶. 4 m.

[0079] Calculate the vertical average flow velocity: Since the sensor is located at the bottom ( z / h ≈7.8%), and introduced a dip After correcting for the decrease in surface velocity, the calculated vertical velocity distribution coefficient is approximately 1.22.

[0080] Vertical average flow velocity v vertical / avg ≈0.660×1.22=0.805m / s.

[0081] Calculate the aspect ratio B / h=1.23; Calculate the lateral distribution shape parameters. a final Due to the small width-to-depth ratio and smooth wall surface, the calculated lateral correction factor (1) is... a / 12) is approximately 0.965.

[0082] Water depth correction: h >0.2m, which is considered deep water. K h ≈1.0.

[0083] Reynolds number correction: A higher Reynolds number indicates a higher Reynolds number. K Re ≈1.05.

[0084] Roughness correction: Kn =0.014 / 0.012=1.167 (compensation for reference model bias).

[0085] Final calculation: Combining the above coefficients, the average flow velocity of the cross-section is calculated. v mean =0.865m / s. Corrected flow rate Q corrected =0.865 × 0.1816 = 0.157m 3 / s.

[0086] The true value of the Barcol groove at that moment was 0.158m. 3 / s. If calculated directly using measured values: Q raw =0.120 m³ / s (error -24%). The calculation error of the method of this invention is only -0.6%, which is extremely high precision.

[0087] The data collected during the monitoring period are summarized in Table 1. It can be seen that, whether correcting the problem of low bottom flow velocity in deep water or correcting the nonlinear effects of the boundary layer in shallow water, the method of this invention is highly consistent with the true value of the Barcol flume.

[0088] Table 1 Error Analysis Table

[0089] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent substitutions, and improvements made by those skilled in the art to the above embodiments without departing from the scope of the technical solution of the present invention, based on the technical essence of the present invention, shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for improving the accuracy of flow monitoring in drainage pipe networks, characterized in that, Includes the following steps: S1: Install flow velocity sensors and liquid level sensors at the monitoring section of the pipeline to be tested, and configure the geometric characteristic parameters of the pipeline, reference roughness coefficient and liquid level gauge installation height, and collect flow velocity data and water depth data at the collection point. S2: After acquiring point velocity data and water depth data, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient and pipe geometry, a vertical velocity distribution model is established to convert the point velocity at the measurement point location into the vertical average velocity. Based on quadratic functions, combined with the water surface width and pipe material corresponding to the water depth data, a transverse velocity distribution model is established to calculate the transverse velocity distribution and convert the vertical average velocity into the surface average velocity at the detection section. S3: Introduce correction coefficients to correct the surface average velocity, and obtain the corrected surface average velocity; the correction coefficients include water depth nonlinearity correction coefficient, Reynolds number correction coefficient, Froude number correction coefficient, and roughness correction coefficient; S4: Obtain the water depth data at the current moment. If the water depth data at the current moment is greater than or equal to the preset sensor blind zone threshold, calculate the monitored flow rate value based on the corrected surface average flow velocity and the cross-sectional area of ​​the water passage and output it. If the current water depth data is less than the sensor blind zone threshold, the flow velocity is estimated based on the Manning formula, and the monitored flow rate is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage.

2. The method for improving the accuracy of flow monitoring in drainage pipe networks according to claim 1, characterized in that: In step S1, the geometric feature parameters of the pipeline include the pipeline shape, pipeline size, and pipeline slope; the reference roughness coefficient is preset based on the pipeline material type or dynamically adjusted based on historical calibration data.

3. The method for improving the accuracy of flow monitoring in drainage pipe networks according to claim 1, characterized in that: In step S2, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient, and pipe and canal geometric characteristics, a vertical velocity distribution model is established. The specific formula is as follows: in, The vertical average velocity; To measure the point velocity at the measurement point; For water depth; The roughness length; The height of the measurement point; This is the submersion correction factor.

