Diffusion coefficient calibration method and device in nitrogen element transportation process

Abstract

The invention provides a diffusion coefficient calibration method and device in a nitrogen element transportation process. The method comprises the following steps: selecting a target reservoir to determine a topological structure; establishing a one-dimensional nitrogen element convective diffusion equation taking the spatial position information of the target reservoir as a variable to obtain a nitrogen element transport model; establishing a neural network model corresponding to a to-be-calibrated diffusion coefficient under the constraint of the nitrogen element transportation process of the target reservoir, and establishing a to-be-optimized target function; collecting historical reservoir area information of each preset monitoring station in the target reservoir; and optimizing the to-be-optimized target function based on the historical reservoir area information to obtain a to-be-solved diffusion coefficient. According to the scheme provided by the invention, the complexity and heterogeneity of the nitrogen element transportation process in the reservoir are considered, the constraint condition of the diffusion coefficient calibration process is added in combination with the actual aquatic environment of the reservoir, and the nitrogen element diffusion coefficient is calibrated through the established one-dimensional nitrogen element transportation equation and the physically-driven neural network inversion model, so that the nitrogen element diffusion coefficient calibration accuracy is improved. And the evaluation and prediction accuracy of the nitrogen element transportation process is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental science and technology, and in particular to a method and device for calibrating the diffusion coefficient of a nitrogen transport process. Background Art

[0002] Reservoirs are important water resource regulation and water supply facilities, which can be divided into natural reservoirs and artificial reservoirs. Due to the influence of factors such as wastewater discharge from agriculture, industry and urban activities and soil erosion, the concentration of nitrogen in reservoirs is relatively high, which will pose a potential threat to the ecological environment and water quality safety of water bodies. In environmental science, the calibration of water quality model parameters is an important step to improve the accuracy and reliability of model prediction. It can be seen that accurately evaluating and predicting the transport process of nitrogen in reservoirs is of great significance to the scientific management and protection of reservoir ecological environment.

[0003] The existing technology has taken a variety of measures to control nitrogen in reservoirs, mainly including source control, sediment management, water quality regulation and ecological restoration. In the simulation of nitrogen transport process, the existing technology describes the nitrogen transport process in reservoirs by constructing a nitrogen transport model including nitrification and denitrification reactions, thereby capturing the distribution characteristics of nitrogen in reservoirs. However, due to the characteristics of nitrogen in reservoirs, the water resource transport process becomes very complicated, which is manifested as non-uniform distribution, time-varying, multi-source input and biological interaction; and the diffusion coefficient, as the core parameter reflecting the diffusion process of nitrogen in water bodies, is mainly affected by temperature, flow rate, and solute interaction, and is a non-constant parameter. The solution provided by the existing technology has multiple solutions in the process of inverting parameters. The existing tracer substance determination method depends on the selection of tracer substances and their stability. The computational fluid dynamics numerical simulation method is difficult to solve the uncertainty of parameters and assumptions, which leads to deviations in simulation results.

[0004] It can be seen that there is an urgent need to provide a diffusion coefficient calibration scheme for the nitrogen transport process in order to accurately evaluate and predict the nitrogen element in the reservoir water resources transport process. Summary of the invention

[0005] The present application shows a method and device for calibrating the diffusion coefficient of the nitrogen transport process, so as to accurately evaluate and predict the nitrogen transport process in a reservoir.

[0006] In a first aspect, the present application provides a method for calibrating the diffusion coefficient of a nitrogen transport process, the method comprising:

[0007] Selecting a target reservoir and determining a topological structure of the target reservoir;

[0008] A one-dimensional nitrogen element convection diffusion equation is established based on the topological structure with the spatial location information of the target reservoir as a variable to obtain a nitrogen element transport model of the target reservoir;

[0009] A neural network model corresponding to the diffusion coefficient to be calibrated under the constraint of the nitrogen transport process of the target reservoir is established according to the nitrogen transport model, and an objective function to be optimized corresponding to the neural network model is established, wherein the neural network model is a physically driven neural network inversion model;

[0010] Collecting historical reservoir area information of each preset monitoring station in the target reservoir;

[0011] The objective function to be optimized is optimized based on the historical reservoir information, and the diffusion coefficient to be determined corresponding to the optimal value of the model parameters of the neural network model during the fitting process is obtained, so as to complete the calibration of the diffusion coefficient of the nitrogen element transport process.

