CN113837493A - Reservoir downstream TDG concentration generation prediction method and device - Google Patents
Reservoir downstream TDG concentration generation prediction method and device Download PDFInfo
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Abstract
The invention relates to a method and a device for generating and predicting TDG concentration at downstream of a reservoir, a storage medium and electronic equipment, wherein the method comprises the following steps: the method comprises the steps of obtaining historical water inflow of a preset time period before a target time period of an upstream of a reservoir and rainfall of the upstream of the reservoir in the target time period, inputting the historical water inflow and the rainfall of the upstream of the reservoir in the target time period into a water inflow prediction model of the upstream of the reservoir, obtaining inflow of the upstream of the reservoir in the target time period, obtaining a reservoir discharge flow, water depth of an outlet of a reservoir stilling pool and TDG concentration of an outlet of the reservoir stilling pool, establishing a TDG concentration prediction model of the downstream of the reservoir by adopting a response surface method, obtaining the reservoir discharge flow and the water depth of the outlet of the reservoir in the target time period according to a reservoir operation scheduling scheme, obtaining the TDG concentration of the downstream of the reservoir in the target time period according to the established TDG concentration prediction model, and considering the influence of the reservoir discharge flow and the water depth of the outlet of the reservoir stilling pool on the TDG concentration of the outlet of the reservoir stilling pool, so that the TDG concentration generation precision is improved.
Description
Technical Field
The invention relates to the technical field of water environment monitoring, in particular to a reservoir downstream TDG concentration generation prediction method, a reservoir downstream TDG concentration generation prediction device, a storage medium and electronic equipment.
Background
In the process of water discharge of the reservoir, a large amount of air is sucked into flood discharge water flow in a entrainment mode and enters the absorption basin of the reservoir along with the flood discharge water flow. The gas pressure bearing in the absorption basin is increased sharply, so that the gas solubility is increased remarkably, and a large amount of gas is dissolved. After the water flow flows out of the stilling basin and enters a downstream river channel, the Gas solubility is reduced along with the reduction of the pressure of the surrounding environment, and the excessive Dissolved Gas is difficult to be completely released back to the atmosphere in a short time, so that the Total Dissolved Gas (TDG for short) is supersaturated. The release process of the supersaturated TDG in the downstream river channel of the reservoir is slow, and the supersaturated water flow of the TDG exists in a large range for a long time. If the fishes are continuously exposed in the TDG supersaturated water body for a long time, the fishes are susceptible to bubble diseases and even die, and serious adverse effects are generated on the ecological environment of a drainage basin.
At present, the method for slowing down the influence of TDG supersaturation of the downstream river of the reservoir is to effectively control the TDG saturation distribution through ecological scheduling. When the future scheduling design of the reservoir is carried out, the generation prediction of the TDG concentration is the key point for carrying out ecological scheduling on the reservoir. However, in the ecological scheduling, the more the generation function of the saturated TDG concentration at the downstream of the reservoir is an empirical formula, and the influence of multiple factors on the generation of the TDG concentration is not considered, so that the generation of the TDG concentration is inaccurate.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method and an apparatus for predicting TDG concentration generation downstream of a reservoir, a storage medium, and an electronic device, which have an advantage of improving accuracy of TDG concentration generation.
According to a first aspect of the embodiments of the present application, there is provided a method for generating and predicting a TDG concentration downstream of a reservoir, including the steps of:
acquiring historical water inflow of a preset time period before a target time period at the upstream of a reservoir and rainfall of the upstream of the reservoir at the target time period;
inputting the historical water inflow amount of the preset time period before the target time period and the rainfall amount of the upstream of the reservoir in the target time period into a prediction model of the upstream water inflow amount of the reservoir to obtain the water inflow amount of the upstream of the reservoir in the target time period;
acquiring reservoir observation data of a preset time period before a target time period; the reservoir observation data comprise a reservoir lower discharge flow, a reservoir absorption pool outlet water depth and a TDG concentration at a reservoir absorption pool outlet;
according to the lower discharge flow of the reservoir, the water depth of the outlet of the stilling pool and the TDG concentration at the outlet of the stilling pool, establishing a TDG concentration prediction model at the downstream of the reservoir by adopting a response surface method;
inputting the water volume of the reservoir in a target time interval into a reservoir operation scheduling scheme to obtain the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval;
and obtaining the TDG concentration of the downstream of the reservoir in the target time period according to the reservoir discharge flow in the target time period, the stilling pool outlet water depth in the target time period and the TDG concentration prediction model.
