Representative monitoring data-driven water transfer project check gate overflowing formula partition construction method

Abstract

The invention discloses a representative monitoring data-driven water transfer project check gate overflowing formula partition construction method. Comprising the steps of representative monitoring data set construction, lockage flow regime division and partition overcurrent formula description, flow regime transition interval segmentation point optimization model, flow regime partition overcurrent formula coefficient calibration, partition overcurrent formula result model verification and evaluation, check gate lockage flow dynamic prediction and the like. The method has the advantages that partition fitting is carried out for different flow states, and function expressions corresponding to the flow states are constructed to improve the lockage flow simulation precision. Reference is provided for water delivery scheduling working condition rehearsal and scientific scheduling plan making, and then technical support is provided for water supply safety of an open channel water transfer project.

Description

Technical Field

[0001] This invention relates to the field of precision calibration of hydraulic parameters of control gates in open channel water transfer systems, and in particular to a method for constructing zonal flow formulas for control gates in water transfer projects driven by representative monitoring data. Background Technology

[0002] In the water conveyance scheduling system of large-scale open channel water transfer projects, the control gate, as a key flow regulation facility, has significant theoretical value and practical guiding significance for its core hydraulic parameter—the gate flow formula—in engineering structure optimization design, operation scheduling decision support, channel hydraulic control strategy formulation, and research on the dynamic characteristics of the water transfer system. At the same time, accurate calculation of the gate flow is also fundamental to achieving scientific scheduling and stable operation of the water transfer system. Currently, domestic and foreign scholars have formed two basic models in their research on the control gate flow formula: one is based on constructing empirical formulas based on the energy conservation equation (such as the Tsinghua University formula, the Wushui formula, and other classic hydraulic models); the other is using dimensional analysis methods and their derivative techniques (including data assimilation-driven dimensional analysis optimization models and multi-objective dimensional analysis frameworks nested with intelligent algorithms).

[0003] Research reveals that while traditional empirical formula systems are widely used in engineering, their parameter identification suffers from significant ambiguity and uncertainty due to the strong empirical dependence of inundation and flow coefficients, and the nonlinear response characteristics of water level-flow caused by complex flow regimes upstream of gates. In contrast, dimensional analysis methods, through mathematical representation of the coupling mechanism of hydraulic elements combined with data-driven parameter identification methods, effectively improve the numerical accuracy of flow simulation. However, it should be noted that the global fitting strategy of this method is sensitive to data distribution, and the propagation effect of model bias caused by abnormal monitoring data can lead to flow prediction errors. More importantly, existing studies mostly adopt a dimensionless modeling path under dimensional analysis methods dominated by orifice flow regimes, and have not yet established clear characteristic discrimination criteria for the flow regime adaptability of weir flow zones and orifice-weir transition zones. This lack of flow regime representation makes it easy for the predicted flow rate through the gate to exceed the engineering allowable error threshold. Summary of the Invention

[0004] The purpose of this invention is to provide a method for constructing a zonal formula for the flow control gate of a water diversion project driven by representative monitoring data, thereby solving the aforementioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A representative monitoring data-driven method for constructing a zonal overflow formula for a water diversion project's control gate includes the following steps:

[0007] S1. Flow regime division and flow formula characterization of the gate: Based on the different flow regime divisions of orifice flow and weir flow, determine the flow rate formula of the gate under the dimensional analysis method for the corresponding division.

[0008] S2. Optimization model for segmented points in the flow transition zone, and calibration of flow formula coefficients for each flow zone: Based on the weir flow discharge formula under the dimensional analysis method, the actual discharge coefficient is derived from the flow monitoring data, and then the functional expression of the discharge coefficient in the weir flow discharge formula under the dimensional analysis method is calibrated; using the NSGA-II algorithm with the minimum root mean square error as the objective function, the transition points of orifice flow and weir flow are autonomously optimized to determine the optimal transition zone and the important parameters in the discharge formula under the dimensional analysis method.

[0009] S3. Validation and Evaluation of the Flow Formula Result Model for Zoned Overflow: The flow rate formula after the optimal transition zone calibration is used to evaluate the accuracy of the model's flow rate prediction using evaluation indicators.

[0010] Preferably, step S1 specifically includes the following:

[0011] S11, Orifice Flow Interval: Using dimensional analysis, the gate flow relationship under the orifice flow submerged outflow condition is derived.

[0012] q=f(e,g,H E ,β) (1)

[0013] H E =H0-H2 (2)

[0014] Where q is the unit width flow rate; e is the gate opening; g is the acceleration due to gravity; H E H0 is the water level difference before and after the gate; β is the absolute viscosity coefficient; H0 is the water depth upstream of the gate; H2 is the water depth downstream of the gate; if the flow is free outflow, then H2 = 0.