[0012] Optionally, the method further includes:

[0013] Obtaining real-time reservoir area information of each preset monitoring station in the target reservoir;

[0014] Based on the nitrogen transport model corresponding to the calibrated diffusion coefficient, the real-time nitrogen content of the target reservoir is calculated.

[0015] Optionally, the step of collecting historical reservoir area information of each preset monitoring station in the target reservoir includes:

[0016] The historical reservoir data of multiple monitoring stations set up on the main stream and / or tributaries of the target reservoir are obtained, wherein the historical reservoir data at least includes cross-sectional flow, flow parameters, water temperature, nitrogen content, and total length of the reservoir.

[0017] Optionally, the step of establishing a one-dimensional nitrogen element convection-diffusion equation with the spatial position information of the target reservoir as a variable based on the topological structure to obtain a nitrogen element transport model of the target reservoir includes:

[0018] The one-dimensional nitrogen element convection diffusion equation is established according to the following expression to generate the nitrogen element transport model of the target reservoir:

[0019]

[0020] Where x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, D represents the diffusion coefficient, U represents the nitrogen concentration in the target reservoir, and R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0021] Optionally, the step of establishing a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model includes:

[0022] The neural network model is established according to the following expression:

[0023]

[0024] e 2 =U S -U* (3)

[0025] Among them, e 1 represents the residual of the one-dimensional nitrogen transport model of the target reservoir, e 2 represents the residual of nitrogen concentration in the target reservoir, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, and D s is the diffusion coefficient prediction value output by the neural network model, U s represents the predicted value of nitrogen concentration in the target reservoir output by the neural network model, U * Represents the true value of nitrogen concentration in the target reservoir, R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0026] Optionally, the step of establishing the objective function to be optimized corresponding to the neural network model includes:

[0027] The objective function to be optimized is established according to the following expression:

[0028]

[0029] D s =D s (x;θ u )

[0030] U s =U s (x, t; θ n )

[0031] Among them, θ u Represents the output diffusion coefficient prediction value D after the neural network model is fitted s The corresponding model parameters, θ n Represents the output diffusion coefficient prediction value U after neural network model fitting s The corresponding model parameters.

[0032] Optionally, the step of optimizing the objective function to be optimized based on the historical storage area information to obtain the diffusion coefficient to be determined when the model parameters of the neural network model take the optimal value during the fitting process includes:

[0033] Inputting the historical storage area information into the objective function to be optimized;

[0034] The objective function to be optimized is solved by using a gradient descent method until the model parameters of the optimal neural network model are obtained;

[0035] After the objective function is optimized, θ n The diffusion coefficient corresponding to the optimal value is the rated diffusion coefficient.

[0036] In a second aspect, the present application shows a diffusion coefficient calibration device for a nitrogen transport process, the device comprising:

[0037] A reservoir determination module, used for selecting a target reservoir and determining a topological structure of the target reservoir;

[0038] A nitrogen transport model establishment module is used to establish a one-dimensional nitrogen convection diffusion equation with the spatial position information of the target reservoir as a variable based on the topological structure to obtain a nitrogen transport model of the target reservoir;

[0039] A neural network model building module is used to establish a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model, and to establish an objective function to be optimized corresponding to the neural network model, wherein the neural network model is a physically driven neural network inversion model;

[0040] A historical information collection module, used to collect historical reservoir area information of each preset monitoring station in the target reservoir;

[0041] The diffusion coefficient calibration module is used to optimize the objective function to be optimized based on the historical reservoir information, obtain the diffusion coefficient to be calculated when the model parameters of the neural network model take the optimal value during the fitting process, and complete the calibration of the diffusion coefficient of the nitrogen element transport process.

[0042] Optionally, the device also includes a nitrogen content monitoring module, which is used to obtain real-time reservoir area information of each preset monitoring station in the target reservoir; based on the nitrogen transport model corresponding to the calibrated diffusion coefficient, calculate the real-time nitrogen content of the target reservoir.

[0043] Optionally, the historical information collection module is specifically used to obtain historical reservoir data of multiple monitoring stations established on the main stream and / or tributaries of the target reservoir, wherein the historical reservoir data at least includes cross-sectional flow, flow parameters, water temperature, nitrogen content, and total length of the reservoir.