According to a second aspect of the embodiments of the present application, there is provided a reservoir downstream TDG concentration generation prediction apparatus including:
the historical water inflow acquisition module is used for acquiring the historical water inflow of the upstream of the reservoir in a preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period;
the inflow obtaining module is used for inputting the historical inflow of the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period into the upstream inflow forecasting model of the reservoir to obtain the inflow of the upstream of the reservoir in the target time period;
the observation data acquisition module is used for acquiring reservoir observation data of a preset time period before a target time period; the reservoir observation data comprise a reservoir lower discharge flow, a reservoir absorption pool outlet water depth and a TDG concentration at a reservoir absorption pool outlet;
the model establishing module is used for establishing a TDG concentration prediction model at the downstream of the reservoir by adopting a response surface method according to the lower discharge flow of the reservoir, the water depth at the outlet of the stilling pool and the TDG concentration at the outlet of the stilling pool;
the lower discharge flow obtaining module is used for inputting the water volume of the reservoir in a target time interval into a reservoir operation scheduling scheme to obtain the lower discharge flow of the reservoir in the target time interval and the water depth of the stilling pool outlet in the target time interval;
and the concentration obtaining module is used for obtaining the TDG concentration of the downstream of the reservoir in the target time interval according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model.
According to a third aspect of embodiments of the present application, there is provided an electronic apparatus, including: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method of generating and predicting TDG concentration downstream of a reservoir as described in any one of the above.
According to a fourth aspect of the embodiments of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for generating and predicting the TDG concentration downstream of the reservoir as described in any one of the above.
According to the embodiment of the invention, the water inflow of the upstream of the reservoir in the target time period is obtained by obtaining the historical water inflow of the upstream of the reservoir in the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period, inputting the historical water inflow of the upstream of the reservoir in the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period into the prediction model of the water inflow of the upstream of the reservoir, and obtaining the observation data of the reservoir in the preset time period before the target time period; the reservoir observation data comprises reservoir discharge flow, reservoir stilling pool outlet water depth and TDG concentration at a reservoir stilling pool outlet, a TDG concentration prediction model at the downstream of the reservoir is established by adopting a response surface method according to the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet, the water amount of the reservoir in a target time interval is input into a reservoir operation scheduling scheme to obtain the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval, the TDG concentration at the downstream of the reservoir in the target time interval is obtained according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model, the water amount at the upstream of the reservoir in the target time interval is predicted, and the influence of the reservoir discharge flow and the reservoir outlet water depth on the TDG concentration at the reservoir stilling pool outlet of the reservoir is considered at the same time, and the TDG concentration generation is fitted by using a response surface method, so that the TDG concentration generation precision is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic flow chart of a method for generating and predicting the TDG concentration of the downstream of a reservoir according to the invention;
FIG. 2 is a schematic flow chart of S20 in the method for predicting TDG concentration generation downstream of a reservoir according to the present invention;
FIG. 3 is a schematic flow chart of S23 in the method for generating and predicting the TDG concentration downstream of the reservoir according to the invention;
FIG. 4 is a schematic flow chart of S40 in the method for predicting TDG concentration generation downstream of a reservoir according to the present invention;
FIG. 5 is a block diagram of the structure of the downstream TDG concentration generation predicting device of the reservoir of the invention;
fig. 6 is a block diagram of the model building module 54 of the downstream TDG concentration generation prediction device of the reservoir of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims. In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Referring to fig. 1, an embodiment of the present invention provides a method for generating and predicting a TDG concentration downstream of a reservoir, including the following steps:
s10, acquiring the historical water inflow amount of the upstream of the reservoir in a preset time period before the target time period and the rainfall amount of the upstream of the reservoir in the target time period.
In the embodiment of the application, the influence of the historical water inflow amount of the preset time period before the target time period upstream of the reservoir and the rainfall amount of the target time period upstream of the reservoir on the water inflow amount of the target time period upstream of the reservoir is considered. The historical water inflow amount of the upstream of the reservoir in the preset time period before the target time period is obtained by inquiring the reservoir water situation table, and the rainfall amount of the upstream of the reservoir in the target time period can be obtained by searching weather network website data. The historical water inflow is the warehousing flow of the reservoir in different periods in the past. Rainfall is the depth of accumulation at the water level of liquid or solid (after melting) water falling from the sky to the ground without evaporation, penetration, loss. The precipitation amount is in millimeters, for example, the precipitation amount of a heavy rain is 50 to 99.9 millimeters.
S20, inputting the historical water inflow amount of the preset time period before the target time period and the rainfall amount of the upstream of the reservoir in the target time period into a prediction model of the upstream water inflow amount of the reservoir to obtain the water inflow amount of the upstream of the reservoir in the target time period.
