Riverbed flow range calculation method, device, electronic equipment and storage medium

Abstract

The present application discloses a method, device, electronic device and storage medium for calculating the flow range of riverbed formation, which belongs to the field of hydrological data processing. The method includes: inputting the flow corresponding to each flow level in the inlet flow sequence into a pre-trained hydrological prediction model to obtain the cross-section water level prediction value, cross-section flow prediction range and cross-section sediment content prediction range associated with each flow level; determining the cross-section sediment content upper limit and cross-section sediment content lower limit associated with each water level according to the cross-section water level prediction value of each water level level and the first functional relationship; determining the evaluation parameter value of the bed formation degree according to the cross-section water level prediction value, flat beach water level, sediment entrainment, cross-section sediment content upper limit and cross-section sediment content lower limit associated with each water level level; determining the target cross-section water level value according to the target water level level associated with the minimum evaluation parameter value; determining the cross-section riverbed formation flow range according to the target cross-section water level value and the second functional relationship.

Description

Technical Field

[0001] The present application belongs to the field of hydrological data processing, and specifically relates to a method, device, electronic equipment and storage medium for calculating the flow range of river bed formation. Background Art

[0002] The bed-forming flow is the characteristic flow that represents the evolution of the river channel, and it symbolizes the flow value that dominates the shaping of the cross-sectional morphology of the river channel. The bed-forming flow is usually difficult to determine, because the situation of excessive flow is relatively rare and does not play a role in shaping the riverbed morphology; the flow is too small and the intensity is insufficient, which also cannot have a major impact on the riverbed morphology. The traditional method of calculating the bed-forming flow mainly includes: when the water level is flush with the cross-sectional beach, the flow passing through the cross-section is used as the bed-forming flow; or, the bed-forming flow is calculated by the Makaveev method.

[0003] The current technical solutions, on the one hand, rely on measured data and can only obtain the bed-forming flow of the observed section, which cannot be extended to the river without observation data. On the other hand, the calculation using empirical formulas such as the water level being flush with the beach and Makaveyev cannot fully reflect the complex mechanism of river evolution. The accurate determination of the bed-forming flow is very critical for river management planning and design, and a more scientific and complete calculation and analysis method is needed.

[0004] Therefore, the current calculation accuracy of the flow range for river bed formation is insufficient. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method, device, electronic device and storage medium for calculating the flow range of river bed construction, which can solve the current problem of insufficient calculation accuracy of the flow range of river bed construction.

[0006] In a first aspect, an embodiment of the present application provides a method for calculating a flow range of a river bed formation, the method comprising:

[0007] Obtain the inlet flow sequence, flat water level and sediment entrainment of the river to be tested. The inlet flow sequence includes the flow during the process of rising and falling water. The starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range.

[0008] The flow corresponding to each flow level in the inlet flow sequence is input into the pre-trained hydrological prediction model, and the hydrological prediction model is run several times to obtain the cross-section water level prediction value, cross-section flow prediction range and cross-section sediment content prediction range associated with each flow level;

[0009] According to the predicted value of the cross-section water level and the predicted range of the cross-section sediment content, the first functional relationship is established;

[0010] A second functional relationship is established according to the predicted value of the cross-section water level and the predicted range of the cross-section flow;

[0011] According to the cross-sectional water level prediction value of each water level level and the first functional relationship, the upper limit of the cross-sectional sediment content and the lower limit of the cross-sectional sediment content associated with each water level level are determined;

[0012] According to the predicted values ​​of the cross-section water level at each water level level, the flat water level, the sediment entraining force, the upper and lower limits of the cross-section sediment content associated with each water level level, the values ​​of the bed-forming degree evaluation parameters are determined; the values ​​of the bed-forming degree evaluation parameters are inversely proportional to the bed-forming effect;

[0013] Determining a target section water level value according to a target water level level associated with a minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among a plurality of bed formation degree evaluation parameter values;

[0014] According to the water level value of the target section and the second function relationship, the river bed formation flow range of the section is determined.

[0015] In a second aspect, an embodiment of the present application provides a device for calculating a flow range of a river bed formation, the device comprising:

[0016] The acquisition module is used to obtain the inlet flow sequence, flat water level and sediment entrainment of the river to be tested. The inlet flow sequence includes the flow during the process of rising and falling water. The starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range.

[0017] An input module is used to input the flow corresponding to each flow level in the inlet flow sequence into a pre-trained hydrological prediction model, run the hydrological prediction model several times, and obtain the cross-sectional water level prediction value, cross-sectional flow prediction range, and cross-sectional sediment content prediction range associated with each flow level;

[0018] A first establishing module is used to establish a first functional relationship according to a predicted value of a cross-section water level and a predicted range of a cross-section sediment content;

[0019] A second establishing module is used to establish a second functional relationship according to the cross-section water level prediction value and the cross-section flow prediction range;

[0020] A first determination module is used to determine the upper limit and lower limit of the cross-section sediment content associated with each water level level according to the cross-section water level prediction value of each water level level and the first functional relationship;

[0021] The second determination module is used to determine the bed formation degree evaluation parameter value according to the cross-section water level prediction value of each water level level, the flat beach water level, the sediment entrainment force, the cross-section sediment content upper limit and the cross-section sediment content lower limit associated with each water level level; the value of the bed formation degree evaluation parameter value is inversely proportional to the bed formation effect;

[0022] A third determination module is used to determine the target section water level value according to the target water level level associated with the minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among the multiple bed formation degree evaluation parameter values;

[0023] The fourth determination module is used to determine the riverbed-forming flow range of the section according to the target section water level value and the second functional relationship.

[0024] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0025] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0026] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0027] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the first aspect.

