Prediction method, equipment and medium for vertical distribution of suspended sediment in maximum turbidity zone

Abstract

The present application discloses a method, device, and medium for predicting the vertical distribution of suspended sediment in the maximum turbidity zone, which relates to the field of electronic digital data processing. The method comprises: obtaining sample data; constructing a suspended sediment vertical distribution prediction model based on the sample data using the Rouse formula; obtaining parameter data such as the water depth, tidal range, and surface and bottom sediment concentrations of the maximum turbidity zone in the estuary region to be predicted, and using the suspended sediment vertical distribution prediction model to determine the sediment concentration at different vertical distances from the water bottom based on the parameter data; and determining the vertical distribution of suspended sediment in the maximum turbidity zone in the estuary region to be predicted based on the sediment concentration at different vertical distances from the water bottom. The present application can effectively and accurately predict the vertical distribution of suspended sediment in the maximum turbidity zone in the estuary region.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a method, device and medium for predicting the vertical distribution of suspended sediment in a maximum turbidity zone. Background Art

[0002] Maximum turbidity zones often occur in estuaries, and clarifying the vertical distribution of suspended sediment in these zones is crucial for estuary channel regulation and ecological restoration. The theory of vertical sediment distribution under steady uniform flow has been widely studied and is relatively mature. However, the complex dynamic conditions in estuaries far exceed the scope of steady uniform flow, making the vertical distribution of suspended sediment difficult to directly solve theoretically. In addition, the instantaneous suspended sediment distribution in the maximum turbidity zone often exhibits a variety of complex forms, including L-type, step-type, exponential, linear, and irregular types. The commonly used Rouse and Van Rijn formulas cannot effectively reflect the complex vertical distribution of suspended sediment. Therefore, there is currently a lack of effective methods for predicting the vertical distribution of suspended sediment in the maximum turbidity zone in estuaries. Summary of the Invention

[0003] The purpose of this application is to provide a method, equipment and medium for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone, which can effectively and accurately predict the vertical distribution morphology of suspended sediment in the maximum turbidity zone in an estuary.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for predicting the vertical distribution of suspended sediment in a maximum turbidity zone, comprising:

[0006] Get sample data;

[0007] Based on the Rouse formula, a suspended sediment vertical distribution prediction model is constructed based on the sample data;

[0008] Obtaining parameter data of the maximum turbidity zone in the estuary area to be predicted; the parameter data includes water depth, tidal range, and surface sediment content and bottom sediment content;

[0009] Using the suspended sediment vertical distribution prediction model, the sediment content at positions at different vertical distances from the water bottom is obtained based on the parameter data;

[0010] Based on the sediment concentration at different vertical distances from the water bottom, the vertical distribution pattern of suspended sediment in the maximum turbidity zone in the estuary area to be predicted is obtained.

[0011] Optionally, the sample data includes sample water depth, sample sediment content, and a vertical distance from the sample sediment content position to the water bottom;

[0012] Based on the Rouse formula and the sample data, a suspended sediment vertical distribution prediction model is constructed, including:

[0013] Selecting a reference sediment content from the sample sediment content, and constructing a double-layer suspended sediment distribution model using the Rouse formula based on the reference sediment content, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom;

[0014] Determining the suspension index of the double-layer suspended sediment distribution model based on the sediment content of the sample to obtain an updated double-layer suspended sediment distribution model;

[0015] An exponential factor is introduced, and a weight coefficient is constructed based on the exponential factor, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom. The updated two-layer suspended sediment distribution model is normalized and connected using the weight coefficient to obtain a connection model.

[0016] Selecting different exponential factors and using the connection model corresponding to the different exponential factors, based on the sample water depth, to obtain the predicted sediment concentration at different times and at different distances from the sediment concentration position to the vertical line of the water bottom;

[0017] Determine the correlation coefficient between the predicted sediment content at different times and at different sediment content locations and the vertical distance from the water bottom and the sediment content of the sample;

[0018] The weight coefficient corresponding to the maximum correlation coefficient is used as the final weight coefficient of the connection model to obtain the suspended sediment vertical distribution prediction model.

