Method and device for calibrating diffusion coefficient in phosphate transportation process

Abstract

The invention provides a method and a device for calibrating a diffusion coefficient in a phosphate transportation process. The method comprises the steps of collecting historical reservoir area information of a target reservoir; constructing a reservoir two-dimensional phosphate transport model based on topographic features of a target reservoir, and constructing a two-dimensional phosphorus element convection diffusion equation based on parameters contained in historical reservoir area information; setting boundary conditions for the two-dimensional phosphorus convection diffusion equation based on parameters and topographic features included in the historical reservoir area information; based on the two-dimensional phosphorus element convection diffusion equation, an optimization objective function is constructed; according to the historical reservoir area information, training the reservoir two-dimensional phosphate transport model to obtain a two-dimensional phosphate transport model; and inputting the current information of the reservoir area into the two-dimensional phosphate transport model to obtain the diffusion coefficient of the phosphorus element, and calibrating the diffusion coefficient of the phosphate transport process based on the concentration of the phosphate, the diffusion coefficient of the phosphorus element and the two-dimensional phosphorus element convection diffusion equation in the current information of the reservoir area. The precision of the obtained diffusion coefficient can be improved.

Description

Technical Field

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

[0002] Large reservoirs play an important role in water resource storage, flood regulation, power generation, etc. However, due to the influence of human activities, such as fertilization of rice fields and aquaculture, excessive nutrient elements are added to the reservoirs along with rainwater, making the enrichment of phosphorus in the form of phosphate in the reservoirs a serious environmental problem. Excessive enrichment of phosphorus can lead to eutrophication of water bodies, causing a series of ecological and environmental problems such as blue algae and aquatic plants, and at the same time, threatening the water quality and ecosystem stability of the reservoirs.

[0003] Phosphorus exists in water in dissolved and particulate states. Particulate phosphorus tends to attach to suspended matter. Therefore, phosphorus has a strong enrichment in water bodies and is easily accumulated and deposited in reservoirs. In the phosphate (phosphorus) transport process, the diffusion coefficient is the core parameter that describes the diffusion and migration ability of phosphorus in reservoirs. Among them, the diffusion coefficient reflects the transfer rate and migration distance of phosphorus in water bodies. It is affected by factors such as water temperature, pH value, salinity, hydrodynamic conditions and phosphorus form, and is a non-constant parameter. Therefore, how to accurately obtain the diffusion coefficient of the phosphate transport process is crucial to the management of reservoir water quality and the maintenance of the reservoir ecosystem stability. However, existing models such as least squares fitting and linear regression statistics are difficult to solve the problem of multi-objective parameter identification, and the inverted parameters are not unique, which leads to poor model solution accuracy, resulting in low accuracy of the diffusion coefficient of the phosphate transport process. Summary of the invention

[0004] The present application shows a method and apparatus for calibrating the diffusion coefficient of a phosphate transport process.

[0005] In a first aspect, the present application shows a method for calibrating the diffusion coefficient of a phosphate transport process, the method comprising:

[0006] Collect historical reservoir information of the target reservoir;

[0007] Based on the topographic features of the target reservoir, a two-dimensional phosphate transport model of the reservoir is constructed, and based on the parameters included in the historical reservoir area information, a two-dimensional phosphorus element convection-diffusion equation for the two-dimensional phosphate transport model of the reservoir is constructed;

[0008] Based on the parameters included in the historical reservoir area information and the topographic characteristics of the target reservoir, boundary conditions are set for the two-dimensional phosphorus element convection-diffusion equation;

[0009] Based on the two-dimensional phosphorus element convection-diffusion equation, construct an optimization objective function based on the two-dimensional phosphate transport model of the reservoir;

[0010] According to the historical reservoir information of the target reservoir, the two-dimensional phosphate transport model of the reservoir is trained, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained;

[0011] Collecting current information of the target reservoir area, inputting the two-dimensional phosphate transport model, obtaining the diffusion coefficient of phosphorus, and calibrating the diffusion coefficient of the phosphate transport process in the target reservoir based on the phosphate concentration contained in the current information of the reservoir area, the diffusion coefficient of phosphorus and the two-dimensional phosphorus convection-diffusion equation;

[0012] The method of constructing an optimization objective function based on the two-dimensional phosphate transport model of the reservoir based on the two-dimensional phosphorus element convection-diffusion equation includes:

[0013] Based on the parameters in the two-dimensional phosphorus element convection-diffusion equation, a neural network equation for calibrating the residual of the diffusion coefficient is established;

[0014] Based on the output of the neural network equation and the concentration parameter of phosphate in the historical reservoir area information, constructing an approximate error equation for phosphate concentration;

[0015] According to the neural network equation and the approximation error equation, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed.

[0016] Optionally, the two-dimensional phosphate transport model of the reservoir is trained based on the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets a preset condition, the two-dimensional phosphate transport model is obtained, including:

[0017] The two-dimensional phosphate transport model of the reservoir is operated based on the input historical reservoir area information of the previous historical moment to obtain the predicted diffusion coefficient and phosphorus content;

[0018] Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir;

[0019] Based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the current historical moment, an approximation error is obtained;

[0020] Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value;

[0021] If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of calculating the two-dimensional phosphate transport model of the reservoir based on the historical reservoir area information input at the previous historical moment is executed.