[0015] Assume the flow rate through the gate has the following form:

[0016]

[0017] Where α, δ, η, ω, and z are constant coefficients:

[0018] Formula (3) can then be transformed into, through dimensional analysis,

[0019] L 2 T -1 =L α ×(LT -2 ) δ ×L η ×(ML -1 T -1 ) ω (4)

[0020]

[0021]

[0022] Simplifying, we get

[0023]

[0024] in, Taking the logarithm of both sides of the equation, we get... and The linear relationship between them is,

[0025]

[0026] make Given a = j and b = lgi, the simplified linear equation is:

[0027] y = ax + b (9)

[0028] The gate flow formula based on dimensional analysis under the orifice flow submerged outflow condition is derived as follows:

[0029]

[0030] Among them, Q 孔 B is the flow rate through the gate in the orifice section; B is the width of the water passage cross section.

[0031] S12. For the weir flow section: the unit width flow rate function relationship is:

[0032]

[0033] Where k, r, s, and t are constant coefficients;

[0034] Formula (11) can then be transformed into, through dimensional analysis,

[0035] L 2 T -1 =(LT) -2 ) r ×L s ×(ML -1 T -1 ) t (12)

[0036]

[0037]

[0038] By transforming equations and following the form of the conventional gate flow formula, the flow formula for an arc-shaped gate based on dimensional analysis is derived as follows:

[0039]

[0040] Among them, Q 堰 λ represents the flow rate through the gate in the weir flow section; λ is the flow rate coefficient under weir flow conditions.

[0041] Preferably, step S2 specifically includes,

[0042] S21. Based on the dimensional analysis method, the formula for the flow rate through the gate of the weir is used, and combined with representative monitoring datasets, the expression for the flow rate coefficient through the gate is fitted by introducing gate parameters.

[0043] S22. Taking the minimum root mean square error of the flow calculation formula in each segment as the objective function, construct a segmented point optimization model for the flow relationship of the control gate. By setting constraints and decision variables for the segmented point optimization model, use the NSGA-Ⅱ algorithm to perform autonomous optimization and obtain the optimal transition interval corresponding to each control gate.

[0044] S23. Based on the flow sequence of the corresponding orifice flow interval and weir flow interval, calculate the flow rate of the optimal transition interval.

[0045] Preferably, S21 specifically involves using the weir flow through the gate formula based on dimensional analysis to deduce the actual gate flow coefficient λ from flow monitoring data.

[0046]

[0047] Based on representative monitoring datasets, quadratic function curve fitting is performed according to λ~e / (H0-H2), gate parameters are introduced, and the expression for the flow coefficient through the gate is fitted.

[0048] λ = l[e / (H0-H2)] 2 +m[e / (H0-H2)]+n (18)

[0049] Wherein, l, m, and n are three gate parameters introduced.

[0050] Preferably, S22 specifically includes the following:

[0051] S221. Using the minimum root mean square error of the flow calculation formula in each segment as the objective function, construct an optimization model for the flow relationship segment of the control gate.

[0052]

[0053] MIN f(Q) =RMSE(Q obs Q cal ) min (20)

[0054] Among them, Q obs For gate flow monitoring data; Q cal Data for calculating gate throughput; RMSE Q The root mean square error between the calculated and monitored values ​​of the gate flow rate is N; N0 is the total number of data points.

[0055] S222. Set constraints for the segmented point optimization model, including constraints on the number of segments and the maximum / minimum values ​​of the relative opening segment intervals.

[0056] section min =3 (21)0.00<u<v<1.00 (22)

[0057] Where section is the number of segments; u and v are the relative opening segmentation points;

[0058] S223. The segmented point optimization model uses the relative opening segmented points u and v as decision variables. Using the NSGA-II algorithm combined with the objective function, it iterative calculations and autonomous optimization are performed on the two segmented points within the intervals where the relative opening of the control gate is 0 and the maximum opening is 1. The segmented points are respectively set as the right-side closed interval and the left-side closed interval.

[0059] Y section =(0.00,u]+(u,v)+[v,1.00) (23)

[0060] Among them, Y section The relative opening intervals for each segment;

[0061] S224. The flow equation for the arc gate is divided into three sections: orifice flow, weir flow, and the transition from orifice flow to weir flow. When the relative opening Y ≤ the upper limit opening Ya of the orifice flow, it is orifice flow; when the relative opening Y ≥ the lower limit opening Yb of the weir flow, it is weir flow; when Ya ≤ Y ≤ Yb, it is the transition from orifice flow to weir flow. The flow rate in the transition section is the average of the flow rates under orifice flow and weir flow.

[0062] Q = (Q1 + Q2) × 0.5 (24)

[0063] Where Q1 is the flow rate calculated by the orifice flow formula in the transition interval data sequence, Q2 is the flow rate calculated by the weir flow formula in the transition interval data sequence, and Q is the flow rate in the transition section;

[0064] Within an initial interval pre-defined based on the relative opening discrimination criterion, the corresponding flow rate sequence for each candidate transition interval is calculated and smoothed to obtain the average simulated transition zone flow rate. The simulated transition zone flow rate is compared with the measured flow rate data, and the root mean square error between the two is calculated to measure the simulation effect. By performing a global search and optimization on all possible combinations of the entire initial interval, the combination of transition intervals that minimizes the root mean square error is determined, which is the optimal transition zone range for the current control gate. Finally, the optimal transition interval, the optimal flow rate sequence within the interval, and the minimum root mean square error value between each control gate and the measured data are output.