[0044] Optionally, the nitrogen transport model establishment module is specifically used to establish the one-dimensional nitrogen convection-diffusion equation according to the following expression to generate the nitrogen transport model of the target reservoir:

[0045]

[0046] Where x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, D represents the diffusion coefficient, U represents the nitrogen concentration in the target reservoir, and R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0047] Optionally, the neural network model building module is specifically used to build the neural network model according to the following expression:

[0048]

[0049] e 2 =U S -U* (3)

[0050] Among them, e 1 represents the residual of the one-dimensional nitrogen transport model of the target reservoir, e 2 represents the residual of nitrogen concentration in the target reservoir, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, and D s is the diffusion coefficient prediction value output by the neural network model, U s represents the predicted value of nitrogen concentration in the target reservoir output by the neural network model, U * Represents the true value of nitrogen concentration in the target reservoir, R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0051] Optionally, the neural network model building module is specifically used to establish the objective function to be optimized according to the following expression:

[0052]

[0053] D s =D s (x;θ u )

[0054] Us =U s (x, t; θ n )

[0055] Among them, θ u Represents the output diffusion coefficient prediction value D after the neural network model is fitted s The corresponding model parameters, θ n Represents the output diffusion coefficient prediction value U after neural network model fitting s The corresponding model parameters.

[0056] Optionally, the diffusion coefficient calibration module is specifically used to input the historical reservoir information into the target function to be optimized; solve the target function to be optimized by using the gradient descent method until the model parameters of the optimal neural network model are obtained; determine θ after the target function is optimized. n The diffusion coefficient corresponding to the optimal value is the rated diffusion coefficient.

[0057] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0058] In a fourth aspect, the present application illustrates a non-temporary computer-readable storage medium, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a method as described in any of the above aspects.

[0059] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above aspects.

[0060] The technical solution provided by this application may have the following beneficial effects:

[0061] First, a target reservoir is selected and the topological structure of the target reservoir is determined; then, based on the topological structure, a one-dimensional nitrogen convection-diffusion equation with the spatial position information of the target reservoir as a variable is established to obtain a nitrogen transport model of the target reservoir; then, a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir is established according to the nitrogen transport model, and an objective function to be optimized corresponding to the neural network model is established; historical reservoir area information of each preset monitoring station in the target reservoir is collected; finally, based on the historical reservoir area information, the objective function to be optimized is optimized to obtain the diffusion coefficient to be determined when the model parameters of the neural network model take the optimal value during the fitting process, and the calibration of the diffusion coefficient of the nitrogen transport process is completed.

[0062] It can be seen that the solution provided in this application also takes into account the complexity and heterogeneity of the nitrogen transport process in the reservoir during the diffusion coefficient calibration process, combines the actual aquatic environment of the reservoir, increases the constraints of the diffusion coefficient calibration process, and calibrates the nitrogen diffusion coefficient by establishing a one-dimensional nitrogen transport equation and a physically driven neural network inversion model, thereby improving the precision and accuracy of the evaluation and prediction of the nitrogen transport process. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the diffusion coefficient calibration method of the nitrogen element transport process provided in this application.

[0064] Figure 2 This is a schematic diagram of the reservoir nitrogen transport process provided by this application.

[0065] Figure 3 This is another flow chart of the diffusion coefficient calibration method for the nitrogen element transport process provided in the present application.

[0066] Figure 4 It is a structural diagram of a diffusion coefficient calibration device for the nitrogen element transport process provided in the present application.

[0067] Figure 5 This is another structural diagram of the diffusion coefficient calibration device for the nitrogen element transport process provided by the present application.

[0068] Figure 6 It is a structural block diagram of an electronic device provided by this application.

[0069] Figure 7 This is another structural block diagram of the electronic device provided by this application. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0071] It should be noted that the diffusion coefficient reflects the diffusion capacity of nitrogen in a reservoir. In order to accurately evaluate and predict the transport process of nitrogen in a reservoir, the present application provides a method and device for calibrating the diffusion coefficient of the nitrogen transport process. The following first describes the method for calibrating the diffusion coefficient of the nitrogen transport process provided by the present application.

[0072] Embodiment 1

[0073] Reference Figure 1, is a flow chart of a method for calibrating the diffusion coefficient of the nitrogen element transport process provided by the present application, which can be applied to electronic devices, wherein the method can specifically include the following steps:

[0074] Step S101: Select a target reservoir and determine the topological structure of the target reservoir.

