A Method and System for Calibrating the Discharge Coefficient and Hydrodynamic Parameters of Gates in Irrigation Districts

Abstract

The present invention provides a method and system for calibrating the flow coefficient and hydrodynamic parameters of a gate in an irrigation area. The method includes: constructing a one-dimensional grid model, embedding the design and survey parameters of the channel section on the one-dimensional grid model to construct a hydrodynamic data matrix; using the measured hydraulic process data and operating conditions at the gate to calibrate the reference value of the flow coefficient of the gate; deducing the water level-flow calculation field; taking the uncertain undetermined parameters as background values, constructing an error function based on the relative deviation degree of the background values, and establishing a data assimilation optimization algorithm model; using the data assimilation optimization algorithm model to obtain the hydrodynamic parameters of the irrigation area canal system and the reference value of the flow coefficient of the gate; in the next period, repeating the above steps to update the calibration results. The present invention can realize the automatic calibration of the flow coefficient and hydrodynamic parameters of the gate in the irrigation area, is applicable to the water resource allocation and water volume deduction calculation of the canal system in the digital and information-based irrigation area, and assists in analyzing and optimizing the irrigation area scheduling scheme.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy engineering, and in particular to a method and system for calibrating a flow coefficient and hydrodynamic parameters of a gate in an irrigation area. Background Art

[0002] Irrigation districts are important water resource allocation systems. Traditional irrigation district scheduling management mainly relies on human control and historical experience. There is a lack of understanding of the physical laws of water transportation and evolution in the canal system, which easily leads to water waste. There is a lot of room for optimization in water allocation and gate scheduling. Therefore, today's irrigation district digitization and informatization gradually involve the mechanism model of canal water distribution. The irrigation district canal system is modeled by building a hydrodynamic mechanism model, and the flow process of the gate is considered, so that the physical process of water allocation and transportation in the irrigation district can be reasonably simulated, and the analysis and optimization of the irrigation district scheduling plan can be assisted.

[0003] However, the calculation accuracy of the simulation of the canal hydrodynamic process and gate flow process is highly dependent on the values ​​of the gate flow coefficient and hydrodynamic parameters. These parameters are usually numerous and correlated, and need to be distinguished and adjusted for different irrigation periods and different gate scheduling conditions in the irrigation area. On the other hand, in most of the current physical models of the canal system, the hydrodynamic parameters of the channel and the gate flow coefficient are usually considered and calibrated independently. However, in the actual operation of the model, these parameters will affect the model calculation results at the same time. Therefore, it is difficult to use a simple and independent method to complete the calibration of the irrigation area gate flow coefficient and hydrodynamic parameters, which is not conducive to the digital information process of modern irrigation areas. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation district gate, aiming to solve the technical problems mentioned in the background technology.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] A method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate comprises the following steps:

[0007] S10, based on the topological structure of the irrigation canal system, construct a one-dimensional grid model of the irrigation canal system, embed the design and mapping parameters of the channel section located at the grid node on each grid node of the one-dimensional grid model, and construct a hydrodynamic data matrix on the grid node to perform simulation calculation on the hydrodynamic process of the irrigation canal system;

[0008] S20. According to the design parameters of the gates in the irrigation district canal system, use the measured hydraulic process data and operating conditions at the gates to calibrate the reference value of the flow coefficient of the gates.

[0009] S30. Based on the water level and flow monitoring data of the irrigation district canal system at the current moment, the upstream and downstream boundary conditions of the irrigation district canal system, the scheduling and operation conditions of the gates, the designed roughness of the cross-sectional areas of each channel in the irrigation district canal system, and the reference value of the flow coefficient of the gates, construct a one-dimensional hydrodynamic numerical simulation model, and then deduce and obtain the water level and flow calculation field within a period of the irrigation district canal system.

[0010] S40. Take the undetermined parameters in the one-dimensional hydrodynamic numerical simulation model as the background values, and take the actual monitoring values of the irrigation district canal system as the measurement field. Based on the degree of change of the background values within a certain range, and the deviation degrees of the water level and flow calculation field and the measurement field relative to the background values, construct an error function and establish a data assimilation optimization algorithm model to make the water level and flow calculation field and the measurement field achieve the best fit.

[0011] S50. Use the data assimilation optimization algorithm model to obtain a set of hydrodynamic parameters of the irrigation district canal system and the reference value of the flow coefficient of the gates, and apply this set of hydrodynamic parameters of the irrigation district canal system and the reference value of the flow coefficient of the gates to the irrigation district canal system for water resource allocation and water volume deduction simulation calculation.

[0012] S60. In the next period, repeat steps S20 - S50 to update the calibration results of the hydrodynamic parameters of the irrigation district canal system and the reference value of the flow coefficient of the gates, so as to optimize the accuracy of the water resource allocation and the water volume deduction simulation calculation.

[0013] According to one aspect of the above technical solution, in step S10, the scope of the one-dimensional grid model includes the main canals, branch canals, rivers, gate positions in the irrigation district canal system and the connection relationships among the four. The design and survey parameters include the cross-sectional geometric data of the canal bottom elevation, canal bottom width, side slope and canal height. The parameters of the hydrodynamic data matrix include water level, flow rate, flow velocity and water depth.

[0014] According to one aspect of the above technical solution, the specific steps of step S20 include:

[0015] Obtain the design parameters of the gates in the irrigation district canal system. The design parameters of the gates include the gate opening width, gate opening height, number of gate openings and gate bottom sill elevation.

[0016] Obtain the measured water conservancy process data and operation condition data of each gate in the canal system of the irrigation area. The water conservancy process data and operation condition data include the process data of the water depth before the gate, the water depth after the gate, the measured discharge through the gate, and the gate opening.

[0017] Clean the measured water conservancy process data and operation condition data, exclude the data when any one of the four process data of the water depth before the gate, the water depth after the gate, the measured discharge through the gate, and the gate opening is missing, and exclude the data when the gate is closed and the measured discharge through the gate ≤ 0.

[0018] Based on the cleaned water conservancy process data and operation condition data, construct a gate operation data matrix:

[0019]

[0020] Where k is the gate number, t0, t1, ……, t M is the time of the water conservancy process data and operation condition data, H i is the water depth before the gate, h i is the water depth after the gate, e i is the gate opening, Q i is the measured discharge through the gate, , M is the number of the last time;

[0021] Classify and screen the flow regime through the gate according to the gate operation data matrix to calculate the theoretical discharge through the gate.

[0022] Calculate the ratio of the measured discharge through the gate to the theoretical discharge through the gate to obtain the gate flow coefficient at each time:

[0023]

[0024] Where Q ti is the theoretical discharge through the gate;

[0025] Establish a frequency distribution histogram for the gate flow coefficient in the current period, statistically obtain the confidence interval with the highest probability of occurrence of the gate flow coefficient, and take the gate flow coefficient corresponding to the confidence interval with the highest probability of occurrence as the reference value of the gate flow coefficient.