[0065]

[0066] Where N is the number of moments within the transition interval time step; Let be the i-th time point; RMSE(tra) is the root mean square error of the flow rate in the transition interval; Q sim and Q obs These are the simulated and monitored values ​​of the flow rate in the transition zone, respectively.

[0067] Preferably, S23 specifically involves taking the average value of the flow rate sequences of the corresponding orifice flow interval and weir flow interval as the flow rate value of the optimal transition interval;

[0068]

[0069] Among them, Q 孔 Q represents the flow rate within the orifice flow range. 堰 Q represents the flow rate within the weir section. 过 This refers to the flow rate during the transition period.

[0070] Preferably, step S3 specifically involves using the optimal transition zone calibration and verification formula for the flow rate through the gate, predicting the flow rate based on the non-calibrated annual control gate monitoring data, and using the root mean square error, mean absolute error, and Nash coefficient as evaluation indicators to evaluate the accuracy of the model's flow rate prediction.

[0071] Preferably, the procedure before step S1 includes:

[0072] S0. Acquisition of representative monitoring datasets: Based on the data requirements of model simulation, preprocess long-sequence historical water and engineering monitoring data to obtain representative monitoring datasets.

[0073] Preferably, step S0 specifically includes,

[0074] S01. Remove obviously erroneous data such as null values, outliers, and missing values ​​to initially eliminate interference;

[0075] S02. Based on the following criteria, the relatively steady-state data are screened: on the one hand, it is determined whether the dynamic change of the flow rate under the combined action of the two gates, one channel pool and the water distribution point in the research section changes; on the other hand, it is determined whether the adjacent control gates of the research object are activated; by considering the above two factors, the stability of the control gate monitoring data is screened for steady-state stability.

[0076] S03. Use the filtered data as a representative monitoring dataset to provide a data foundation for subsequent characterization of partition relationships.

[0077] Preferably, step S3 is followed by:

[0078] S4. Dynamic prediction of gate flow: Based on the gate flow formula of each zone after evaluation and verification, the gate flow is dynamically simulated according to the basic water conditions and engineering conditions of the input project, and the gate flow is dynamically predicted.

[0079] The beneficial effects of this invention are as follows: 1. Based on historical long-sequence hydrological data, the method of this invention divides the flow regime into three zones, calibrates and verifies the fitted flow formulas for different zones, aiming to improve the accuracy of flow simulation and thus achieve precise perception of the hydrological status of the water transfer system. 2. This invention, on the one hand, determines whether the dynamic change in flow rate under the combined action of the two gates, one channel pool, and the outlet in the study section changes; on the other hand, it identifies whether the adjacent control gates of the study object have been activated, considering both factors to perform steady-state screening on the stability of the control gate monitoring data. This method avoids the interference of abnormal fluctuations in monitoring data on the accurate construction of the flow formula. Attached Figure Description

[0080] Figure 1 This is a logical sequence diagram of the partition construction method in this embodiment of the invention;

[0081] Figure 2 This is a comparison chart of the zoning prediction effects of the turbulent river control gate in this embodiment of the invention;

[0082] Figure 3 This is a comparison chart of the overall prediction effect of the turbulent river control gate in an embodiment of the present invention. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0084] Example 1

[0085] This embodiment addresses the problem of characterizing the gate passage relationship in water transfer projects by providing a representative monitoring data-driven method for constructing flow formula partitions. This method better solves the shortcomings and defects of existing technologies and improves the prediction accuracy of gate passage flow. The method selects steady-state data as the basis for constructing a representative monitoring dataset by considering the dynamic flow of the two gates, one channel pool, and the diversion point, as well as whether adjacent gates are activated. Simultaneously, it performs partition fitting for different flow regimes and constructs corresponding function expressions to improve the simulation accuracy of gate passage flow. This provides a reference for water transfer scheduling condition simulation and scientifically formulating scheduling plans, thereby providing technical support for the water supply safety of open channel water transfer projects. Figure 1 As shown, it specifically includes the following parts:

[0086] I. Construction of Representative Monitoring Dataset

[0087] Based on the data requirements of the model simulation, the historical long-term hydrological data were preprocessed. This preprocessing mainly included removing obviously erroneous data such as null values, outliers, and missing values ​​to initially eliminate interference. Then, relatively steady-state data were screened based on the following criteria: firstly, whether the dynamic changes in flow under the combined action of the two sluice gates, one channel pool, and the diversion point in the study section changed; secondly, whether the adjacent control gates of the study object were activated. Considering these two factors, the stability of the control gate monitoring data was determined through steady-state screening. The screened data was then used as a representative monitoring dataset to provide a data foundation for subsequent characterization of zoning relationships.

[0088] II. Flow regime division and characterization of zoned flow formulas

[0089] The representative monitoring dataset is divided into zones based on flow regime: orifice flow zone, transition zone, and weir flow zone. Using dimensional analysis, dimensionless flow rate formulas (i.e., dimensionless function expressions) are derived for each zone. Specifically, dimensionless function expressions for the flow rate formulas of the orifice flow and weir flow zones are derived for each zone.

