Method and device for predicting vertical distribution pattern of suspended sand in maximum turbid zone and medium

Abstract

The invention discloses a maximum turbid zone suspended sediment vertical distribution pattern prediction method and device and a medium, and relates to the field of electric digital data processing, and the method comprises the steps: obtaining sample data; on the basis of a Rouse formula, constructing a suspended sediment vertical distribution prediction model based on the sample data; obtaining parameter data such as water depth, tidal range, surface layer sand content and bottom layer sand content of a maximum turbid zone of a to-be-predicted estuary area, and obtaining sand content at positions with different vertical line distances from the water bottom based on the parameter data by adopting the suspended sediment vertical distribution prediction model; and obtaining the suspended sediment vertical distribution pattern of the maximum turbid zone of the to-be-predicted estuary area based on the sediment content at the positions away from the water bottom by different vertical line distances. According to the method, the suspended sediment vertical distribution pattern of the maximum turbid zone of the estuary can be effectively and accurately predicted.

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 pattern of suspended sediment in the turbidity maximum zone. Background Art

[0002] The turbidity maximum zone often appears in estuarine areas. Clarifying the vertical distribution of suspended sediment in the turbidity maximum zone is crucial for estuarine waterway regulation and ecological environment 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 have far exceeded the scope of steady uniform flow, and it is difficult to directly solve the vertical distribution of suspended sediment theoretically. In addition, the instantaneous suspended sediment distribution in the turbidity maximum zone often exhibits various complex patterns such as L-shaped, stepped, exponential, linear, and irregular. The common Rouse formula and Van Rijn formula cannot effectively reflect the complex vertical distribution pattern of suspended sediment. Therefore, there is currently a lack of an effective method for predicting the vertical distribution pattern of suspended sediment in the estuarine turbidity maximum zone. Summary of the Invention

[0003] The purpose of the present application is to provide a method, device, and medium for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum zone, which can effectively and accurately predict the vertical distribution pattern of suspended sediment in the estuarine turbidity maximum zone.

[0004] To achieve the above object, the present application provides the following solutions: In a first aspect, the present application provides a method for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum zone, including: Obtaining sample data; Based on the Rouse formula, constructing a vertical suspended sediment distribution prediction model based on the sample data; Obtaining parameter data of the turbidity maximum zone in the estuarine area to be predicted; the parameter data includes water depth, tidal range, surface sediment concentration, and bottom sediment concentration; Using the vertical suspended sediment distribution prediction model, obtaining the sediment concentration at different vertical distances from the bottom based on the parameter data; Based on the sediment concentration at different vertical distances from the bottom, obtaining the vertical distribution pattern of suspended sediment in the turbidity maximum zone of the estuarine area to be predicted.

[0005] Optionally, the sample data includes sample water depth, sample sediment concentration, and the vertical distance from the sample sediment concentration position to the bottom; Based on the Rouse formula, constructing a vertical suspended sediment distribution prediction model based on the sample data, including: Selecting a reference sediment concentration from the sample sediment concentration, and using the Rouse formula to construct a two-layer suspended sediment distribution model based on the reference sediment concentration, the sample water depth, and the vertical distance from the sample sediment concentration position to the bottom; Based on the sediment concentration of the sample, determine the suspension index of the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model; Introduce an exponential factor, and construct a weight coefficient based on the exponential factor, the sample water depth, and the vertical distance from the position of the sample sediment concentration to the bottom. Use the weight coefficient to perform normalization connection on the updated double-layer suspended sediment distribution model to obtain a connection model; Select different exponential factors, and use the connection model corresponding to different exponential factors. Based on the sample water depth, obtain the predicted sediment concentrations at different times and at different vertical distances from the position of the sediment concentration to the bottom; Determine the correlation coefficient between the predicted sediment concentrations at different times and at different vertical distances from the position of the sediment concentration to the bottom and the sample sediment concentration; Take the weight coefficient corresponding to the maximum correlation coefficient as the final weight coefficient of the connection model to obtain the predicted model of the vertical distribution of suspended sediment.