The water inflow of the target time interval is the water inflow of a certain time interval in the future which needs to be predicted. In the embodiment of the application, the upstream incoming water amount prediction model of the reservoir is used for predicting the incoming water amount of the upstream of the reservoir in a target time period, inputting the historical incoming water amount of a preset time period before the target time period and the rainfall amount of the upstream of the reservoir in the target time period into the upstream incoming water amount prediction model of the reservoir, and outputting the incoming water amount of the upstream of the reservoir in the target time period by the upstream incoming water amount prediction model of the reservoir.
S30, acquiring reservoir observation data of a preset time period before a target time period; the reservoir observation data comprise the lower discharge flow of the reservoir, the water depth of the outlet of the reservoir absorption basin and the TDG concentration of the outlet of the reservoir absorption basin.
The reservoir discharge flow refers to the flow of reservoir operation and discharge, and comprises power generation flow, flood discharge flow of a spillway and the like. The reservoir stilling pool is an energy dissipation facility which promotes the generation of underflow type hydraulic jump at the downstream of the reservoir, can quickly change a downward-discharging rapid flow into a slow flow, and can generally eliminate the kinetic energy of the downward-discharging water flow by 40-70%. In the embodiment of the application, the reservoir observation data of the preset time period before the target time period is obtained by inquiring the reservoir water regime table.
And S40, establishing a TDG concentration prediction model at the downstream of the reservoir by adopting a response surface method according to the lower discharge flow of the reservoir, the water depth of the outlet of the stilling pool and the TDG concentration at the outlet of the stilling pool.
The Response Surface Method (RSM) is a statistical test method for optimizing random processes, and aims to find the quantitative rule between test indexes and each factor and find out the optimal combination of each factor level. Data is actively collected on the basis of multiple linear regression to obtain a regression equation with better properties. The established complex multi-dimensional space curved surface is closer to the actual situation, the number of required test sets is relatively less, and the method is widely applied to simulation and system dynamics. In the embodiment of the application, a response surface method is adopted to perform regression fitting on the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet, and a TDG concentration prediction model at the downstream of the reservoir is established.
S50, inputting the water volume of the reservoir in the target time interval into a reservoir operation scheduling scheme, and obtaining the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval.
The reservoir operation scheduling scheme means that all reservoirs need to compile scheduling regulations, the reservoir management units or the administrative departments organize and compile guidance rules according to the reservoir scheduling regulations, the reservoir management units or the administrative departments report the water administration departments above county level to examine and approve according to the administration authority, when the safety condition, the scheduling task and the operation condition of the reservoir are greatly changed, the reservoir needs to be revised and re-approved, and the reservoir is put on records every year if the reservoir is not changed. In the embodiment of the application, the water volume of the reservoir in the target time interval is input into the reservoir operation scheduling scheme, and the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval are obtained.
And S60, obtaining the TDG concentration of the downstream of the reservoir in the target time interval according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model.
In the embodiment of the application, the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval are input into the established TDG concentration prediction model, and the TDG concentration prediction model can output the TDG concentration of the reservoir downstream in the target time interval.
By applying the embodiment of the invention, the water inflow of the upstream of the reservoir in the target time period is obtained and the observation data of the reservoir in the preset time period before the target time period is obtained by obtaining the historical water inflow of the upstream of the reservoir in the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period, inputting the historical water inflow of the upstream of the reservoir in the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period into the prediction model of the water inflow of the upstream of the reservoir; the reservoir observation data comprises reservoir discharge flow, reservoir stilling pool outlet water depth and TDG concentration at a reservoir stilling pool outlet, a TDG concentration prediction model at the downstream of the reservoir is established by adopting a response surface method according to the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet, the water amount of the reservoir in a target time interval is input into a reservoir operation scheduling scheme to obtain the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval, the TDG concentration at the downstream of the reservoir in the target time interval is obtained according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model, the water amount at the upstream of the reservoir in the target time interval is predicted, and the influence of the reservoir discharge flow and the reservoir outlet water depth on the TDG concentration at the reservoir stilling pool outlet of the reservoir is considered at the same time, and the TDG concentration generation is fitted by utilizing a response surface method, so that the accuracy of the TDG concentration generation is improved.
Referring to fig. 2, in an embodiment of the present application, the method for generating and predicting TDG concentration downstream of a reservoir further includes training a prediction model of the amount of incoming water upstream of the reservoir, where the training of the prediction model of the amount of incoming water upstream of the reservoir includes S21 to S23:
s21, carrying out correlation analysis on the historical water inflow amount of the upstream of the reservoir in a plurality of training time periods and the historical water inflow amount of a corresponding preset time period before the plurality of training time periods to obtain a Pearson correlation coefficient.