[0090] 2.1 Orifice Flow Range

[0091] Using dimensional analysis, the gate flow relationship under submerged outflow conditions is derived:

[0092] q=f(e,g,H E ,β) (1)

[0093] H E =H0-H2 (2)

[0094] Where q is the unit width flow rate, in meters (m). 2 / s; e is the gate opening degree, in meters; g is the acceleration due to gravity, in meters per second. 2 H EH0 is the water level difference before and after the gate, in meters; β is the absolute viscosity coefficient; H0 is the water depth upstream of the gate, in meters; H2 is the water depth downstream of the gate, in meters. If the flow is free outflow, then H2 = 0.

[0095] Assume the flow rate through the gate has the following form, where α, δ, η, ω, and z are constant coefficients:

[0096]

[0097] This can be transformed through dimensional analysis:

[0098] L 2 T -1 =L α ×(LT -2 ) δ ×L η ×(ML -1 T -1 ) ω (4)

[0099]

[0100]

[0101] Simplifying, we get:

[0102]

[0103] in,

[0104] Taking the logarithm of both sides of the equation, we can transform it into... and The linear relationship between them is:

[0105]

[0106] make a = j, b = lgi, which simplifies to a linear equation:

[0107] y = ax + b (9)

[0108] The gate flow formula based on dimensional analysis under orifice flow submerged outflow conditions is derived as follows:

[0109]

[0110] Among them, Q 孔 The flow rate through the gate is expressed in m³. 3 / s; B is the width of the water passage section, in meters.

[0111] 2.2 Weir Flow Section

[0112] Unit width flow function relationship:

[0113]

[0114] Where k is a constant coefficient, and r, s, and t are constant coefficients, which can be transformed into the following through dimensional analysis:

[0115] L 2 T -1 =(LT) -2 ) r ×L s ×(ML -1 T -1 ) t (12)

[0116]

[0117]

[0118] By transforming equations and following the form of conventional gate flow formulas, the flow formula for an arc-shaped gate based on dimensional analysis is derived as follows:

[0119]

[0120] Among them, Q 堰 The flow rate through the gate is expressed in m³. 3 / s; λ is the overcurrent coefficient.

[0121] III. Optimization model for segmented points in the flow regime transition zone; Calibration of flow formula coefficients for each flow regime zone.

[0122] Based on the filtered steady-state data of the weir flow area and the back-calculation of the flow formula coefficient λ, the flow formula coefficient λ is obtained by fitting a quadratic function combination curve with λ~e / H and λ~e / (H0-H2). After determining the weir flow area coefficient λ, basic engineering data is collected and organized. Using the NSGA-Ⅱ optimization algorithm, with the minimum root mean square error as the optimization objective, the two transition points of the transition zone, i.e., the orifice weir flow zone, are obtained, and each flow regime zone is determined. The transition points divided by the relative opening e / H (where e is the gate opening and H is the upstream water depth) are u and v (where 0 < u < v < 1), and the following relationship holds:

[0123] Orifice flow range: 0 < e / H ≤ u

[0124] Transition interval: u < e / H < v

[0125] Weir flow interval: v≤e / H<1

[0126] Using the dimensional analysis method of orifice and weir flow zones, the average value of the calculation results of orifice flow and weir flow is taken in the transition zone to calibrate the parameters of the gate flow formula for each zone.

[0127] 3.1 Determine the functional expression of the flow formula in the weir flow zone.

[0128] Based on the weir flow formula under dimensional analysis, the actual flow coefficient λ can be derived from flow monitoring data, as shown in the following formula:

[0129] Weir flow - submerged outflow condition:

[0130]

[0131] Based on the filtered relative steady-state data, quadratic function curves are successively fitted to λ~e / H and λ~e / (H0-H2). Three gate parameters, l, m, and n, are introduced to fit the expression for the flow relationship through the gate. Here, the upstream and downstream head difference H0-H2 of the control gate is taken as an example, with e / (H0-H2) as the variable. The specific formula is as follows:

[0132] λ = l[e / (H0-H2)] 2 +m[e / (H0-H2)]+n (18)

[0133] Where λ is the flow coefficient through the gate under weir flow conditions; e is the gate opening degree in meters; l, m, and n are gate parameters; H0 and H2 are the water levels before and after the control gate, respectively, in meters.

[0134] 3.2 Autonomous Partitioning Coupled with NSGA-II Algorithm

[0135] (1) Objective function

[0136] The objective function is to minimize the root mean square error (RMSE) of the flow calculation in each segment of the gate flow relationship fitting expression. A segmented point optimization model for the gate flow relationship is then constructed, with the objective function taking the following form:

[0137]

[0138] MIN f(Q) =RMSE(Q obs Q cal ) min (20)

[0139] Among them, Q obs This is the flow rate monitoring data for the gate, in meters (m³). 3 / s, Q cal The data is for calculating the flow rate through the gate, in cubic meters (m³). 3 / s,RMSE Q MIN represents the root mean square error between the calculated and monitored values ​​of the gate flow rate. f(Q) This represents the fitting result under the condition of minimizing the root mean square error of each segment.