[0022] Optionally, the two-dimensional phosphate transport model of the reservoir is trained based on the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets a preset condition, the two-dimensional phosphate transport model is obtained, including:

[0023] From the historical reservoir area information at the target historical moment, the phosphorus content is filtered out to obtain the data to be trained;

[0024] The reservoir two-dimensional phosphate transport model performs calculations based on the input data to be trained to obtain predicted diffusion coefficients and phosphorus content;

[0025] Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir;

[0026] Obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the target historical moment;

[0027] Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value;

[0028] If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of filtering out the phosphorus content is performed from historical reservoir area information at another historical moment.

[0029] Optionally, the two-dimensional phosphate transport model of the reservoir is trained based on the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets a preset condition, the two-dimensional phosphate transport model is obtained, including:

[0030] From the acquired historical storage area information, sort them according to the order of collection time to obtain a sorting sequence;

[0031] According to the number of sample data sets set in advance, the historical library area information is extracted from the sorting sequence with the number as interval to obtain the number of sample data sets;

[0032] From each sample data set, the phosphorus content is filtered out to obtain the corresponding training data set;

[0033] Sorting the training data sets according to the collection time of the first data in each training data set;

[0034] Selecting a first training data set with the earliest collection time, and training the reservoir two-dimensional phosphate transport model based on the first training data set and a first sample data set corresponding to the first training data set, until the optimization value of the reservoir two-dimensional phosphate transport model is less than a preset first optimization threshold, so as to obtain an initial training transport model;

[0035] A second training data set collected earlier in time is selected, and the initial training transport model is trained based on the second training data set and a second sample data set corresponding to the second training data set, until the optimization value of the initial training transport model is less than a preset second optimization threshold, and the second optimization threshold is less than the first optimization threshold, until the training of the training data set with the latest collection time is completed.

[0036] Optionally, the two-dimensional phosphorus element convection-diffusion equation is as follows:

[0037]

[0038] in:

[0039] x, y, and t represent the diffusion spatial position of phosphorus and the current time, respectively;

[0040] U represents the concentration of phosphate in the reservoir;

[0041] R represents the rate of water adsorption and desorption;

[0042] v x 、v y are water flow direction parameters and vertical flow parameters respectively;

[0043] D x , D y They are the water flow diffusion parameter and vertical diffusion parameter of phosphorus element respectively.

[0044] Optionally, the neural network equation for determining the diffusion coefficient is as follows:

[0045]

[0046] In the formula,

[0047] e1 is the residual of the two-dimensional phosphate transport model of the reservoir;

[0048] U s calculated concentrations of phosphate output from a two-dimensional phosphate transport model for the reservoir;

[0049] D xs , D ysThey are the water flow diffusion calculation parameters and vertical diffusion calculation parameters of phosphorus element respectively.

[0050] Optionally, the method further comprises:

[0051] The calibrated diffusion coefficient of the phosphate transport process in the target reservoir is sent to a pre-set terminal device.

[0052] Optionally, the historical reservoir information includes: flow rate, water temperature, pH value, salinity, hydrodynamic conditions, phosphorus content and form.

[0053] Optionally, the historical reservoir area information also includes: water adsorption and desorption rates.

[0054] Optionally, if the water depth of the target reservoir area exceeds a preset water depth threshold, the historical reservoir area information includes: hydrological historical data of multiple preset depths in the reservoir area.

[0055] Optionally, the method further comprises:

[0056] At each time corresponding to the diffusion coefficient of the phosphate transport process in the target reservoir and the position corresponding to the time, respectively collect the measured information of the reservoir area at the position corresponding to the time;

[0057] The two-dimensional phosphate transport model is retrained based on the diffusion coefficient of the target reservoir at the corresponding position at that moment and the phosphorus concentration information contained in the measured information of the reservoir area.

[0058] In a second aspect, the present application shows a device for calibrating the diffusion coefficient of a phosphate transport process, the device comprising:

[0059] A data collection module is used to collect historical reservoir information of the target reservoir;

[0060] A model building module, used to build a two-dimensional phosphate transport model of the reservoir based on the topographic features of the target reservoir, and to build a two-dimensional phosphorus element convection diffusion equation for the two-dimensional phosphate transport model of the reservoir based on the parameters included in the historical reservoir area information;

[0061] A boundary setting module, used to set boundary conditions for the two-dimensional phosphorus element convection-diffusion equation based on the parameters included in the historical reservoir area information and the topographic characteristics of the target reservoir;

[0062] An optimization construction module, used to construct an optimization objective function based on the two-dimensional phosphate transport model of the reservoir based on the two-dimensional phosphorus element convection-diffusion equation;

[0063] A model training module, used to train the two-dimensional phosphate transport model of the reservoir according to the historical reservoir information of the target reservoir, and obtain the two-dimensional phosphate transport model when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions;

[0064] A diffusion coefficient acquisition module, used to collect the current information of the reservoir area of ​​the target reservoir, input the two-dimensional phosphate transport model, obtain the diffusion coefficient of phosphorus, and calibrate the diffusion coefficient of the phosphate transport process in the target reservoir based on the phosphate concentration contained in the current information of the reservoir area, the diffusion coefficient of phosphorus and the two-dimensional phosphorus convection-diffusion equation;

[0065] The optimization building block is specifically used for:

[0066] Based on the parameters in the two-dimensional phosphorus convection-diffusion equation, a neural network equation for calibrating the residual of the diffusion coefficient is established;

[0067] Based on the output of the neural network equation and the concentration parameter of phosphate in the historical reservoir area information, constructing an approximate error equation for phosphate concentration;

[0068] According to the neural network equation and the approximation error equation, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed.