[0075] The application scenarios of this application include reservoirs, such as artificial reservoirs, natural lakes, etc., and are particularly suitable for large reservoirs. Figure 2 This is a schematic diagram of the reservoir nitrogen transport process provided by this application. Figure 2 The topological structure of the target reservoir can be clearly seen. Specifically, Figure 2 In the middle is the reservoir dam, with the upstream river channel on the left side of the dam and the downstream river channel on the right side. For the convenience of description, the direction from the upstream river channel to the downstream river channel along the reservoir water surface is defined as the x direction. The total length of the reservoir along the x direction selected in this application is represented by h.

[0076] Step S102: Based on the topological structure, a one-dimensional nitrogen element convection-diffusion equation is established with the spatial location information of the target reservoir as a variable to obtain a nitrogen element transport model of the target reservoir.

[0077] In one implementation, the one-dimensional nitrogen element convection diffusion equation can be established according to expression (1) to generate the nitrogen element transport model of the target reservoir.

[0078]

[0079] Where x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, D represents the diffusion coefficient, U represents the nitrogen concentration in the target reservoir, and R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0080] In one implementation, the boundary conditions of the one-dimensional nitrogen convection-diffusion equation corresponding to expression (1) can be determined in the following manner: the upper boundary condition is a fixed boundary condition, that is, the nitrogen concentration of the inflow; the lower boundary is a free boundary condition, preferably, the nitrogen change rate is a constant, generally taken as 0.

[0081] In one case, the flow parameter of the target reservoir may be the average flow velocity of the water body of the target reservoir in the selected reservoir area. It should be noted that the specific calculation method of the flow parameter will not be repeated here, and the calculation may be performed by referring to the relevant methods in the prior art.

[0082] It should be noted that R 1 represents the nitrogen fixation rate in water, R 2It represents the denitrification rate of water bodies and can be used to reflect the activity of plankton and microorganisms in water bodies. It is affected by the adsorption of oxygen content in the bottom mud of the reservoir water bodies. It can be measured by collecting target reservoir water bodies and bottom mud in the laboratory. The existing measurement methods of reservoir water bodies and bottom mud can be referred to, which will not be elaborated here.

[0083] Step S103: establishing a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model, and establishing an objective function to be optimized corresponding to the neural network model.

[0084] In one implementation of the present application, the neural network model can be established according to Expression (2) and Expression (3):

[0085]

[0086] e 2 =U S -U* (3)

[0087] Among them, e 1 represents the residual of the one-dimensional nitrogen transport model of the target reservoir, e 2 represents the residual of nitrogen concentration in the target reservoir, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, and D s is the diffusion coefficient prediction value output by the neural network model, U s represents the predicted value of nitrogen concentration in the target reservoir output by the neural network model, U * Represents the true value of nitrogen concentration in the target reservoir, R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0088] It should be noted that in expression (2) 1 represents the residual of the one-dimensional nitrogen transport model of the target reservoir, which is the physically driven neural network model corresponding to the diffusion coefficient D. The output of the neural network model D s =D s (x;θ u ) is the approximate value of the true diffusion coefficient, and the corresponding model parameter to be fitted is denoted as θ u , that is, θ u Output diffusion coefficient prediction value D after fitting the neural network model s The corresponding model parameters. The output of the network U s =U s (x, t; θ n ) is the approximate value of the actual nitrogen concentration, and the corresponding model parameter to be fitted is denoted as θ n , that is, θ nThe diffusion coefficient prediction value U is output after fitting the neural network model s The corresponding model parameters. In expression (3), e 2 The residual represents the nitrogen concentration in the target reservoir, reflecting the approximation error of the output of the neural network model relative to the observed nitrogen element in the section.

[0089] Furthermore, the objective function to be optimized is established according to the following expression (4):

[0090]

[0091] D s =D s (x;θ u )

[0092] U s =U s (x, t; θ n )

[0093] Among them, θ u Represents the output diffusion coefficient prediction value D after the neural network model is fitted s The corresponding model parameters, θ n Represents the output diffusion coefficient prediction value U after neural network model fitting s The corresponding model parameters.