[0140] (2) Constraints

[0141] The constraints of the piecewise fitting model include the number of segments constraint and the maximum / minimum value constraint of the relative opening interval, expressed as follows:

[0142] section min =3 (21)0.00<u<v<1.00 (22)

[0143] Where section is the number of segments; u and v are the relative opening segment points.

[0144] (3) Decision variables

[0145] The model decision variables are relative opening segment points u and v. Using the NSGA-II algorithm combined with the objective function, iterative calculations and autonomous optimization are performed on the two segment points within the intervals where the relative opening of the control gate is 0 and the maximum opening is 1. The segment points are respectively set as the right closed interval and the left closed interval, expressed in the following form:

[0146] Y section =(0.00,u]+(u,v)+[v,1.00) (23)

[0147] Among them, Y section The relative opening intervals for each segment are given, with u and v having the same meaning as above.

[0148] (4) Optimization of flow transition interval

[0149] The flow equation for an arc-shaped gate can be divided into three sections: orifice flow, weir flow, and the transition from orifice flow to weir flow. Let Ya and Yb represent the upper limit opening of the orifice flow and the lower limit opening of the weir flow, respectively. That is, relative openings Y≤Ya represent orifice flow, Y≥Yb represent weir flow, and Ya≤Y≤Yb represent the transition from orifice flow to weir flow. After calculating the upper limit opening of the orifice flow and the lower limit opening of the weir flow, the flow rate calculation formula for the transition section uses the average flow rate calculated under the orifice flow and weir flow methods.

[0150] Q = (Q1 + Q2) × 0.5 (24)

[0151] Where Q1 is the flow rate calculated by the orifice flow formula in the transition zone data sequence, Q2 is the flow rate calculated by the weir flow formula in the transition zone data sequence, and Q is the flow rate in the transition section.

[0152] Artificially setting the flow transition interval may lead to significant errors compared to the actual interval. Therefore, data sequences for calculating the gate flow coefficient λ are calculated using both orifice flow and weir flow formulas. With λ as the dependent variable and the relative opening e / h as the independent variable, the relationship between λ and e / h is fitted. The approximate range of the transition between orifice flow and weir flow is qualitatively analyzed based on the distribution of each data point. Appropriate initial values ​​for the flow transition interval are preset to avoid the lack of reasonableness in the results due to divergence during the optimization algorithm's search for the optimal solution. In this invention, the model aims to determine the actual transition interval range of the control gate and improve the accuracy of the gate flow simulation. The transition interval is optimized using a method that minimizes the root mean square error.

[0153] Specifically, firstly, possible transition zone flow sequences are extracted within an initial interval pre-defined around the preceding relative opening discrimination criterion, such as a floating range of [0.55, 0.75]. For each candidate transition zone, its corresponding gate flow sequence is calculated and smoothed to obtain an average simulated transition zone flow value. Then, this simulated value is compared with the measured flow data, and its RMSE value is calculated to measure the quality of the simulation. By performing a global search and optimization on all possible combinations of the entire initial interval, the transition zone combination that minimizes the RMSE is determined, and this combination is the optimal transition zone range for the current control gate. Finally, the optimal transition zone for each control gate, the optimal gate flow sequence within the zone, and the minimum RMSE value between the optimal transition zone and the measured data are output.

[0154]

[0155] Where n represents the number of moments within the transition interval time step, t i Let RMSE(tra) be the root mean square error of the transition interval sequence at the i-th time point, and Q be the root mean square error of the sequence. sim and Q obs These are the simulated and monitored values ​​of the flow rate in the transition zone, respectively. The final results for the transition zone will be output in tabular form, including the optimal transition zone range for each control gate and the error evaluation index of the model fitting.

[0156] 3.3 Transitional Zone Division and Flow Calculation

[0157] The average flow rate sequence of the orifice flow interval and the weir flow interval is used as the result of the flow rate value in the transition zone.

[0158]

[0159] Among them, Q 孔 The flow rate through the gate in the orifice section is expressed in cubic meters per second (m³). 3 / s;Q 堰 The flow rate through the gate in the weir section is expressed in cubic meters per second (m³). 3 / s;Q过 The flow rate through the gate in the transition section is expressed in cubic meters per second (m³). 3 / s.

[0160] IV. Verification and Evaluation of the Regional Overcurrent Formula Model

[0161] The flow rate formulas for each zone under the optimal transition interval are derived through calibration. The Nash efficiency coefficient, mean absolute error, and root mean square error are used as evaluation indicators to evaluate the accuracy of the model's flow rate prediction.

[0162] (1) Nash efficiency coefficient (NSE)

[0163]

[0164] Among them, Q obs This is the flow rate monitoring data for the gate, in meters (m³). 3 / s;Q cal The data is for calculating the flow rate through the gate, in cubic meters (m³). 3 / s; NSE is the Nash coefficient between the calculated and monitored values ​​of the gate flow rate.

