Integral time-delay zero point model parameter identification and water level rolling simulation method

Abstract

The invention discloses an integral time-delay zero point model parameter identification and water level rolling simulation method, and relates to the technical field of power station optimization scheduling. The method comprises the following steps: constructing a river channel one-dimensional hydrodynamic model based on a Saint-Venant equation set; wherein the river channel one-dimensional hydrodynamic model is used for model Manning coefficient calibration, working condition design and working condition stable operation state acquisition; when the one-dimensional hydrodynamic model of the river channel is in a stable operation state, a flow step is applied to the hydrodynamic model in stable operation for parameter identification, and key parameters under different working conditions are obtained; fitting each key parameter by using a multivariable fitting method, and determining a parameter function relationship; based on the parameter function relationship, introducing a time domain expression of an integral time lag zero point model, and calculating the water level difference of adjacent moments; and performing rolling simulation of the river water level according to the set upstream and downstream flows, the initial water level of the river and the water level difference between adjacent moments. According to the invention, the efficiency and accuracy of real-time scheduling of the power station can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of power station optimization dispatching, and in particular to an integral time-delay zero-point model parameter identification and water level rolling simulation method. Background Art

[0002] In the water resource dispatching of power stations, accurate water level prediction is a key link to ensure the safe operation of water conservancy projects and improve the efficiency of water resource utilization. With the frequent occurrence of climate change and extreme hydrological events, the water level changes in power stations have become more complex and difficult to predict, posing a huge challenge to real-time dispatching. In order to cope with these problems, a variety of water level prediction methods have been proposed, such as hydrodynamic models based on numerical simulation and data-driven machine learning models.

[0003] However, these methods still have certain limitations in practical applications. Although the hydrodynamic model can accurately reflect the changes in river water levels, its computational complexity is high and the parameter debugging process is cumbersome, making it difficult to meet the rapid response needs of frontline dispatchers. On the other hand, although the machine learning model has good prediction capabilities under certain conditions, it relies on large-scale historical data, and the black box characteristics of the model are difficult to explain, which also limits its widespread application in actual water level scheduling. Summary of the invention

[0004] The purpose of the present invention is to provide an integral time-delay zero-point model parameter identification and water level rolling simulation method, which can improve the efficiency and accuracy of real-time dispatching of power stations.

[0005] To achieve the above object, the present invention provides the following solutions: A method for identifying integral time-delay zero-point model parameters and simulating water level rolling, comprising: A one-dimensional hydrodynamic model of a river channel is constructed based on the Saint-Venant equations; the one-dimensional hydrodynamic model of a river channel is used for calibration of the Manning coefficient of the model, design of operating conditions, and acquisition of stable operating conditions; When the one-dimensional hydrodynamic model of the river channel is in a stable operation state, a flow step is applied to the stable hydrodynamic model to perform parameter identification, and key parameters under different working conditions are obtained; Using a multivariate fitting method to fit each of the key parameters to determine the parameter function relationship; Based on the parameter function relationship, the time domain expression of the integral time-delay zero point model is introduced to calculate the water level difference at adjacent moments; The rolling simulation of the river water level is performed according to the set upstream and downstream flow, the initial water level of the river and the water level difference at adjacent moments.

[0006] Optionally, the expression of the one-dimensional hydrodynamic model of the river channel is: , in, B is the surface width of the water-passing section, in m; Z is the water level, in m; t is the time, in seconds; Q is the flow rate, in m³ / s; x is the longitudinal distance of the channel along the main flow direction, in meters; α is the momentum correction factor; A is the water flow area, in m²; g is the acceleration due to gravity, in m / s²; S f is the friction ratio; q is the lateral water volume per unit length of the river channel. Positive values ​​indicate inflow, negative values ​​indicate outflow, and zero indicates no lateral exchange. The unit is m 2 / s.

[0007] Optionally, the process of calibrating the model Manning coefficient includes: automatically calibrating the model Manning coefficient using a particle swarm optimization algorithm, and iterating with the minimum error between the model simulation result and the observed data as the objective function; the objective function is specifically: , in: is the objective function with the goal of minimizing the error; N represents the number of observations; is the model simulation result; is the observation data; i Indicates i data.