[0028] In an embodiment of the present application, by obtaining the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range; the flow corresponding to each flow level in the inlet flow sequence is input into a pre-trained hydrological prediction model, and the hydrological prediction model is run several times to quickly obtain the cross-sectional water level prediction value, cross-sectional flow prediction range and cross-sectional sediment content prediction range associated with each flow level; according to the cross-sectional water level prediction value and the cross-sectional sediment content prediction range, a cross-sectional water level prediction model is established. The first functional relationship; the second functional relationship is established according to the section water level prediction value and the section flow prediction range; through the first functional relationship, the corresponding section sediment content upper limit and section sediment content lower limit can be deduced according to the section water level prediction value of different water level levels, and the bed formation degree evaluation parameter value is determined according to the section water level prediction value of each water level level, the flat beach water level, the sediment entrainment capacity, and the section sediment content upper limit and section sediment content lower limit associated with each water level level. Here, by integrating multi-factor analysis, the water level, sediment content characteristics, sediment entrainment capacity and other factors are comprehensively considered to calculate the bed formation degree evaluation parameter value corresponding to each water level level, which can improve the scientific nature of decision-making through quantified evaluation parameter values. Since the numerical value of the bed-forming degree evaluation parameter is inversely proportional to the bed-forming effect, the target water level associated with the minimum evaluation parameter value is the water level with the strongest bed-forming effect. The target section water level value is determined based on the target water level associated with the minimum evaluation parameter value. According to the target section water level value and the second function relationship, the river bed-forming flow range of the section can be accurately determined, which is convenient for providing a more scientific basis for the subsequent design and operation of water conservancy projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for calculating the flow range of river bed formation provided in an embodiment of the present application;

[0030] Figure 2 is a schematic diagram of an import flow sequence provided in an embodiment of the present application;

[0031] Figure 3 is a schematic diagram of a first functional relationship provided in an embodiment of the present application;

[0032] Figure 4 is a schematic diagram of a second functional relationship provided in an embodiment of the present application;

[0033] Figure 5 It is a structural diagram of a river bed flow range calculation device provided in an embodiment of the present application;

[0034] Figure 6 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application belong to the scope of protection of this application.

[0036] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0037] The method for calculating the flow range of river bed construction provided in the embodiment of the present application can be applied to at least the following application scenarios, which are explained below.

[0038] Bed-forming flow refers to the flow that has the greatest effect on the riverbed compared with other flows in the same period within a certain period of time (usually a hydrological year). This flow is not equal to the maximum flood flow, but refers to the flow whose bed-forming effect is equal to the comprehensive bed-forming effect of multi-year flow processes.

[0039] In practical applications, the bed-making flow is often used as the design flow for reclaimed water regulation, referred to as the regulation flow. It is the basis for drawing up the regulation line and designing the regulation buildings. There are many methods for calculating the bed-making flow, which needs to be determined through comparison. In practical applications, the flat beach flow is often used as the regulation flow. The bed-making effect of the flood season is strong, but the duration is short and it has no control effect on the bed-making; although the dry season lasts for a long time, the bed-making effect is not obvious due to the small flow.

[0040] The main functions of bed flow prediction are as follows:

[0041] Understanding the evolution trend of the river channel and predicting the bed-forming flow can help analyze the morphological changes of the river channel under different flow conditions and understand the long-term evolution trend of the river channel. This is very important for river management planning and decision-making.

[0042] Guiding engineering design, bed flow prediction can provide a basis for river engineering design, such as the design of embankments, revetments, riverbed stabilization measures, etc., to ensure the effectiveness and safety of the project.

[0043] Guiding the scouring and silting control and mastering the prediction results of bed-building flow can provide reference for the scouring and silting control of the river, such as reasonable scheduling of sediment discharge, dredging of the river, etc. This helps to maintain the flood discharge capacity and ecological environment of the river.

[0044] Supporting disaster prevention and reduction, bed-making flow prediction helps predict river channel deformation during floods, provides a scientific basis for flood prevention and disaster reduction warnings, and reduces disaster risks.

[0045] Optimize water resource allocation, and the bed flow prediction results can provide information support for the rational allocation of water resources, such as reservoir scheduling, water supply planning, etc.

[0046] Therefore, bed-forming flow prediction provides an important scientific basis for river management and planning, and is of great significance for promoting the sustainable utilization of river resources.

[0047] In response to the problems arising in the related art, the embodiments of the present application provide a method and device for calculating the flow range of river bed construction, which can solve the problem of insufficient calculation accuracy of the flow range of river bed construction in the related art.

[0048] The method for calculating the flow range of river bed formation provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0049] Figure 1 A flow chart of a method for calculating the flow range of river bed formation provided in an embodiment of the present application.

[0050] like Figure 1 As shown, the method for calculating the flow range of riverbed construction may include steps 110 to 180. The method is applied to a device for calculating the flow range of riverbed construction, as shown below:

[0051] Step 110, obtaining the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, wherein the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and the ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range;

[0052] Step 120, inputting the flow corresponding to each flow level in the inlet flow sequence into a pre-trained hydrological prediction model, running the hydrological prediction model several times, and obtaining the cross-sectional water level prediction value, cross-sectional flow prediction range, and cross-sectional sediment content prediction range associated with each flow level;

[0053] Step 130, establishing a first functional relationship according to the predicted value of the cross-section water level and the predicted range of the cross-section sediment content;

[0054] Step 140, establishing a second functional relationship according to the cross-sectional water level prediction value and the cross-sectional flow prediction range;

[0055] Step 150, determining the upper limit and lower limit of the cross-section sediment content associated with each water level level according to the cross-section water level prediction value of each water level level and the first functional relationship;

[0056] Step 160, determining the bed formation degree evaluation parameter value according to the cross-sectional water level prediction value of each water level level, the flat beach water level, the sediment entrainment force, the cross-sectional sediment content upper limit and the cross-sectional sediment content lower limit associated with each water level level; the value of the bed formation degree evaluation parameter value is inversely proportional to the bed formation effect;

[0057] Step 170, determining a target cross-section water level value according to a target water level level associated with a minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among a plurality of bed formation degree evaluation parameter values;

[0058] Step 180, determining the riverbed-forming flow range of the section according to the target section water level value and the second functional relationship.