[0006] Optionally, select a reference sediment concentration from the sample sediment concentration, and use the Rouse formula to construct a double-layer suspended sediment distribution model based on the reference sediment concentration, the sample water depth, and the vertical distance from the position of the sample sediment concentration to the bottom, including: Take the surface sediment concentration in the sample sediment concentration as the reference sediment concentration for the upper half of the water depth, and take the bottom sediment concentration in the sample sediment concentration as the reference sediment concentration for the lower half of the water depth; the upper half of the water depth refers to the area where the vertical distance from the position of the sediment concentration to the 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 position of the sediment concentration to the bottom is less than a set multiple of the water depth; Use the Rouse formula to construct a double-layer suspended sediment distribution model based on the reference sediment concentration for the upper half of the water depth, the reference sediment concentration for the lower half of the water depth, the sample water depth, and the vertical distance from the position of the sample sediment concentration to the bottom.

[0007] Optionally, based on the sample sediment concentration, determine the suspension index of the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model, including: Take the natural logarithm of the double-layer suspended sediment distribution model, and use the least squares method to fit the sediment concentrations at different positions in the sample sediment concentration at different times to obtain the suspension index; the suspension index includes an upper layer suspension index and a lower layer suspension index; Substitute the fitted suspension index into the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model.

[0008] Optionally, the double-layer suspended sediment distribution model is expressed as: ; In the formula, represents the sediment concentration,h represents the water depth, z represents the vertical distance from the sediment concentration position to the bottom, represents the surface sediment concentration, represents the bottom sediment concentration, represents the upper-layer suspension index, represents the lower-layer suspension index.

[0009] Optionally, take the natural logarithm of the double-layer suspended sediment distribution model, and use the least squares method to fit the suspension index based on the sediment concentrations at different positions in the sample sediment concentrations at different times, including: Take the natural logarithm of the upper-layer suspended sediment distribution model in the double-layer suspended sediment distribution model, and use the least squares method, based on the sample sediment concentrations at different times z = h -0.5 m, z = 0.8 h and z = 0.6 h to fit the upper-layer suspension index from the sediment concentrations; m represents the length unit meter; Take the natural logarithm of the lower-layer suspended sediment distribution model in the double-layer suspended sediment distribution model, and use the least squares method, based on the sediment concentrations at different times z= 0.4 h , z = 0.2 h and z = 0.5 m to fit the lower-layer suspension index from the sediment concentrations.

[0010] Optionally, the connection model is expressed as: ; In the formula, represents the sediment concentration, h represents the water depth, z represents the vertical distance from the sediment concentration position to the bottom, represents the surface sediment concentration, represents the bottom sediment concentration, represents the upper-layer suspension index, represents the lower-layer suspension index, represents the weight coefficient, represents the exponential factor.

[0011] Optionally, the vertical distribution prediction model of suspended sediment is expressed as: ; In the formula, represents the sediment concentration, h represents the water depth, z represents the vertical distance from the sediment concentration position to the bottom, Indicates the sediment concentration of the surface layer, Indicates the sediment concentration of the bottom layer, Indicates the suspended index of the upper layer, Indicates the suspended index of the lower layer, Indicates the exponential factor corresponding to the maximum correlation coefficient between the predicted sediment concentration and the sample sediment concentration at the vertical distance from the water bottom at different times and different sediment concentration positions k The value of 0.

[0012] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-provided method for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum zone.

[0013] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-provided method for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum zone are implemented.

[0014] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides a method, device and medium for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum zone. By building a vertical distribution prediction model of suspended sediment based on the Rouse formula and sample data, after measuring data such as the water depth of the turbidity maximum zone in the estuary area to be predicted, the sediment concentration at different vertical distance positions from the water bottom can be obtained, thereby effectively realizing the accurate prediction of the vertical distribution pattern of suspended sediment and filling the technical gap in the lack of prediction of the vertical distribution pattern of suspended sediment in the estuary turbidity maximum zone. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of a method for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum zone provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the measuring point positions of the sediment concentration in the turbidity maximum zone of the North Passage of the Yangtze Estuary provided by an embodiment of the present application; Figure 3 It is a comparison array diagram of the prediction effects of the vertical distribution prediction model of suspended sediment, the Rouse formula and the Van Rijn formula on different types of suspended sediment profiles provided by an embodiment of the present application; Figure 4 Array diagram of the comparison results of the sediment concentration predicted by the vertical suspended sediment distribution prediction model provided in an embodiment of the present application and the measured sediment concentration; Figure 5 Vertical suspended indexes of different tidal cycles provided in an embodiment of the present application and Intra-tidal variation array diagram; Figure 6 Average value of the upper-layer suspension index in the tidal cycle and Scatter relationship schematic diagram; Figure 7 Lower-layer suspension index and Relationship array diagram; Figure 8 Array diagram of sediment concentration verification results provided in an embodiment of the present application; Figure 9 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] In order to accurately predict the vertical distribution pattern of suspended sediment in the estuarine turbidity maximum, the present application provides a prediction method, device and medium for the vertical distribution pattern of suspended sediment in the estuarine turbidity maximum, which can fill the technical gap in the lack of prediction of the vertical distribution pattern of suspended sediment in the estuarine turbidity maximum.