[0026] According to one aspect of the above technical solution, the specific steps of classifying and screening the flow regime through the gate according to the gate operation data matrix to calculate the theoretical discharge through the gate include:

[0027] If , then determine that the flow regime through the gate is free weir flow, and the calculation formula for the theoretical discharge through the gate is:

[0028]

[0029] If , it is determined that the flow state through the sluice is submerged weir flow, and the calculation formula for the theoretical flow rate through the sluice is:

[0030]

[0031] If , it is determined that the flow state through the sluice is free orifice flow, and the calculation formula for the theoretical flow rate through the sluice is:

[0032]

[0033] If , it is determined that the flow state through the sluice is submerged orifice flow, and the calculation formula for the theoretical flow rate through the sluice is:

[0034]

[0035] Among them, g is the acceleration of gravity, b is the width of the sluice opening, .

[0036] According to one aspect of the above technical solution, the specific steps of the step S30 include:

[0037] Put the water level and flow rate monitoring data of the current moment of the irrigation area canal system, the upstream and downstream boundary conditions of the irrigation area canal system, the scheduling operation conditions of the gate, and the designed roughness of the water-crossing section of each canal in the irrigation area canal system into the grid nodes of the one-dimensional grid model, and refer to the reference value of the gate flow coefficient to construct a one-dimensional hydrodynamic numerical simulation model to obtain the water level and flow rate calculation field of the irrigation area canal system:

[0038] The water level and flow rate calculation field includes a water level calculation field and a flow rate calculation field, and the water level calculation field and the flow rate calculation field are expressed at each water-crossing section as:

[0039]

[0040] Among them, n is the section number, w i is the section water level, q i is the section flow rate;

[0041] Superimpose the water level calculation field and the flow rate calculation field to form a water level and flow rate calculation field matrix:

[0042] .

[0043] According to one aspect of the above technical solution, the specific steps of the step S40 include:

[0044] Based on the designed roughness coefficient of the cross-sectional area of each channel in the irrigation area canal system and the reference value of the gate flow coefficient, set the uncertain parameter to be determined x as the background value x b ;

[0045] According to the variation range of the parameter to be determined x set the regulation range of the background value x b to establish the covariance matrix for quantifying the error of the parameter to be determined:

[0046]

[0047] Obtain the actual measured water level data of the cross-section of the channel and the actual measured flow data , and superimpose the actual measured water level data and the actual measured flow data to form the measurement field matrix:

[0048]

[0049] According to the difference between the water level - flow calculation field matrix and the measurement field matrix, establish the covariance matrix for quantifying the observation error:

[0050]

[0051] According to the deviation between the parameter to be determined x and the background value x b , use the inverse of the covariance matrix for quantifying the error of the parameter to be determined for weighting to form the background constraint evaluation function;

[0052] According to the deviation between the water level - flow calculation field matrix and the measurement field matrix, use the inverse of the covariance matrix for quantifying the observation error for weighting to form the observation evaluation function;

[0053] Superimpose the background constraint evaluation function and the observation evaluation function to form the objective function for parameter calibration:

[0054]

[0055] Among them, the designed roughness coefficient of the cross - sectional area determines the feasible region of parameter calibration through the sensitivity analysis method, and the reference value of the gate flow coefficient obtains the feasible region of parameter calibration through the confidence interval: , in the formula, a j 、b j are respectively the upper and lower limits of the j th parameter, ;

[0056] Establish a data assimilation optimization algorithm model to minimize the objective function for parameter calibration, so as to achieve an optimal fit between the water level-discharge calculation field matrix and the measurement field matrix.

[0057] According to one aspect of the above technical solution, the specific steps of establishing the data assimilation optimization algorithm model to minimize the objective function for parameter calibration, so as to achieve an optimal fit between the water level-discharge calculation field matrix and the measurement field matrix include:

[0058] By using the three-dimensional variational method, represent the objective function for parameter calibration as:

[0059]

[0060] Take the derivative of the above formula to obtain:

[0061]

[0062] where is H the adjoint matrix of the linear tangent matrix with respect to x ;

[0063] Obtain the optimal undetermined parameters in the one-dimensional hydrodynamic numerical simulation model through a perturbation method.

[0064] According to one aspect of the above technical solution, the specific steps of step S50 include:

[0065] Obtain a set of hydrodynamic parameters of the irrigation district canal system and reference values of the flow coefficient of the gate based on the optimal undetermined parameters, and use them as the optimal reference values of the flow coefficient;

[0066] Apply the set of hydrodynamic parameters of the irrigation district canal system and the optimal reference values of the flow coefficient to the irrigation district canal system for water resource allocation and water volume deduction simulation calculation, so as to obtain the optimal water level-discharge calculation field matrix and the measurement field matrix.

[0067] The present invention also provides an irrigation district gate flow coefficient and hydrodynamic parameter calibration system, including:

[0068] A construction module: used to construct a one-dimensional grid model of the irrigation district canal system based on the topological structure of the irrigation district canal system, embed the design and surveying and mapping parameters of the channel cross-section located at each grid node of the one-dimensional grid model, and construct a hydrodynamic data matrix at the grid nodes to perform a deduction simulation calculation on the hydrodynamic process of the irrigation district canal system;

[0069] In the construction module: The scope of the one-dimensional grid model includes the main canals, branch canals, rivers, gate positions in the irrigation district canal system, and the connection relationships among the four. The design and survey parameters include cross-sectional geometric data such as canal bottom elevation, canal bottom width, side slope, and canal height. The parameters of the hydrodynamic data matrix include water level, flow rate, flow velocity, and water depth.

[0070] Calibration module: Used to calibrate the reference value of the flow coefficient of the gate according to the design parameters of the gate in the irrigation district canal system, using the measured hydraulic process data and operating conditions at the gate.

[0071] Specifically, the calibration module is used to: Obtain the design parameters of the gate in the irrigation district canal system, and the design parameters of the gate include gate opening width, gate opening height, number of gate openings, and bottom sill elevation of the gate.

[0072] Obtain the measured hydraulic process data and operating condition data of each gate in the irrigation district canal system. The hydraulic process data and operating condition data include process data such as water depth in front of the gate, water depth behind the gate, measured flow through the gate, and gate opening.

[0073] Clean the measured hydraulic process data and operating condition data, excluding the data when any one of the four process data of water depth in front of the gate, water depth behind the gate, measured flow through the gate, and gate opening is missing, and excluding the data when the gate is closed and the measured flow through the gate ≤ 0.

[0074] Based on the cleaned hydraulic process data and operating condition data, construct a gate operation data matrix:

[0075]

[0076] Where, k is the gate number, t0, t1, ……, t M is the time of the hydraulic process data and operating condition data, H i is the water depth in front of the gate, h i is the water depth behind the gate, e i is the gate opening, Q i is the measured flow through the gate, , M is the number of the last moment;

[0077] Classify and screen the flow states through the gate according to the gate operation data matrix to calculate the theoretical flow through the gate.