[0008] Optionally, the process of the operating condition design includes: performing parameter analysis based on historical flow and water level data, determining a historical operating range, and setting different operating conditions in sequence from low to high according to the historical operating range.

[0009] Optionally, the process of obtaining the stable operating state of the working condition includes: setting a fixed upstream flow for different working conditions based on a model calibrated with the Manning coefficient, maintaining a stable state of the downstream water level by adjusting the output, and obtaining a stable operating state under different working conditions.

[0010] Optionally, when the one-dimensional hydrodynamic model of the river channel is in a stable operating state, a flow step is applied to the stable operating hydrodynamic model for parameter identification to obtain key parameters under different working conditions, specifically including: Under a stable operation state, a flow step is applied to the stable operation hydrodynamic model, the water level change in the downstream is calculated using the one-dimensional hydrodynamic model of the river channel, and characteristic parameters are identified based on the simulation results of the water level change to obtain key parameters under different working conditions; The simulation result of the water level change includes the backwater area; the backwater area is calculated as follows: , high frequency response and , and according to the formula Calculate the backwater area; the lag time is the time interval between the moment when the downstream water level changes and the moment when the upstream flow pulse disturbance occurs. and In order to deal with the high-frequency response caused by transient mutations, the constants under different working conditions are given by the stable hydrodynamic model; The calculation formula of the backwater area is: , in, is the water level change after applying the upstream flow step, m³ / s; is the duration of change, s; is the slope of water level change, m / s; is the flow step amount, m 3 / s.

[0011] Optionally, the parameter setting under the stable operation state includes: giving a fixed 5% step to the upstream, the downstream output is fixed unchanged, and the output result step size is set to 1 min.

[0012] Optionally, the time domain expression for introducing the integral time-delay zero point model is specifically: , in: is the model output, which is the deviation of the downstream water depth value relative to the steady-state value, m; is the change in the upstream inflow flow relative to the steady-state value, m³ / s; is the change in the downstream outflow flow of the river relative to the steady-state value, m³ / s; is the area of ​​the backwater zone, m 2 ; is the lag time, s; and Constant coefficient.

[0013] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention discloses a method for parameter identification and water level rolling simulation of an integral time-delay zero-point model, the method comprising constructing a one-dimensional hydrodynamic model of a river channel based on the Saint-Venant equations; wherein the one-dimensional hydrodynamic model of the river channel is used for calibration of the model Manning coefficient, operating condition design, and acquisition of the stable operating state of the operating condition; when the one-dimensional hydrodynamic model of the river channel is in a stable operating state, a flow step is applied to the stable operating hydrodynamic model for parameter identification to obtain key parameters under different operating conditions; each key parameter is fitted using a multivariable fitting method to determine the parameter function relationship; based on the parameter function relationship, the time domain expression of the integral time-delay zero-point model is introduced to calculate the water level difference at adjacent moments; and a rolling simulation of the river channel water level is performed according to the set upstream and downstream flows, the initial water level of the river channel, and the water level difference at adjacent moments. The present invention can improve the efficiency and accuracy of real-time dispatching of power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a flow chart of the integral time-delay zero-point model parameter identification and water level rolling simulation method of the present invention; Figure 2 A water level rolling prediction diagram considering adaptive integral time-lag zero point model parameters for this embodiment; Figure 3 Schematic diagram of modules for applying the method in this embodiment. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The purpose of the present invention is to provide an integral time-delay zero-point model parameter identification and water level rolling simulation method, which can improve the efficiency and accuracy of real-time dispatching of power stations.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1As shown, the present invention provides an integral time-delay zero-point model parameter identification and water level rolling simulation method, comprising: Step 100: Construct a one-dimensional hydrodynamic model of the river channel based on the Saint-Venant equations; the one-dimensional hydrodynamic model of the river channel is used for the calibration of the model's Manning coefficient, the design of operating conditions, and the acquisition of the stable operating state of the operating conditions. Among them, the design operating conditions should be based on a comprehensive analysis of historical flow and water level data to extract the range of historical operations. On this basis, the formulated design operating conditions should cover flow and water level changes from the lowest to the highest to ensure the safety and reliability of the project operation under various possible conditions. In addition, under the model after the Manning coefficient is automatically calibrated, a fixed upstream flow is set for different design conditions, and the downstream water level is ensured to remain stable by adjusting the output, thereby achieving steady-state operation of the project.