[0059] In an embodiment of the present application, by obtaining the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range; the flow corresponding to each flow level in the inlet flow sequence is input into a pre-trained hydrological prediction model, and the hydrological prediction model is run several times to quickly obtain the cross-sectional water level prediction value, cross-sectional flow prediction range and cross-sectional sediment content prediction range associated with each flow level; according to the cross-sectional water level prediction value and the cross-sectional sediment content prediction range, a cross-sectional water level prediction model is established. The first functional relationship; the second functional relationship is established according to the section water level prediction value and the section flow prediction range; through the first functional relationship, the corresponding section sediment content upper limit and section sediment content lower limit can be deduced according to the section water level prediction value of different water level levels, and the bed formation degree evaluation parameter value is determined according to the section water level prediction value of each water level level, the flat beach water level, the sediment entrainment capacity, and the section sediment content upper limit and section sediment content lower limit associated with each water level level. Here, by integrating multi-factor analysis, the water level, sediment content characteristics, sediment entrainment capacity and other factors are comprehensively considered to calculate the bed formation degree evaluation parameter value corresponding to each water level level, which can improve the scientific nature of decision-making through quantified evaluation parameter values. Since the numerical value of the bed-forming degree evaluation parameter is inversely proportional to the bed-forming effect, the target water level associated with the minimum evaluation parameter value is the water level with the strongest bed-forming effect. The target section water level value is determined based on the target water level associated with the minimum evaluation parameter value. According to the target section water level value and the second function relationship, the river bed-forming flow range of the section can be accurately determined, which is convenient for providing a more scientific basis for the subsequent design and operation of water conservancy projects.

[0060] Steps 110 to 180 are described below respectively:

[0061] Step 110, obtaining the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, wherein the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and the ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range;

[0062] It is necessary to obtain the entire flow time series data of the river inlet, including the flow values ​​at each moment during the process of rising and falling water. The flat water level refers to the water level under normal conditions without flooding, and the sediment carrying capacity refers to the amount of sediment that the river can carry and transport. These data are the basic information required for subsequent analysis. The following are explained separately:

[0063] The flat water level refers to the water level when the cross-section of the river reaches a balanced state under a certain flow rate. It reflects the sediment holding capacity of the river and is an important indicator for measuring the river's passing capacity. The higher the flat water level, the stronger the sediment holding capacity of the river and the faster the siltation rate.

[0064] Sediment-carrying capacity, also known as sand-carrying capacity or pushing capacity, refers to the ability of a river to carry or push sand of a certain particle size in a certain section. The sediment-carrying capacity is determined by factors such as flow velocity, flow rate, and water depth. The greater the sediment-carrying capacity, the easier it is to scour the river channel and the slower the siltation rate.

[0065] The flat water level and sediment-carrying capacity reflect the movement characteristics of the river channel and are important physical factors in the river development process. They are interrelated and have important significance for river management and water environment management.

[0066] like Figure 2 As shown, the horizontal axis is time and the vertical axis is flow rate. An "isosceles triangle" type import flow sequence is established to simulate the process of rising and falling water. The flow sequence uses Ra as the valley value of the start and end positions of the sequence, and the starting flow and ending flow of the import flow sequence are equal to the lower limit of the flow research range. Rb is used as the peak of the sharp angle, and the flow sequence is divided according to the flow level, that is, the peak flow of the import flow sequence is equal to the upper limit of the flow research range.

[0067] Step 120, inputting the flow corresponding to each flow level in the inlet flow sequence into a pre-trained hydrological prediction model, running the hydrological prediction model several times, and obtaining the cross-sectional water level prediction value, cross-sectional flow prediction range, and cross-sectional sediment content prediction range associated with each flow level;

[0068] The previously collected inlet flow series data is used to enter a pre-established hydrological prediction model. The prediction model is run multiple times to obtain the output of the model, including the predicted values ​​and ranges of the future water level, flow and sediment content of the river section.

[0069] In a possible embodiment, before step 120, the following steps may also be included:

[0070] Obtain flow research range and bed-making flow accuracy;

[0071] The flow research range is divided into multiple flow levels according to the bed-making flow accuracy.

[0072] According to the characteristics of the river and the management objectives, determine the flow range that needs to be covered by the study. Usually including: daily base flow, general flood flow, extreme flood flow. Collect flow observation data for as long as possible to fully reflect the flow change characteristics. For rivers without observation data, flow simulation can be carried out using methods such as hydrological models.

[0073] Specifically, the flow research range [Ra, Rb] is determined according to the hydrological frequency from 10-year (Ra) to 100-year (Rb). After the range is determined, the flow level is further divided according to the accuracy requirements. If the bed-making flow accuracy is required to be 50m3 / s, the flow level can be set to 50m3 / s per flow level.

[0074] For example, the flow research range is determined as [2000, 8000] according to the hydrological frequency from 10-year (Ra) to 100-year (Rb). After the range is determined, the flow level is further divided according to the accuracy requirements. 3 / s, so the flow rate is set to every 50m 3 / s is a flow level, which divides the flow range.

[0075] Therefore, by reasonably dividing the flow research scope and accuracy level, targeted analysis methods can be adopted according to actual needs to improve the scientificity and effectiveness of river management.

[0076] In a possible embodiment, before step 120, the following steps may also be included:

[0077] Step 210, obtaining the inlet flow and label hydrological data of the river section, the label hydrological data including: section water level label data, section flow label data and section sediment content label data;

[0078] Step 220, inputting the river section inlet flow into a preset LSTM to obtain predicted hydrological data;

[0079] Step 230, training a preset LSTM according to the predicted hydrological data and the labeled hydrological data until the preset LSTM meets the preset training conditions to obtain a hydrological prediction model.

[0080] Involving step 210, obtaining the river section inlet flow and labeled hydrological data, obtaining the river section inlet flow data from relevant data sources, and at the same time obtaining the section water level label data, section flow label data and section sediment content label data of the river section through the water-sediment mathematical model.

[0081] In a possible embodiment, step 210 includes:

[0082] Obtain measured cross-section topographic data, river section inlet flow and river section sediment content process data;

[0083] The measured cross-section topographic data, river section inlet flow and river section sediment content process data are input into the pre-established water-sediment mathematical model to obtain the labeled hydrological data, flat water level and sediment-carrying capacity of each section of the river.

