Method and system for calibrating overflow coefficient and hydrodynamic parameter of gate in irrigation area

Abstract

The invention provides an irrigation area gate overflow coefficient and hydrodynamic parameter calibration method and system, and the method comprises the steps: constructing a one-dimensional grid model, embedding the design and mapping parameters of a channel section into the one-dimensional grid model, and constructing a hydrodynamic data matrix; calibrating an overflow coefficient reference value of the gate by using water conservancy process data and operation conditions actually measured at the gate; deducing a water level flow calculation field; uncertain undetermined parameters are used as background values, an error function is constructed based on the relative deviation degree of the background values, and a data assimilation optimization algorithm model is established; a data assimilation optimization algorithm model is used for obtaining hydrodynamic parameters of an irrigation area canal system and an overflow coefficient reference value of a gate; in the next period, the steps are repeated, the calibration result is updated, automatic calibration of the overflow coefficient and the hydrodynamic parameter of the gate of the irrigation area can be achieved, and the method is suitable for water resource allocation and water volume deduction calculation of a digitized and informationized irrigation area canal system of the irrigation area, and assists in analysis and optimization of an 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: A method for calibrating the flow coefficient and hydrodynamic parameters of an irrigation area gate comprises the following steps: 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; 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.

[0006] According to one aspect of the above technical solution, 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 channel bottom elevation, channel bottom width, side slope and channel height, and the parameters of the hydrodynamic data matrix include water level, flow, flow velocity and water depth.

[0007] According to one aspect of the above technical solution, 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:

[0008] in, k is the gate number, t 0 , t 1 ,……,t M is the time of the water conservancy process data and the operating condition data, H 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:

[0009] 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.

[0010] According to one aspect of the above technical solution, 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:

[0011] 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:

[0012] like , then the gate flow state is determined to be free hole flow, and the calculation formula for the theoretical gate flow rate is:

[0013] like , then the gate flow state is determined to be submerged hole flow, and the calculation formula for the theoretical gate flow is:

[0014] Among them, g is the acceleration of gravity, b is the width of the gate hole, .

[0015] According to one aspect of the above technical solution, 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:

[0016] 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: .

[0017] According to one aspect of the above technical solution, the specific steps of step S40 include: 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 gate flow coefficient, 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:

[0018] 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:

[0019] 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:

[0020] 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:

[0021] 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 gate flow coefficient is obtained by the 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, ; 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.

[0022] According to one aspect of the above technical solution, 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 of 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:

[0023] Taking the derivative of the above formula, we can get:

[0024] 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.

[0025] According to one aspect of the above technical solution, 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.

[0026] The present invention also provides an irrigation area gate flow coefficient and hydrodynamic parameter calibration system, comprising: 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; In the construction module: 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; the parameters of the hydrodynamic data matrix include water level, flow rate, flow velocity and water depth; 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; The calibration module 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 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:

[0027] in, k is the gate number, t 0 , t 1 ,……,tM is the time of the water conservancy process data and the operating condition data, H 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; Specifically: If , 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:

[0028] 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:

[0029] like , then the gate flow state is determined to be free hole flow, and the calculation formula for the theoretical gate flow rate is:

[0030] like , then the gate flow state is determined to be submerged hole flow, and the calculation formula for the theoretical gate flow is:

[0031] Among them, g is the acceleration of gravity, b is the width of the gate hole, ; 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:

[0032] Among them, Q ti is the theoretical gate flow rate; A frequency distribution histogram is established for the gate flow coefficient of the current period, and the confidence interval with the maximum probability of occurrence of the gate flow coefficient is obtained by statistics, 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; 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; The deduction module is specifically used to: place 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 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 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:

[0033] 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: ; 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 gate flow coefficient, 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:

[0034] 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:

[0035] 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:

[0036] 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:

[0037] 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 gate flow coefficient is obtained by the 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, ; 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; Specifically, by using the three-dimensional variation method, the objective function of parameter calibration is expressed as:

[0038] Taking the derivative of the above formula, we can get:

[0039] 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.

[0040] 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; 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; 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; 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.

[0041] Compared with the prior art, the present invention has the following beneficial effects: 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.

[0042] 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

[0043] 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: 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; Figure 2 It is a schematic structural diagram of a one-dimensional grid model in the first embodiment of the present invention; 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; The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0044] 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.

[0045] 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.

[0046] 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.

[0047] Embodiment 1 See also Figure 1 to Figure 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: 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; 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.

[0048] It can be understood that 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, and then calibrates the reference value of the gate's flow coefficient according to the design parameters of the gate in the irrigation canal system, and then obtains the water level and flow calculation field within a period of time by constructing a one-dimensional hydrodynamic numerical simulation model. By taking the uncertain parameters to be determined as background values, a data assimilation optimization algorithm model is constructed based on the degree of deviation of each data from the background value to update the water level and flow calculation field and the measurement field, thereby optimizing the accuracy of water resource allocation and water volume deduction simulation calculation to obtain the best gate flow coefficient and hydrodynamic parameters.

[0049] 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.

[0050] Specifically, 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 channel bottom elevation, channel bottom width, side slope and channel height, and the parameters of the hydrodynamic data matrix include water level, flow, flow velocity and water depth.