[0094] It should be emphasized that in the diffusion coefficient calibration method for the nitrogen element transport process provided in the present application, the established neural network model is a physically driven neural network inversion model, that is, the residual of the physical equation is added on the basis of the loss function of the neural network model. The advantage is that by adding physical constraints to the neural network model, the fitting results of the model in the inversion process have physically meaningful considerations and are easier to approach the actual values.

[0095] Step S104: Collecting historical reservoir area information of each preset monitoring station in the target reservoir.

[0096] In one case, the historical reservoir area information of each preset monitoring station in the target reservoir can be collected in the following manner:

[0097] The historical reservoir data of multiple monitoring stations set up on the main stream and / or tributaries of the target reservoir are obtained, wherein the historical reservoir data at least includes cross-sectional flow, flow parameters, water temperature, nitrogen content, and total length h of the reservoir.

[0098] Step S105: Optimizing the objective function to be optimized based on the historical reservoir information, obtaining the diffusion coefficient to be determined when the model parameters of the neural network model take the optimal value during the fitting process, and completing the calibration of the diffusion coefficient of the nitrogen transport process.

[0099] In one case, the objective function to be optimized is optimized in the following manner, and the diffusion coefficient to be determined corresponding to the optimal value of the model parameter of the neural network model during the fitting process is obtained:

[0100] i. inputting the historical storage area information into the objective function to be optimized;

[0101] ii. Using the gradient descent method to solve the objective function to be optimized until the model parameters of the optimal neural network model are obtained;

[0102] iii. Determine θ after the objective function is optimized n The diffusion coefficient corresponding to the optimal value is the rated diffusion coefficient.

[0103] It should be noted that by optimizing the objective function in this way, the output of the neural network model can satisfy the control equation as much as possible and be as close to the observed data as possible, thereby improving the authenticity of the diffusion coefficient calibration of the nitrogen transport process.

[0104] In summary, the scheme provided in the present application also takes into account the complexity and heterogeneity of the nitrogen transport process in the reservoir during the diffusion coefficient calibration process, combines the actual aquatic environment of the reservoir, increases the constraints of the diffusion coefficient calibration process, and calibrates the nitrogen diffusion coefficient by establishing a one-dimensional nitrogen transport equation and a physically driven neural network inversion model. The influence of temperature, flow rate, and solute interaction on the diffusion coefficient calibration can be comprehensively considered, so that the neural network model obtains the optimal parameters, thereby improving the precision and accuracy of the evaluation and prediction of the nitrogen transport process.

[0105] Embodiment 2

[0106] Reference Figure 3 , is a flow chart of a method for calibrating the diffusion coefficient of the nitrogen element transport process provided by the present application, which can be applied to electronic devices, wherein the method can specifically include the following steps:

[0107] Step S201: Select a target reservoir and determine the topological structure of the target reservoir.

[0108] Step S202: Based on the topological structure, a one-dimensional nitrogen element convection-diffusion equation is established with the spatial location information of the target reservoir as a variable to obtain a nitrogen element transport model of the target reservoir.

[0109] Step S203: Establish a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model, and establish an objective function to be optimized corresponding to the neural network model, wherein the neural network model is a physically driven neural network inversion model.

[0110] Step S204: Collecting historical reservoir area information of each preset monitoring station in the target reservoir.

[0111] Step S205: Optimizing the objective function to be optimized based on the historical reservoir information, obtaining the diffusion coefficient to be determined when the model parameters of the neural network model take the optimal value during the fitting process, and completing the calibration of the diffusion coefficient of the nitrogen transport process.

[0112] It should be noted that steps S201 to S205 in the second embodiment are similar to steps S101 to S105 in the first embodiment. For the relevant details, please refer to the first embodiment and will not be described again here.

[0113] Step S206: obtaining real-time reservoir area information of each preset monitoring station in the target reservoir;

[0114] Step S207: Calculate the real-time nitrogen content of the target reservoir based on the nitrogen transport model corresponding to the calibrated diffusion coefficient.

[0115] It should be noted that Figure 3 The second embodiment shown has Figure 1 All the beneficial effects of the embodiment 1 shown in the figure, in addition, after completing the calibration of the diffusion coefficient of the nitrogen transport process in the target reservoir, the embodiment can further realize real-time monitoring of the nitrogen content in the target reservoir by acquiring the real-time reservoir information, i.e., real-time data, of each preset monitoring point, thereby dynamically analyzing the change process of the nitrogen content in the reservoir.