[0019] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0020] In an exemplary embodiment, the present application provides a prediction method for the vertical distribution pattern of suspended sediment in the estuarine turbidity maximum. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, this method is described by taking it as an example applied to a server.

[0021] As Figure 1As shown in the figure, the prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone provided by this application includes: Step 100: Obtain sample data.

[0022] Among them, the sample data includes sample water depth, sample sediment concentration, and the vertical distance from the position of the sample sediment concentration to the bottom of the water.

[0023] For example, obtain n the water depth data at multiple moments ( , ...), 3 ), as the sample water depth. The stratified sediment concentration data is measured by the vertical six-point method as the sample sediment concentration (unit: kg / m z ). From the surface layer to the bottom layer, the vertical distances from the positions of the sediment concentration to the bottom of the water h are respectively taken as h -0.5 m, 0.8 h , 0.6 h , 0.4 h , 0.2 h and 0.5 m, where m is the length unit meter,

[0024] Step 101: Based on the Rouse formula, construct a prediction model for the vertical distribution of suspended sediment based on the sample data.

[0025] Step 102: Obtain the parameter data of the maximum turbidity zone in the estuary area to be predicted.

[0026] Among them, the parameter data includes water depth, tidal range, surface sediment concentration, bottom sediment concentration, etc.

[0027] These parameter data are mainly used to fit the suspension index in the prediction model for the vertical distribution of suspended sediment. For the specific fitting method, please refer to the following content.

[0028] Step 103: Use the prediction model for the vertical distribution of suspended sediment to obtain the sediment concentration at different vertical distances from the bottom of the water based on the parameter data.

[0029] Step 104: Based on the sediment concentration at different vertical distances from the bottom of the water, obtain the vertical distribution pattern of suspended sediment in the maximum turbidity zone of the estuary area to be predicted.

[0030] By implementing the above steps 100 - 104, based on the data such as the water depth of the maximum turbidity zone in the estuary area to be predicted, the sediment concentration at different vertical distances from the bottom of the water can be obtained, thereby realizing the accurate prediction of the vertical distribution pattern of suspended sediment and filling the technical gap in the lack of prediction of the vertical distribution pattern of suspended sediment in the estuary maximum turbidity zone.

[0031] In another exemplary embodiment of the present application, since the Rouse formula for the vertical distribution of suspended sediment is the most commonly used and the formula form is relatively simple, therefore, in order to reduce the complexity of predicting the vertical distribution of suspended sediment while improving the prediction efficiency of the vertical distribution of suspended sediment, in step 101, a prediction model for the vertical distribution of suspended sediment is constructed based on the Rouse formula. The specific implementation of this process includes the following steps 1 to 6.

[0032] Step 1: Select a reference sediment concentration from the sample sediment concentration, and use the Rouse formula to construct a two-layer suspended sediment distribution model based on the reference sediment concentration, the sample water depth, and the vertical distance from the position of the sample sediment concentration to the bottom of the water.

[0033] Among them, the surface sediment concentration in the sample sediment concentration is used as the reference sediment concentration for the upper half of the water depth (for example ), and the bottom sediment concentration in the sample sediment concentration is used as the reference sediment concentration for the lower half of the water depth (for example ).

[0034] Based on the above description, use the Rouse formula to construct a two-layer suspended sediment distribution model based on the reference sediment concentration of the upper half of the water depth, the reference sediment concentration of the lower half of the water depth, the sample water depth, and the vertical distance from the position of the sample sediment concentration to the bottom of the water.

[0035] The constructed two-layer suspended sediment distribution model is expressed as: (1) In the formula, represents the sediment concentration, represents the upper layer suspension index, represents the lower layer suspension index.