[0019] Optionally, a reference sediment content is selected from the sample sediment content, and a double-layer suspended sediment distribution model is constructed using the Rouse formula based on the reference sediment content, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom, including:

[0020] The surface sediment content of the sample sediment content is used as the reference sediment content of the upper half of the water depth, and the bottom sediment content of the sample sediment content is used as the reference sediment content of the lower half of the water depth; the upper half of the water depth refers to the area where the vertical distance from the sediment content position to the water bottom is greater than or equal to a set multiple of the water depth; the lower half of the water depth refers to the area where the vertical distance from the sediment content position to the water bottom is less than a set multiple of the water depth;

[0021] The Rouse formula is used to construct a double-layer suspended sediment distribution model based on the reference sediment content of the upper half of the water depth, the reference sediment content of the lower half of the water depth, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom.

[0022] Optionally, determining a suspension index of the double-layer suspended sediment distribution model based on the sediment content of the sample to obtain an updated double-layer suspended sediment distribution model includes:

[0023] Taking the natural logarithm of the double-layer suspended sediment distribution model, and using the least squares method to fit the sediment content at different locations in the sample sediment content at different times to obtain a suspension index; the suspension index includes an upper layer suspension index and a lower layer suspension index;

[0024] The suspended index obtained by fitting is substituted into the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model.

[0025] Optionally, the double-layer suspended sediment distribution model is expressed as:

[0026] ;

[0027] Where, Indicates the sand content, h Indicates water depth. z Indicates the vertical distance from the sediment content position to the water bottom. Indicates the surface sand content, Indicates the bottom sediment content, Indicates the upper floating index, Indicates the lower layer suspension index.

[0028] Optionally, the natural logarithm of the double-layer suspended sediment distribution model is taken, and the suspension index is obtained by fitting the sediment concentration at different positions in the sample sediment concentration at different times using the least squares method, including:

[0029] The natural logarithm of the upper suspended sediment distribution model in the double-layer suspended sediment distribution model is taken, and the least squares method is used to calculate the distribution of suspended sediment at different times based on the sample sediment content. z=h -0.5m, z =0.8 h as well as z =0.6 h The upper layer suspension index is obtained by fitting the sediment content; m Indicates the length unit meter;

[0030] The natural logarithm of the lower suspended sediment distribution model in the double-layer suspended sediment distribution model is taken, and the least squares method is used to calculate the distribution of suspended sediment at different times. z= 0.4 h , z =0.2 h as well as z =0.5m sediment content fitting to obtain the lower layer suspension index.

[0031] Optionally, the connection model is expressed as:

[0032] ;

[0033] Where, Indicates the sand content, hIndicates water depth. z Indicates the vertical distance from the sediment content position to the water bottom. Indicates the surface sand content, Indicates the bottom sediment content, Indicates the upper floating index, Indicates the lower layer suspension index, represents the weight coefficient, Represents the exponential factor.

[0034] Optionally, the suspended sediment vertical distribution prediction model is expressed as:

[0035] ;

[0036] Where, Indicates the sand content, h Indicates water depth. z Indicates the vertical distance from the sediment content position to the water bottom. Indicates the surface sand content, Indicates the bottom sediment content, Indicates the upper floating index, Indicates the lower layer suspension index, The exponential factor corresponding to the maximum correlation coefficient between the predicted sediment content and the sample sediment content at different times and different sediment content positions and the vertical line distance from the water bottom is k The value is 0.

[0037] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone provided above.

[0038] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone provided above.

[0039] According to the specific embodiments provided in this application, this application has the following technical effects:

[0040] The present application provides a method, device and medium for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone. By constructing a suspended sediment vertical distribution prediction model based on sample data based on the Rouse formula, the sediment content at different vertical distances from the water bottom can be obtained after measuring data such as the water depth of the maximum turbidity zone in the estuary area to be predicted, thereby effectively realizing the accurate prediction of the vertical distribution morphology of suspended sediment, thereby filling the current technical gap in the prediction of the vertical distribution morphology of suspended sediment in the maximum turbidity zone in the estuary. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A schematic flow chart of a method for predicting the vertical distribution of suspended sediment in a maximum turbidity zone provided in one embodiment of the present application;

[0043] Figure 2 A schematic diagram of the locations of the measurement points for the maximum turbidity zone in the northern trough of the Yangtze River Estuary provided in one embodiment of the present application;