[0069] Optionally, the model training module is specifically used for:

[0070] The two-dimensional phosphate transport model of the reservoir is operated based on the input historical reservoir area information of the previous historical moment to obtain the predicted diffusion coefficient and phosphorus content;

[0071] Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir;

[0072] Based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the current historical moment, an approximation error is obtained;

[0073] Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value;

[0074] If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of calculating the two-dimensional phosphate transport model of the reservoir based on the historical reservoir area information input at the previous historical moment is executed.

[0075] Optionally, the model training module is specifically used for:

[0076] From the historical reservoir area information at the target historical moment, the phosphorus content is filtered out to obtain the data to be trained;

[0077] The reservoir two-dimensional phosphate transport model performs calculations based on the input data to be trained to obtain predicted diffusion coefficients and phosphorus content;

[0078] Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir;

[0079] Obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the target historical moment;

[0080] Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value;

[0081] If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of filtering out the phosphorus content is performed from historical reservoir area information at another historical moment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The technical solution provided by this application may have the following beneficial effects:

[0086] By analyzing the parameters contained in the historical reservoir information, a two-dimensional phosphorus convection-diffusion equation is constructed, and based on the parameters contained in the historical reservoir information and the terrain characteristics, boundary conditions are set for the two-dimensional phosphorus convection-diffusion equation. Then, based on the two-dimensional phosphorus convection-diffusion equation, an optimization objective function is constructed. In this way, the actual aquatic environment of the target reservoir is combined, and various transport mechanisms of the phosphorus transport process in the target reservoir are considered. The phosphorus transport process is accurately simulated through model training, and the diffusion coefficient of the phosphate transport process is calibrated. This can effectively improve the accuracy of the evaluation of the phosphorus transport process and the accuracy of the calibrated phosphorus diffusion coefficient, providing more accurate data support for managing the water quality of the reservoir and maintaining the stability of the reservoir ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a flow chart of the steps of a method for calibrating the diffusion coefficient of the phosphate transport process of the present application.

[0088] Figure 2 It is a schematic diagram of the reservoir phosphorus transport process of the present application.

[0089] Figure 3 It is a structural block diagram of a device for calibrating the diffusion coefficient of the phosphate transport process of the present application.

[0090] Figure 4 It is a block diagram of an electronic device of the present application.

[0091] Figure 5 It is a block diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0092] 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.

[0093] The diffusion coefficient is a core parameter that describes the diffusion and migration capacity of phosphorus in a reservoir. It is affected by factors such as water temperature, pH value, salinity, hydrodynamic conditions and phosphorus form, and is a non-constant parameter. The existing method of calculating the diffusion coefficient using a model constructed by least squares fitting and linear regression statistics has a low accuracy in the calculated diffusion coefficient due to the non-uniqueness of the inversion target parameters, which in turn affects the water treatment based on the diffusion coefficient of phosphorus.

[0094] In this embodiment, a method for calibrating the diffusion coefficient of the phosphorus (phosphate) transport process in a large reservoir is proposed. By utilizing the two-dimensional phosphorus transport equation and combining it with the actual aquatic environment of the reservoir, the constraints of the diffusion coefficient calibration process are added, thereby considering various transport mechanisms of the phosphorus transport process in the reservoir, calibrating the diffusion coefficient, and accurately simulating the phosphorus transport process, thereby improving the accuracy of the evaluation of the phosphorus transport process and the accuracy of the calibrated diffusion coefficient of the phosphorus element, providing more accurate data support for managing the water quality of the reservoir and maintaining the stability of the reservoir ecosystem.

[0095] Reference Figure 1 , shows a flow chart of the steps of a method for calibrating the diffusion coefficient of the phosphate transport process of the present application, the method can be applied to electronic devices, wherein the method specifically may include the following steps:

[0096] S101, collecting historical reservoir information of the target reservoir;

[0097] In step S101, the historical reservoir area information is the historical parameter information that affects the diffusion coefficient of phosphorus. As an optional embodiment, the content contained in the historical reservoir area information can be determined based on the parameters related to the diffusion coefficient of phosphorus selected by professional and technical personnel, including but not limited to: historical data of monitoring stations established on each main and tributary stream in the target reservoir area and reservoir capacity information, wherein the historical data includes but is not limited to: flow rate, water temperature, pH value, salinity, hydrodynamic conditions, phosphorus content and form. Hydrodynamic conditions include but are not limited to: water flow direction parameters and vertical flow parameters. As another optional embodiment, the historical reservoir area information also includes water body adsorption and desorption rates.

[0098] In this embodiment, as an optional embodiment, since the diffusion coefficient of phosphorus in the reservoir area is related to the water temperature, and the water surface at different depths in the reservoir area has different water temperatures, if it is determined that the water depth of the target reservoir area exceeds the preset water depth threshold, the historical reservoir area information also includes: hydrological historical data of multiple preset depths in the reservoir area. The hydrological historical data contains the same content as the historical data of the monitoring site, including but not limited to: flow, water temperature, pH value, salinity, hydrodynamic conditions, phosphorus content and form at the preset depth.

[0099] S102, constructing a two-dimensional phosphate transport model of the reservoir based on the topographic features of the target reservoir, and constructing a two-dimensional phosphorus element convection-diffusion equation for the two-dimensional phosphate transport model of the reservoir based on the parameters included in the historical reservoir area information;

[0100] Figure 2 Schematic diagram of the reservoir phosphorus transport process of the present application. Figure 2As shown, the target reservoir is divided into the upstream river surface (reservoir water surface) and the downstream river surface with the dam as the interface. The reservoir water surface corresponds to the riverbed in the reservoir area, and the downstream river surface corresponds to the downstream riverbed. In this embodiment, as an optional embodiment, a two-dimensional phosphate transport model of the reservoir is established based on the topological structure of the reservoir water surface, the riverbed in the reservoir area, the dam, the downstream river surface and the downstream riverbed including the target reservoir. In the two-dimensional phosphate transport model of the reservoir, the water depth (vertical) direction is the y direction and the water flow (horizontal) direction is the x direction.