[0036] Step 2: Based on the sample sediment concentration, determine the suspension index of the two-layer suspended sediment distribution model to obtain an updated two-layer suspended sediment distribution model.

[0037] Among them, take the natural logarithm of the two-layer suspended sediment distribution model, and use the least squares method to fit the suspension index based on the sediment concentrations at different positions in the sample sediment concentration at different times.

[0038] Substitute the fitted suspension index into the two-layer suspended sediment distribution model to obtain an updated two-layer suspended sediment distribution model.

[0039] For example, (1) The method of fitting the upper layer suspension index , ,..., at different times can be: Take the natural logarithm of both sides of the upper layer suspended sediment distribution model of the two-layer suspended sediment distribution model [i.e., the upper half of formula (1)], and we have: (2) Based on z = h -0.5m, z =0.8 h and z =0.6 h sediment concentration , the upper-layer suspension index is obtained by least-squares fitting .

[0040] Among them, least-squares fitting can be implemented through tools such as excel and matlab.

[0041] (2) The method of fitting the lower-layer suspension index at different times ( , ,..., ) can be as follows: Take the natural logarithm of both sides of the lower-layer suspended sediment distribution model of the double-layer suspended sediment distribution model [i.e., the lower half of formula (1)], and we get: (3) Based on different times z= 0.4 h , z =0.2 h and z =0.5m sediment concentration , the lower-layer suspension index is obtained by least-squares fitting .

[0042] Step 3: Introduce an exponential factor, and construct a weight coefficient based on the exponential factor, the sample water depth, and the perpendicular distance from the sample sediment concentration position to the bottom. Normalize and connect the updated double-layer suspended sediment distribution model with the weight coefficient to obtain a connection model.

[0043] In this step, to solve the drawback of inconvenient use of piecewise functions, the constructed weight coefficient is . Among them, represents the exponential factor.

[0044] Based on the above description, the obtained connection model is expressed as: (4) Step 4: Select different exponential factors, and use the connection models corresponding to different exponential factors. Based on the sample water depth, obtain the predicted sediment concentration at different times and the perpendicular distance from different sediment concentration positions to the bottom.

[0045] Step 5: Determine the correlation coefficient between the predicted sediment concentration at different times and the perpendicular distance from different sediment concentration positions to the bottom and the sample sediment concentration.​

[0046] Step 6: Take the weight coefficient corresponding to the maximum correlation coefficient as the final weight coefficient of the connection model, and obtain the suspended sediment vertical distribution prediction model.

[0047] Furthermore, the specific implementation manners of Steps 4 to 6 given above are illustrated by way of example: For example, let k 0 = 1, and at different times ( , ,..., ), when taking z = 0.2 h , 0.4 h , 0.6 h and 0.8 h respectively, 4 n predicted sediment concentrations are obtained through formula (4), and the correlation coefficients n between the 4 R predicted sediment concentrations and the corresponding measured sediment concentrations in the sample sediment concentration are statistically calculated. k In turn, select R 0 = 2, 3, 4, 5..., repeat the above steps, and select the k value of (5) wherein, represents the k value of

[0048] 0 corresponding to the maximum correlation coefficient between the predicted sediment concentration representing the vertical distance from the water bottom at different times and different sediment concentration positions and the sample sediment concentration.

[0049] 1) Obtain data: As Figure 2 shown, select the CSW station as the suspended sediment measurement point, and obtain the water depth and stratified sediment concentration data at 25 moments during the medium flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide in 2016, 2017, and 2018 respectively. That is, there are 3 × 25 = 75 moments ( , ,..., ) of water depth data and sediment concentration data for the medium flood season spring tide, flood season neap tide, dry season spring tide, and dry season neap tide respectively.

[0050] 2) Based on the above-mentioned water depth data and sediment concentration data obtained, the upper-layer suspension index is obtained by fitting using the above step 2 and the lower-layer suspension index . The specific values of the upper-layer suspension index and the lower-layer suspension index obtained by fitting are shown in Table 1, Table 2, Table 3-1 and Table 3-2.