[0116] The diffusion coefficient calibration device for the nitrogen element transport process provided by the present application is described below.

[0117] Embodiment 3

[0118] Reference Figure 4 , is a structural diagram of a diffusion coefficient calibration device for a nitrogen element transport process provided in the present application, the device comprising:

[0119] A reservoir determination module 310 is used to select a target reservoir and determine the topological structure of the target reservoir;

[0120] A nitrogen transport model building module 320 is used to build a one-dimensional nitrogen convection diffusion equation with the spatial location information of the target reservoir as a variable based on the topological structure to obtain a nitrogen transport model of the target reservoir;

[0121] A neural network model building module 330 is used to build a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model, and to build an objective function to be optimized corresponding to the neural network model, wherein the neural network model is a physically driven neural network inversion model;

[0122] A historical information collection module 340 is used to collect historical reservoir area information of each preset monitoring station in the target reservoir;

[0123] The diffusion coefficient calibration module 350 is used to optimize the target function to be optimized based on the historical reservoir information, obtain the diffusion coefficient to be calculated when the model parameters of the neural network model take the optimal value during the fitting process, and complete the calibration of the diffusion coefficient of the nitrogen element transport process.

[0124] In one case, the historical information collection module 340 is specifically used to obtain historical reservoir data of multiple monitoring stations established on the main stream and / or tributaries of the target reservoir, wherein the historical reservoir data at least includes cross-sectional flow, flow parameters, water temperature, nitrogen content, and total length of the reservoir.

[0125] In one case, the nitrogen transport model establishment module 320 is specifically used to establish the one-dimensional nitrogen convection diffusion equation according to the following expression to generate the nitrogen transport model of the target reservoir:

[0126]

[0127] Where x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, D represents the diffusion coefficient, U represents the nitrogen concentration in the target reservoir, and R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0128] In one case, the neural network model building module 330 is specifically used to build the neural network model according to the following expression:

[0129]

[0130] e 2 =U S -U* (3)

[0131] Among them, e 1represents the residual of the one-dimensional nitrogen transport model of the target reservoir, e 2 represents the residual of nitrogen concentration in the target reservoir, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, and D s is the diffusion coefficient prediction value output by the neural network model, U s represents the predicted value of nitrogen concentration in the target reservoir output by the neural network model, U * Represents the true value of nitrogen concentration in the target reservoir, R 1 represents the nitrogen fixation rate in water, R 2 It represents the denitrification rate of water body.

[0132] In one case, the neural network model building module 330 is specifically used to establish the objective function to be optimized according to the following expression:

[0133]

[0134] D s =D s (x;θ u )

[0135] U s =U s (x, t; θ n )

[0136] Among them, θ u Represents the output diffusion coefficient prediction value D after the neural network model is fitted s The corresponding model parameters, θ n Represents the output diffusion coefficient prediction value U after neural network model fitting s The corresponding model parameters.

[0137] In one case, the diffusion coefficient calibration module 350 is specifically used to input the historical reservoir information into the target function to be optimized; solve the target function to be optimized by using the gradient descent method until the model parameters of the optimal neural network model are obtained; determine θ after the target function is optimized. n The diffusion coefficient corresponding to the optimal value is the rated diffusion coefficient.

[0138] In summary, the solution provided in this application also takes into account the complexity and heterogeneity of the nitrogen transport process in the reservoir during the diffusion coefficient calibration process, combines the actual aquatic environment of the reservoir, increases the constraints of the diffusion coefficient calibration process, and calibrates the nitrogen diffusion coefficient by establishing a one-dimensional nitrogen transport equation and a physically driven neural network inversion model, thereby improving the precision and accuracy of the evaluation and prediction of the nitrogen transport process.

[0139] Furthermore, if Figure 5As shown, it is another structural diagram of the diffusion coefficient calibration device for the nitrogen transport process provided by the present application, and the device also includes a nitrogen content monitoring module 360, which is used to obtain the real-time reservoir area information of each preset monitoring station in the target reservoir; based on the nitrogen transport model corresponding to the diffusion coefficient after calibration, the real-time nitrogen content of the target reservoir is calculated.

[0140] It should be noted that Figure 5 After the device shown is implemented to complete the calibration of the diffusion coefficient of the nitrogen transport process in the target reservoir, it can further realize real-time monitoring of the nitrogen content in the target reservoir by acquiring real-time reservoir information, i.e., real-time data, of each preset monitoring point, thereby dynamically analyzing the change process of the nitrogen content in the reservoir.