[0101] In this embodiment, as an optional embodiment, considering that the diffusion coefficient of phosphorus in the reservoir varies with space, the diffusion coefficient is set as a spatial step function, and a two-dimensional phosphorus convection diffusion equation is established based on the parameters contained in the historical reservoir area information, as follows:

[0102]

[0103] in:

[0104] x, y, and t represent the diffusion spatial position of phosphorus and the current time, respectively;

[0105] U represents the concentration of phosphate (element phosphorus concentration) in the reservoir;

[0106] R represents the adsorption and desorption rate of water, which is affected by factors such as water temperature, pH value and phosphorus form, and can be measured by collecting target reservoir water in the laboratory.

[0107] v x 、v y are water flow direction parameters and vertical flow parameters respectively;

[0108] D x , D y They are the water flow diffusion parameter and vertical diffusion parameter of phosphorus, which are used to describe the diffusion capacity of phosphate in the reservoir. That is, the diffusion coefficient of phosphorus includes the water flow diffusion parameter and vertical diffusion parameter.

[0109] S103, setting boundary conditions for the two-dimensional phosphorus convection-diffusion equation based on the parameters included in the historical reservoir area information and the topographic characteristics of the target reservoir;

[0110] In this embodiment, the boundary conditions of the two-dimensional phosphorus convection-diffusion equation are determined based on the correlation between the parameters included in the reservoir area information and the topographical characteristics of the target reservoir. As an optional embodiment, the boundary conditions include an upper boundary condition and a lower boundary condition. Among them, the upper boundary condition is a fixed boundary condition (inflow phosphorus concentration), and the lower boundary is a free boundary condition (phosphorus change rate), which is a fixed value. As an optional embodiment, it is taken as 0. For example, the upper boundary condition is a first-class boundary condition, and the lower boundary condition is a second-class boundary condition.

[0111] S104, constructing an optimization objective function based on the two-dimensional phosphate transport model of the reservoir based on the two-dimensional phosphorus element convection-diffusion equation;

[0112] In this embodiment, according to the two-dimensional phosphorus element convection-diffusion equation and boundary conditions, a neural network equation for determining the diffusion coefficient under the constraints of the reservoir phosphate transport process is established, so as to perform residual calculation of the two-dimensional phosphate transport model of the reservoir based on the neural network equation.

[0113] In this embodiment, as an optional embodiment, based on the two-dimensional phosphorus element convection-diffusion equation, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed, including:

[0114] A11, based on the parameters in the two-dimensional phosphorus convection-diffusion equation, a neural network equation for calibrating the residual of the diffusion coefficient is established;

[0115] In this embodiment, the neural network equation for determining the diffusion coefficient is established as follows:

[0116]

[0117] In the formula,

[0118] e1 is the residual of the two-dimensional phosphate transport model of the reservoir, which is the physically driven neural network model corresponding to the diffusion coefficients Dx and Dy, that is, the physical driver of the physically driven neural network model is the two-dimensional phosphate transport model of the reservoir;

[0119] U s The calculated concentration of phosphate (predicted phosphorus concentration) output by the two-dimensional phosphate transport model for the reservoir;

[0120] D xs , D ys They are the water flow diffusion calculation parameters and vertical diffusion calculation parameters of phosphorus element respectively.

[0121] In this embodiment, the output of the two-dimensional phosphate transport model of the reservoir includes water flow directional diffusion calculation parameters and vertical diffusion calculation parameters of the phosphorus element, which are approximate values ​​of the true diffusion coefficient.

[0122] In this embodiment, Dxs =D xs (x,y,q u ), D ys =D ys (x,y,q u ), U s =U s (x,y,t;q n ), where q u ,q n are the model parameters of the two-dimensional phosphate transport model for reservoirs.

[0123] In this embodiment, the model parameters need to be fitted using a neural network algorithm. The output of the neural network model (reservoir two-dimensional phosphate transport model) includes: an approximate value (predicted value) of phosphorus concentration and an approximate value of the diffusion coefficient of phosphorus.

[0124] A12, constructing an approximate error equation for phosphate concentration based on the output of the neural network equation and the phosphate concentration parameter in the historical reservoir area information;

[0125] In this embodiment, as an optional embodiment, the approximation error of the phosphate concentration is obtained based on the output of the two-dimensional phosphate transport model of the reservoir and the measured concentration of phosphate in the reservoir area information.

[0126] In this embodiment, as an optional embodiment, the approximation error (approximation error equation) of phosphate concentration is calculated using the following formula:

[0127] e2=U s -U *

[0128] Where e2 is the approximation error of the output of the neural network model to the observed concentration of phosphate in the cross section;

[0129] U * It is the observed phosphate concentration of the section, that is, the phosphorus content in the historical reservoir information (the measured concentration of phosphate).

[0130] A13, constructing an optimization objective function based on the two-dimensional phosphate transport model of the reservoir according to the neural network equation and the approximation error equation.

[0131] In this embodiment, based on the residual of the two-dimensional phosphate transport model of the reservoir and the approximation error of the phosphate concentration, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed.

