Digital twinning-based intelligent metering method for water diversion data of open channel easy to deposit

Abstract

The invention discloses an intelligent metering method for water diversion data of an easily-deposited open channel based on digital twinning. The intelligent metering method comprises the following steps: S1, acquiring basic data of the open channel through a data sensing system; s2, transmitting the basic data to a storage system, and establishing a mapping relationship between the basic data and the digital twin system one by one; and S3, predicting change trends of the flow velocity, the flow and the erosion and deposition change trends in small-scale time through a digital twin system in combination with the basic data and the time sequence, so as to obtain dynamic flow data of the open channel. The influence of the flow velocity of the bottom of the open channel easy to deposit on deposition is fully considered, the change trend of all basic data can be predicted within a small time scale under the condition that erosion and deposition change is violent, and the metering precision of water diversion data of the open channel easy to deposit is improved.

Description

Technical Field

[0001] The present invention belongs to the field of open channel metering technology, and specifically relates to an intelligent metering method for water diversion data in an open channel prone to siltation based on digital twins. Background Art

[0002] In water diversion channels with high sediment content, flow measurement is greatly affected by sediment deposition. The processing method after basic data acquisition is the most important means to solve the intelligent measurement of open channel flow. The selection of accurate processing methods is critical to the accuracy of flow measurement. Based on the empirical formula of cross-sectional flow velocity distribution, a "flow velocity-water level method" is proposed, which can be used to measure flow. The flow soft measurement method based on particle image velocimetry technology and radial basis function neural network model are both for the case of no sedimentation and are not suitable for the open channel with high sediment content in the Yellow River.

[0003] The patent with announcement number CN112052425B proposes an automated and precise flow measurement method for artificial high-sediment open channels. However, this technology can only obtain the changes in scouring and silting of open channels in real time during a fixed period of time. It does not consider the impact of the flow velocity at the bottom of the open channel on siltation. In addition, when scouring and silting change dramatically, it is impossible to predict the changing trends of various basic data within a small time scale.

[0004] Therefore, the present invention provides an intelligent measurement method for water diversion data in open channels prone to siltation based on digital twins, which fully considers the influence of the bottom flow velocity of open channels prone to siltation on siltation, and can predict the changing trends of various basic data within a small time scale under the condition of drastic changes in scouring and siltation, thereby improving the measurement accuracy of water diversion data in open channels prone to siltation. Summary of the invention

[0005] The present invention provides an intelligent metering method for water diversion data in open channels prone to siltation based on digital twins, which is used to solve the technical problems that the existing open channel flow measurement method cannot take into account the impact of the flow velocity at the bottom of the open channel on siltation, and cannot predict the changing trends of various basic data within a small time scale when the scouring and silting changes dramatically.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows: an intelligent measurement method for water diversion data in an open channel prone to siltation based on digital twins, the method comprising the following steps:

[0007] S1. Collect basic data of open channels through data sensing system;

[0008] S2. Transmit the basic data to a storage system, and establish a one-to-one mapping relationship between the basic data and the digital twin system;

[0009] S3. The digital twin system combines basic data and time series to predict the changing trends of flow velocity, flow rate, and erosion and siltation in a small-scale time, and thus the dynamic flow data of the open channel can be obtained.

[0010] Furthermore, the data sensing system includes a water level sensor, a surface flow meter and a transducer, and the basic data includes the open channel water level elevation H, the average flow velocity V of each layered section 分层 , average flow velocity V on the open channel surface 表面 and average sediment thickness h 淤积 ; H is obtained by: the liquid level sensor measures the water level elevation of the open channel; V 分层 The method of obtaining V is as follows: a pair of transducers are installed at each layered section, and each layered section transducer measures the layered flow velocity of the section; V 表面 The acquisition method is: through the surface velocity meter; h 淤积 Measured by sedimentation measurement system.

[0011] Furthermore, the step S3 comprises:

[0012] S3-1. Construct a velocity-area Bayesian sparse hierarchical model and estimate the surface velocity V of the sedimentation layer through the message passing algorithm 淤积估 ;

[0013] S3-2, by introducing the correction coefficient α to obtain the stratified flow velocity V 1 Relationship with the new velocity of the silted surface layer V silted new;

[0014] S3-3, by introducing the correction coefficient β, combined with the change in sediment thickness △h 淤积 , get the new sedimentation h 淤积新 and △h 淤积 relationship;

[0015] S3-4. Calculate the open channel flow prediction data and water level prediction data Q m+1 '、H m+1 '.