[0020] Step 200: When the one-dimensional hydrodynamic model of the river channel is in a stable operating state, a flow step is applied to the stable operating hydrodynamic model to perform parameter identification to obtain key parameters under different working conditions.

[0021] Step 300: Use a multivariable fitting method to fit each of the key parameters and determine the parameter function relationship to facilitate rapid calling of parameters under different working conditions.

[0022] Step 400: Based on the parameter function relationship, the time domain expression of the integral time-delay zero point model is introduced to calculate the water level difference at adjacent moments.

[0023] Step 500: Perform a rolling simulation of the river water level according to the set upstream and downstream flow, the initial water level of the river, and the water level difference at adjacent moments. By using the measured flow and initial water level values ​​to simulate the water level in time series, the model can gradually achieve dynamic updates. Specifically, after each step, the model will update the relevant parameters based on the operation results to ensure that the new input can better reflect the current water level status. Subsequently, the water level simulation of the next step is continued using the updated parameters, and the process is gradually advanced. The entire process is repeated until the entire simulation is completed.

[0024] Based on the above technical solution, the specific implementation methods of each step are provided as shown below.

[0025] Step 110, constructing a one-dimensional hydrodynamic model of the river channel.

[0026] Among them, the construction of the one-dimensional hydrodynamic model of the river channel includes the calibration of the Manning coefficient of the model, the design of the working conditions, and the acquisition of the stable operating state of the working conditions. Specifically, the construction of the one-dimensional hydrodynamic model of the river channel is based on the Saint-Venant equations and is solved by the pursuit method.

[0027] In terms of water level simulation, since the one-dimensional hydrodynamic model needs to adjust relevant parameters to minimize the deviation between the model simulated water level and flow and the actual value, only the Manning coefficient can be adjusted for the one-dimensional hydrodynamic model of the river. When the Manning coefficient increases, the simulated water level increases and the Manning coefficient decreases; when the simulated water level decreases, within the range of the Manning coefficient of the river, the particle swarm algorithm is used to automatically calibrate the Manning coefficient of the river.

[0028] In the process of working condition design, since it is necessary to use the hydrodynamic model for parameter identification, the wider the working condition coverage, the more comprehensive the parameter distribution obtained; the more refined the working condition setting, the richer the parameter data collected. This has a decisive influence on the subsequent parameter fitting accuracy.

[0029] In terms of stable operating conditions, it is necessary to set a fixed upstream flow under given operating conditions, and ensure that the downstream water level remains stable by adjusting the output to avoid the impact of the downstream water level on parameter identification.

[0030] Preferably, the equation group of the one-dimensional hydrodynamic model of the river channel is: , in, B is the surface width of the water-passing section, in m; Z is the water level, in m; t is the time, in s; Q is the flow rate, in m³ / s; x is the longitudinal distance of the channel along the main flow direction, in m; α is the momentum correction factor; A is the water flow area, in m²; g is the acceleration due to gravity, in m / s²; S f is the friction ratio drop.

[0031] The basic idea of ​​the catch-up method is to gradually solve the unknown water level and flow rate from the known initial conditions by gradually advancing the formula. First, the matrix A is converted into an upper triangular matrix U, and the vector Y is updated to maintain the equivalence of the equation system. The formula is as follows: , Where: i = 2, 3, ..., n. The elements in A and Y are all known values.

[0032] Next, back-substitute to solve for X, the formula is as follows: , In the formula: i=n-1, n-2,...,0, the elements in X are the quantities to be solved, namely the water level and flow rate of each control section.

[0033] As a preferred method, the one-dimensional hydrodynamic model of the river channel needs to be calibrated. Since the Manning coefficient is the only controllable parameter, the particle swarm optimization algorithm is used to automatically calibrate the Manning coefficient. The objective function is set to minimize the error between the model simulation results and the observed data, and the error is expressed as a square error. The specific objective function is: , in, To minimize the error; N represents the number of observations; is the model simulation result; For observation data.