[0141] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0142] Embodiment 4

[0143] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0144] Embodiment 5

[0145] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0146] Figure 6 800 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0147] Reference Figure 6, the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0148] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0149] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0150] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0151] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0152] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0153] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0154] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0155] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0156] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0157] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0158] Figure 7 1 is a structural block diagram of the electronic device 1900 provided in the present application. For example, the electronic device 1900 may be provided as a server.

[0159] Reference Figure 7 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0160] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0161] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0162] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0163] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0164] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0165] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0166] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0169] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0170] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for calibrating the diffusion coefficient of nitrogen transport process, characterized in that: The method comprises: Selecting a target reservoir and determining a topological structure of the target reservoir; A one-dimensional nitrogen element convection diffusion equation is established based on the topological structure with the spatial location information of the target reservoir as a variable to obtain a nitrogen element transport model of the target reservoir; A neural network model corresponding to the diffusion coefficient to be calibrated under the constraint of the nitrogen transport process of the target reservoir is established according to the nitrogen transport model, and an objective function to be optimized corresponding to the neural network model is established, wherein the neural network model is a physically driven neural network inversion model; Collecting historical reservoir area information of each preset monitoring station in the target reservoir; The objective function to be optimized is optimized based on the historical reservoir information, and the diffusion coefficient to be determined corresponding to the optimal value of the model parameters of the neural network model during the fitting process is obtained, so as to complete the calibration of the diffusion coefficient of the nitrogen element transport process.

2. The method for calibrating the diffusion coefficient of nitrogen transport process according to claim 1, characterized in that: The method further comprises: Obtaining real-time reservoir area information of each preset monitoring station in the target reservoir; Based on the nitrogen transport model corresponding to the calibrated diffusion coefficient, the real-time nitrogen content of the target reservoir is calculated.

3. The method for calibrating the diffusion coefficient of nitrogen transport process according to claim 1 or 2, characterized in that: The step of collecting historical reservoir area information of each preset monitoring station in the target reservoir comprises: The historical reservoir data of multiple monitoring stations set up on the main stream and / or tributaries of the target reservoir are obtained, wherein the historical reservoir data at least includes cross-sectional flow, flow parameters, water temperature, nitrogen content, and total length of the reservoir.

4. The method for calibrating the diffusion coefficient of nitrogen transport process according to claim 1 or 2, characterized in that: The step of establishing a one-dimensional nitrogen element convection diffusion equation based on the topological structure with the spatial position information of the target reservoir as a variable to obtain the nitrogen element transport model of the target reservoir includes: The one-dimensional nitrogen element convection diffusion equation is established according to the following expression to generate the nitrogen element transport model of the target reservoir: Among them, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, D represents the diffusion coefficient, U represents the nitrogen concentration in the target reservoir, R1 represents the nitrogen fixation rate of the water body, and R2 represents the denitrification rate of the water body.

5. The method for calibrating the diffusion coefficient of nitrogen transport process according to claim 1 or 2, characterized in that: The step of establishing a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model includes: The neural network model is established according to the following expression: e2=U S -U* (3) Where e1 represents the residual of the one-dimensional nitrogen transport model of the target reservoir, e2 represents the residual of the nitrogen concentration in the target reservoir, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, and D s is the diffusion coefficient prediction value output by the neural network model, U s represents the predicted value of nitrogen concentration in the target reservoir output by the neural network model, U * It represents the true value of nitrogen concentration in the target reservoir, R1 represents the nitrogen fixation rate of the water body, and R2 represents the denitrification rate of the water body.

6. The method for calibrating the diffusion coefficient of nitrogen transport process according to claim 5, characterized in that: The step of establishing the objective function to be optimized corresponding to the neural network model comprises: The objective function to be optimized is established according to the following expression: D s =D s (x;θ u ) U s =U s (x,t;θ n ) Among them, θ u Represents the output diffusion coefficient prediction value D after the neural network model is fitted s The corresponding model parameters, θ n Represents the output diffusion coefficient prediction value U after neural network model fitting s The corresponding model parameters.