The correlation analysis refers to the analysis of two or more variable elements with correlation, so as to measure the degree of closeness of correlation of the two variable elements. The Pearson Correlation Coefficient (Pearson Correlation Coefficient) is a linear Correlation Coefficient, denoted as r, and is used to reflect the linear Correlation degree of two variables X and Y, and the value of r is between-1 and 1, and a larger absolute value indicates a stronger Correlation. In the embodiment of the application, SPSS (Statistical Product and Service Solutions) is adopted to perform correlation analysis on the historical water inflow of a plurality of training time periods at the upstream of a reservoir and the historical water inflow of a corresponding plurality of preset time periods before the training time periods, so as to obtain a Pearson correlation coefficient.
And S22, obtaining a target historical water inflow amount of a preset time period before a plurality of training time periods according to the Pearson correlation coefficient.
In the embodiment of the present application, the pearson correlation coefficient is greater than 0.6, and the correlation is considered to be strong. And reserving the historical water inflow quantity of the preset time interval before the training time interval with strong correlation, namely obtaining the target historical water inflow quantity of the preset time interval before the training time interval.
And S23, inputting the target historical water inflow amount of a preset time period before a plurality of training time periods and the rainfall amount of the corresponding upstream reservoir in the plurality of training time periods as input, and inputting the historical water inflow amount of the upstream reservoir in the plurality of training time periods as output to a BP neural network for training and learning to obtain a prediction model of the upstream water inflow amount of the reservoir.
The BP neural network (Back Propagation) is a multi-layer feedforward neural network trained according to an error Back Propagation algorithm, and comprises an input layer, a hidden layer and an output layer. In the embodiment of the application, the target historical inflow amount of a preset time period before a plurality of training time periods and the corresponding rainfall amount of the upstream of the reservoir in the plurality of training time periods are input to an input layer of a BP (back propagation) neural network, the historical inflow amount of the upstream of the reservoir in the plurality of training time periods is used as the output of an output layer of the BP neural network, the input layer, the hidden layer and the output layer of the BP neural network are trained and learned, and a prediction model of the upstream inflow amount of the reservoir is obtained.
In an alternative embodiment, referring to fig. 3, the step S23 includes steps S231 to S233, which are as follows:
s231, taking the target historical water inflow amount of a preset time period before a plurality of training time periods and the rainfall amount of the upstream of the corresponding reservoir in the plurality of training time periods as input variables of the BP neural network, and taking the historical water inflow amount of the upstream of the reservoir in the plurality of training time periods as output variables of the BP neural network;
s232, preprocessing the input variable and the output variable to obtain a preprocessed input variable and a preprocessed output variable;
s233, inputting the preprocessed input variable into a BP neural network for calculation to obtain a predicted value; and calculating a square loss value between the predicted value and the output variable according to a square loss function, optimizing network parameters of the BP neural network through a back propagation algorithm and a gradient descent algorithm when the square loss value is larger than a preset threshold value, repeatedly calculating the square loss value according to the optimized BP neural network until the square loss value is descended to the preset threshold value, and taking the corresponding BP neural network as a reservoir upstream incoming water quantity prediction model.
In the embodiment of the application, the preprocessed input variables and output variables are divided into a training set, a verification set and a test set, and the number of neuron nodes of an input layer, a hidden layer and an output layer of the BP neural network is determined. The number of the neuron nodes of the input layer is determined to be m according to the target historical inflow amount of a preset time period before a plurality of preprocessed training time periods and the rainfall amount of the upstream of the preprocessed reservoir in the plurality of training time periods, the number of the neuron nodes of the output layer is determined to be n according to the historical inflow amount of the upstream of the preprocessed reservoir in the plurality of training time periods, and the number of the neuron nodes of the hidden layer is calculated by the following empirical formula:
wherein l represents the number of neuron nodes of the hidden layer, and a is [1, 10 ]]Constant in between. The hidden layers all adopt Sigmoid type activation functions, and the output layer adopts linear type activation functions. Initializing weights and biases of input layers to hidden layers and hidingThe method comprises the steps of including weights and offsets from a layer to an output layer, using training set data, using a gradient descent algorithm to train a model, using a square loss function to calculate errors between predicted values and expected values, using an error back propagation method to calculate gradients, and adjusting the weights and the offsets to obtain different models. And obtaining errors on different models by using the same verification set, and selecting the model with the minimum error on the test set as an optimal model to obtain a trained reservoir upstream inflow prediction model.