[0078] Specifically: If , then determine that the flow state through the gate is free weir flow, and the calculation formula for the theoretical flow through the gate is:

[0079]

[0080] If , it is determined that the flow state through the sluice is submerged weir flow, and the calculation formula for the theoretical flow rate through the sluice is:

[0081]

[0082] If , it is determined that the flow state through the sluice is free orifice flow, and the calculation formula for the theoretical flow rate through the sluice is:

[0083]

[0084] If , it is determined that the flow state through the sluice is submerged orifice flow, and the calculation formula for the theoretical flow rate through the sluice is:

[0085]

[0086] where g is the acceleration due to gravity, b is the width of the sluice opening, ;

[0087] Calculate the ratio of the measured flow rate through the sluice to the theoretical flow rate through the sluice to obtain the flow coefficient of the sluice at each moment:

[0088]

[0089] where Q ti is the theoretical flow rate through the sluice;

[0090] Construct a frequency distribution histogram for the flow coefficient of the sluice in the current period, statistically obtain the confidence interval with the highest probability of occurrence of the flow coefficient of the sluice, and take the flow coefficient of the sluice corresponding to the confidence interval with the highest probability of occurrence as the reference value of the flow coefficient of the sluice;

[0091] Deduction module: used to construct a one-dimensional hydrodynamic numerical simulation model based on the water level and flow rate monitoring data of the current moment of the irrigation district canal system, the upstream and downstream boundary conditions of the irrigation district canal system, the scheduling operation conditions of the sluice, the designed roughness of the water-crossing section of each channel in the irrigation district canal system, and the reference value of the flow coefficient of the sluice, and then deduce and obtain the water level and flow rate calculation field within a period of the irrigation district canal system;

[0092] The deduction module is specifically used to: place the water level and flow rate monitoring data of the current moment of the irrigation district canal system, the upstream and downstream boundary conditions of the irrigation district canal system, the scheduling operation conditions of the sluice, and the designed roughness of the water-crossing section of each channel in the irrigation district canal system into the grid nodes of the one-dimensional grid model, and construct a one-dimensional hydrodynamic numerical simulation model with reference to the reference value of the flow coefficient of the sluice to obtain the water level and flow rate calculation field of the irrigation district canal system:

[0093] The water level - discharge calculation field includes a water level calculation field and a discharge calculation field. The water level calculation field and the discharge calculation field are expressed at each cross - section as follows:

[0094]

[0095] where, n is the cross - section number, w i is the cross - section water level, and q i is the cross - section discharge;

[0096] Superimpose the water level calculation field and the discharge calculation field to form a water level - discharge calculation field matrix:

[0097] ;

[0098] Optimization module: It is used to take the undetermined parameters in the one - dimensional hydrodynamic numerical simulation model as the background values, and take the actual monitoring values of the irrigation area canal system as the measurement field. Based on the degree of change of the background values within a certain range, and the deviation degrees of the water level - discharge calculation field and the measurement field from the background values, an error function is constructed, and a data assimilation optimization algorithm model is established to make the water level - discharge calculation field and the measurement field achieve the best fit;

[0099] Specifically, the optimization module is used to: based on the designed roughness coefficients of the cross - sections of each channel in the irrigation area canal system and the reference values of the gate flow coefficients, set the undetermined parameter x as the background value x b ;

[0100] Set the regulation range of the background value x according to the change range of the undetermined parameter x b to establish an undetermined parameter error quantization covariance matrix:

[0101]

[0102] Obtain the measured water level data of the cross - section of the actual monitored channel and the measured discharge data , superimpose the measured water level data and the measured discharge data to form a measurement field matrix:

[0103]

[0104] Establish an observation error quantization covariance matrix according to the difference between the water level - discharge calculation field matrix and the measurement field matrix:

[0105]

[0106] According to the to-be-determined parameter x and the background value x b The deviation between them is weighted using the inverse of the error quantization covariance matrix of the to-be-determined parameter to form a background constraint evaluation function;

[0107] According to the deviation between the water level - discharge calculation field matrix and the measurement field matrix, it is weighted using the inverse of the observation error quantization covariance matrix to form an observation evaluation function;

[0108] Superimpose the background constraint evaluation function and the observation evaluation function to form an objective function for parameter calibration:

[0109]

[0110] Among them, the designed roughness coefficient of the cross - section of the water flow determines the feasible region of the parameter calibration through the sensitivity analysis method, and the reference value of the gate flow coefficient obtains the feasible region of the parameter calibration through the confidence interval: , in the formula a j and b j are respectively j the upper and lower limits of the th parameter,

[0111] Establish a data assimilation optimization algorithm model to minimize the objective function of the parameter calibration so that the water level - discharge calculation field matrix and the measurement field matrix achieve the best fit;

[0112] Specifically: Through the three - dimensional variational method, the objective function of the parameter calibration is expressed as:

[0113]

[0114] Taking the derivative of the above formula gives:

[0115]

[0116] Among them, is H the adjoint matrix of the linear tangent matrix of x with respect to

[0117] The optimal to - be - determined parameter in the one - dimensional hydrodynamic numerical simulation model is obtained through the perturbation method.

[0118] Application module: used to use the data assimilation optimization algorithm model to obtain a set of hydrodynamic parameters of the irrigation canal system and the flow coefficient reference value of the gate, and apply the set of hydrodynamic parameters of the irrigation canal system and the flow coefficient reference value of the gate to the irrigation canal system to perform water resource allocation and water volume deduction simulation calculation;

[0119] The application module is specifically used to: obtain a set of hydrodynamic parameters of the irrigation canal system and a flow coefficient reference value of the gate according to the optimal undetermined parameters, and use them as the optimal flow coefficient reference value;

[0120] Applying the set of hydrodynamic parameters of the irrigation canal system and the optimal flow coefficient reference value to the irrigation canal system to perform water resource allocation and water volume deduction simulation calculation to obtain the optimal water level flow calculation field matrix and the measurement field matrix;

[0121] Update module: used to repeat the contents of the calibration module, deduction module, optimization module and application module in the next period, and update the calibration results of the hydrodynamic parameters of the irrigation canal system and the reference value of the flow coefficient of the gate to optimize the accuracy of the water resource allocation and the water volume deduction simulation calculation.

[0122] Compared with the prior art, the present invention has the following beneficial effects:

[0123] The present invention first establishes a one-dimensional grid model through a topological structure, and then embeds the design and mapping parameters of the channel section into the grid nodes of the one-dimensional grid model to obtain a hydrodynamic data matrix. Then, the reference value of the gate flow coefficient is calibrated according to the design parameters of the gate in the irrigation canal system, and then a water level flow calculation field within a time period is obtained by constructing a one-dimensional hydrodynamic numerical simulation model. By taking uncertain parameters as background values ​​and constructing a data assimilation optimization algorithm model based on the degree of deviation of each data from the background value, the water level flow calculation field and the measurement field are updated, thereby optimizing the accuracy of water resource allocation and water volume deduction simulation calculation to obtain the best gate flow coefficient and hydrodynamic parameters.