[0084] For example, the river channel to be measured is 150 km long. In order to obtain the approximate range of bed-forming flow at a certain section A in this river channel, a water-sediment mathematical model can be established.

[0085] Specifically, a one-dimensional water-sediment mathematical model was established, and the topographic scattered point data of the river section measured in 2012 were input. The average interval of the sections was 1 to 1.5 km, with a total of 80 sections, and each section consisted of a series of xz elevation scattered points. The model was fed with the changes in the river inlet flow Qin (t) and sediment content Sin (t) over time in the flood year of 2012, the dry year of 2013, and the normal year of 2015. The water level Z(t), flow Q(t), and sediment content S(t) of the river section A were simulated and calculated over time, and the flat water level Z*=15.5m and sediment carrying capacity S*=0.64kg / m 3 The time interval between data input and output is interpolated to 30s, so the mapping relationship between the inlet flow and the water level, flow rate and sediment content of section A is obtained, which are 3156480 sets of data respectively.

[0086] In step 220, a preset LSTM (Long Short-Term Memory) model is selected or built, and the acquired river section inlet flow data is sent as input to the LSTM model. The LSTM model will predict the corresponding water level, flow and sediment content data based on the input flow data.

[0087] Involving step 230, the hydrological data predicted by the LSTM model in step 220 is compared with the labeled hydrological data obtained in step 210, and the difference between the predicted data and the labeled data is used to continuously optimize the weight parameters of the LSTM model through the back propagation algorithm, and the training process is repeated until the LSTM model meets the preset training conditions, such as the prediction error is less than the preset error threshold, and finally a trained hydrological prediction model is obtained.

[0088] Therefore, the LSTM model can be used to predict and simulate the hydrological process of the river section. The trained hydrological prediction model can handle large-scale import flow sequences and improve prediction efficiency.

[0089] In a possible embodiment, the preset LSTM includes: a first LSTM, a second LSTM and a third LSTM, and step 230 includes:

[0090] Input the inlet flow of the river section into the first LSTM, the second LSTM and the third LSTM respectively, and obtain the cross-section water level prediction data, the cross-section flow prediction data and the cross-section sediment content prediction data respectively;

[0091] Training the first LSTM according to the section water level prediction data and the section water level label data; and,

[0092] Training the second LSTM according to the cross-sectional flow prediction data and the cross-sectional flow label data; and,

[0093] The third LSTM is trained based on the cross-section sediment content prediction data and cross-section sediment content label data;

[0094] Until the first LSTM, the second LSTM and the third LSTM meet the preset training conditions, a hydrological prediction model is obtained.

[0095] The preset LSTM includes: the first LSTM, the second LSTM and the third LSTM, a total of 3 deep learning LSTMs, taking the inlet flow process Qin (t) of the water-sediment mathematical model in step 210 as input, and the water level, flow, and sediment content process of section A calculated by the model as the prediction targets of the output of the 3 LSTM models, respectively, and the amount of training data is 3156480 groups. After completing the training and testing of the 3 LSTM models, it is determined that the LSTM prediction of each step is most accurate based on the past 7 flow values.

[0096] The flow corresponding to each flow level in the inlet flow sequence is input into the pre-trained hydrological prediction model, and the hydrological prediction model is run several times. That is, the flow sequence is input into 3 LSTM models in batches according to n flow levels each time, and the LSTM model is run several times to obtain the section water level prediction value, section flow prediction range and section sediment content prediction range associated with each flow level.

[0097] According to the cross-section water level prediction value, cross-section flow prediction range and cross-section sediment content prediction range associated with each flow level, the first functional relationship and the second functional relationship are established. They are explained below:

[0098] Step 130, establishing a first functional relationship according to the predicted value of the cross-section water level and the predicted range of the cross-section sediment content;

[0099] Taking the predicted value of the section water level as the independent variable and the predicted range of the section sediment content as the dependent variable, the functional relationship between the section water level and the section sediment content is established through curve fitting or other mathematical modeling methods.

[0100] For example, the flow sequence will be 2000m 3 / s as the valley value of the sequence start and end positions, and 8000m 3 / s is taken as the peak value of the sharp angle, and the flow sequence is divided into 241 flow levels according to the flow level. According to the 7 flow levels determined in the above steps, the flow sequence is input into 3 LSTM models in batches, and the LSTM models are run several times to predict the water level and sediment content range of each section of the river, and the relationship between the cross-section water level and sediment content range is established accordingly.

[0101]

[0102] In formula (1), z is the predicted value of the cross-section water level, S range This is the predicted range of cross-section sediment content.

[0103] The first functional relationship between the predicted value of the cross-section water level and the predicted range of the cross-section sediment content is as follows: Figure 3 As shown in the figure, the horizontal axis is the water level, the left vertical axis is the sediment content, and the right vertical axis is the bed formation degree evaluation parameter value. The two solid lines represent the cross-section sediment content range, and the dotted lines represent the bed formation degree evaluation parameter values ​​under different water levels and sediment contents.

[0104] Step 140, establishing a second functional relationship according to the cross-sectional water level prediction value and the cross-sectional flow prediction range;

[0105]

[0106] In formula (2), z is the predicted value of the cross-section water level, Q range is the cross-sectional flow prediction range.

[0107] Taking the predicted value of the section water level as the independent variable and the predicted range of the section flow as the dependent variable, the functional relationship between the section water level and the section flow is established through curve fitting or mathematical modeling methods.

[0108] The second functional relationship between the cross-section water level prediction value and the cross-section flow prediction range is as follows: Figure 4 As shown, the horizontal axis is the water level and the vertical axis is the flow rate.

[0109] Therefore, based on the water level prediction value, the corresponding sediment content prediction range and flow prediction range can be obtained, and two important functional relationships can be established.