[0034] As a preference, the design conditions should be based on a comprehensive analysis of historical flow and water level data to extract the historical operating range. On this basis, the formulated design conditions should cover flow and water level changes from the lowest to the highest to ensure the safety and reliability of the project operation under various possible conditions.

[0035] Preferably, under the model after the Manning coefficient is automatically calibrated, a fixed upstream flow is set for different design conditions, and the downstream water level is ensured to remain stable by adjusting the output, thereby achieving steady-state operation of the project and avoiding the impact of the downstream water level on parameter identification.

[0036] The following is an example of the Shenxigou Power Station. First, a one-dimensional hydrodynamic model of the Shenxigou river channel is constructed based on the Saint-Venant equations, and the Manning coefficient n of the river channel is calibrated, with the Manning coefficient being 0.065. The design conditions are based on the water level and flow that occurred in 23 years, with a flow of 550-3550m³ / s, an interval of 1000m³ / s, and a water level of 655-660m, with an interval of 1-2m. The upstream flow is given at 550m³ / s, 1550m³ / s, 2550m³ / s, and 3550m³ / s, and the outflow is controlled so that the downstream water level is stabilized at 655m, 657m, 659m, and 660m.

[0037] Step 120: Identification of characteristic parameters of the integral time-delay zero point model.

[0038] Among them, the identification of characteristic parameters of the integral time-delay zero-point model is to identify the steps based on the steady-state hydrodynamic model.

[0039] Specifically, the step percentage is considered according to the magnitude of flow. For example, a large step with a large magnitude will lead to non-convergence of the hydrodynamic simulation and no results. A small step with a large flow will cause the water level to rise slowly for projects with large storage capacity, and the simulation time needs to be increased. The output step size cannot be too large to avoid covering the lag time.

[0040] As a preferred method, a 5% step of the fixed upstream flow is given under stable operation conditions, and the downstream output remains unchanged. The downstream water level change is calculated using the river hydraulic model.

[0041] Preferably, the characteristic parameters are identified based on the simulation results of the downstream water level. In the simulation water level results, the time interval between the time when the downstream water level changes and the time when the upstream flow pulse disturbance occurs is the lag time. ; , In order to deal with the high-frequency response caused by transient mutations, the hydrodynamic model gives constants under different working conditions; the backwater area It can be calculated by the following formula: , in, Water level change after applying upstream flow step, m³ / s; is the duration of change, s; is the water level change slope, m / s.

[0042] Preferably, the output step length of the model is set to 1 minute to avoid the lag time being less than the output step length, which would cause a lag time error and affect parameter accuracy.

[0043] The following is an example of Shenxigou Power Station. Under the stable working condition of S110, a 5% flow step is given to the upstream initial flow, namely 577.5m³ / s, 1627.5m³ / s, 2782.5m³ / s, and 3832.5m³ / s. The detailed parameter identification is shown in Tables 1 to 4 below: Table 1 Parameter results (㎡) , Table 2 τ Parameter result (min) , Table 3 Parameter results , Table 4 Parameter results , Step 130: fitting the adaptive functional relationship of the integral time-delay model parameters.

[0044] The adaptive function relationship fitting of the integral time-delay model parameters is to fit the detailed parameter identification results.

[0045] Specifically, the detailed parameter identification results are fitted to perform multivariate fitting.

[0046] Preferably, the parameters of the integral time-lag zero-point model under different working conditions are fitted with a dual variable (stable water level, initial flow) relationship.

[0047] The following is an example of Shenxigou Power Station. The two-variable fitting is performed on Tables 1 to 4 in S120. The fitting function relationship is as follows: Relation 1: , Relation 2: , Relation 3: , Relation 4: , Where: x is the downstream stable water level, m; y is the initial flow, m³ / s.

[0048] Step 140: introducing the time domain function of the integral time-delay zero point model.

[0049] The time domain function of the integral time-delay zero-point model is introduced, that is, the formula is introduced to calculate the water level.