In an optional embodiment, the input variable and the output variable are preprocessed to obtain preprocessed input variable and output variable by:
at xiX is the target historical water inflow of a preset time period before a plurality of training time periodsimaxIs the maximum value, x, of the target historical water inflow of a preset time period before a plurality of the training time periodsiminIs the minimum value, x 'of the target historical water inflow of a preset period before a plurality of the training periods'iThe target historical water inflow amount of a preset time period before a plurality of pre-processed training time periods;
at xiWhen the rainfall of a plurality of training time intervals at the upstream of the reservoir is ximaxIs the maximum value, x, of the rainfall upstream of the reservoir in a number of said training sessionsiminIs the minimum value, x ', of rainfall upstream of the reservoir for a number of said training sessions'iThe rainfall of the upstream of the reservoir in a plurality of training time periods after pretreatment;
at xiWhen the historical water inflow of the upstream of the reservoir in a plurality of training periods is ximaxIs the maximum value x of the historical water inflow of the upstream reservoir in a plurality of training periodsiminIs the minimum value, x ', of the historical amount of water coming from the reservoir upstream of several of the training sessions'iIs the history of the pretreated upstream reservoir in a plurality of training periodsThe amount of the incoming water.
In the neural network learning process, because the activation function of the neuron is a bounded function, in order to eliminate the influence of different factors due to different dimensions and units, part of the neuron is prevented from reaching an oversaturation state, and meanwhile, a larger input is required to fall in a region with a large gradient of the activation function of the neuron. To this end, the present application preprocesses input variables and output variables, where x is x, prior to the BP neural network training and predictionimaxAnd ximinRespectively the maximum value and the minimum value of each input component of the ith neuron; x is the number ofiAnd x'iRespectively are input variables and output variables before and after the ith neuron preprocessing.
In an alternative embodiment, the TDG concentration prediction model is:
y is the TDG concentration at the outlet of the stilling pool, X1Is the lower discharge quantity, X, of the reservoir2Is the water depth at the outlet of the stilling pool, K1、K2、K3、K4、K5And K6Are model parameters of the TDG concentration prediction model.
In an alternative embodiment, referring to fig. 4, the step S40 includes steps S41-S42, which are as follows:
s41, performing regression fitting on the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet by adopting a response surface method to obtain a relation model taking the reservoir discharge flow and the stilling pool outlet water depth as independent variables and the TDG concentration at the stilling pool outlet as a dependent variable;
and S42, carrying out variance analysis on the TDG concentration at the outlet of the stilling pool, the reservoir discharge flow and the water depth at the outlet of the stilling pool in the relation model, and obtaining a TDG concentration prediction model according to the significance level of the variance analysis.
Analysis of Variance (ANOVA) is also called Variance Analysis or F test and is used for significance test of mean difference between two or more samples. In the embodiment of the application, the SPSS is used to perform variance analysis on the TDG concentration at the stilling pool outlet, the reservoir discharge flow and the stilling pool outlet water depth in the relational model, and the variance analysis is used to determine whether the influence of the reservoir discharge flow and the stilling pool outlet water depth on the TDG concentration at the stilling pool outlet is significant, specifically including: only considering the factor of reservoir discharge flow, only considering the factor of stilling pool outlet water depth, and considering the interaction of the two factors of reservoir discharge flow and stilling pool outlet water depth, respectively calculating corresponding significance levels, namely P values, if the P value is less than 0.05, representing that the influence is significant, and keeping the corresponding model parameters of the item; if the P value is greater than 0.05, the influence is not significant, and the model parameter corresponding to the item is made to be 0.
Referring to fig. 5, an embodiment of the present invention provides a device 5 for generating and predicting a TDG concentration downstream of a reservoir, including:
a historical water inflow acquisition module 51, configured to acquire a historical water inflow of a preset time period before the target time period upstream of the reservoir and a rainfall of the target time period upstream of the reservoir;
the inflow obtaining module 52 is configured to input the historical inflow of the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period into the upstream inflow prediction model of the reservoir, so as to obtain the inflow of the upstream of the reservoir in the target time period;
the observation data acquisition module 53 is configured to acquire reservoir observation data of a preset time period before a target time period; the reservoir observation data comprise a reservoir lower discharge flow, a reservoir absorption pool outlet water depth and a TDG concentration at a reservoir absorption pool outlet;
the model establishing module 54 is used for establishing a TDG concentration prediction model at the downstream of the reservoir by adopting a response surface method according to the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet;
the lower discharge flow obtaining module 55 is configured to input the water volume of the reservoir in the target time period to a reservoir operation scheduling scheme, and obtain the lower discharge flow of the reservoir in the target time period and the water depth of the stilling pool outlet in the target time period;
and the concentration obtaining module 56 is used for obtaining the TDG concentration of the downstream of the reservoir in the target time interval according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model.