[0124] The present invention realizes automatic calibration of gate flow coefficient and hydrodynamic parameters of irrigation districts based on data assimilation method, obtains optimal hydrodynamic parameters and gate flow coefficient that can simulate calculation results that conform to measured hydraulic data of irrigation districts, and automatically updates calibration for different periods. It is suitable for water resource allocation and water volume deduction calculation of irrigation district canals with digital information of irrigation districts, and assists in analyzing and optimizing irrigation district scheduling plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0126] Figure 1 It is a flow chart of the method for calibrating the flow coefficient and hydrodynamic parameters of the irrigation area gate in the first embodiment of the present invention;

[0127] Figure 2 It is a schematic structural diagram of a one-dimensional grid model in the first embodiment of the present invention;

[0128] Figure 3 It is a structural block diagram of the irrigation area gate flow coefficient and hydrodynamic parameter calibration system in the second embodiment of the present invention;

[0129] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0130] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0131] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0132] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0133] Embodiment 1

[0134] See also Figures 1 to 2 , which is a method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate in the first embodiment of the present invention, comprises the following steps:

[0135] S10, based on the topological structure of the irrigation canal system, construct a one-dimensional grid model of the irrigation canal system, embed the design and mapping parameters of the channel section located at the grid node on each grid node of the one-dimensional grid model, and construct a hydrodynamic data matrix on the grid node to perform simulation calculation on the hydrodynamic process of the irrigation canal system;

[0136] S20. According to the design parameters of the gates in the irrigation district canal system, use the measured hydraulic process data and operating conditions at the gates to calibrate the reference value of the flow coefficient of the gates.

[0137] S30. Based on the water level and flow monitoring data of the irrigation district canal system at the current moment, the upstream and downstream boundary conditions of the irrigation district canal system, the scheduling and operating conditions of the gates, the designed roughness of the cross-sectional areas of each channel in the irrigation district canal system, and the reference value of the flow coefficient of the gates, construct a one-dimensional hydrodynamic numerical simulation model, and then deduce and obtain the water level and flow calculation field within a period of the irrigation district canal system.

[0138] S40. Take the undetermined parameters in the one-dimensional hydrodynamic numerical simulation model as the background values, and take the actual monitoring values of the irrigation district canal system as the measurement field. Based on the degree of change of the background values within a certain range, and the deviation degrees of the water level and flow calculation field and the measurement field relative to the background values, construct an error function and establish a data assimilation optimization algorithm model to make the water level and flow calculation field and the measurement field achieve the best fit.

[0139] S50. Use the data assimilation optimization algorithm model to obtain a set of hydrodynamic parameters of the irrigation district canal system and the reference value of the flow coefficient of the gates, and apply this set of hydrodynamic parameters of the irrigation district canal system and the reference value of the flow coefficient of the gates to the irrigation district canal system for water resource allocation and water volume deduction simulation calculations.

[0140] S60. In the next period, repeat steps S20 - S50 to update the calibration results of the hydrodynamic parameters of the irrigation district canal system and the reference value of the flow coefficient of the gates, so as to optimize the accuracy of the water resource allocation and the water volume deduction simulation calculations.

[0141] It can be understood that the present invention first establishes a one-dimensional grid model through the topological structure, then embeds the design and survey parameters of the channel cross-section into the grid nodes of the one-dimensional grid model to obtain a hydrodynamic data matrix. Then, according to the design parameters of the gates in the irrigation district canal system, calibrate the reference value of the flow coefficient of the gates. Then, obtain the water level and flow calculation field within a period by constructing a one-dimensional hydrodynamic numerical simulation model. By taking the undetermined parameters as the background values and constructing a data assimilation optimization algorithm model based on the deviation degrees of each data from the background values, update the water level and flow calculation field and the measurement field, and further optimize the accuracy of the water resource allocation and the water volume deduction simulation calculations to obtain the best gate flow coefficient and hydrodynamic parameters.

[0142] The present invention realizes the automatic calibration of the flow coefficient and hydrodynamic parameters of the gates in the irrigation area based on the data assimilation method, obtains the optimal hydrodynamic parameters and gate flow coefficients that can simulate the calculation results consistent with the measured hydraulic data in the irrigation area, and automatically updates the calibration for different periods, which is applicable to the water resource allocation and water volume deduction calculation of the irrigation canal system in the digital and information-based irrigation area, and assists in analyzing and optimizing the irrigation area scheduling plan.

[0143] Specifically, in the step S10, the range of the one-dimensional grid model includes the main canals, branch canals, rivers, gate positions in the irrigation canal system and the connection relationships among the four. The design and survey parameters include the cross-sectional geometric data such as the bottom elevation of the canal, the bottom width of the canal, the slope and the height of the canal. The parameters of the hydrodynamic data matrix include water level, flow rate, flow velocity and water depth.

[0144] In this embodiment, there are 2 main canals, 7 branch canals, 1 natural river and 10 main gates, and the connection relationships among the four are also included. By collecting the design parameters and survey parameters of the channel cross-section, a physical cross-section grid is established, and virtual cross-section grids are constructed near the key positions, gates, etc. The irrigation canal system is discretized into a one-dimensional grid model.

[0145] Furthermore, the specific steps of the step S20 include:

[0146] Obtain the design parameters of the gates in the irrigation canal system. The design parameters of the gates include the width of the gate opening, the height of the gate opening, the number of gate openings and the elevation of the bottom sill of the gate; these parameters can all be obtained through measurement or by referring to the data during construction.

[0147] Obtain the measured hydraulic process data and operating condition data of each gate in the irrigation canal system. The hydraulic process data and operating condition data include the process data of the water depth in front of the gate, the water depth behind the gate, the measured flow rate through the gate and the opening of the gate; the data in this step is obtained through measurement in the current period.

[0148] Clean the measured hydraulic process data and operating condition data, exclude the data when any one of the four process data of the water depth in front of the gate, the water depth behind the gate, the measured flow rate through the gate and the opening of the gate is missing, and exclude the data when the gate is closed and the measured flow rate through the gate ≤ 0;

[0149] Based on the cleaned hydraulic process data and operating condition data, construct a gate operation data matrix:

[0150]

[0151] Among them, k is the gate number, t0, t1, ……, t M is the time of the hydraulic process data and operating condition data, H iis the water depth in front of the sluice, h i is the water depth behind the sluice, e i is the sluice opening, Q i is the measured discharge through the sluice, , M is the number of the last moment; here k takes values from 1 to 10.