[0110] Step 150, determining the upper limit and lower limit of the cross-section sediment content associated with each water level level according to the cross-section water level prediction value of each water level level and the first functional relationship;

[0111] By substituting the water level prediction values ​​of different water level levels into the first functional relationship (the functional relationship between the cross-section water level and the cross-section sediment content), the upper and lower limits of the sediment content corresponding to each water level level can be determined.

[0112] Assuming that every 0.2m is one water level level, the water level Z is substituted into the first functional relationship to obtain the upper and lower limits of sediment content at each water level.

[0113] Step 160, determining the bed formation degree evaluation parameter value according to the cross-sectional water level prediction value of each water level level, the flat beach water level, the sediment entrainment force, the cross-sectional sediment content upper limit and the cross-sectional sediment content lower limit associated with each water level level; the value of the bed formation degree evaluation parameter value is inversely proportional to the bed formation effect;

[0114] In a possible embodiment, step 160 may specifically include the following steps:

[0115] For any water level, the water level evaluation parameter value is determined based on the cross-section water level prediction value and the flat beach water level;

[0116] Determine the average sediment content based on the upper limit of the sediment content range of the cross section and the lower limit of the sediment content range of the cross section;

[0117] Determine the sediment content evaluation parameter value based on the average sediment content and sediment carrying capacity;

[0118] The bed formation degree evaluation parameter values ​​are determined based on the water level evaluation parameter values ​​and the sediment content evaluation parameter values.

[0119] The evaluation parameter values ​​of bed formation degree under each water level and sediment content are as follows: Figure 3 As shown in the figure, the horizontal axis is the water level, the left vertical axis is the sediment content, and the right vertical axis is the bed formation degree evaluation parameter value. The two solid lines represent the cross-section sediment content range, and the dotted lines represent the bed formation degree evaluation parameter values ​​under different water levels and sediment contents.

[0120] For any water level, the water level evaluation parameter value is determined based on the section water level prediction value and the flat beach water level.

[0121] Water level evaluation parameter value = (cross-section water level prediction value - flat beach water level) / maximum water level difference;

[0122] The water level evaluation parameter value indicates the difference between the current water level and the flat beach water level, reflecting the influence of the water level on the bed formation process.

[0123] Average sand content = (upper limit of sand content + lower limit of sand content) / 2;

[0124] The average sediment content is used to represent the sediment content characteristics of the section corresponding to the water level.

[0125] Sediment content evaluation parameter value = (average sediment content - sediment holding capacity) / maximum sediment content difference

[0126] The sediment content evaluation parameter value reflects the influence of the current water level and cross-section sediment content characteristics on the bed formation process.

[0127] Bed formation degree evaluation parameter value = water level evaluation parameter value + sediment content evaluation parameter value.

[0128] The bed-forming degree evaluation parameter values ​​reflect the influence of water level and sediment content on the bed-forming process.

[0129] Thus, the bed-forming degree evaluation parameter values ​​corresponding to each water level can be calculated. The smaller the parameter value, the stronger the bed-forming effect. This information can provide a basis for the subsequent determination of the target water level and flow range.

[0130] In a possible embodiment, determining a water level evaluation parameter value according to a section water level prediction value and a flat beach water level includes:

[0131] Determine the maximum water level difference based on the predicted cross-section water level and the flat beach water level;

[0132] Determine the water level evaluation parameter value based on the cross-section water level prediction value, the maximum water level difference value and the flat beach water level;

[0133] Determining the sediment content evaluation parameter value according to the average sediment content and the sediment holding capacity of the cross section includes:

[0134] According to the average sediment content and sediment-carrying capacity, the maximum sediment content difference is determined;

[0135] The sediment content evaluation parameter values ​​are determined based on the average sediment content, the maximum sediment content difference and the sediment carrying capacity.

[0136]

[0137] The smaller it is, the closer the water level is to the flat beach water level, and the closer the sand content in the section is to the sand-carrying capacity, and the more obvious the bed-forming effect is.

[0138] Z represents the cross-section water level value predicted by LSTM, Z* represents the flat water level of the cross-section; △Z max Represents the maximum difference between the water level prediction value of the current section and the flat water level;

[0139] S represents the cross-section sediment content value predicted by LSTM, S* represents the cross-section sediment holding capacity, △S max Represents the maximum difference between the predicted sediment content of the current section and the sediment-carrying capacity.

[0140] Step 170, determining a target cross-section water level value according to a target water level level associated with a minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among a plurality of bed formation degree evaluation parameter values;

[0141] Among the previously calculated values ​​of the bed formation evaluation parameters for each water level, find the water level corresponding to the minimum value, which is the target water level. After calculating the values ​​of the bed formation evaluation parameters for all water levels and corresponding sediment contents, take the target section water level value when the bed formation evaluation parameter value is the minimum.

[0142] For example, Figure 4 As shown, the minimum value among multiple bed formation degree evaluation parameter values ​​corresponds to a target section water level value of 15.89m.

[0143] Step 180, determining the riverbed-forming flow range of the section according to the target section water level value and the second functional relationship.

[0144] The flow range [Qa, Qb] corresponding to the target section water level value is calculated through the second functional relationship, and this range is the river bed formation flow range of the current section.

[0145] Exemplarily, the flow range [Qa, Qb] corresponding to the water level calculated by providing a second functional relationship is approximately [5350, 5650], which is the approximate range value of the bed-forming flow rate of the current section.

[0146] Substituting the determined target water level value into the second functional relationship, the corresponding river bed formation flow range can be obtained.

[0147] Therefore, the embodiment of the present application can calculate the approximate range of the bed-making flow rate of any section of the river, which can be used as an enrichment and supplement to the traditional bed-making flow rate. It solves the problem that the current reliance on measured data can only obtain the bed-making flow rate of the observed section, and cannot be extended to the river without observation data.