[0044] Figure 3 This is an array diagram comparing the prediction effects of the suspended sediment vertical distribution prediction model, Rouse formula, and Van Rijn formula on different types of suspended sediment profiles provided in one embodiment of the present application;

[0045] Figure 4 This is an array diagram comparing the results of the suspended sediment vertical distribution prediction model and the measured sediment content provided in one embodiment of the present application;

[0046] Figure 5 The upper and lower layer suspension indicators at different tides provided in one embodiment of the present application and Array diagram of intratidal variations;

[0047] Figure 6 The upper suspension indicator provided in one embodiment of the present application The average value of the tidal cycle and Schematic diagram of the scatter relationship;

[0048] Figure 7 The lower layer suspension indicator provided in one embodiment of the present application and Relationship array diagram;

[0049] Figure 8 An array diagram of the sediment content verification results provided in one embodiment of the present application;

[0050] Figure 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] In order to accurately predict the vertical distribution morphology of suspended sediment in the maximum turbidity zone of an estuary, the present application provides a method, equipment and medium for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone, which can fill the current technical gap in predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone of an estuary.

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, the present application provides a method for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone. The method is executed by a computer device, and can be executed separately by a computer device such as a terminal or a server, or jointly by a terminal and a server. In the embodiment of the present application, the method is applied to a server as an example for illustration.

[0055] like Figure 1 As shown, the prediction method for the vertical distribution of suspended sediment in the maximum turbidity zone provided in this application includes:

[0056] Step 100: Obtain sample data.

[0057] The sample data includes the sample water depth, sample sediment content, and the vertical distance from the sample sediment content position to the water bottom.

[0058] For example, get n moments ( , ,..., ) as the sample water depth. The vertical 6-point method was used to measure the layered sediment content data as the sample sediment content (unit: kg / m 3 ). From the surface to the bottom layer, the vertical distance from the sediment content position to the water bottom z Take separately h -0.5m, 0.8 h , 0.6 h , 0.4 h , 0.2 h and 0.5m, m is the length unit meter, h is the water depth (unit: m).

[0059] Step 101: Based on the Rouse formula and sample data, a suspended sediment vertical distribution prediction model is constructed.

[0060] Step 102: Obtain parameter data of the maximum turbidity zone in the estuary area to be predicted.

[0061] Among them, parameter data include water depth, tidal range, surface sand content, bottom sand content, etc.

[0062] These parameter data are mainly used to fit the suspension index in the suspended sediment vertical distribution prediction model. The specific fitting method is shown below.

[0063] Step 103: Using a suspended sediment vertical distribution prediction model, the sediment concentration at different vertical distances from the water bottom is obtained based on parameter data.

[0064] Step 104 : Obtain the vertical distribution pattern of suspended sediment in the maximum turbidity zone of the estuary region to be predicted based on the sediment concentration at positions at different vertical distances from the water bottom.

[0065] By implementing the above steps 100 to 104, the sediment content at different vertical distances from the water bottom can be obtained based on data such as the water depth of the maximum turbidity zone in the estuary area to be predicted, thereby achieving an accurate prediction of the vertical distribution morphology of suspended sediment, filling the current technical gap in the prediction of the vertical distribution morphology of suspended sediment in the maximum turbidity zone in the estuary.

[0066] In another exemplary embodiment of the present application, the Rouse formula for suspended sediment vertical distribution is the most commonly used and relatively simple formula. Therefore, in order to reduce the complexity of suspended sediment vertical distribution prediction while improving the efficiency of suspended sediment vertical distribution prediction, in step 101, a suspended sediment vertical distribution prediction model is constructed based on the Rouse formula. The specific implementation of this process includes the following steps 1 to 6.

[0067] Step 1: Select the reference sediment content from the sample sediment content, and use the Rouse formula to construct a double-layer suspended sediment distribution model based on the reference sediment content, sample water depth, and the vertical distance from the sample sediment position to the water bottom.

[0068] Among them, the surface sand content in the sample sand content As the upper half of the water depth (e.g. ) of the reference sediment content, the bottom sediment content in the sample sediment content As the lower half of the water depth (e.g. ) reference sand content.

[0069] Based on the above description, the Rouse formula was used to construct a double-layer suspended sediment distribution model based on the reference sediment concentration in the upper half of the water depth, the reference sediment concentration in the lower half of the water depth, the sample water depth, and the vertical distance from the sample sediment concentration position to the water bottom.