[0132] In this embodiment, as an optional embodiment, the optimization objective function constructed is as follows:

[0133]

[0134] S105, training the two-dimensional phosphate transport model of the reservoir according to the historical reservoir area information of the target reservoir, and obtaining the two-dimensional phosphate transport model when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets a preset condition;

[0135] In this embodiment, as an optional embodiment, the two-dimensional phosphate transport model of the reservoir is trained according to the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained, including:

[0136] B11, the two-dimensional phosphate transport model of the reservoir is calculated based on the input historical reservoir area information at the previous historical moment to obtain the predicted diffusion coefficient and phosphorus content;

[0137] In this embodiment, the historical reservoir area information is sorted in chronological order, and the reservoir area information collected at each collection time is used as a unit to train the reservoir two-dimensional phosphate transport model.

[0138] In this embodiment, as an optional embodiment, the historical warehouse area information may also be preprocessed, including but not limited to: abnormal data detection, missing data completion, abnormal data correction, etc.

[0139] In this embodiment, the model parameters of the two-dimensional phosphate transport model of the reservoir are initialized, and the historical reservoir area information at the previous moment is input into the initialized two-dimensional phosphate transport model of the reservoir. The two-dimensional phosphate transport model of the reservoir is calculated according to a preset neural network algorithm, and the predicted diffusion coefficient and phosphorus content are input.

[0140] B12, obtaining the residual of the two-dimensional phosphate transport model of the reservoir based on the predicted diffusion coefficient and phosphorus content;

[0141] B13, obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the current historical moment;

[0142] B14, based on the residual and the approximation error, using the optimization objective function to obtain an optimization value;

[0143] B15, if the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of calculating the two-dimensional phosphate transport model of the reservoir based on the historical reservoir area information input at the previous historical moment is executed.

[0144] In this embodiment, if the optimization value is greater than the minimum optimization threshold, it indicates that the training of the two-dimensional phosphate transport model of the reservoir is not completed. Based on the back propagation algorithm, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted, and the two-dimensional phosphate transport model of the reservoir with the adjusted parameters continues to be trained according to the historical reservoir area information of the previous historical moment until the optimization value obtained is no greater than the minimum optimization threshold. This training ends, and the historical reservoir area information of another historical moment is selected to continue the training until the data set used for training is trained or the accuracy of the trained two-dimensional phosphate transport model of the reservoir reaches a preset accuracy threshold.

[0145] In this embodiment, as another optional embodiment, the two-dimensional phosphate transport model of the reservoir is trained according to the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained, including:

[0146] C11, filtering out the phosphorus content from the historical reservoir area information at the target historical moment to obtain the data to be trained;

[0147] C12, the reservoir two-dimensional phosphate transport model performs calculations based on the input data to be trained to obtain predicted diffusion coefficients and phosphorus content;

[0148] C13, obtaining the residual of the two-dimensional phosphate transport model of the reservoir based on the predicted diffusion coefficient and phosphorus content;

[0149] C14, obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the target historical moment;

[0150] C15, based on the residual and the approximation error, using the optimization objective function to obtain an optimization value;

[0151] C16, if the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of filtering out the phosphorus content is performed from historical reservoir area information at another historical moment.

[0152] In this embodiment, as another optional embodiment, the two-dimensional phosphate transport model of the reservoir is trained according to the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained, including:

[0153] D11, sorting the acquired historical storage area information according to the order of collection time to obtain a sorting sequence;

[0154] D12, according to the preset number of sample data sets, taking the number as interval, extracting the historical library area information from the sorting sequence to obtain the number of sample data sets;

[0155] In this embodiment, multiple sample data sets are constructed for subsequent tiered training. As an optional embodiment, if the number of sample data sets is 3, the 1st, 4th, ..., 3n-2th historical storage area information is extracted from the sorting sequence as the first sample data set, and then the 2nd, 5th, ..., 3n-1th historical storage area information is extracted as the second sample data set, and finally, the 3rd, 6th, ..., 3nth historical storage area information is extracted as the third sample data set. In this way, by extracting the historical storage area information at intervals, better discrimination is achieved.

[0156] D13, filtering out the phosphorus content from each sample data set to obtain a corresponding training data set;

[0157] D14, sorting the training data sets according to the collection time of the first data in each training data set;

[0158] D15, selecting a first training data set with the earliest collection time, and training the reservoir two-dimensional phosphate transport model based on the first training data set and a first sample data set corresponding to the first training data set, until the optimization value of the reservoir two-dimensional phosphate transport model is less than a preset first optimization threshold, thereby obtaining an initial training transport model;

[0159] D16, select the second training data set with the earliest collection time, and train the initial training transport model based on the second training data set and the second sample data set corresponding to the second training data set, until the optimization value of the initial training transport model is less than the preset second optimization threshold, and the second optimization threshold is less than the first optimization threshold, until the training of the training data set with the last collection time is completed.

[0160] In this embodiment, as an optional embodiment, for each monitoring data of the target reservoir (historical reservoir information), the two-dimensional phosphate transport model of the reservoir is input, and the two-dimensional phosphate transport model of the reservoir is calculated based on the two-dimensional phosphorus convection-diffusion equation to obtain the predicted diffusion coefficient and phosphorus content corresponding to the next monitoring data. Based on the predicted diffusion coefficient and phosphorus content and the actual diffusion coefficient and phosphorus content contained in the next monitoring data and the optimization objective function, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted until the preset conditions are met.