[0050] Specifically, the time domain expression of the integral time-delay zero-point model can calculate the water level difference and can iteratively calculate the water level.

[0051] As a preferred method, the time domain expression of the integral time-delay zero point model is introduced: , in: is the model output, which is the deviation of the downstream water depth value relative to the steady-state value, m; is the change in the upstream inflow flow relative to the steady-state value, m³ / s; is the change in the downstream outflow flow of the river relative to the steady-state value, m³ / s; is the area of ​​the backwater zone, m 2 ; is the lag time, s; and Constant coefficient.

[0052] The water level difference at adjacent moments can be calculated through four characteristic parameters, but the characteristic parameters at the next moment will change, and the initial characteristic parameters have narrow applicability to working conditions.

[0053] The following is an explanation using the Shenxigou Power Station. The upstream time-delay flow at a certain moment is subtracted from the outflow flow, and the four characteristic parameters are interpolated between the current upstream flow and the water level at the initial moment. The characteristic parameters are brought into the time domain expression, the water level difference is calculated, and the water level at the previous moment is added to the water level difference, which is the water level at the current moment.

[0054] Step 150: Adaptive rolling prediction of river water level As a preferred method, the measured flow and initial water level values ​​are used to simulate the water level in time series, and dynamic updates are gradually achieved. Specifically, after each step, the model will update the relevant parameters according to the operation results to ensure that the new input can better reflect the current water level status. Subsequently, the water level simulation of the next step is continued using the updated parameters, and the process is gradually advanced. The whole process is repeated until the entire simulation is completed.

[0055] The following is an example of Shenxigou Power Station. The upstream outflow of Shenxigou Power Station in February 2023, the outflow of this power station and the water level at the initial time are used to gradually advance and dynamically cycle until the simulation ends. The results are as follows Figure 2 As shown in Table 5, the Nash coefficient (NSE), root mean square error (RMSE), and mean absolute error (MAE) are used as evaluation indicators, and the simulation accuracy is relatively high. The specific results are shown in Table 5: Table 5 Evaluation index results , Figure 3 It is a module diagram of a method for parameter identification and water level rolling simulation based on an adaptive integral time-lag zero-point model as shown in some embodiments of this specification. The adaptive integral time-lag zero-point model parameter identification and water level rolling simulation may include a model building module, a parameter identification module, a function fitting module, an introduction time domain module and a dynamic simulation module.

[0056] A model building module is used to construct a one-dimensional hydrodynamic model of a river channel based on the Saint-Venant equations, wherein the one-dimensional hydrodynamic model of a river channel includes the calibration of the Manning coefficient of the model, the design of the working condition and the stable operation state of the working condition; The parameter identification module is used to apply a flow step in the stable hydrodynamic model, perform parameter identification through this step change, and then obtain high-precision key parameters; Function fitting module, used to perform multivariable fitting of parameters under different working conditions, so as to generate functional relationships between parameters; The time domain module is introduced to introduce the time domain expression of the integral time lag zero point model and calculate the water level difference at adjacent moments; The dynamic simulation module is used to perform rolling simulation of river water level using upstream and downstream flows and the initial water level of the river.

[0057] River channels and river-type reservoirs can be used to execute a method for parameter identification and water level rolling simulation based on an adaptive integral time-lag zero-point model. For more descriptions of the rolling update simulation of water levels, please refer to the relevant description of a method for parameter identification and water level rolling simulation based on an adaptive integral time-lag zero-point model, which will not be repeated here.

[0058] Compared with the prior art, this embodiment has at least the following beneficial effects: 1. This method uses a hydrodynamic model to identify parameters, ensuring high parameter accuracy and accurately reflecting the hydraulic characteristics of the river. 2. This method introduces an integral time-delay zero-point model, which overcomes the limitation that the integral time-delay model can only handle low-frequency responses of the river. It has the ability to handle high-frequency responses caused by instantaneous mutations, significantly improving simulation accuracy and being closer to actual engineering needs. 3. This method uses multivariable function relationship fitting to achieve rapid generation of parameters under different working conditions, greatly facilitating real-time calling and application by front-line dispatchers, and improving dispatch efficiency and operational convenience.