[0070] The constructed double-layer suspended sediment distribution model is expressed as:

[0071] (1)

[0072] Where, Indicates the sand content, Indicates the upper floating index, Indicates the lower layer suspension index.

[0073] Step 2: Based on the sediment content of the sample, the suspension index of the double-layer suspended sediment distribution model is determined to obtain an updated double-layer suspended sediment distribution model.

[0074] Among them, the natural logarithm of the double-layer suspended sediment distribution model is taken, and the least squares method is used to fit the sediment content at different positions in the sample sediment content at different times to obtain the suspension index.

[0075] The fitted suspension index is substituted into the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model.

[0076] For example, (1) fitting different moments ( , ,..., ) Upper floating indicator The method can be:

[0077] Taking the natural logarithm of both sides of the upper suspended sediment distribution model of the double-layer suspended sediment distribution model [i.e. the upper half of formula (1)], we have:

[0078] (2)

[0079] based on z=h -0.5m, z =0.8 h as well as z =0.6 h The sand content , the upper suspension index is obtained by least square fitting .

[0080] Among them, the least squares fitting can be achieved through tools such as Excel and Matlab.

[0081] (2) Fitting different moments ( , ,..., ) Lower floating index The method can be:

[0082] Taking the natural logarithm of both sides of the lower suspended sediment distribution model of the double-layer suspended sediment distribution model [i.e. the lower half of formula (1)], we have:

[0083] (3)

[0084] Based on different moments z= 0.4 h , z =0.2 h as well as z =0.5m3 sand content , the least squares method is used to fit the lower suspension index .

[0085] Step 3: Introduce an exponential factor and construct a weight coefficient based on the exponential factor, sample water depth, and the vertical distance from the sample sediment content position to the water bottom. Use the weight coefficient to normalize and connect the updated double-layer suspended sediment distribution model to obtain a connection model.

[0086] In this step, in order to solve the disadvantage of the inconvenience of using piecewise functions, the constructed weight coefficient is .in, Represents the exponential factor.

[0087] Based on the above description, the obtained connection model is expressed as:

[0088] (4)

[0089] Step 4: Select different exponential factors and use the connection model corresponding to the different exponential factors to obtain the predicted sediment content at different times and different distances from the vertical line of the water bottom based on the sample water depth.

[0090] Step 5: Determine the correlation coefficient between the predicted sediment content and the sample sediment content at different times and at different distances from the vertical line of the water bottom.

[0091] Step 6: The weight coefficient corresponding to the maximum correlation coefficient is used as the final weight coefficient of the connection model to obtain the suspended sediment vertical distribution prediction model.

[0092] Furthermore, the specific implementation of steps 4 to 6 given above is illustrated by way of example:

[0093] For example, let k 0=1, at different times ( , ,..., ), respectively z =0.2 h , 0.4 h , 0.6 h and 0.8 h When , we can get 4 by formula (4) n Predicted sediment content, statistics 4 nThe correlation coefficient between the predicted sediment content and the corresponding measured sediment content in the sample sediment content R . In sequence, select k 0=2,3,4,5..., repeat the above steps and select R Maximum k 0 is taken to obtain the final suspended sediment vertical distribution prediction model, which is expressed as:

[0094] (5)

[0095] Where, The maximum correlation coefficient between the predicted sediment content and the sample sediment content at different times and different sediment content positions and the vertical line distance from the water bottom corresponds to k The value is 0.

[0096] In another exemplary embodiment of the present application, taking the prediction of the vertical distribution morphology of suspended sediment in the maximum turbidity zone in the northern trough of the Yangtze River Estuary as an example, the specific implementation process and advantages of the prediction method of the vertical distribution morphology of suspended sediment in the maximum turbidity zone provided above in the present application are explained.

[0097] 1) Get data:

[0098] like Figure 2 As shown in the figure, the CSW station was selected as the suspended sediment measurement point, and the water depth and sediment content data of each layer were obtained at 25 moments of the flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide in 2016, 2017, and 2018. That is, there are 3×25=75 moments of flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide ( , ,..., )’s water depth data and sediment content data.