[0152] Classify and screen the flow states through the sluice according to the sluice operation data matrix to calculate the theoretical discharge through the sluice;

[0153] Calculate the ratio of the measured discharge through the sluice to the theoretical discharge through the sluice to obtain the flow coefficient of the sluice at each moment:

[0154]

[0155] where, Q ti is the theoretical discharge through the sluice;

[0156] Establish a frequency distribution histogram for the flow coefficient of the sluice in the current period, statistically obtain the confidence interval with the highest occurrence probability of the flow coefficient of the sluice, and take the flow coefficient of the sluice corresponding to the confidence interval with the highest occurrence probability as the reference value of the flow coefficient of the sluice.

[0157] Specifically, the specific steps of classifying and screening the flow states through the sluice according to the sluice operation data matrix to calculate the theoretical discharge through the sluice include:

[0158] If , it is determined that the flow state through the sluice is free weir flow, and the calculation formula for the theoretical discharge through the sluice is:

[0159]

[0160] If , it is determined that the flow state through the sluice is submerged weir flow, and the calculation formula for the theoretical discharge through the sluice is:

[0161]

[0162] If , it is determined that the flow state through the sluice is free orifice flow, and the calculation formula for the theoretical discharge through the sluice is:

[0163]

[0164] If , it is determined that the flow state through the sluice is submerged orifice flow, and the calculation formula for the theoretical discharge through the sluice is:

[0165]

[0166] where, g is the acceleration due to gravity, b is the width of the sluice opening, .

[0167] In this embodiment, all the gates obtained in return belong to the flow state of submerged orifice flow. Based on the calculation formula of the theoretical discharge through the gate under the condition of submerged orifice flow, the theoretical flow state through the gate is calculated. Then, by the ratio of the measured flow state through the gate to the theoretical flow state through the gate, the flow coefficient of the gate at each moment is obtained;

[0168] A frequency distribution histogram is established for the flow coefficient of the gate obtained in the current calculation period, and the flow coefficient of the gate corresponding to the confidence interval with the highest probability of occurrence of the flow coefficient of the gate is statistically obtained as the reference value of the flow coefficient of the gate. The reference value of the flow coefficient of the gate obtains the feasible region of the parameter calibration through the confidence interval: , where a j and b j are the upper and lower limits of the j th parameter respectively, N represents the number of parameters, ; In this embodiment, the 70% confidence interval is shown in the following table:

[0169]

[0170] Furthermore, the specific steps of the step S30 include:

[0171] Place the water level and discharge monitoring data of the current moment of the irrigation area canal system, the upstream and downstream boundary conditions of the irrigation area canal system, the scheduling operation conditions of the gate, and the designed roughness of the cross-sectional area of each canal in the irrigation area canal system into the grid nodes of the one-dimensional grid model, and construct a one-dimensional hydrodynamic numerical simulation model with reference to the reference value of the gate flow coefficient to obtain the water level and discharge calculation field of the irrigation area canal system:

[0172] The water level and discharge calculation field includes a water level calculation field and a discharge calculation field, and the water level calculation field and the discharge calculation field are expressed at each cross-sectional area as:

[0173]

[0174] where n is the cross-sectional area number, w i is the cross-sectional water level, and q i is the cross-sectional discharge;

[0175] Superimpose the water level calculation field and the discharge calculation field to form a water level and discharge calculation field matrix:

[0176] .

[0177] It can be understood that the designed roughness coefficients of the cross-sections of each channel can be obtained based on prior values such as design parameters and literature. The setting range of the designed roughness coefficients of the cross-sections of each channel is [0.01, 0.3], as shown in the following table:

[0178]

[0179] In this embodiment, only the roughness coefficient of 0.023 for the channel and the roughness coefficient of 0.03 for the natural river are used.

[0180] Furthermore, the specific steps of step S40 include:

[0181] Based on the designed roughness coefficients of the cross-sections of each channel in the irrigation area canal system and the reference value of the gate flow coefficient, set the uncertain parameter to be determined x as the background value x b ;

[0182] According to the variation range of the parameter to be determined x set the regulation range of the background value x b to establish a covariance matrix for quantifying the error of the parameter to be determined:

[0183]

[0184] Obtain the actual measured water level data of the channel cross-section and the actual measured flow data , and superimpose the actual measured water level data and the actual measured flow data to form a measurement field matrix:

[0185]

[0186] According to the difference between the water level-flow calculation field matrix and the measurement field matrix, establish an observation error quantification covariance matrix:

[0187]

[0188] According to the deviation between the parameter to be determined x and the background value x b , use the inverse of the covariance matrix for quantifying the error of the parameter to be determined for weighting to form a background constraint evaluation function;

[0189] According to the deviation between the water level-flow calculation field matrix and the measurement field matrix, use the inverse of the observation error quantification covariance matrix for weighting to form an observation evaluation function;

[0190] The background constraint evaluation function and the observation evaluation function are superimposed to form the objective function for parameter calibration:

[0191]

[0192] Wherein, the design roughness of the water-passing section is determined by a sensitivity analysis method to determine the feasible domain of the parameter calibration;

[0193] A data assimilation optimization algorithm model is established to minimize the objective function of the parameter calibration so that the water level flow calculation field matrix and the measurement field matrix achieve optimal fit.

[0194] Furthermore, the specific steps of establishing a data assimilation optimization algorithm model and minimizing the objective function of parameter calibration so as to achieve optimal fitting between the water level flow calculation field matrix and the measurement field matrix include:

[0195] By using the three-dimensional variational method, the objective function of parameter calibration is expressed as:

[0196]

[0197] Taking the derivative of the above formula, we can get:

[0198]

[0199] in, for H about x The adjoint matrix of the linear tangent matrix;

[0200] Objective Function J The minimum value is obtained to satisfy ∇Jx (dx)=0, and the optimal undetermined parameters in the one-dimensional hydrodynamic numerical simulation model are obtained by a perturbation method.

[0201] Furthermore, the specific steps of step S50 include:

[0202] According to the optimal undetermined parameters, a set of hydrodynamic parameters of the irrigation canal system and a flow coefficient reference value of the gate are obtained, and the reference values ​​are used as the optimal flow coefficient reference values;

[0203] The set of hydrodynamic parameters of the irrigation canal system and the optimal flow coefficient reference value are applied to the irrigation canal system to perform water resource allocation and water volume simulation calculations to obtain the optimal water level flow calculation field matrix and the measurement field matrix.

[0204] It can be understood that in the next calculation period divided according to water and drought conditions, farming cycles, and seasons, the above automatic calibration steps are repeated using the data from the first 30% period, and the optimal parameters are obtained by numerical simulation calculation of the irrigation canal water volume in the next 70% period.

[0205] In summary, the irrigation district gate flow coefficient and hydrodynamic parameter calibration method in the above-mentioned embodiment of the present invention realizes automatic calibration of the irrigation district gate flow coefficient and hydrodynamic parameters based on the data assimilation method, obtains the optimal hydrodynamic parameters and gate flow coefficient that can simulate the calculation results that conform to the measured hydraulic data of the irrigation district, and automatically updates the calibration for different periods, which is suitable for water resource allocation and water volume deduction calculation of irrigation district canals with digital information of irrigation districts, and assists in analyzing and optimizing irrigation district scheduling plans.