[0165] (2) Mean Absolute Error (MAE)

[0166]

[0167] Where N0 is the total number of samples; Q obs Q cal The meaning is the same as above. MAE is the average absolute error between the calculated value and the monitored value of the gate flow rate.

[0168] (3) Root Mean Square Error (RMSE)

[0169]

[0170] Where N0 is the total number of samples; Q obs Q cal The meaning is the same as above. RMSE is the root mean square error between the calculated value and the monitored value of the gate flow rate.

[0171] V. Dynamic Prediction of Throughflow at Control Gates

[0172] Finally, the formula for the flow rate through the gate is obtained. The flow rate through the gate is dynamically simulated based on the basic hydrological and engineering data of the input project. At the same time, the flow rate formula calibrated in segments further improves the accuracy of the flow rate prediction and provides a reference for scheduling and operation.

[0173] Example 2

[0174] In this embodiment, taking the Tuanhe Sluice Gate of a certain central route project as an example, the method described in this invention is used to automatically identify relatively steady-state data, construct and calculate the optimal model of segmented points in the flow transition interval, calibrate the flow formula coefficients, and characterize the function expression by partition, thereby improving the accuracy of flow prediction.

[0175] The Central Route Project spans 1432 km and includes a variety of water-passing structures, among which 61 control gates are crucial regulatory structures. In actual project operation, water level and flow sensing equipment often exhibits monitoring errors, or even anomalies or errors, such as missing or out-of-range data. Furthermore, due to disturbances from the objective environment, seasonal changes, and the operation of control structures, the hydraulic parameters of various structures exhibit dynamic characteristics in time and space, subtly affecting the project's water conveyance. These disturbances all contribute to the difficulty of characterizing the gate flow relationship and place higher demands on high-precision flow prediction. Therefore, this invention uses representative monitoring data, taking the Tuanhe control gate upstream of the Central Route Project as an example, to characterize the gate flow formula in segments, achieving dynamic prediction of gate flow and improving calculation accuracy.

[0176] I. Construction of Representative Monitoring Dataset

[0177] Long-series monitoring data are preprocessed: obviously erroneous data such as null values, invalid values, and missing values ​​are removed, and then the measured data are screened for relative steady-state conditions. On the one hand, it is determined whether the dynamic changes in flow rate under the combined action of the two gates, one channel pool, and the water diversion point in the study section have changed; on the other hand, it is determined whether the adjacent control gates of the study object have been activated. Considering both factors, the stability of the control gate monitoring data is screened, and the screened data is used as a representative monitoring dataset.

[0178] II. Flow regime division and characterization of zoned flow formulas

[0179] Based on the different flow regimes of orifice flow and weir flow, the dimensionless function relationship derived from the dimensional analysis method of gate flow rate is determined.

[0180] III. Optimization model for segmented points in the flow regime transition zone; Calibration of flow formula coefficients for each flow regime zone.

[0181] Based on the weir flow formula under dimensional analysis, the actual gate flow coefficient λ can be derived from flow monitoring data, thereby calibrating the functional expression of the gate flow coefficient in the dimensionless weir flow formula. Using the NSGA-II multi-objective genetic algorithm with the minimum root mean square error as the optimization objective, the transition points of the orifice and weir flow zones are autonomously optimized, ultimately determining the data sequence length of each zone and the important parameters in the flow formula.

[0182] IV. Verification and Evaluation of the Regional Overcurrent Formula Model

[0183] Based on steady-state data and zoning results, the flow formulas for the orifice and weir flow intervals were piecewise fitted using dimensional analysis, with the average of the two results taken for the transition interval. After calibration, root mean square error, mean absolute error, and Nash efficiency coefficient were selected as evaluation indicators to verify the calculation accuracy of the simulation results of the evaluation model.

[0184] V. Dynamic Prediction of Throughflow at Control Gates

[0185] The final formulas describing the flow in each segment were verified and compared with the overall fitting results of the dimensional analysis method. The calculation results are as follows: Figure 1 , Figure 2 As shown in Table 1, the calculation results of the indicators are as follows.

[0186] After introducing the transition zone between the orifice and weir flow, the accuracy of flow prediction is significantly improved. The root mean square error (RMSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE) of the flow simulation value and the measured value are compared by the overall dimensional analysis method of the turbulent river control gate. The results of the three indicators of the simulated and measured flow values ​​after piecewise fitting are shown in Table 1.

[0187] Table 1. Fitting index results of the Tuohe Control Gate

[0188]

[0189] Therefore, the results show that the method of this invention can achieve high simulation accuracy of the flow formula for water diversion engineering control gates driven by representative monitoring data. This method introduces the concept of a transition interval, segmenting long-sequence historical monitoring data to fit the flow relationship. The method uses the NSGA-II genetic algorithm to autonomously optimize the transition interval segmentation points, more accurately reflecting the gate flow relationship. It mainly achieves high-precision prediction of gate flow processes driven by representative data, supported by a dedicated control gate model, providing necessary support for engineering scheduling and operation. This method is simple, convenient, and universal, and can basically meet the simulation accuracy requirements for flow prediction based on flow relationship.