[0099] 2) Based on the water depth data and sediment content data obtained above, the upper suspension index is fitted using the above step 2 and lower floating indicators The upper suspension index obtained by fitting and lower floating indicators The specific values ​​are shown in Table 1, Table 2, Table 3-1 and Table 3-2.

[0100] Table 1 The times of the flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide in 2016 and Value table of

[0101]

[0102] Table 2 Times of flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide in 2017 and Value table of

[0103]

[0104] Table 3-1 Times of flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide in 2018 and One of the values

[0105]

[0106] Table 3-2 Times of flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide in 2018 and The second value of

[0107]

[0108] 3) Upper layer suspension index obtained based on fitting and lower floating indicators , combined with the data obtained in step (1), the suspended sediment vertical distribution prediction model [Formula (5)] constructed by the above embodiment of this application can obtain the prediction results of the vertical distribution of suspended sediment in the maximum turbidity zone of the northern channel of the Yangtze River Estuary. Among them, the corresponding flood season high tide, flood season low tide, dry season high tide, dry season low tide index factors are k The values ​​of the correlation coefficient R when 0=1, 2, 3, 4, 5 can be found in Table 4.

[0109] Table 4 Correlation coefficient R value table

[0110]

[0111] Therefore, in this embodiment, the formula (5) =2. We get:

[0112] (6)

[0113] Based on Figure 3 The comparison results of the prediction effects of the present application formula (i.e., the suspended sediment vertical distribution prediction model) with the Rouse formula and the Van Rijn formula on different types of suspended sediment profiles (L-type, step-type, exponential-type, linear-type, irregular-type, etc.), as well as the following: Figure 4 The comparison results of the predicted values ​​and measured values ​​among the three are shown. Based on this, compared with the Rouse formula and the Van Rijn formula, the suspended sediment vertical distribution prediction model provided by this application can better fit a variety of complex suspended sediment distribution patterns, and the predicted values ​​are closer to the measured values, with a better fitting effect. Figure 3In the figure, columns (a) to (e) respectively represent the prediction effect comparison results of L-type, step-type, exponential-type, linear-type, and irregular-type suspended sediment profiles, and rows (A) and (B) represent the prediction effect comparison results of different types corresponding to sediment content. Figure 4 Part (a) shows the comparison results of the predicted and measured values ​​corresponding to the spring tide in the flood season, part (b) shows the comparison results of the predicted and measured values ​​corresponding to the neap tide in the flood season, part (c) shows the comparison results of the predicted and measured values ​​corresponding to the spring tide in the dry season, and part (d) shows the comparison results of the predicted and measured values ​​corresponding to the neap tide in the dry season.

[0114] In another exemplary embodiment of the present application, the dynamic conditions of the estuary are non-constant, and the suspension index must change continuously with the ebb and flow of the tide. and lower floating indicators The changing rules of the upper suspension index are established and lower floating indicators The numerical relationship with the easily observable parameters can be used to obtain an applicable empirical formula. Based on this, in this embodiment, the 2016 and 2017 data are used for calibration, and the 2018 data are used for verification.

[0115] Upper-layer suspension indicators at different tides in 2016 and 2017 and lower floating indicators The tidal variation curve is as follows Figure 5 As shown in the figure, since the surface water is easily affected by external disturbances such as wind and waves, the upper suspended index The changes in the tidal cycle are more complex, but the upper suspension index It has obvious tidal change characteristics. Upper suspension index in the neap tide stage The overall value is smaller than that of the spring tide stage. This is because the tidal stratification is stronger, the L-shaped distribution appears more frequently, the sediment content in the upper water body is low and very uniform along the water depth, so the upper suspension index The value of is small. Take the upper floating index The average value of the tidal cycle To approximate the upper suspension index at all times during the tidal cycle , the average value of the tidal cycle It has a good positive correlation with tidal dynamics. and water depth Ratio Characterize tidal dynamics and plot tidal cycle averages and dimensionless tidal parameters Scatter plot of . Figure 6 As shown, the scatter points are exponentially distributed, and the numerical relationship shown in formula (7) can be fitted, with a fitting correlation coefficient as high as 0.92.