4. The method for improving the accuracy of flow monitoring in drainage pipe networks according to claim 1, characterized in that: In step S2, the calculation of the transverse velocity distribution involves converting the vertical average velocity into the surface average velocity at the detection section, specifically as follows: The formula for calculating the transverse velocity distribution is: Integrating the formula for calculating the transverse velocity distribution along the width direction yields the average transverse velocity. : Surface average velocity for: in, Let y be the flow velocity at a distance y from the side wall; The velocity is the centerline velocity. B is the shape parameter; B is the channel bottom width; These are the corrected shape parameters; The vertical average velocity; This is the centerline velocity correction factor.

5. The method for improving the accuracy of flow monitoring in drainage pipe networks according to claim 1, characterized in that: In step S4, the process of estimating the flow velocity based on Manning's formula, calculating and outputting the monitored flow rate value based on the estimated flow velocity and the cross-sectional area of ​​the water passage, specifically involves: in, To estimate flow velocity; This is the actual roughness. R The hydraulic radius; I For hydraulic gradient; To monitor flow rate values; This refers to the cross-sectional area of ​​the water passage.

6. The method for improving the accuracy of flow monitoring in drainage pipe networks according to claim 1, characterized in that: In step 3, the correction coefficients include water depth nonlinearity correction coefficients, Reynolds number correction coefficients, Froude number correction coefficients, and roughness correction coefficients, specifically: The water depth nonlinear correction coefficient is determined based on the ratio of the current water depth to the full well water depth or the historical characteristic water depth, through a preset nonlinear function relationship; The Reynolds number correction factor is calculated based on the hydraulic radius, surface average velocity, and kinematic viscosity. The Froude number correction factor is calculated based on the surface average velocity, gravitational acceleration, and hydraulic depth. The roughness correction coefficient is obtained by dynamically calibrating by comparing the actual hydraulic gradient with the theoretical hydraulic gradient under the current working conditions, or by comparing the difference between the calculated flow velocity and the measured flow velocity.

7. The method for improving the accuracy of flow monitoring in drainage pipe networks according to claim 1, characterized in that: Also includes: The data collected by the flow velocity sensor and the liquid level sensor are synchronized in time, and the point flow velocity data and water depth data are filtered according to the preset outlier removal rules. The outlier removal rules are: removing data that exceeds the preset flow velocity threshold and water depth threshold, as well as data in continuously collected data with a deviation greater than the threshold.

8. A system for improving the accuracy of flow monitoring in drainage pipe networks, characterized in that, include: The parameter configuration and data acquisition module is used to install flow velocity sensors and liquid level sensors at the monitoring section of the pipeline to be tested, and to configure the geometric characteristic parameters of the pipeline, the reference roughness coefficient and the installation height of the liquid level gauge, and to collect flow velocity data and water depth data at the acquisition point. The velocity distribution model conversion module is used to acquire point velocity data and water depth data, and then, based on the logarithmic law distribution function, combined with water depth data, reference roughness coefficient, and pipe and channel geometric characteristics, establish a vertical velocity distribution model to convert the point velocity at the measurement point location into a vertical average velocity. Based on a quadratic function, combined with the water surface width corresponding to the water depth data and the pipe and channel material, a transverse velocity distribution model is established to calculate the transverse velocity distribution and convert the vertical average velocity into a surface average velocity at the detection section. The multi-coefficient velocity correction module is used to introduce correction coefficients to correct the surface average velocity, thus obtaining the corrected surface average velocity. The correction factors include water depth nonlinearity correction factor, Reynolds number correction factor, Froude number correction factor, and roughness correction factor; The flow calculation and low water level mode switching module is used to acquire the water depth data at the current moment. If the water depth data at the current moment is greater than or equal to the preset sensor blind zone threshold, the monitored flow value is calculated and output based on the corrected surface average flow velocity and the cross-sectional area of ​​the water passage. If the water depth data at the current moment is less than the sensor blind zone threshold, the flow velocity is estimated based on the Manning formula, and the monitored flow value is calculated and output based on the estimated flow velocity and the cross-sectional area of ​​the water passage.

9. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a method for improving the accuracy of flow monitoring in a drainage network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program causes the computer to execute a method for improving the accuracy of flow monitoring in drainage pipe networks as described in any one of claims 1 to 7.

Images