Optionally, the TDG concentration generation prediction device for the downstream of the reservoir further includes a model for predicting the upstream amount of water from the reservoir, and the model for predicting the upstream amount of water from the reservoir includes:
the correlation analysis unit is used for carrying out correlation analysis on the historical water inflow amount of the upstream of the reservoir in a plurality of training time periods and the historical water inflow amount of a corresponding preset time period before the plurality of training time periods to obtain a Pearson correlation coefficient;
a target historical water inflow obtaining unit, configured to obtain a target historical water inflow of a preset time period before a plurality of training time periods according to the pearson correlation coefficient;
and the training learning unit is used for inputting the target historical water inflow amount of a preset time period before a plurality of training time periods and the rainfall amount of the corresponding upstream reservoir in the plurality of training time periods, inputting the historical water inflow amount of the upstream reservoir in the plurality of training time periods as output into a BP neural network for training and learning, and obtaining a reservoir upstream water inflow prediction model.
Optionally, referring to fig. 6, the model building module 54 includes:
a regression fitting unit 542, configured to perform regression fitting on the reservoir let-down flow, the stilling pool outlet water depth, and the TDG concentration at the stilling pool outlet by using a response surface method, to obtain a relationship model using the reservoir let-down flow and the stilling pool outlet water depth as independent variables, and using the TDG concentration at the stilling pool outlet as a dependent variable;
and the variance analysis unit 544 is configured to perform variance analysis on the TDG concentration at the stilling pool outlet, the reservoir discharge flow, and the stilling pool outlet water depth in the relationship model, and obtain a TDG concentration prediction model according to a significance level of the variance analysis.
By applying the embodiment of the invention, the water inflow of the upstream of the reservoir in the target time period is obtained and the observation data of the reservoir in the preset time period before the target time period is obtained by obtaining the historical water inflow of the upstream of the reservoir in the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period, inputting the historical water inflow of the upstream of the reservoir in the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period into the prediction model of the water inflow of the upstream of the reservoir; the reservoir observation data comprises reservoir discharge flow, reservoir stilling pool outlet water depth and TDG concentration at a reservoir stilling pool outlet, a TDG concentration prediction model at the downstream of the reservoir is established by adopting a response surface method according to the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet, the water amount of the reservoir in a target time interval is input into a reservoir operation scheduling scheme to obtain the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval, the TDG concentration at the downstream of the reservoir in the target time interval is obtained according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model, the water amount at the upstream of the reservoir in the target time interval is predicted, and the influence of the reservoir discharge flow and the reservoir outlet water depth on the TDG concentration at the reservoir stilling pool outlet of the reservoir is considered at the same time, and the TDG concentration generation is fitted by utilizing a response surface method, so that the accuracy of the TDG concentration generation is improved.
The present application further provides an electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of the above embodiments.
The present application also provides a computer-readable storage medium, on which a computer program is stored, which is characterized in that the computer program, when being executed by a processor, performs the method steps of the above-mentioned embodiments.
For the apparatus embodiment, since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment for relevant points. The above-described device embodiments are merely illustrative, wherein the components described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart block or blocks and/or flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for generating and predicting TDG concentration at downstream of a reservoir is characterized by comprising the following steps:
acquiring historical water inflow of a preset time period before a target time period at the upstream of a reservoir and rainfall of the upstream of the reservoir at the target time period;
inputting the historical water inflow amount of the preset time period before the target time period and the rainfall amount of the upstream of the reservoir in the target time period into a prediction model of the upstream water inflow amount of the reservoir to obtain the water inflow amount of the upstream of the reservoir in the target time period;
acquiring reservoir observation data of a preset time period before a target time period; the reservoir observation data comprise a reservoir lower discharge flow, a reservoir absorption pool outlet water depth and a TDG concentration at a reservoir absorption pool outlet;
according to the lower discharge flow of the reservoir, the water depth of the outlet of the stilling pool and the TDG concentration at the outlet of the stilling pool, establishing a TDG concentration prediction model at the downstream of the reservoir by adopting a response surface method;
inputting the water volume of the reservoir in a target time interval into a reservoir operation scheduling scheme to obtain the reservoir discharge flow in the target time interval and the stilling pool outlet water depth in the target time interval;
and obtaining the TDG concentration of the downstream of the reservoir in the target time period according to the reservoir discharge flow in the target time period, the stilling pool outlet water depth in the target time period and the TDG concentration prediction model.