7. The method for calibrating the diffusion coefficient of nitrogen transport process according to claim 6, characterized in that: The step of optimizing the objective function to be optimized based on the historical storage area information to obtain the diffusion coefficient to be calculated corresponding to the optimal value of the model parameter of the neural network model during the fitting process includes: Inputting the historical storage area information into the objective function to be optimized; The objective function to be optimized is solved by using a gradient descent method until the model parameters of the optimal neural network model are obtained; After the objective function is optimized, θ n The diffusion coefficient corresponding to the optimal value is the rated diffusion coefficient.

8. A device for calibrating the diffusion coefficient of nitrogen transport process, characterized in that: The device comprises: A reservoir determination module, used for selecting a target reservoir and determining a topological structure of the target reservoir; A nitrogen transport model establishment module is used to establish a one-dimensional nitrogen convection diffusion equation with the spatial position information of the target reservoir as a variable based on the topological structure to obtain a nitrogen transport model of the target reservoir; A neural network model building module is used to establish a neural network model corresponding to the diffusion coefficient to be calibrated under the constraints of the nitrogen transport process of the target reservoir according to the nitrogen transport model, and to establish an objective function to be optimized corresponding to the neural network model, wherein the neural network model is a physically driven neural network inversion model; A historical information collection module, used to collect historical reservoir area information of each preset monitoring station in the target reservoir; The diffusion coefficient calibration module is used to optimize the objective function to be optimized based on the historical reservoir information, obtain the diffusion coefficient to be calculated when the model parameters of the neural network model take the optimal value during the fitting process, and complete the calibration of the diffusion coefficient of the nitrogen element transport process.

9. The diffusion coefficient calibration device for nitrogen transport process according to claim 8, characterized in that: The device also includes a nitrogen content monitoring module for obtaining real-time reservoir area information of each preset monitoring station in the target reservoir; and calculating the real-time nitrogen content of the target reservoir based on a nitrogen transport model corresponding to the calibrated diffusion coefficient.

10. The diffusion coefficient calibration device for nitrogen transport process according to claim 8 or 9, characterized in that: The historical information collection module is specifically used to obtain historical reservoir data of multiple monitoring stations established on the main stream and / or tributaries of the target reservoir, wherein the historical reservoir data at least includes cross-sectional flow, flow parameters, water temperature, nitrogen content, and total length of the reservoir.

11. The diffusion coefficient calibration device for nitrogen transport process according to claim 8 or 9, characterized in that: The nitrogen transport model establishment module is specifically used to establish the one-dimensional nitrogen convection diffusion equation according to the following expression to generate the nitrogen transport model of the target reservoir. Among them, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, D represents the diffusion coefficient, U represents the nitrogen concentration in the target reservoir, R1 represents the nitrogen fixation rate of the water body, and R2 represents the denitrification rate of the water body.

12. The diffusion coefficient calibration device for nitrogen transport process according to claim 8 or 9, characterized in that: The neural network model building module is specifically used to build the neural network model according to the following expression: e2=U S -U* (3) Where e1 represents the residual of the one-dimensional nitrogen transport model of the target reservoir, e2 represents the residual of the nitrogen concentration in the target reservoir, x represents the spatial position of the target reservoir, t represents the current time, v represents the flow parameter of the target reservoir, and D s is the diffusion coefficient prediction value output by the neural network model, U s represents the predicted value of nitrogen concentration in the target reservoir output by the neural network model, U * It represents the true value of nitrogen concentration in the target reservoir, R1 represents the nitrogen fixation rate of the water body, and R2 represents the denitrification rate of the water body.

13. The diffusion coefficient calibration device for nitrogen transport process according to claim 12, characterized in that: The neural network model building module is specifically used to establish the objective function to be optimized according to the following expression: D s =D s (x;θ u ) U s =U s (x,t;θ n ) Among them, θ u Represents the output diffusion coefficient prediction value D after the neural network model is fitted s The corresponding model parameters, θ n Represents the output diffusion coefficient prediction value U after neural network model fitting s The corresponding model parameters.

14. The diffusion coefficient calibration device for nitrogen transport process according to claim 13, characterized in that: The diffusion coefficient calibration module is specifically used to input the historical reservoir information into the target function to be optimized; solve the target function to be optimized by using the gradient descent method until the model parameters of the optimal neural network model are obtained; determine θ after the target function is optimized n The diffusion coefficient corresponding to the optimal value is the rated diffusion coefficient.

15. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by the processor.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

17. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method according to any one of claims 1 to 7.

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