[0016] Furthermore, the calculation process of step S3-1 is:

[0017] V=λ∑S T Q (1)

[0018] ∑=λS T S+A(γ) -1 (2)

[0019]

[0020] V 淤积估 =VV i (6)

[0021] Where λ, η, and γ are Bayesian hierarchical distribution coefficients, which are set according to the characteristics of the scene. The coefficients are a=0.1, b=1 and c=0.5, i represents the i-th layer, and Vi represents the stratified flow velocity of the i-th layer, measured by the transducer of the i-th layer; V represents the average flow velocity; S is the cross-sectional area of ​​the open channel, S T represents the transpose of S, K p+n , K p It is a custom symbol, representing P+n and P-order Bessel functions, and Q is the flow rate.

[0022] Furthermore, in step S3-2, the stratified flow rate V 1 The new velocity V of the sediment surface 淤积新 The relationship is: V 淤积新 =α×V 1 +V 淤积估 ; V 1 is the flow velocity of the first channel of the transducer.

[0023] Furthermore, in step S3-3, the newly deposited 淤积新 and △h 淤积 The relationship is: h 淤积 =β×V 淤积新 +c;h 淤积新 =h 淤积 +△h 淤积 , where △h 淤积 is the change of sedimentation, V 淤积新 is the surface velocity of sedimentation, and c is the correction coefficient.

[0024] Furthermore, the calculation method of step S3-4 predicts the flow change Q and the water level change H by exponential smoothing method, and the specific calculation is:

[0025] Q m+1 '=α 流量 ×Q m+1 +(1-α 流量 )×Q m

[0026] H m+1 '=α 水位 ×H m+1 +(1-α 水位 )×H m

[0027] Where Q m+1 '、H m+1 ' are the flow prediction value and water level prediction value at time m+1, Q m+1 , H m+1 are the flow value and water level value at time m+1 respectively, Q m , H m is the flow value and water level value at time m, α 流量 and α 水位 are the correction factors for flow rate and water level respectively.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention is based on the objective law of dynamic changes of silt deposition in fluids, fully considers the influence of the flow velocity at the bottom of an open channel prone to siltation on siltation, and can predict the changing trends of various basic data within a small time scale under the condition of drastic changes in scouring and silting, thereby improving the measurement accuracy of water diversion data in open channels prone to siltation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a diagram of the working principle of the layered data perception system.

[0031] Figure 2 It is a structural block diagram of data perception, transmission and storage.

[0032] Figure 3 It is a framework diagram for predicting sedimentation changes.

[0033] Figure 4 It is the flow and water level prediction block diagram. DETAILED DESCRIPTION

[0034] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0035] See attached Figure 1-4 , a specific embodiment of the present invention comprises the following steps:

[0036] S1. Collect basic data of open channels through data sensing system;

[0037] S2. Transfer the basic data to the storage system and establish a one-to-one mapping relationship between the basic data and the digital twin system;

[0038] S3. By combining the basic data and time series through the digital twin system, the changing trends of flow velocity, flow rate, erosion and siltation in small-scale time can be predicted to obtain the dynamic flow data of the open channel.

[0039] The step S3 comprises:

[0040] S3-1. Construct a velocity-area Bayesian sparse hierarchical model and estimate the surface velocity V of the sedimentation layer through the message passing algorithm 淤积估 ;

[0041] The calculation process of the transfer algorithm is:

[0042] V=λΣS TQ (1)

[0043] ∑=λS T S + A(γ) -1 (2)

[0044]

[0045]

[0046] V 淤积估 =V - V i (6)

[0047] where λ, η, γ are Bayesian hierarchical distribution coefficients, V i represents the hierarchical flow velocity of the i-th layer; V represents the average flow velocity. S is the cross-sectional area of the open channel, and Q is the flow rate.

[0048] S3 - 2. Obtain the relationship between the hierarchical flow velocity V 1 and the velocity V 淤积 of the deposition surface layer by introducing a correction coefficient α;

[0049] V 淤积新 =α×V 1 + V 淤积估 ;

[0050] V 1 is the flow velocity of the first sound channel of the transducer.

[0051] S3 - 3. By introducing a correction coefficient β and combining the change amount △h 淤积 of the deposition thickness, obtain the relationship between the new deposition h 淤积新 and △h 淤积 ; the relationship between the new deposition h 淤积新 and △h 淤积 in the step S3 - 3 is: h 淤积 =β×V 淤积新 + c; h 淤积新 =h 淤积 + △h 淤积 where △h 淤积 is the change amount of the deposition thickness, V 淤积新 is the new flow velocity of the deposition surface layer, and c is a correction coefficient, generally taking 0.3 - 0.5.

[0052] S3 - 4. Calculate the flow prediction data and water level prediction data Q m+1 ’ and H m+1 ’ of the open channel. Predict the flow change Q and water level change H by the exponential smoothing method, and the specific calculation is:

[0053] Q m+1 ’=α 流量 ×Q m+1 +(1 - α流量 )×Q m

[0054] H m+1 '=α 水位 ×H m+1 +(1-α 水位 )×H m

[0055] Where Q m+1 '、H m+1 ' are the flow prediction value and water level prediction value at time m+1, Q m+1 , H m+1 are the flow value and water level value at time m+1 respectively, Q m , H m is the flow value and water level value at time m, α 流量 and α 水位 are the correction factors for flow rate and water level respectively.