[0206] Embodiment 2

[0207] See also Figure 3 The second embodiment of the present invention provides a system for calibrating a gate flow coefficient and a hydrodynamic parameter of an irrigation area, comprising:

[0208] Construction module 11: used to construct a one-dimensional grid model of the irrigation canal system based on the topological structure of the irrigation canal system, embed the design and mapping parameters of the channel section located at the grid node on each grid node of the one-dimensional grid model, and construct a hydrodynamic data matrix on the grid node to perform simulation calculation on the hydrodynamic process of the irrigation canal system;

[0209] In the construction module 11: the scope of the one-dimensional grid model includes the main canal, branch canal, river channel, gate location and the connection relationship between the four in the irrigation canal system, the design and mapping parameters include the cross-sectional geometric data of the canal bottom elevation, canal bottom width, side slope and canal height, and the parameters of the hydrodynamic data matrix include water level, flow, flow velocity and water depth;

[0210] The calibration module 12 is used to calibrate the flow coefficient reference value of the gate according to the design parameters of the gate in the irrigation canal system, using the hydraulic process data and operating conditions measured at the gate;

[0211] The calibration module 12 is specifically used to: obtain the design parameters of the gate in the irrigation canal system, the design parameters of the gate include gate hole width, gate hole height, gate hole number and gate bottom sill elevation;

[0212] Acquire the measured hydraulic process data and operating condition data of each gate in the irrigation canal system, wherein the hydraulic process data and operating condition data include the process data of the water depth before the gate, the water depth after the gate, the measured flow rate through the gate and the gate opening;

[0213] Clean the measured water conservancy process data and operating condition data, and exclude the data when any one of the four process data of the water depth in front of the gate, the water depth behind the gate, the measured flow rate through the gate, and the gate opening is missing, and exclude the data when the gate is closed and the measured flow rate through the gate ≤ 0;

[0214] Based on the cleaned water conservancy process data and operating condition data, construct a gate operation data matrix:

[0215]

[0216] Among them, k is the gate number, t0, t1, ……, t M is the time of the water conservancy process data and operating condition data, H i is the water depth in front of the gate, h i is the water depth behind the gate, e i is the gate opening, Q i is the measured flow rate through the gate, , M is the number of the last moment;

[0217] Classify and screen the flow states through the gate according to the gate operation data matrix to calculate the theoretical flow rate through the gate;

[0218] Specifically: If , then it is determined that the flow state through the gate is free weir flow, and the calculation formula for the theoretical flow rate through the gate is:

[0219]

[0220] If , then it is determined that the flow state through the gate is submerged weir flow, and the calculation formula for the theoretical flow rate through the gate is:

[0221]

[0222] If , then it is determined that the flow state through the gate is free orifice flow, and the calculation formula for the theoretical flow rate through the gate is:

[0223]

[0224] If , then it is determined that the flow state through the gate is submerged orifice flow, and the calculation formula for the theoretical flow rate through the gate is:

[0225]

[0226] Among them, g is the acceleration of gravity, b is the width of the gate opening, ;

[0227] Calculate the ratio of the measured discharge through the gate to the theoretical discharge through the gate to obtain the gate flow coefficient at each moment:

[0228]

[0229] where Q ti is the theoretical discharge through the gate;

[0230] Establish a frequency distribution histogram for the gate flow coefficient in the current period, statistically obtain the confidence interval with the highest probability of occurrence of the gate flow coefficient, and take the gate flow coefficient corresponding to the confidence interval with the highest probability of occurrence as the reference value of the gate flow coefficient;

[0231] Deduction module 13: Used to construct a one-dimensional hydrodynamic numerical simulation model based on the water level and flow monitoring data of the irrigation area canal system at the current moment, the upstream and downstream boundary conditions of the irrigation area canal system, the scheduling operation conditions of the gate, the designed roughness of the water-crossing section of each channel in the irrigation area canal system, and the reference value of the gate flow coefficient, and then deduce and obtain the water level and flow calculation field within a period of the irrigation area canal system;

[0232] The deduction module 13 is specifically used to: Place the water level and flow monitoring data of the irrigation area canal system at the current moment, the upstream and downstream boundary conditions of the irrigation area canal system, the scheduling operation conditions of the gate, and the designed roughness of the water-crossing section of each channel in the irrigation area canal system into the grid nodes of the one-dimensional grid model, and refer to the reference value of the gate flow coefficient to construct a one-dimensional hydrodynamic numerical simulation model to obtain the water level and flow calculation field of the irrigation area canal system:

[0233] The water level and flow calculation field includes a water level calculation field and a flow calculation field, and the water level calculation field and the flow calculation field are expressed at each water-crossing section as:

[0234]

[0235] where n is the section number, w i is the section water level, and q i is the section flow;

[0236] Superimpose the water level calculation field and the flow calculation field to form a water level and flow calculation field matrix:

[0237] ;

[0238] Optimization Module 14: It is used to take the uncertain undetermined parameters in the one-dimensional hydrodynamic numerical simulation model as the background values, and take the actual monitoring values of the irrigation district canal system as the measurement field. Based on the degree of change of the background values within a certain range, and the deviation degrees of the water level-flow calculation field and the measurement field from the background values, an error function is constructed, and a data assimilation optimization algorithm model is established to make the water level-flow calculation field and the measurement field achieve the best fit;

[0239] The Optimization Module 14 is specifically used for: Based on the designed roughness of the cross-section of each channel in the irrigation district canal system and the reference value of the gate flow coefficient, the uncertain undetermined parameters x are set as the background values x b ;

[0240] According to the change range of the undetermined parameters x the regulation range of the background values is set x b to establish an undetermined parameter error quantization covariance matrix:

[0241]

[0242] Obtain the actual measured water level data of the cross-section of the channel and the actual measured flow data , and superimpose the actual measured water level data and the actual measured flow data to form a measurement field matrix:

[0243]

[0244] According to the gap between the water level-flow calculation field matrix and the measurement field matrix, an observation error quantization covariance matrix is established:

[0245]

[0246] According to the deviation between the undetermined parameters x and the background values x b weighting is performed using the inverse of the undetermined parameter error quantization covariance matrix to form a background constraint evaluation function;

[0247] According to the deviation between the water level-flow calculation field matrix and the measurement field matrix, weighting is performed using the inverse of the observation error quantization covariance matrix to form an observation evaluation function;

[0248] The background constraint evaluation function and the observation evaluation function are superimposed to form an objective function for parameter calibration:

[0249]

[0250] Among them, the designed roughness of the cross-sectional area of the flowing water determines the feasible region of the parameter calibration through the sensitivity analysis method, and the reference value of the gate flow coefficient obtains the feasible region of the parameter calibration through the confidence interval: , in the formula, a j and b j are respectively j the upper and lower limits of the th parameter;

[0251] Establish a data assimilation optimization algorithm model to minimize the objective function of the parameter calibration, so that the water level-discharge calculation field matrix and the measurement field matrix achieve the best fit;

[0252] Specifically: Through the three-dimensional variational method, the objective function of the parameter calibration is expressed as:

[0253]

[0254] Taking the derivative of the above formula, we get:

[0255]

[0256] Among them, is H the adjoint matrix of the linear tangent matrix of x with respect to;

[0257] Obtain the optimal undetermined parameters in the one-dimensional hydrodynamic numerical simulation model through the perturbation method.