[0161] In this embodiment, if the historical reservoir information is the hydrological historical data of multiple preset depths within the reservoir area, training is performed separately based on the hydrological historical data of each depth to obtain the two-dimensional phosphate transport sub-model of the reservoir corresponding to the depth, and the two-dimensional phosphate transport sub-models of each reservoir constitute a two-dimensional phosphate transport model of the reservoir.

[0162] In this embodiment, by setting up multiple training data sets, subsequent training data sets are trained based on the model trained by the previous training data set, which can effectively reduce the amount of calculation of the training model and improve the model training efficiency. At the same time, the training data sets used for training before and after have similar characteristics but have a certain degree of distinction between each other, which can effectively reduce the impact of neighbor interference factors.

[0163] S106, collecting the current information of the reservoir area of ​​the target reservoir, inputting the two-dimensional phosphate transport model, obtaining the diffusion coefficient of phosphorus, and calibrating the diffusion coefficient of the phosphate transport process in the target reservoir based on the phosphate concentration contained in the current information of the reservoir area, the diffusion coefficient of phosphorus and the two-dimensional phosphorus convection-diffusion equation.

[0164] In this embodiment, the diffusion coefficient of the phosphate transport process in the target reservoir is calibrated to characterize the real-time dynamic change process of the phosphate content in the reservoir.

[0165] In this embodiment, the collected historical phosphate content flow data is used to train using a classical gradient descent method until the optimal neural network model parameters are found so that the output of the neural network model satisfies the control equation as much as possible and approaches the observed data as much as possible. After the optimization is completed, the current monitoring data is input into the reservoir two-dimensional phosphate transport model to obtain the predicted diffusion coefficient to be sought. The phosphate content data observed in real time at the monitoring site and the rated diffusion coefficient function are then input into the two-dimensional phosphorus element convection diffusion equation, and the finite difference method is used to solve it, so that the reservoir phosphate content change process can be calculated in real time.

[0166] In this embodiment, as an optional embodiment, the method further includes:

[0167] The calibrated diffusion coefficient of the phosphate transport process in the target reservoir is sent to a pre-set terminal device.

[0168] In this embodiment, as an optional embodiment, the terminal device is a terminal device of a person related to the reservoir management department, such as a mobile phone, a personal mailbox, a cloud server, etc. The calculation results are fed back to the reservoir management department to provide support for its decision-making.

[0169] In this embodiment, as another optional embodiment, the method further includes:

[0170] At each time corresponding to the diffusion coefficient of the phosphate transport process in the target reservoir and the position corresponding to the time, respectively collect the measured information of the reservoir area at the position corresponding to the time;

[0171] The two-dimensional phosphate transport model is retrained based on the diffusion coefficient of the target reservoir at the corresponding position at that moment and the phosphorus concentration information contained in the measured information of the reservoir area.

[0172] In this embodiment, the model can be retrained according to the feedback until a result that meets the actual situation is obtained.

[0173] It should be noted that, for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by the present application.

[0174] Reference Figure 3 , showing a structural block diagram of a device for calibrating the diffusion coefficient of the phosphate transport process of the present application, the device comprising:

[0175] The data collection module 301 is used to collect historical reservoir information of the target reservoir;

[0176] In this embodiment, as an optional embodiment, the historical reservoir area information includes but is not limited to: flow rate, water temperature, pH value, salinity, hydrodynamic conditions, phosphorus content and form.

[0177] In this embodiment, as an optional embodiment, the historical reservoir area information also includes: water body adsorption and desorption rates.

[0178] In this embodiment, as an optional embodiment, if the water depth of the target reservoir area exceeds a preset water depth threshold, the historical reservoir area information includes: hydrological historical data of multiple preset depths in the reservoir area.

[0179] A model building module 302 is used to build a two-dimensional phosphate transport model of the reservoir based on the topographic characteristics of the target reservoir, and to build a two-dimensional phosphorus element convection diffusion equation for the two-dimensional phosphate transport model of the reservoir based on the parameters included in the historical reservoir area information;

[0180] In this embodiment, as an optional embodiment, the two-dimensional phosphorus element convection diffusion equation is as follows:

[0181]

[0182] in:

[0183] x, y, and t represent the diffusion spatial position of phosphorus and the current time, respectively;

[0184] U represents the concentration of phosphate in the reservoir;

[0185] R represents the rate of water adsorption and desorption;

[0186] v x 、v y are water flow direction parameters and vertical flow parameters respectively;

[0187] D x , D y They are the water flow diffusion parameter and vertical diffusion parameter of phosphorus element respectively.

[0188] A boundary setting module 303 is used to set boundary conditions for the two-dimensional phosphorus element convection-diffusion equation based on the parameters included in the historical reservoir area information and the topographic characteristics of the target reservoir;

[0189] An optimization construction module 304 is used to construct an optimization objective function based on the two-dimensional phosphate transport model of the reservoir based on the two-dimensional phosphorus element convection diffusion equation;

[0190] The model training module 305 is used to train the two-dimensional phosphate transport model of the reservoir according to the historical reservoir area information of the target reservoir, and obtain the two-dimensional phosphate transport model when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions;

[0191] The diffusion coefficient acquisition module 306 is used to collect the current information of the reservoir area of ​​the target reservoir, input the two-dimensional phosphate transport model, obtain the diffusion coefficient of phosphorus, and calibrate the diffusion coefficient of the phosphate transport process in the target reservoir based on the phosphate concentration contained in the current information of the reservoir area, the diffusion coefficient of phosphorus and the two-dimensional phosphorus convection-diffusion equation;

[0192] The optimization construction module 304 is specifically used for:

[0193] Based on the parameters in the two-dimensional phosphorus element convection-diffusion equation, a neural network equation for calibrating the residual of the diffusion coefficient is established;

[0194] Based on the output of the neural network equation and the concentration parameter of phosphate in the historical reservoir area information, constructing an approximate error equation for phosphate concentration;

[0195] According to the neural network equation and the approximation error equation, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed.