[0056] The above calculation steps fully consider the impact of the bottom flow velocity of the open channel prone to siltation on siltation. Under the condition of drastic changes in scouring and silting, the changing trend of various basic data can be predicted within a small time scale, thereby improving the measurement accuracy of water diversion data in the open channel prone to siltation.

[0057] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application.

Claims

1. An intelligent measurement method for water diversion data in open channels prone to siltation based on digital twins, characterized in that: The following steps are involved: S1. Collect basic data of open channels through data sensing system; S2. Transmit the basic data to a storage system, and establish a one-to-one mapping relationship between the basic data and the digital twin system; S3. The digital twin system combines basic data and time series to predict the changing trends of flow velocity, flow rate, and erosion and siltation in a small-scale time, and thus the dynamic flow data of the open channel can be obtained.

2. The method for intelligent measurement of water diversion data in open channels prone to siltation based on digital twins according to claim 1 is characterized in that: The data sensing system includes a water level sensor, a surface flow meter and a transducer. The basic data includes the open channel water level elevation H, the average flow velocity V of each layered section 分层 , average flow velocity V on the open channel surface 表面 and average sediment thickness h 淤积 ; H is obtained by: the liquid level sensor measures the water level elevation of the open channel; V 分层 The method of obtaining V is as follows: a pair of transducers are installed at each layered section, and each layered section transducer measures the layered flow velocity of the section; V 表面 The acquisition method is: through the surface velocity meter; h 淤积 Measured by sedimentation measurement system.

3. According to claim 1, a digital twin-based intelligent measurement method for water diversion data in silt-prone open channels is characterized in that: The step S3 comprises: S3-1. Construct a velocity-area Bayesian sparse hierarchical model and estimate the surface velocity V of the sedimentation layer through the message passing algorithm 淤积估 ; S3-2, by introducing the correction coefficient α, the velocity V1 of the first channel of the transducer and the new velocity V of the silted surface layer are obtained. 淤积新 relationship; S3-3, by introducing the correction coefficient β, combined with the change in sediment thickness Δh 淤积 , get the new sedimentation h 淤积新 With Δh 淤积 relationship; S3-4. Calculate the open channel flow prediction data and water level prediction data Q m+1 '、H m+1 '.

4. According to claim 3, a digital twin-based intelligent measurement method for water diversion data in silt-prone open channels is characterized in that: The calculation process of step S3-1 is: V=λ∑S T Q (1) ∑=λS T S+A(c) -1 (2) V 淤积估 =VV i (6) Where λ, η, and γ are Bayesian hierarchical distribution coefficients, which are set according to the characteristics of the scene. The coefficients are a=0.1, b=1 and c=0.5, i represents the i-th layer, and V i represents the stratified flow velocity of the i-th layer, measured by the transducer of the i-th layer; V represents the average flow velocity; S is the cross-sectional area of ​​the open channel, S T represents the transpose of S, K p+n , K p It is a custom symbol, representing P+n and P-order Bessel functions, and Q is the flow rate.

5. According to claim 3, a digital twin-based intelligent measurement method for water diversion data in silt-prone open channels is characterized in that: In step S3-2, the stratified flow velocity V1 and the new flow velocity V 淤积新 The relationship is: V 淤积新 =α×V1+V 淤积估 ; V1 is the flow velocity of the first channel of the transducer.

6. The method for intelligent measurement of water diversion data in open channels prone to siltation based on digital twins according to claim 3 is characterized in that: In step S3-3, the newly deposited 淤积新 With Δh 淤积 The relationship is: h 淤积 =β×V 淤积新 +c;h 淤积新 =h 淤积 +Δh 淤积 , where Δh 淤积 is the change of sedimentation, V 淤积新 is the new flow velocity of the sedimentation surface layer, and c is the correction coefficient.

7. The method for intelligent measurement of water diversion data in open channels prone to siltation based on digital twins according to claim 3 is characterized in that: in, The calculation method of step S3-4 predicts the flow change Q and the water level change H by exponential smoothing method, and the specific calculation is: Q m+1 '=a 流量 ×Q m+1 +(1-a 流量 )×Q m H m+1 '=a 水位 ×H m+1 +(1-a 水位 )×H m Where Q m+1 '、H m+1 ' are the flow prediction value and water level prediction value at time m+1, Q m , H m is the flow value and water level value at time m, α 流量 and α 水位 are the correction factors for flow rate and water level respectively.

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