[0258] Application module 15: Used to obtain a set of hydrodynamic parameters of the irrigation district canal system and the reference value of the gate flow coefficient using the data assimilation optimization algorithm model, and apply the set of hydrodynamic parameters of the irrigation district canal system and the reference value of the gate flow coefficient to the irrigation district canal system for water resource allocation and water volume deduction simulation calculation;

[0259] The application module 15 is specifically used for: obtaining a set of hydrodynamic parameters of the irrigation district canal system and the reference value of the gate flow coefficient according to the optimal undetermined parameters, and using them as the optimal reference value of the flow coefficient;

[0260] Apply the set of hydrodynamic parameters of the irrigation district canal system and the optimal reference value of the flow coefficient to the irrigation district canal system for water resource allocation and water volume deduction simulation calculation to obtain the optimal water level-discharge calculation field matrix and the measurement field matrix;

[0261] Update Module 16: It is used to repeat the content of the calibration module, the deduction module, the optimization module, and the application module in the next period, and update the calibration results of the hydrodynamic parameters of the irrigation area canal system and the reference value of the flow coefficient of the gate, so as to optimize the accuracy of the water resource allocation and the water volume deduction simulation calculation.

[0262] In the description of this specification, the description of reference terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0263] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. A method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate, characterized in that: The steps include: S10, based on the topological structure of the irrigation canal system, construct a one-dimensional grid model of the irrigation canal system, embed the design and mapping parameters of the channel section located at the grid node on each grid node of the one-dimensional grid model, and construct a hydrodynamic data matrix on the grid node to perform simulation calculation on the hydrodynamic process of the irrigation canal system; S20, according to the design parameters of the gate in the irrigation canal system, using the hydraulic process data and operating conditions actually measured at the gate, calibrating the reference value of the flow coefficient of the gate; S30, constructing a one-dimensional hydrodynamic numerical simulation model based on the water level and flow monitoring data of the irrigation canal system at the current moment, the upstream and downstream boundary conditions of the irrigation canal system, the dispatching and operating conditions of the gate, the design roughness of the water flow section of each channel in the irrigation canal system, and the reference value of the flow coefficient of the gate, and then deriving and obtaining the water level and flow calculation field of the irrigation canal system within a period of time; S40, taking the uncertain parameters to be determined in the one-dimensional hydrodynamic numerical simulation model as background values, and taking the actual monitoring values ​​of the irrigation canal system as the measurement field, constructing an error function based on the degree of change of the background value within a certain range, and the degree of deviation of the water level and flow calculation field and the measurement field relative to the background value, and establishing a data assimilation optimization algorithm model, so that the water level and flow calculation field and the measurement field achieve optimal fit; The specific steps of step S40 include: Based on the design roughness of the water flow section of each channel in the irrigation canal system and the reference value of the flow coefficient of the gate, the uncertain parameters to be determined are x Set as background value x b ; According to the parameters to be determined x The background value is set within the range of x b The control range of is used to establish the quantized covariance matrix of the unknown parameter error: Obtain the actual water level data of the channel section under actual monitoring and flow measurement data , superimposed on the measured water level data And the measured flow data Form the measurement field matrix: According to the gap between the water level flow calculation field matrix and the measurement field matrix, the observation error quantization covariance matrix is ​​established: Among them, H represents the water level flow calculation field matrix; According to the parameters to be determined x With the background value x b The deviation between them is weighted using the inverse of the undetermined parameter error quantization covariance matrix to form a background constraint evaluation function; According to the deviation between the water level flow calculation field matrix and the measurement field matrix, the inverse of the observation error quantization covariance matrix is ​​used for weighting to form an observation evaluation function; The background constraint evaluation function and the observation evaluation function are superimposed to form the objective function for parameter calibration: The design roughness of the water-passing section is determined by a sensitivity analysis method to determine the feasible domain of the parameter calibration, and the reference value of the flow coefficient of the gate is obtained by a confidence interval to obtain the feasible domain of the parameter calibration: , where a j , b j Respectively j The upper and lower limits of the parameters, N Indicates the number of parameters, ; Establishing a data assimilation optimization algorithm model to minimize the objective function of the parameter calibration so that the water level flow calculation field matrix and the measurement field matrix achieve optimal fit; S50, using the data assimilation optimization algorithm model to obtain a set of hydrodynamic parameters of the irrigation canal system and a reference value of the flow coefficient of the gate, and applying the set of hydrodynamic parameters of the irrigation canal system and the reference value of the flow coefficient of the gate to the irrigation canal system to perform water resource allocation and water volume deduction simulation calculation; S60, in the next period, repeat steps S20-S50 to update the calibration results of the hydrodynamic parameters of the irrigation canal system and the reference value of the flow coefficient of the gate to optimize the accuracy of the water resource allocation and the water volume simulation calculation.

2. The method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate according to claim 1 is characterized in that: In step S10, the scope of the one-dimensional grid model includes the main canal, branch canal, river channel, gate location and the connection relationship between the four in the irrigation canal system, the design and surveying parameters include the cross-sectional geometry data of the canal bottom elevation, canal bottom width, side slope and canal height, and the parameters of the hydrodynamic data matrix include water level, flow, flow velocity and water depth.

3. The method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate according to claim 1 is characterized in that: The specific steps of step S20 include: Obtaining design parameters of the gate in the irrigation canal system, wherein the design parameters of the gate include gate hole width, gate hole height, gate hole number and gate sill elevation; Acquire the measured hydraulic process data and operating condition data of each gate in the irrigation canal system, wherein the hydraulic process data and operating condition data include the process data of the water depth before the gate, the water depth after the gate, the measured flow rate through the gate and the gate opening; Clean the measured water conservancy process data and operating condition data, exclude data when any one of the four process data of the water depth before the gate, the water depth after the gate, the measured gate flow rate and the gate opening is missing, and exclude data when the gate is closed and the measured gate flow rate is ≤0; Based on the cleaned hydraulic process data and operating condition data, a gate operation data matrix is ​​constructed: in, k Number the gates, t0, t1, ..., t M H is the discrete moment of the water conservancy process data and operation condition data, i is the water depth before the gate, h i is the water depth behind the gate, e i is the gate opening, Q i To measure the flow rate through the gate, , M is the number of the last moment; Classifying and screening the gate flow pattern according to the gate operation data matrix to calculate the theoretical gate flow rate; The ratio of the measured gate flow rate to the theoretical gate flow rate is calculated to obtain the gate flow coefficient at each moment: Among them, Q ti is the theoretical gate flow rate; A frequency distribution histogram is established for the gate flow coefficient in the current period, and the confidence interval with the maximum probability of occurrence of the gate flow coefficient is obtained statistically, and the gate flow coefficient corresponding to the confidence interval with the maximum probability of occurrence is taken as the reference value of the gate flow coefficient.