[0196] In this embodiment, as an optional embodiment, the model training module 305 is specifically used for:

[0197] The two-dimensional phosphate transport model of the reservoir is operated based on the input historical reservoir area information of the previous historical moment to obtain the predicted diffusion coefficient and phosphorus content;

[0198] Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir;

[0199] Based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the current historical moment, an approximation error is obtained;

[0200] Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value;

[0201] If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of calculating the two-dimensional phosphate transport model of the reservoir based on the historical reservoir area information input at the previous historical moment is executed.

[0202] In this embodiment, as another optional embodiment, the model training module 305 is specifically used for:

[0203] From the historical reservoir area information at the target historical moment, the phosphorus content is filtered out to obtain the data to be trained;

[0204] The reservoir two-dimensional phosphate transport model performs calculations based on the input data to be trained to obtain predicted diffusion coefficients and phosphorus content;

[0205] Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir;

[0206] Obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the target historical moment;

[0207] Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value;

[0208] If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of filtering out the phosphorus content is performed from historical reservoir area information at another historical moment.

[0209] In this embodiment, as an optional embodiment, the device further includes:

[0210] A model retraining module (not shown in the figure) is used to collect the measured information of the reservoir area at the corresponding position of the target reservoir at each time corresponding to the diffusion coefficient of the phosphate transport process in the target reservoir and the position corresponding to the time;

[0211] The two-dimensional phosphate transport model is retrained based on the diffusion coefficient of the target reservoir at the corresponding position at that moment and the phosphorus concentration information contained in the measured information of the reservoir area.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] FIG4 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.

[0216] Reference Figure 4 , 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 .

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] Figure 5 1 is a block diagram of an electronic device 1900 shown in the present application. For example, the electronic device 1900 may be provided as a server.

[0228] Reference Figure 5 , 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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 determining the diffusion coefficient of a phosphate transport process, characterized in that: The method comprises: Collect historical reservoir information of the target reservoir; Based on the topographic features of the target reservoir, a two-dimensional phosphate transport model of the reservoir is constructed, and based on the parameters included in the historical reservoir area information, a two-dimensional phosphorus element convection-diffusion equation for the two-dimensional phosphate transport model of the reservoir is constructed; Based on the parameters included in the historical reservoir area information and the topographic characteristics of the target reservoir, boundary conditions are set for the two-dimensional phosphorus element convection-diffusion equation; Based on the two-dimensional phosphorus element convection-diffusion equation, construct an optimization objective function based on the two-dimensional phosphate transport model of the reservoir; According to the historical reservoir information of the target reservoir, the two-dimensional phosphate transport model of the reservoir is trained, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained; Collecting current information of the target reservoir area, inputting the two-dimensional phosphate transport model, obtaining the diffusion coefficient of phosphorus, and calibrating the diffusion coefficient of the phosphate transport process in the target reservoir based on the phosphate concentration contained in the current information of the reservoir area, the diffusion coefficient of phosphorus and the two-dimensional phosphorus convection-diffusion equation; The method of constructing an optimization objective function based on the two-dimensional phosphate transport model of the reservoir based on the two-dimensional phosphorus element convection-diffusion equation includes: Based on the parameters in the two-dimensional phosphorus element convection-diffusion equation, a neural network equation for calibrating the residual of the diffusion coefficient is established; Based on the output of the neural network equation and the concentration parameter of phosphate in the historical reservoir area information, constructing an approximate error equation for phosphate concentration; According to the neural network equation and the approximation error equation, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed.

2. The method according to claim 1, characterized in that The two-dimensional phosphate transport model of the reservoir is trained based on the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained, including: The two-dimensional phosphate transport model of the reservoir is operated based on the input historical reservoir area information of the previous historical moment to obtain the predicted diffusion coefficient and phosphorus content; Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir; Based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the current historical moment, an approximation error is obtained; Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value; If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of calculating the two-dimensional phosphate transport model of the reservoir based on the historical reservoir area information input at the previous historical moment is executed.

3. The method according to claim 1, characterized in that The two-dimensional phosphate transport model of the reservoir is trained based on the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained, including: From the historical reservoir area information at the target historical moment, the phosphorus content is filtered out to obtain the data to be trained; The reservoir two-dimensional phosphate transport model performs calculations based on the input data to be trained to obtain predicted diffusion coefficients and phosphorus content; Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir; Obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the target historical moment; Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value; If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of filtering out the phosphorus content is performed from historical reservoir area information at another historical moment.

4. The method according to claim 1, characterized in that: The two-dimensional phosphate transport model of the reservoir is trained based on the historical reservoir area information of the target reservoir, and when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions, the two-dimensional phosphate transport model is obtained, including: From the acquired historical storage area information, sort them according to the order of collection time to obtain a sorting sequence; According to the number of sample data sets set in advance, the historical library area information is extracted from the sorting sequence with the number as interval to obtain the number of sample data sets; From each sample data set, the phosphorus content is filtered out to obtain the corresponding training data set; Sorting the training data sets according to the collection time of the first data in each training data set; Selecting a first training data set with the earliest collection time, and training the reservoir two-dimensional phosphate transport model based on the first training data set and a first sample data set corresponding to the first training data set, until the optimization value of the reservoir two-dimensional phosphate transport model is less than a preset first optimization threshold, so as to obtain an initial training transport model; A second training data set collected earlier in time is selected, and the initial training transport model is trained based on the second training data set and a second sample data set corresponding to the second training data set, until the optimization value of the initial training transport model is less than a preset second optimization threshold, and the second optimization threshold is less than the first optimization threshold, until the training of the training data set with the latest collection time is completed.