[0190] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained:

[0191] This invention provides a method for constructing flow formulas for control gates in water transfer projects based on representative monitoring data. The method divides the flow regime into three zones based on long-term historical hydrological data, calibrates and verifies the fitted flow formulas for each zone, aiming to improve the accuracy of flow simulation and thus achieve precise perception of the hydrological status of the water transfer system. This method considers two factors: firstly, whether the dynamic changes in flow under the combined action of the two gates, one channel pool, and the outlet in the study section change; and secondly, whether the adjacent control gates of the study object are activated. This dual-factor approach allows for steady-state screening of the control gate monitoring data to ensure stability. This method avoids the interference of abnormal fluctuations in monitoring data on the accurate construction of the flow formula.

[0192] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data, characterized in that: Includes the following steps, S1. Flow regime division and flow formula characterization of the gate: Based on the different flow regime divisions of orifice flow and weir flow, determine the flow rate formula of the gate under the dimensional analysis method for the corresponding division. S2. Optimization model for segmented points in the flow transition zone, and calibration of the flow formula coefficients for each flow zone: Based on the weir flow through the gate flow formula under the dimensional analysis method, the actual gate flow coefficient is derived from the flow monitoring data, and then the functional expression of the gate flow coefficient in the weir flow through the gate flow formula under the dimensional analysis method is calibrated. Using the NSGA-II algorithm with the minimum root mean square error as the objective function, the transition zone segmentation point between orifice flow and weir flow is obtained through autonomous optimization, and the optimal transition zone and important parameters in the gate flow formula under the dimensional analysis method are determined. S3. Validation and Evaluation of the Flow Formula Result Model for Zoned Overflow: The flow rate formula after the optimal transition zone calibration is used to evaluate the accuracy of the model's flow rate prediction using evaluation indicators.

2. The method for constructing a zoned overflow formula for a water diversion project control gate driven by representative monitoring data according to claim 1, characterized in that: Step S1 specifically includes the following: S11, Orifice Flow Interval: Using dimensional analysis, the gate flow relationship under the orifice flow submerged outflow condition is derived. q=f(e,g,H E ,β) (1) H E =H0-H2 (2) Where q is the unit width flow rate; e is the gate opening; g is the acceleration due to gravity; H E H0 is the water level difference before and after the gate; β is the absolute viscosity coefficient; H0 is the water depth upstream of the gate; H2 is the water depth downstream of the gate; if the flow is free outflow, then H2 = 0. Assume the flow rate through the gate has the following form: Where α, δ, η, ω, and z are constant coefficients: Formula (3) can then be transformed into, through dimensional analysis, L 2 T -1 =L α ×(LT -2 ) δ ×L η ×(ML -1 T -1 ) ω (4) Simplifying, we get in, Taking the logarithm of both sides of the equation, we get... and The linear relationship between them is, make Given a = j and b = lgi, the simplified linear equation is: y = ax + b (9) The gate flow formula based on dimensional analysis under the orifice flow submerged outflow condition is derived as follows: Among them, Q 孔 B is the flow rate through the gate in the orifice section; B is the width of the water passage cross section. S12. For the weir flow section: the unit width flow rate function relationship is: Where k, r, s, and t are constant coefficients; Formula (11) can then be transformed into, through dimensional analysis, L 2 T -1 =(LT -2 ) r ×L s ×(ML -1 T -1 ) t (12) By transforming equations and following the form of the conventional gate flow formula, the flow formula for an arc-shaped gate based on dimensional analysis is derived as follows: Among them, Q 堰 λ represents the flow rate through the gate in the weir flow section; λ is the flow rate coefficient under weir flow conditions.

3. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 2, characterized in that: Step S2 specifically includes, S21. Based on the dimensional analysis method, the formula for the flow rate through the gate of the weir is used, and combined with representative monitoring datasets, the expression for the flow rate coefficient through the gate is fitted by introducing gate parameters. S22. Taking the minimum root mean square error of the flow calculation formula in each segment as the objective function, construct a segmented point optimization model for the flow relationship of the control gate. By setting constraints and decision variables for the segmented point optimization model, use the NSGA-Ⅱ algorithm to perform autonomous optimization and obtain the optimal transition interval corresponding to each control gate. S23. Based on the flow sequence of the corresponding orifice flow interval and weir flow interval, calculate the flow rate of the optimal transition interval.

4. The method for constructing a zoned overflow formula for a water diversion project control gate driven by representative monitoring data according to claim 3, characterized in that: S21 specifically involves using the weir flow through-gate formula based on dimensional analysis to deduce the actual flow coefficient λ from flow monitoring data. Based on representative monitoring datasets, quadratic function curve fitting is performed according to λ~e / (H0-H2), gate parameters are introduced, and the expression for the flow coefficient through the gate is fitted. λ=l[e / (H0-H2)] 2 +m[e / (H0-H2)]+n (18) Wherein, l, m, and n are three gate parameters introduced.

5. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 4, characterized in that: S22 specifically includes the following contents. S221. Using the minimum root mean square error of the flow calculation formula in each segment as the objective function, construct an optimization model for the flow relationship segment of the control gate. MIN f(Q) =RMSE(Q obs ,Q cal ) min (20) Among them, Q obs For gate flow monitoring data; Q cal Data for calculating gate throughput; RMSE Q The root mean square error between the calculated and monitored values ​​of the gate flow rate is N; N0 is the total number of data points. S222. Set constraints for the segmented point optimization model, including constraints on the number of segments and the maximum / minimum values ​​of the relative opening segment intervals. section min =3 (21) 0.00 < u < v < 1.00 (22) Where section is the number of segments; u and v are the relative opening segment points; S223. The segment point optimization model uses the relative opening segment points u and v as decision variables, combines the NSGA-II algorithm with the objective function, and performs iterative calculations and autonomous optimization on the two segment points within the range of the relative opening of the sluice gate from 0 to the maximum opening of 1. The segment points are respectively set as the right closed interval and the left closed interval. Y section =(0.00,u]+(u,v)+[v,1.00) (23) Among them, Y section The relative opening intervals for each segment; S224. The flow equation of the radial gate is divided into three segments: orifice flow, weir flow, and the transition from orifice flow to weir flow; when the relative opening Y ≤ the upper limit opening Ya of orifice flow, it is orifice flow; when the relative opening Y ≥ the lower limit opening Yb of weir flow, it is weir flow; when Ya ≤ Y ≤ Yb, it is the transition from orifice flow to weir flow; the flow rate in the transition segment uses the average value of the flow rates under orifice flow and weir flow as the flow rate in the transition segment. Q = (Q1 + Q2) × 0.5 (24) Where Q1 is the flow rate calculated by the orifice flow formula in the data sequence of the transition interval, Q2 is the flow rate calculated by the weir flow formula in the data sequence of the transition interval; Q is the flow rate in the transition segment. Within the initial interval preset around the relative opening discrimination criterion, for each group of candidate transition intervals, calculate the corresponding flow rate sequence through the sluice gate, and smooth the flow rate sequence through the sluice gate to obtain the average simulated value of the flow rate in the transition area; compare the simulated value of the flow rate in the transition area with the measured flow rate data, calculate the root mean square error between the two, and measure the quality of the simulation effect; through global search and optimization of all possible combinations in the entire initial interval, determine the combination of transition intervals that minimizes the root mean square error, which is the optimal transition area range of the current sluice gate; finally, output the optimal transition interval corresponding to each sluice gate, the optimal flow rate sequence through the sluice gate within the interval, and the minimum root mean square error value between the measured data. Where N is the number of moments within the transition interval time step; Let be the i-th time point; RMSE(tra) is the root mean square error of the flow rate in the transition interval; Q sim and Q obs These are the simulated and monitored values ​​of the flow rate in the transition zone, respectively.

6. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 5, characterized in that: S23 specifically is to take the mean value of the flow rate sequences in the corresponding orifice flow interval and weir flow interval as the flow rate value result of the optimal transition interval. Among them, Q 孔 Q represents the flow rate within the orifice flow range. 堰 Q represents the flow rate within the weir section. 过 This refers to the flow rate during the transition period.

7. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 6, characterized in that: Step S3 specifically is to use the flow rate formula through the sluice gate after calibration and verification of the optimal transition partition ratio to predict the flow rate of the sluice gate monitoring data in non-calibration years, and use the root mean square error, mean absolute error, and Nash coefficient as evaluation indicators to evaluate the accuracy of the model's predicted flow rate.

8. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 1, characterized in that: Before step S1, it also includes S0. Obtaining the representative monitoring data set: Based on the data requirements of the model simulation, preprocess the long-sequence historical water regime and project operation monitoring data to obtain the representative monitoring data set.

9. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 8, characterized in that: Step S0 specifically includes S01.剔除空值、异常值、缺失值这些明显错误数据,初步排除干扰;Eliminate obvious error data such as null values, outliers, and missing values, and initially exclude interference; S02.基于如下标准对相对稳态数据展开筛选:一方面通过判别研究段两闸一渠池及分水口共同作用下的流量动态变化量是否变化,另一方面鉴别研究对象的相邻节制闸是否动作;通过考虑以上双重因素对节制闸监测数据是否稳定进行稳态筛选;Screen the relatively steady-state data based on the following criteria: on the one hand, determine whether the dynamic change in the flow rate under the combined action of the two sluice gates, one channel pond, and the water diversion outlet in the study section changes, and on the other hand, identify whether the adjacent sluice gates of the research object are operating; perform steady-state screening on the stability of the sluice gate monitoring data by considering these two factors. S03.将筛选后的数据作为代表性监测数据集,为后续的分区关系刻画提供数据基础。Take the screened data as the representative monitoring data set to provide a data basis for the subsequent characterization of the partition relationship.

10. The method for constructing a zoned overcurrent formula for a water diversion project control gate driven by representative monitoring data according to claim 1, characterized in that: After step S3, it also includes S4. Dynamic prediction of gate flow: Based on the gate flow formula of each zone after evaluation and verification, the gate flow is dynamically simulated according to the basic water conditions and engineering conditions of the input project, and the gate flow is dynamically predicted.

Images