[0051] In this embodiment, there are 2 main canals, 7 branch canals, 1 natural river channel and 10 main gates, and the connection relationship between the four is also included. A physical section grid is established by collecting the design parameters and surveying parameters of the channel section, and a virtual section grid is constructed near key locations and gates, so that the irrigation area canal system is discretized into a one-dimensional grid model.

[0052] Furthermore, the specific steps of step S20 include: The design parameters of the gates in the irrigation canal system are obtained, wherein the design parameters of the gates include gate hole width, gate hole height, gate hole number and gate bottom sill elevation; these parameters can be obtained by measuring or consulting the data during construction.

[0053] The hydraulic process data and operating condition data measured for each gate in the irrigation district canal system are obtained, wherein the hydraulic process data and operating condition data include the water depth before the gate, the water depth after the gate, the measured flow rate through the gate and the process data of the gate opening; the data in this step is obtained through measurement in the current time period.

[0054] 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:

[0055] in, k is the gate number, t 0 , t 1 ,……,t M is the time of the water conservancy process data and the operating condition data, H 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; here k The value range is 1~10.

[0056] 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:

[0057] 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.

[0058] Specifically, 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:

[0059] 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:

[0060] like , then the gate flow state is determined to be free hole flow, and the calculation formula for the theoretical gate flow rate is:

[0061] like , then the gate flow state is determined to be submerged hole flow, and the calculation formula for the theoretical gate flow is:

[0062] Among them, g is the acceleration of gravity, b is the width of the gate hole, .

[0063] In this embodiment, all gates returned are gate flow states belonging to submerged hole flow. Based on the calculation formula of theoretical gate flow under submerged hole flow, the theoretical gate flow state is calculated, and then the flow coefficient of the gate at each moment is obtained by the ratio of the measured gate flow state to the theoretical gate flow state. A frequency distribution histogram is established for the gate flow coefficient obtained in the current calculation period, and the gate flow coefficient corresponding to the confidence interval with the maximum probability of occurrence of the gate flow coefficient is obtained by statistics as the reference value of the gate flow coefficient. The reference value of the gate flow coefficient obtains the feasible domain of the parameter calibration through the confidence interval: , where a j 、b j Respectively j The upper and lower limits of the parameters, N Indicates the number of parameters, In this embodiment, the 70% confidence interval is shown in the following table:

[0064] Furthermore, 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:

[0065] 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: .

[0066] It can be understood that the design roughness of the water-passing section of each channel can be obtained based on the prior values ​​of design parameters, literature, etc. The setting range of the design roughness of the water-passing section of each channel is [0.01, 0.3], as shown in the following table:

[0067] In this embodiment, only the roughness of the channel 0.023 and the roughness of the natural river channel 0.03 are used.

[0068] Furthermore, the specific steps of step S40 include: 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 gate flow coefficient, 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:

[0069] 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:

[0070] 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:

[0071] 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:

[0072] 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; 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.

[0073] 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: By using the three-dimensional variational method, the objective function of parameter calibration is expressed as:

[0074] Taking the derivative of the above formula, we can get:

[0075] in, for H about x The adjoint matrix of the linear tangent matrix; 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.

[0076] Furthermore, 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.

[0077] 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.

[0078] 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.

[0079] Embodiment 2 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: 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; 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; 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; 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; 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:

[0080] in, k is the gate number, t 0 , t 1 ,……,t M is the time of the water conservancy process data and the operating condition data, H 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; Specifically: If , 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:

[0081] 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:

[0082] like , then the gate flow state is determined to be free hole flow, and the calculation formula for the theoretical gate flow rate is:

[0083] like , then the gate flow state is determined to be submerged hole flow, and the calculation formula for the theoretical gate flow is:

[0084] Among them, g is the acceleration of gravity, b is the width of the gate hole, ; 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:

[0085] Among them, Q ti is the theoretical gate flow rate; A frequency distribution histogram is established for the gate flow coefficient of the current period, and the confidence interval with the maximum probability of occurrence of the gate flow coefficient is obtained by statistics, 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; 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 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; The deduction module 13 is specifically used to: place 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 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 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:

[0086] 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: ; Optimization module 14: 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 14 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 gate flow coefficient, 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:

[0087] 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:

[0088] 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:

[0089] 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:

[0090] 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 gate flow coefficient is obtained by the 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, ; 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; Specifically, by using the three-dimensional variation method, the objective function of parameter calibration is expressed as:

[0091] Taking the derivative of the above formula, we can get:

[0092] 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.

[0093] Application module 15: used to use 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 apply 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; The application module 15 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; 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; Update module 16: 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.

[0094] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0095] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached 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; 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 step S40 include: Based on the design roughness of the water-passing section of each channel in the irrigation canal system and the reference value of the gate flow coefficient, the uncertain parameters to be determined are x Set as background value x b ; According to the pending parameters 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: 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 gate flow coefficient is obtained by the 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, ; 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.

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 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.

8. The method for calibrating the flow coefficient and hydrodynamic parameters of irrigation gates according to claim 7 is 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.

9. 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; 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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