[0059] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0060] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for parameter identification of integral time-delay zero-point model and water level rolling simulation, characterized in that: include: Construct a one-dimensional hydrodynamic model of the river channel based on Saint-Venant equations; The one-dimensional hydrodynamic model of the river channel is used for calibration of the Manning coefficient of the model, design of working conditions and acquisition of stable operating state of working conditions; When the one-dimensional hydrodynamic model of the river channel is in a stable operation state, a flow step is applied to the stable hydrodynamic model to perform parameter identification, and key parameters under different working conditions are obtained; Using a multivariate fitting method to fit each of the key parameters to determine the parameter function relationship; Based on the parameter function relationship, the time domain expression of the integral time-delay zero point model is introduced to calculate the water level difference at adjacent moments; The rolling simulation of the river water level is performed according to the set upstream and downstream flow, the initial water level of the river and the water level difference at adjacent moments.

2. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 1 is characterized in that: The expression of the one-dimensional hydrodynamic model of the river channel is: ， in, B is the surface width of the water-passing section, in m; Z is the water level, in m; t is time, unit is s; Q is the flow rate, in m³ / s; x is the longitudinal distance of the channel along the main flow direction, in meters; α is the momentum correction factor; A is the water flow area, in m²; g is the acceleration due to gravity, in m / s²; S f is the friction ratio; q is the lateral water volume per unit length of the river channel. Positive values ​​indicate inflow, negative values ​​indicate outflow, and zero indicates no lateral exchange. The unit is m 2 / s.

3. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 1 is characterized in that: The process of calibrating the model Manning coefficient includes: automatically calibrating the model Manning coefficient using a particle swarm optimization algorithm, and iterating with the minimum error between the model simulation result and the observed data as the objective function; the objective function is specifically: ， in: is the objective function with the goal of minimizing the error; N represents the number of observations; is the model simulation result; is the observation data; i Indicates i data.

4. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 1 is characterized in that: The process of the operating condition design includes: performing parameter analysis based on historical flow and water level data, determining the range of historical operation, and setting different operating conditions in sequence from low to high according to the range of historical operation.

5. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 1 is characterized in that: The process of obtaining the stable operating state of the working condition includes: setting a fixed upstream flow for different working conditions based on the model calibrated by the Manning coefficient, maintaining the stable state of the downstream water level by adjusting the output, and obtaining the stable operating state under different working conditions.

6. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 1 is characterized in that: When the one-dimensional hydrodynamic model of the river channel is in a stable operating state, a flow step is applied to the stable operating hydrodynamic model for parameter identification to obtain key parameters under different working conditions, including: Under a stable operation state, a flow step is applied to the stable operation hydrodynamic model, the water level change in the downstream is calculated using the one-dimensional hydrodynamic model of the river channel, and characteristic parameters are identified based on the simulation results of the water level change to obtain key parameters under different working conditions; The simulation result of the water level change includes the backwater area; the backwater area is calculated as follows: , high frequency response and , and according to the formula Calculate the backwater area; the lag time is the time interval between the moment when the downstream water level changes and the moment when the upstream flow pulse disturbance occurs. and In order to deal with the high-frequency response caused by transient mutations, the constants under different working conditions are given by the stable hydrodynamic model; The calculation formula of the backwater area is: ， in, is the water level change after applying the upstream flow step, m³ / s; is the duration of change, s; is the slope of water level change, m / s; is the flow step amount, m 3 / s.

7. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 6 is characterized in that: The parameter setting under the stable operation state includes: giving a fixed 5% step to the upstream, keeping the downstream output unchanged, and setting the output result step size to 1 minute.

8. The integral time-delay zero point model parameter identification and water level rolling simulation method according to claim 1 is characterized in that: The time domain expression for introducing the integral time-delay zero point model is specifically: ， in: is the model output, which is the deviation of the downstream water depth value relative to the steady-state value, m; is the change in the upstream inflow flow relative to the steady-state value, m³ / s; is the change in the downstream outflow flow of the river relative to the steady-state value, m³ / s; is the area of ​​the backwater zone, m 2 ; is the lag time, s; and Constant coefficient.

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