[0148] In an embodiment of the present application, by obtaining the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range; the flow corresponding to each flow level in the inlet flow sequence is input into a pre-trained hydrological prediction model, and the hydrological prediction model is run several times to quickly obtain the cross-sectional water level prediction value, cross-sectional flow prediction range and cross-sectional sediment content prediction range associated with each flow level; according to the cross-sectional water level prediction value and the cross-sectional sediment content prediction range, a cross-sectional water level prediction model is established. The first functional relationship; the second functional relationship is established according to the section water level prediction value and the section flow prediction range; through the first functional relationship, the corresponding section sediment content upper limit and section sediment content lower limit can be deduced according to the section water level prediction value of different water level levels, and the bed formation degree evaluation parameter value is determined according to the section water level prediction value of each water level level, the flat beach water level, the sediment entrainment capacity, and the section sediment content upper limit and section sediment content lower limit associated with each water level level. Here, by integrating multi-factor analysis, the water level, sediment content characteristics, sediment entrainment capacity and other factors are comprehensively considered to calculate the bed formation degree evaluation parameter value corresponding to each water level level, which can improve the scientific nature of decision-making through quantified evaluation parameter values. Since the numerical value of the bed-forming degree evaluation parameter is inversely proportional to the bed-forming effect, the target water level associated with the minimum evaluation parameter value is the water level with the strongest bed-forming effect. The target section water level value is determined based on the target water level associated with the minimum evaluation parameter value. According to the target section water level value and the second function relationship, the river bed-forming flow range of the section can be accurately determined, which is convenient for providing a more scientific basis for the subsequent design and operation of water conservancy projects.

[0149] The method for calculating the flow range of a river bed formation provided in the embodiment of the present application can be executed by a device for calculating the flow range of a river bed formation. In the embodiment of the present application, the device for calculating the flow range of a river bed formation is used as an example to illustrate the method for calculating the flow range of a river bed formation.

[0150] Figure 5 1 is a block diagram of a device for calculating a flow range of a river bed formation provided in an embodiment of the present application. The device 500 includes:

[0151] The acquisition module 510 is used to obtain the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and the ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range;

[0152] An input module 520 is used to input the flow corresponding to each flow level in the inlet flow sequence into a pre-trained hydrological prediction model, run the hydrological prediction model several times, and obtain the cross-sectional water level prediction value, cross-sectional flow prediction range, and cross-sectional sediment content prediction range associated with each flow level;

[0153] A first establishing module 530 is used to establish a first functional relationship according to the cross-section water level prediction value and the cross-section sediment content prediction range;

[0154] The second establishing module 540 is used to establish a second functional relationship according to the cross-section water level prediction value and the cross-section flow prediction range;

[0155] The first determination module 550 is used to determine the upper limit and the lower limit of the cross-section sediment content associated with each water level level according to the cross-section water level prediction value of each water level level and the first functional relationship;

[0156] The second determination module 560 is used to determine the bed formation degree evaluation parameter value according to the cross-sectional water level prediction value of each water level level, the flat beach water level, the sediment entrainment force, the cross-sectional sediment content upper limit and the cross-sectional sediment content lower limit associated with each water level level; the value of the bed formation degree evaluation parameter value is inversely proportional to the bed formation effect;

[0157] The third determination module 570 is used to determine the target cross-section water level value according to the target water level level associated with the minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among the multiple bed formation degree evaluation parameter values;

[0158] The fourth determination module 580 is used to determine the riverbed-forming flow range of the section according to the target section water level value and the second functional relationship.

[0159] In a possible embodiment, the second determining module 560 is specifically configured to:

[0160] For any water level, the water level evaluation parameter value is determined based on the cross-section water level prediction value and the flat beach water level;

[0161] Determine the average sediment content based on the upper limit of the sediment content range of the cross section and the lower limit of the sediment content range of the cross section;

[0162] Determine the sediment content evaluation parameter value based on the average sediment content and sediment carrying capacity;

[0163] The bed formation degree evaluation parameter values ​​are determined based on the water level evaluation parameter values ​​and the sediment content evaluation parameter values.

[0164] In a possible embodiment, the second determining module 560 is specifically configured to:

[0165] Determine the maximum water level difference based on the predicted cross-section water level and the flat beach water level;

[0166] Determine the water level evaluation parameter value based on the cross-section water level prediction value, the maximum water level difference value and the flat beach water level;

[0167] Determining the sediment content evaluation parameter value according to the average sediment content and the sediment holding capacity of the cross section includes:

[0168] According to the average sediment content and sediment-carrying capacity, the maximum sediment content difference is determined;

[0169] The sediment content evaluation parameter values ​​are determined based on the average sediment content, the maximum sediment content difference and the sediment carrying capacity.

[0170] In a possible embodiment, the acquisition module 510 is further used to obtain the flow research range and bed-making flow accuracy;

[0171] The apparatus 500 may further include:

[0172] The division module is used to divide the flow research range into multiple flow levels according to the bed-making flow accuracy.

[0173] In a possible embodiment, the acquisition module 510 is further used to acquire the river section inlet flow and label hydrological data, the label hydrological data including: section water level label data, section flow label data and section sediment content label data;

[0174] The input module 520 is also used to input the river section inlet flow into the preset LSTM to obtain the predicted hydrological data;

[0175] The apparatus 500 may further include:

[0176] The training module is used to train the preset LSTM according to the predicted hydrological data and the labeled hydrological data until the preset LSTM meets the preset training conditions to obtain the hydrological prediction model.

[0177] In a possible embodiment, the preset LSTM includes: a first LSTM, a second LSTM and a third LSTM, and a training module, which is specifically used for:

[0178] Input the inlet flow of the river section into the first LSTM, the second LSTM and the third LSTM respectively, and obtain the cross-section water level prediction data, the cross-section flow prediction data and the cross-section sediment content prediction data respectively;

[0179] Training the first LSTM according to the section water level prediction data and the section water level label data; and,

[0180] Training the second LSTM according to the cross-sectional flow prediction data and the cross-sectional flow label data; and,

[0181] The third LSTM is trained based on the cross-section sediment content prediction data and cross-section sediment content label data;

[0182] Until the first LSTM, the second LSTM and the third LSTM meet the preset training conditions, a hydrological prediction model is obtained.