2. The method of generating and predicting TDG concentration downstream of a reservoir of claim 1, further comprising training a model for predicting the amount of incoming water upstream of a reservoir; the step of training the water inflow prediction model at the upstream of the reservoir comprises the following steps:
carrying out correlation analysis on the historical water inflow amount of the upstream of the reservoir in a plurality of training time periods and the historical water inflow amount of a corresponding preset time period before the plurality of training time periods to obtain a Pearson correlation coefficient;
obtaining a target historical water inflow amount of a preset time period before a plurality of training time periods according to the Pearson correlation coefficient;
and inputting the target historical water inflow of a preset time period before the training time periods and the corresponding rainfall of the upstream of the reservoir in the training time periods as inputs, and inputting the historical water inflow of the upstream of the reservoir in the training time periods as outputs to a BP neural network for training and learning to obtain a prediction model of the upstream water inflow of the reservoir.
3. The method for generating and predicting the TDG concentration at the downstream of the reservoir according to claim 2, wherein the step of inputting the target historical water inflow in a preset time period before a plurality of training time periods and the corresponding rainfall at the upstream of the reservoir in the plurality of training time periods as inputs, inputting the historical water inflow at the upstream of the reservoir in the plurality of training time periods as outputs to a BP neural network for training and learning, and obtaining the water inflow prediction model at the upstream of the reservoir comprises the following steps:
taking the target historical water inflow of a preset time period before a plurality of training time periods and the rainfall of the upstream of the corresponding reservoir in the plurality of training time periods as input variables of the BP neural network, and taking the historical water inflow of the upstream of the reservoir in the plurality of training time periods as output variables of the BP neural network;
preprocessing the input variable and the output variable to obtain a preprocessed input variable and a preprocessed output variable;
inputting the preprocessed input variable into a BP neural network for calculation to obtain a predicted value; and calculating a square loss value between the predicted value and the output variable according to a square loss function, optimizing network parameters of the BP neural network through a back propagation algorithm and a gradient descent algorithm when the square loss value is larger than a preset threshold value, repeatedly calculating the square loss value according to the optimized BP neural network until the square loss value is descended to the preset threshold value, and taking the corresponding BP neural network as a reservoir upstream incoming water quantity prediction model.
4. The method for predicting TDG concentration downstream of a reservoir of claim 3, wherein,
preprocessing the input variable and the output variable to obtain a preprocessed input variable and a preprocessed output variable by the following method:
at xiX is the target historical water inflow of a preset time period before a plurality of training time periodsimaxIs the maximum value, x, of the target historical water inflow of a preset time period before a plurality of the training time periodsiminIs the minimum value, x 'of the target historical water inflow of a preset period before a plurality of the training periods'iThe target historical water inflow amount of a preset time period before a plurality of pre-processed training time periods;
at xiWhen the rainfall of a plurality of training time intervals at the upstream of the reservoir is ximaxIs the maximum value, x, of the rainfall upstream of the reservoir in a number of said training sessionsiminIs the minimum value, x ', of rainfall upstream of the reservoir for a number of said training sessions'iThe rainfall of the upstream of the reservoir in a plurality of training time periods after pretreatment;
at xiWhen the historical water inflow of the upstream of the reservoir in a plurality of training periods is ximaxIs a calendar of a number of said training sessions upstream of the reservoirMaximum value of amount of water, ximinIs the minimum value, x ', of the historical amount of water coming from the reservoir upstream of several of the training sessions'iThe historical water inflow of the upstream reservoir after pretreatment in a plurality of training periods.
5. The method for generating and predicting the TDG concentration of the downstream of the reservoir according to claim 1, wherein the step of establishing a TDG concentration prediction model of the downstream of the reservoir by using a response surface method according to the lower discharge flow of the reservoir, the water depth of the outlet of the stilling pool and the TDG concentration of the outlet of the stilling pool comprises the following steps:
performing regression fitting on the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet by adopting a response surface method to obtain a relation model taking the reservoir discharge flow and the stilling pool outlet water depth as independent variables and the TDG concentration at the stilling pool outlet as a dependent variable;
and carrying out variance analysis on the TDG concentration at the outlet of the stilling pool, the reservoir discharge flow and the water depth at the outlet of the stilling pool in the relation model, and obtaining a TDG concentration prediction model according to the significance level of the variance analysis.
6. The method for predicting TDG concentration downstream of a reservoir of claim 1,
the TDG concentration prediction model is as follows:
y is the TDG concentration at the outlet of the stilling pool, X1Is the lower discharge quantity, X, of the reservoir2Is the water depth at the outlet of the stilling pool, K1、K2、K3、K4、K5And K6Are model parameters of the TDG concentration prediction model.