[0116] (7)

[0117] Figure 7 The lower suspension index is given Logarithmic form of the ratio of bottom and surface sediment content The correlation between the lower suspension index It can be expressed as:

[0118] (8)

[0119] Formula (8) shows that the lower suspension index and A linear relationship, the slope ,intercept and the correlation coefficient Marked on Figure 7 The ratio of bottom and surface sediment content reflects to some extent the stratification and mixing alternation process during the tidal cycle: the strengthening of stratification can drive the suspended sediment to gather near the bottom layer, the ratio of bottom and surface sediment content increases, the vertical distribution of suspended sediment tends to be uneven, and the lower suspended index The value increases; the enhanced mixing can drive the suspended sediment to diffuse to the surface layer, the ratio of sediment content in the bottom and surface layers decreases, the vertical distribution of suspended sediment tends to be uniform, and the lower suspended index Therefore, the physical meaning of formula (8) is more reasonable. For the flood season high tide, flood season low tide and dry season low tide, the lower suspension index and The linear relationship is significant, and the correlation coefficient exceeds 0.8. The linear relationship of the dry season tide is weak, and the lower suspension index Follow The change is not obvious, so we can take the average value as an approximate substitute, that is, , .

[0120] Substituting formula (7) and formula (8) into the above formula (6), we can obtain the application-oriented unified expression:

[0121] (9)

[0122] in, .

[0123] Based on the above description, the above formula (9) provided in this application is verified using data from different festivals and different tide types in 2018. The verification results are as follows: Figure 8As shown in Figure 2 . The correlation coefficients between the predicted and measured sediment concentrations for the validation year (2018) were 0.85, 0.78, 0.80, and 0.72 for the spring and neap tides in the flood season and the spring and neap tides in the dry season, respectively. The correlation coefficients for the calibration years (2016 and 2017) were 0.81, 0.72, 0.85, and 0.76, respectively. The correlation coefficients for the validation and calibration years are very close, and both have high correlations, indicating that formula (9) can reasonably predict sediment concentration distribution.

[0124] Predicting the vertical distribution of suspended sediment in the maximum turbidity zone of an estuary is extremely difficult. Although some of the predicted values ​​of formula (9) still deviate from the measured values, the prediction accuracy has been significantly improved compared to the existing Rouse formula and Van Rijn formula. In addition, the parameters involved in the empirical formula proposed in this application [i.e., formula (9)] are all easy to obtain. Water depth, tidal range, and surface sediment content can be obtained by buoys, surface sediment content can also be obtained from satellite imagery, and bottom sediment content can be obtained from bottom-mounted observation instruments. With the help of the above measurement methods, this application can provide a possibility for predicting long-term suspended sediment in the maximum turbidity zone of the North Trough.

[0125] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store predicted data on the vertical distribution morphology of suspended sediment in the maximum turbidity zone. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone can be implemented.

[0126] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0128] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] In an exemplary embodiment, a computer program product may be provided. The computer program product includes a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0131] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0132] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0133] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting the vertical distribution of suspended sediment in the maximum turbidity zone, characterized by: include: Obtaining sample data; the sample data includes sample water depth, sample sediment content, and a vertical distance from the sample sediment content position to the water bottom; Based on the Rouse formula, a suspended sediment vertical distribution prediction model is constructed based on the sample data; Obtaining parameter data of the maximum turbidity zone in the estuary area to be predicted; the parameter data includes water depth, tidal range, and surface sediment content and bottom sediment content; Using the suspended sediment vertical distribution prediction model, the sediment content at positions at different vertical distances from the water bottom is obtained based on the parameter data; Based on the sediment concentration at different vertical distances from the water bottom, the vertical distribution pattern of suspended sediment in the maximum turbidity zone of the estuary area to be predicted is obtained; Among them, based on the Rouse formula, a suspended sediment vertical distribution prediction model is constructed based on the sample data, including: Selecting a reference sediment content from the sample sediment content, and constructing a double-layer suspended sediment distribution model using the Rouse formula based on the reference sediment content, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom; Determining the suspension index of the double-layer suspended sediment distribution model based on the sediment content of the sample to obtain an updated double-layer suspended sediment distribution model; An exponential factor is introduced, and a weight coefficient is constructed based on the exponential factor, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom. The updated two-layer suspended sediment distribution model is normalized and connected using the weight coefficient to obtain a connection model; the connection model is expressed as: ； Where, Indicates the sand content, h Indicates water depth. z Indicates the vertical distance from the sediment content position to the water bottom. Indicates the surface sand content, Indicates the bottom sediment content, Indicates the upper floating index, Indicates the lower layer suspension index, represents the weight coefficient, represents the exponential factor; Selecting different exponential factors and using the connection model corresponding to the different exponential factors, based on the sample water depth, to obtain the predicted sediment concentration at different times and at different distances from the sediment concentration position to the vertical line of the water bottom; Determine the correlation coefficient between the predicted sediment content at different times and at different sediment content locations and the vertical distance from the water bottom and the sediment content of the sample; The weight coefficient corresponding to the maximum correlation coefficient is used as the final weight coefficient of the connection model to obtain the suspended sediment vertical distribution prediction model.