[0183] In a possible embodiment, the acquisition module 510 is specifically configured to:

[0184] Obtain measured cross-section topographic data, river section inlet flow and river section sediment content process data;

[0185] The measured cross-section topographic data, river section inlet flow and river section sediment content process data are input into the pre-established water-sediment mathematical model to obtain the labeled hydrological data, flat water level and sediment-carrying capacity of each section of the river.

[0186] In an embodiment of the present application, by obtaining the inlet flow sequence, flat water level and sediment entrainment of the river to be tested, the inlet flow sequence includes the flow during the process of rising and falling water, the starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range; the flow corresponding to each flow level in the inlet flow sequence is input into a pre-trained hydrological prediction model, and the hydrological prediction model is run several times to quickly obtain the cross-sectional water level prediction value, cross-sectional flow prediction range and cross-sectional sediment content prediction range associated with each flow level; according to the cross-sectional water level prediction value and the cross-sectional sediment content prediction range, a cross-sectional water level prediction model is established. The first functional relationship; the second functional relationship is established according to the section water level prediction value and the section flow prediction range; through the first functional relationship, the corresponding section sediment content upper limit and section sediment content lower limit can be deduced according to the section water level prediction value of different water level levels, and the bed formation degree evaluation parameter value is determined according to the section water level prediction value of each water level level, the flat beach water level, the sediment entrainment capacity, and the section sediment content upper limit and section sediment content lower limit associated with each water level level. Here, by integrating multi-factor analysis, the water level, sediment content characteristics, sediment entrainment capacity and other factors are comprehensively considered to calculate the bed formation degree evaluation parameter value corresponding to each water level level, which can improve the scientific nature of decision-making through quantified evaluation parameter values. Since the numerical value of the bed-forming degree evaluation parameter is inversely proportional to the bed-forming effect, the target water level associated with the minimum evaluation parameter value is the water level with the strongest bed-forming effect. The target section water level value is determined based on the target water level associated with the minimum evaluation parameter value. According to the target section water level value and the second function relationship, the river bed-forming flow range of the section can be accurately determined, which is convenient for providing a more scientific basis for the subsequent design and operation of water conservancy projects.

[0187] The river bed flow range calculation device provided in the embodiment of the present application can realize each process implemented by the above method embodiment, and will not be described again here to avoid repetition.

[0188] Optionally, Figure 6A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0189] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0190] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0191] The memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 402 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 402 is a non-volatile solid-state memory. In a specific embodiment, the memory 402 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0192] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the river bed formation flow range calculation methods in the embodiments shown in the figure.

[0193] In one example, the electronic device may further include a communication interface 404 and a bus 410. Figure 6 As shown, the processor 401, the memory 402, and the communication interface 404 are connected via a bus 410 and communicate with each other.

[0194] The communication interface 404 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0195] Bus 410 includes hardware, software or both, and couples the components of the electronic device to each other. For example, but not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnect.

[0196] The electronic device can execute the flow range calculation method of the riverbed construction in the embodiment of the present application, thereby realizing the combination of Figure 1-Figure 4 Described is a method for calculating the flow range for channel formation.

[0197] In addition, in combination with the flow range calculation method for riverbed construction in the above embodiment, the embodiment of the present application can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the Figure 1 Calculation method for flow range of river bed construction.

[0198] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0199] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0200] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0201] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application.

Claims

1. A method for calculating the flow range of river bed construction, characterized in that: The method comprises: Obtain the inlet flow sequence, flat water level and sediment entrainment of the river to be tested. The inlet flow sequence includes the flow during the process of rising and falling water. The starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range. The flow corresponding to each flow level in the inlet flow sequence is input into the pre-trained hydrological prediction model, and the hydrological prediction model is run several times to obtain the cross-section water level prediction value, cross-section flow prediction range and cross-section sediment content prediction range associated with each flow level; According to the predicted value of the cross-section water level and the predicted range of the cross-section sediment content, the first functional relationship is established; A second functional relationship is established according to the predicted value of the cross-section water level and the predicted range of the cross-section flow; According to the cross-sectional water level prediction value of each water level level and the first functional relationship, the upper limit of the cross-sectional sediment content and the lower limit of the cross-sectional sediment content associated with each water level level are determined; According to the predicted values ​​of the cross-section water level at each water level level, the flat water level, the sediment entraining force, the upper and lower limits of the cross-section sediment content associated with each water level level, the values ​​of the bed-forming degree evaluation parameters are determined; the values ​​of the bed-forming degree evaluation parameters are inversely proportional to the bed-forming effect; Determining a target section water level value according to a target water level level associated with a minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among a plurality of bed formation degree evaluation parameter values; According to the water level value of the target section and the second function relationship, the flow range of the riverbed formation of the section is determined; The bed formation degree evaluation parameter values ​​are determined according to the predicted values ​​of the cross-section water level at each water level level, the flat beach water level, the sediment entrainment capacity, the upper limit of the cross-section sediment content and the lower limit of the cross-section sediment content associated with each water level level, including: For any water level, the water level evaluation parameter value is determined based on the cross-section water level prediction value and the flat beach water level; Determine the average sediment content based on the upper limit of the sediment content range of the cross section and the lower limit of the sediment content range of the cross section; Determine the sediment content evaluation parameter value based on the average sediment content and sediment carrying capacity; Determine the bed formation degree evaluation parameter value according to the water level evaluation parameter value and the sediment content evaluation parameter value; Determining the water level evaluation parameter value according to the cross-section water level prediction value and the flat beach water level includes: Determine the maximum water level difference based on the predicted cross-section water level and the flat beach water level; Determine the water level evaluation parameter value based on the cross-section water level prediction value, the maximum water level difference value and the flat beach water level; Determining the sediment content evaluation parameter value according to the average sediment content and the sediment holding capacity of the cross section includes: According to the average sediment content and sediment-carrying capacity, the maximum sediment content difference is determined; The sediment content evaluation parameter values ​​are determined based on the average sediment content, the maximum sediment content difference and the sediment carrying capacity.