4. The method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate according to claim 3 is characterized in that: The specific steps of classifying and screening the gate flow pattern according to the gate operation data matrix to calculate the theoretical gate flow rate include: like , then the flow state through the gate is determined to be free weir flow, and the calculation formula for the theoretical flow through the gate is: like , then the flow state through the gate is determined to be submerged weir flow, and the calculation formula for the theoretical flow through the gate is: like , then the gate flow state is determined to be free hole flow, and the calculation formula for the theoretical gate flow rate is: like , then the gate flow state is determined to be submerged hole flow, and the calculation formula for the theoretical gate flow is: Among them, g is the acceleration of gravity, b is the width of the gate hole, .

5. The method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate according to claim 4 is characterized in that: The specific steps of step S30 include: The water level and flow monitoring data of the irrigation canal system at the current moment, the upstream and downstream boundary conditions of the irrigation canal system, the dispatching and operating conditions of the gate, and the design roughness of the water-passing section of each channel in the irrigation canal system are placed into the grid nodes of the one-dimensional grid model, and the one-dimensional hydrodynamic numerical simulation model is constructed with reference to the reference value of the gate flow coefficient to obtain the water level and flow calculation field of the irrigation canal system: The water level and flow calculation field includes a water level calculation field and a flow calculation field. The water level calculation field and the flow calculation field are expressed in each water flow section as follows: in, n is the section number, w i is the cross-section water level, q i is the cross-sectional flow rate; The water level calculation field and the flow calculation field are superimposed to form a water level and flow calculation field matrix: 。 6. The method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate according to claim 5 is characterized in that: The specific steps of establishing a data assimilation optimization algorithm model and minimizing the objective function of parameter calibration so as to achieve optimal fitting between the water level flow calculation field matrix and the measurement field matrix include: By using the three-dimensional variational method, the objective function of parameter calibration is expressed as: The objective function of parameter calibration obtained by the three-dimensional variational method is derived as follows: in, for H about x The adjoint matrix of the linear tangent matrix; The optimal undetermined parameters in the one-dimensional hydrodynamic numerical simulation model are obtained by a perturbation method.

7. The method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate according to claim 6, characterized in that: The specific steps of step S50 include: According to the optimal undetermined parameters, a set of hydrodynamic parameters of the irrigation canal system and a flow coefficient reference value of the gate are obtained, and the reference values ​​are used as the optimal flow coefficient reference values; The set of hydrodynamic parameters of the irrigation canal system and the optimal flow coefficient reference value are applied to the irrigation canal system to perform water resource allocation and water volume simulation calculations to obtain the optimal water level flow calculation field matrix and the measurement field matrix.

8. A system for calibrating the flow coefficient and hydrodynamic parameters of irrigation gates, characterized in that: include: Construction module: used to construct a one-dimensional grid model of the irrigation canal system based on the topological structure of the irrigation canal system, embed the design and mapping parameters of the channel section located at the grid node on each grid node of the one-dimensional grid model, and construct a hydrodynamic data matrix on the grid node to perform simulation calculation on the hydrodynamic process of the irrigation canal system; Calibration module: used to calibrate the flow coefficient reference value of the gate according to the design parameters of the gate in the irrigation canal system, using the hydraulic process data and operating conditions measured at the gate; Deduction module: used to construct a one-dimensional hydrodynamic numerical simulation model based on the water level and flow monitoring data of the irrigation canal system at the current moment, the upstream and downstream boundary conditions of the irrigation canal system, the dispatching and operating conditions of the gate, the design roughness of the water flow section of each channel in the irrigation canal system, and the reference value of the flow coefficient of the gate, so as to deduce and obtain the water level and flow calculation field of the irrigation canal system within a period of time; Optimization module: used to take the uncertain undetermined parameters in the one-dimensional hydrodynamic numerical simulation model as background values, and take the actual monitoring values ​​of the irrigation canal system as the measurement field, construct an error function based on the variation degree of the background value within a certain range, and the deviation degree of the water level flow calculation field and the measurement field relative to the background value, and establish a data assimilation optimization algorithm model, so that the water level flow calculation field and the measurement field achieve optimal fit; The optimization module is specifically used to: based on the design roughness of the water flow section of each channel in the irrigation area canal system and the reference value of the flow coefficient of the gate, x Set as background value x b ; According to the parameters to be determined x The background value is set within the range of x b The control range of is used to establish the quantized covariance matrix of the unknown parameter error: Obtain the actual water level data of the channel section under actual monitoring and flow measurement data , superimposed on the measured water level data And the measured flow data Form the measurement field matrix: According to the gap between the water level flow calculation field matrix and the measurement field matrix, the observation error quantization covariance matrix is ​​established: Among them, H represents the water level flow calculation field matrix; According to the parameters to be determined x With the background value x b The deviation between them is weighted using the inverse of the undetermined parameter error quantization covariance matrix to form a background constraint evaluation function; According to the deviation between the water level flow calculation field matrix and the measurement field matrix, the inverse of the observation error quantization covariance matrix is ​​used for weighting to form an observation evaluation function; The background constraint evaluation function and the observation evaluation function are superimposed to form the objective function for parameter calibration: The design roughness of the water-passing section is determined by a sensitivity analysis method to determine the feasible domain of the parameter calibration, and the reference value of the flow coefficient of the gate is obtained by a confidence interval to obtain the feasible domain of the parameter calibration: , where a j , b j Respectively j The upper and lower limits of the parameters, N Indicates the number of parameters, ; Establishing a data assimilation optimization algorithm model to minimize the objective function of the parameter calibration so that the water level flow calculation field matrix and the measurement field matrix achieve optimal fit; Application module: used to use the data assimilation optimization algorithm model to obtain a set of hydrodynamic parameters of the irrigation canal system and the flow coefficient reference value of the gate, and apply the set of hydrodynamic parameters of the irrigation canal system and the flow coefficient reference value of the gate to the irrigation canal system to perform water resource allocation and water volume deduction simulation calculation; Update module: used to repeat the contents of the calibration module, deduction module, optimization module and application module in the next period, and update the calibration results of the hydrodynamic parameters of the irrigation canal system and the reference value of the flow coefficient of the gate to optimize the accuracy of the water resource allocation and the water volume deduction simulation calculation.

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