7. A reservoir downstream TDG concentration generation prediction apparatus, comprising:
the historical water inflow acquisition module is used for acquiring the historical water inflow of the upstream of the reservoir in a preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period;
the inflow obtaining module is used for inputting the historical inflow of the preset time period before the target time period and the rainfall of the upstream of the reservoir in the target time period into the upstream inflow forecasting model of the reservoir to obtain the inflow of the upstream of the reservoir in the target time period;
the observation data acquisition module is used for acquiring reservoir observation data of a preset time period before a target time period; the reservoir observation data comprise a reservoir lower discharge flow, a reservoir absorption pool outlet water depth and a TDG concentration at a reservoir absorption pool outlet;
the model establishing module is used for establishing a TDG concentration prediction model at the downstream of the reservoir by adopting a response surface method according to the lower discharge flow of the reservoir, the water depth at the outlet of the stilling pool and the TDG concentration at the outlet of the stilling pool;
the lower discharge flow obtaining module is used for inputting the water volume of the reservoir in a target time interval into a reservoir operation scheduling scheme to obtain the lower discharge flow of the reservoir in the target time interval and the water depth of the stilling pool outlet in the target time interval;
and the concentration obtaining module is used for obtaining the TDG concentration of the downstream of the reservoir in the target time interval according to the reservoir discharge flow in the target time interval, the stilling pool outlet water depth in the target time interval and the TDG concentration prediction model.
8. The apparatus of claim 6, wherein the model building module comprises:
the regression fitting unit is used for performing regression fitting on the reservoir discharge flow, the stilling pool outlet water depth and the TDG concentration at the stilling pool outlet by adopting a response surface method to obtain a relation model taking the reservoir discharge flow and the stilling pool outlet water depth as independent variables and the TDG concentration at the stilling pool outlet as a dependent variable;
and the variance analysis unit is used for carrying out variance analysis on the TDG concentration at the outlet of the stilling pool, the reservoir discharge flow and the water depth at the outlet of the stilling pool in the relation model, and obtaining a TDG concentration prediction model according to the significance level of the variance analysis.
9. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method of reservoir downstream TDG concentration generation prediction according to any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for prediction of TDG concentration generation downstream of a reservoir according to any one of claims 1 to 6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115083134A (en) * | 2022-07-21 | 2022-09-20 | 中国长江三峡集团有限公司 | Hydraulic engineering dam flood discharge dissolved gas supersaturation early warning regulation and control system |
| CN116562469A (en) * | 2023-07-10 | 2023-08-08 | 湖南师范大学 | Fresh water fish pathogen transmission prediction method, device, equipment and storage medium |
| CN117575289A (en) * | 2024-01-15 | 2024-02-20 | 广东工业大学 | Reservoir optimal scheduling method with water quantity-water quality multi-objective cooperation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150065355A1 (en) * | 2012-03-14 | 2015-03-05 | Ruprecht-Karls-Universität Heildelberg | Epigenetic Signatures as Marker for Cardiomyopathies and Myocardial Insufficiencies |
| CN108867582A (en) * | 2018-06-12 | 2018-11-23 | 四川大学 | The step hydropower station ecological dispatching method that fish are influenced based on supersaturated TDG |
-
2021
- 2021-10-27 CN CN202111253419.4A patent/CN113837493B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150065355A1 (en) * | 2012-03-14 | 2015-03-05 | Ruprecht-Karls-Universität Heildelberg | Epigenetic Signatures as Marker for Cardiomyopathies and Myocardial Insufficiencies |
| CN108867582A (en) * | 2018-06-12 | 2018-11-23 | 四川大学 | The step hydropower station ecological dispatching method that fish are influenced based on supersaturated TDG |
Non-Patent Citations (5)
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115083134A (en) * | 2022-07-21 | 2022-09-20 | 中国长江三峡集团有限公司 | Hydraulic engineering dam flood discharge dissolved gas supersaturation early warning regulation and control system |
| CN115083134B (en) * | 2022-07-21 | 2022-12-02 | 中国长江三峡集团有限公司 | Hydraulic engineering dam flood discharge dissolved gas supersaturation early warning regulation and control system |
| CN116562469A (en) * | 2023-07-10 | 2023-08-08 | 湖南师范大学 | Fresh water fish pathogen transmission prediction method, device, equipment and storage medium |
| CN116562469B (en) * | 2023-07-10 | 2023-09-19 | 湖南师范大学 | Fresh water fish pathogen transmission prediction method, device, equipment and storage medium |
| CN117575289A (en) * | 2024-01-15 | 2024-02-20 | 广东工业大学 | Reservoir optimal scheduling method with water quantity-water quality multi-objective cooperation |
| CN117575289B (en) * | 2024-01-15 | 2024-05-03 | 广东工业大学 | Reservoir optimal scheduling method with water quantity-water quality multi-objective cooperation |
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