2. The method according to claim 1, characterized in that: Before inputting the flow corresponding to each flow level in the inlet flow sequence into the pre-trained hydrological prediction model, the method further includes: Obtain flow research range and bed-making flow accuracy; The flow research range is divided into multiple flow levels according to the bed-making flow accuracy.

3. The method according to claim 1, characterized in that Before inputting the flow corresponding to each flow level in the inlet flow sequence into the pre-trained hydrological prediction model, the method further includes: Obtain the river section inlet flow and labeled hydrological data. The labeled hydrological data include: section water level label data, section flow label data and section sediment content label data; Input the river section inlet flow into the preset LSTM to obtain the predicted hydrological data; According to the predicted hydrological data and the labeled hydrological data, the preset LSTM is trained until the preset LSTM meets the preset training conditions to obtain a hydrological prediction model.

4. The method according to claim 3, characterized in that The preset LSTM includes: a first LSTM, a second LSTM and a third LSTM. The preset LSTM is trained according to the predicted hydrological data and the labeled hydrological data until the preset LSTM meets the preset training conditions to obtain a hydrological prediction model, including: Input the inlet flow of the river section into the first LSTM, the second LSTM and the third LSTM respectively, and obtain the cross-section water level prediction data, the cross-section flow prediction data and the cross-section sediment content prediction data respectively; Training the first LSTM according to the section water level prediction data and the section water level label data; and, Training the second LSTM according to the cross-sectional flow prediction data and the cross-sectional flow label data; and, The third LSTM is trained based on the cross-section sediment content prediction data and cross-section sediment content label data; Until the first LSTM, the second LSTM and the third LSTM meet the preset training conditions, a hydrological prediction model is obtained.

5. The method according to claim 3, characterized in that: The method of obtaining the river section inlet flow and label hydrological data includes: Obtain measured cross-section topographic data, river section inlet flow and river section sediment content process data; The measured cross-section topographic data, river section inlet flow and river section sediment content process data are input into the pre-established water-sediment mathematical model to obtain the labeled hydrological data, flat water level and sediment-carrying capacity of each section of the river.

6. A device for calculating the flow range of a river bed formation, characterized in that: The device comprises: The acquisition module is used to obtain the inlet flow sequence, flat water level and sediment entrainment of the river to be tested. The inlet flow sequence includes the flow during the process of rising and falling water. The starting flow and ending flow of the inlet flow sequence are equal to the lower limit of the flow research range, and the peak flow of the inlet flow sequence is equal to the upper limit of the flow research range. An input module is used to input the flow corresponding to each flow level in the inlet flow sequence into a pre-trained hydrological prediction model, run the hydrological prediction model several times, and obtain the cross-sectional water level prediction value, cross-sectional flow prediction range, and cross-sectional sediment content prediction range associated with each flow level; A first establishing module is used to establish a first functional relationship according to a predicted value of a cross-section water level and a predicted range of a cross-section sediment content; A second establishing module is used to establish a second functional relationship according to the cross-section water level prediction value and the cross-section flow prediction range; A first determination module is used to determine the upper limit and lower limit of the cross-section sediment content associated with each water level level according to the cross-section water level prediction value of each water level level and the first functional relationship; The second determination module is used to determine the bed formation degree evaluation parameter value according to the cross-section water level prediction value of each water level level, the flat beach water level, the sediment entrainment force, the cross-section sediment content upper limit and the cross-section sediment content lower limit associated with each water level level; the value of the bed formation degree evaluation parameter value is inversely proportional to the bed formation effect; A third determination module is used to determine the target section water level value according to the target water level level associated with the minimum evaluation parameter value; the minimum evaluation parameter value is the minimum value among the multiple bed formation degree evaluation parameter values; The fourth determination module is used to determine the riverbed-building flow range of the section according to the target section water level value and the second function relationship; The second determination module is specifically used for: For any water level, the water level evaluation parameter value is determined based on the cross-section water level prediction value and the flat beach water level; Determine the average sediment content based on the upper limit of the sediment content range of the cross section and the lower limit of the sediment content range of the cross section; Determine the sediment content evaluation parameter value based on the average sediment content and sediment carrying capacity; Determine the bed formation degree evaluation parameter value according to the water level evaluation parameter value and the sediment content evaluation parameter value; The second determination module is specifically used for: Determine the maximum water level difference based on the predicted cross-section water level and the flat beach water level; Determine the water level evaluation parameter value based on the cross-section water level prediction value, the maximum water level difference value and the flat beach water level; Determining the sediment content evaluation parameter value according to the average sediment content and the sediment holding capacity of the cross section includes: According to the average sediment content and sediment-carrying capacity, the maximum sediment content difference is determined; The sediment content evaluation parameter values ​​are determined based on the average sediment content, the maximum sediment content difference and the sediment carrying capacity.

7. The device according to claim 6, characterized in that The acquisition module is also used to obtain the flow research range and bed-making flow accuracy; The device for calculating the flow range of river bed construction also includes: The division module is used to divide the flow research range into multiple flow levels according to the bed-making flow accuracy.

8. The device according to claim 6, characterized in that The acquisition module is also used to obtain the river section inlet flow and label hydrological data, the label hydrological data includes: section water level label data, section flow label data and section sediment content label data; The input module is also used to input the river section inlet flow into the preset LSTM to obtain the predicted hydrological data; The device for calculating the flow range of river bed construction also includes: The training module is used to train the preset LSTM according to the predicted hydrological data and the labeled hydrological data until the preset LSTM meets the preset training conditions to obtain the hydrological prediction model.

9. The device according to claim 8, characterized in that Get the module, specifically used for: Obtain measured cross-section topographic data, river section inlet flow and river section sediment content process data; The measured cross-section topographic data, river section inlet flow and river section sediment content process data are input into the pre-established water-sediment mathematical model to obtain the labeled hydrological data, flat water level and sediment-carrying capacity of each section of the river.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for calculating the flow range of river bed formation as claimed in any one of claims 1-5 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the method for calculating the flow range of river bed formation as described in any one of claims 1-5.

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