5. The method according to claim 1, characterized in that The two-dimensional phosphorus element convection-diffusion equation is as follows: in: x, y, and t represent the diffusion spatial position of phosphorus and the current time, respectively; U represents the concentration of phosphate in the reservoir; R represents the rate of water adsorption and desorption; v x 、v y are water flow direction parameters and vertical flow parameters respectively; D x , D y They are the water flow diffusion parameter and vertical diffusion parameter of phosphorus element respectively.

6. The method according to claim 5, characterized in that The neural network equation for determining the diffusion coefficient is as follows: In the formula, e1 is the residual of the two-dimensional phosphate transport model of the reservoir; U s calculated concentrations of phosphate output from a two-dimensional phosphate transport model for the reservoir; D xs , D ys They are the water flow diffusion calculation parameters and vertical diffusion calculation parameters of phosphorus element respectively.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: The calibrated diffusion coefficient of the phosphate transport process in the target reservoir is sent to a pre-set terminal device.

8. The method according to any one of claims 1 to 6, characterized in that: The historical reservoir information includes: flow, water temperature, pH value, salinity, hydrodynamic conditions, phosphorus content and form.

9. The method according to claim 8, characterized in that The historical reservoir area information also includes: water body adsorption and desorption rates.

10. The method according to any one of claims 1 to 6, characterized in that: If the water depth of the target reservoir area exceeds a preset water depth threshold, the historical reservoir area information includes: hydrological historical data of multiple preset depths in the reservoir area.

11. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: At each time corresponding to the diffusion coefficient of the phosphate transport process in the target reservoir and the position corresponding to the time, respectively collect the measured information of the reservoir area at the position corresponding to the time; The two-dimensional phosphate transport model is retrained based on the diffusion coefficient of the target reservoir at the corresponding position at that moment and the phosphorus concentration information contained in the measured information of the reservoir area.

12. A device for determining the diffusion coefficient of a phosphate transport process, characterized in that: The device comprises: A data collection module is used to collect historical reservoir information of the target reservoir; A model building module, used to build a two-dimensional phosphate transport model of the reservoir based on the topographic features of the target reservoir, and to build a two-dimensional phosphorus element convection diffusion equation for the two-dimensional phosphate transport model of the reservoir based on the parameters included in the historical reservoir area information; A boundary setting module, used to set boundary conditions for the two-dimensional phosphorus element convection-diffusion equation based on the parameters included in the historical reservoir area information and the topographic characteristics of the target reservoir; An optimization construction module, used to construct an optimization objective function based on the two-dimensional phosphate transport model of the reservoir based on the two-dimensional phosphorus element convection-diffusion equation; A model training module, used to train the two-dimensional phosphate transport model of the reservoir according to the historical reservoir information of the target reservoir, and obtain the two-dimensional phosphate transport model when the optimization objective function of the two-dimensional phosphate transport model of the reservoir meets the preset conditions; A diffusion coefficient acquisition module, used to collect the current information of the reservoir area of ​​the target reservoir, input the two-dimensional phosphate transport model, obtain the diffusion coefficient of phosphorus, and calibrate the diffusion coefficient of the phosphate transport process in the target reservoir based on the phosphate concentration contained in the current information of the reservoir area, the diffusion coefficient of phosphorus and the two-dimensional phosphorus convection-diffusion equation; The optimization building block is specifically used for: Based on the parameters in the two-dimensional phosphorus element convection-diffusion equation, a neural network equation for calibrating the residual of the diffusion coefficient is established; Based on the output of the neural network equation and the concentration parameter of phosphate in the historical reservoir area information, constructing an approximate error equation for phosphate concentration; According to the neural network equation and the approximation error equation, an optimization objective function based on the two-dimensional phosphate transport model of the reservoir is constructed.

13. The device according to claim 12, characterized in that The model training module is specifically used for: The two-dimensional phosphate transport model of the reservoir is operated based on the input historical reservoir area information of the previous historical moment to obtain the predicted diffusion coefficient and phosphorus content; Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir; Based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the current historical moment, an approximation error is obtained; Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value; If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of calculating the two-dimensional phosphate transport model of the reservoir based on the historical reservoir area information input at the previous historical moment is executed.

14. The device according to claim 12, characterized in that The model training module is specifically used for: From the historical reservoir area information at the target historical moment, the phosphorus content is filtered out to obtain the data to be trained; The reservoir two-dimensional phosphate transport model performs calculations based on the input data to be trained to obtain predicted diffusion coefficients and phosphorus content; Based on the predicted diffusion coefficient and phosphorus content, obtaining the residual of the two-dimensional phosphate transport model of the reservoir; Obtaining an approximation error based on the predicted phosphorus content and the phosphorus content contained in the historical reservoir area information at the target historical moment; Based on the residual and the approximation error, using the optimization objective function, obtaining an optimization value; If the optimization value is greater than a preset minimum optimization threshold, the model parameters of the two-dimensional phosphate transport model of the reservoir are adjusted using a preset back propagation algorithm, and the step of filtering out the phosphorus content is performed from historical reservoir area information at another historical moment.

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 11 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 11 is implemented.

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