2. The method for predicting the vertical distribution of suspended sediment in the maximum turbidity zone according to claim 1 is characterized in that: A reference sediment content is selected from the sample sediment content, and a double-layer suspended sediment distribution model is constructed using the Rouse formula based on the reference sediment content, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom, including: The surface sediment content of the sample sediment content is used as the reference sediment content of the upper half of the water depth, and the bottom sediment content of the sample sediment content is used as the reference sediment content of the lower half of the water depth; the upper half of the water depth refers to the area where the vertical distance from the sediment content position to the water bottom is greater than or equal to a set multiple of the water depth; the lower half of the water depth refers to the area where the vertical distance from the sediment content position to the water bottom is less than a set multiple of the water depth; The Rouse formula is used to construct a double-layer suspended sediment distribution model based on the reference sediment content of the upper half of the water depth, the reference sediment content of the lower half of the water depth, the sample water depth, and the vertical distance from the sample sediment content position to the water bottom.

3. The method for predicting the vertical distribution of suspended sediment in the maximum turbidity zone according to claim 1 is characterized in that: Determining the suspension index of the double-layer suspended sediment distribution model based on the sediment content of the sample to obtain an updated double-layer suspended sediment distribution model includes: Taking the natural logarithm of the double-layer suspended sediment distribution model, and using the least squares method to fit the sediment content at different locations in the sample sediment content at different times to obtain a suspension index; the suspension index includes an upper layer suspension index and a lower layer suspension index; The suspended index obtained by fitting is substituted into the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model.

4. The method for predicting the vertical distribution of suspended sediment in the maximum turbidity zone according to claim 3 is characterized in that: The double-layer suspended sediment distribution model is expressed as: ； Where, Indicates the sand content, h Indicates water depth. z Indicates the vertical distance from the sediment content position to the water bottom. Indicates the surface sand content, Indicates the bottom sediment content, Indicates the upper floating index, Indicates the lower layer suspension index.

5. The method for predicting the vertical distribution of suspended sediment in the maximum turbidity zone according to claim 4, characterized in that: The natural logarithm of the double-layer suspended sediment distribution model is taken, and the suspension index is obtained by fitting the sediment content at different positions in the sample sediment content at different times using the least squares method, including: The natural logarithm of the upper suspended sediment distribution model in the double-layer suspended sediment distribution model is taken, and the least squares method is used to calculate the distribution of suspended sediment at different times based on the sample sediment content. z=h -0.5m, z =0.8 h as well as z =0.6 h The upper layer suspension index is obtained by fitting the sediment content; m Indicates the length unit meter; The natural logarithm of the lower suspended sediment distribution model in the double-layer suspended sediment distribution model is taken, and the least squares method is used to calculate the distribution of suspended sediment at different times. z= 0.4 h , z =0.2 h as well as z =0.5m sediment content is fitted to obtain the lower layer suspension index.

6. The method for predicting the vertical distribution of suspended sediment in the maximum turbidity zone according to claim 1, characterized in that: The suspended sediment vertical distribution prediction model is expressed as: ； Where, Indicates the sand content, h Indicates water depth. z Indicates the vertical distance from the sediment content position to the water bottom. Indicates the surface sand content, Indicates the bottom sediment content, Indicates the upper floating index, Indicates the lower layer suspension index, The exponential factor corresponding to the maximum correlation coefficient between the predicted sediment content and the sample sediment content at different times and different sediment content positions and the vertical line distance from the water bottom is The value of .

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the vertical distribution morphology of suspended sediment in the maximum turbidity zone according to any one of claims 1 to 6 is realized.

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