[0051] Table 1 Values at each moment of spring tide in the flood season, neap tide in the flood season, spring tide in the dry season, and neap tide in the dry season in 2016 and value table

[0052] Table 2 Values at each moment of spring tide in the flood season, neap tide in the flood season, spring tide in the dry season, and neap tide in the dry season in 2017 and value table

[0053] Table 3-1 One of the values at each moment of spring tide in the flood season, neap tide in the flood season, spring tide in the dry season, and neap tide in the dry season in 2018 and value

[0054] Table 3-2 The second value at each moment of spring tide in the flood season, neap tide in the flood season, spring tide in the dry season, and neap tide in the dry season in 2018 and value

[0055] 3) Based on the upper-layer suspension index and the lower-layer suspension index obtained by fitting, combined with the data obtained in step (1), the prediction result of the vertical distribution pattern of suspended sediment in the maximum turbidity zone of the North Passage of the Yangtze Estuary can be obtained by using the suspended sediment vertical distribution prediction model [Formula (5)] constructed in the above-mentioned embodiment of the present application. Among them, the values of the correlation coefficient R when the index factors k 0 = 1, 2, 3, 4, 5 for spring tide in the flood season, neap tide in the flood season, spring tide in the dry season, and neap tide in the dry season can be seen in Table 4.

[0056] Table 4 Table of values of correlation coefficient R

[0057] Therefore, in this embodiment, in Formula (5) (6) Based on as Figure 3The comparison results of the formula of the present application (i.e., the prediction model of vertical suspended sediment distribution) with the Rouse formula and the VanRijn formula for the prediction effects of different types of suspended sediment profiles (L-type, stepped-type, exponential-type, linear-type, irregular-type, etc.), and the comparison results of the predicted values and measured values among the three as shown in Figure 4 Based on this, compared with the Rouse formula and the VanRijn formula, the prediction model of vertical suspended sediment distribution provided by the present application can better fit various complex suspended sediment distribution patterns, the predicted values and measured values are closer, and it has a better fitting effect. Figure 3 In [figure], columns (a)-(e) respectively represent the comparison results of the prediction effects of L-type, stepped-type, exponential-type, linear-type, and irregular-type suspended sediment profiles, and rows (A) and (B) represent the comparison results of different types corresponding to the sediment concentration. Figure 4 In [figure], part (a) represents the comparison results of the predicted values and measured values corresponding to the spring tide during the flood season, part (b) represents the comparison results of the predicted values and measured values corresponding to the neap tide during the flood season, part (c) represents the comparison results of the predicted values and measured values corresponding to the spring tide during the dry season, and part (d) represents the comparison results of the predicted values and measured values corresponding to the neap tide during the dry season.

[0058] In another exemplary embodiment of the present application, the estuarine dynamic conditions are in a non-steady state, and the suspension index will necessarily change continuously with the ebb and flood tide processes. From the perspective of facilitating application, studying the variation laws of the upper-layer suspension index and the lower-layer suspension index , and establishing the numerical relationship between the upper-layer suspension index and the lower-layer suspension index and the easily observable parameters, an applied empirical formula can be obtained. Based on this, in this embodiment, the data of 2016 and 2017 are used for calibration, and the data of 2018 are used for verification.

[0059] The intra-tidal variation curves of the upper-layer suspension index and the lower-layer suspension index in different tidal cycles in 2016 and 2017 are as shown in Figure 5 . Since the surface water is easily affected by external disturbances such as wind and waves, the variation of the upper-layer suspension index in the tidal cycle is relatively complex, but the upper-layer suspension index has obvious tidal type variation characteristics. The numerical value of the upper-layer suspension index during the neap tide stage is generally smaller than that during the spring tide stage. This is because the stratification during the neap tide is stronger, the L-type distribution appears more frequently, the sediment concentration in the upper-layer water is low and very uniform along the water depth, so the numerical value of the upper-layer suspension index is small. Taking the tidal cycle average value of the upper-layer suspension index to approximately replace the upper-layer suspension index at all times within the tidal cycle , the average value over the tidal cycle has a good positive correlation with the tidal dynamics. The tidal range and the water depth ratio is used to characterize the tidal dynamics, and a scatter plot of the average value over the tidal cycle and the dimensionless tidal dynamics parameter is plotted. As Figure 6 shown, the scatter points are exponentially distributed, and a numerical relationship as shown in formula (7) can be fitted, with a fitting correlation coefficient as high as 0.92.

[0060] (7) Figure 7 gives the correlation between the lower-layer suspension index and the logarithmic form of the ratio of the bottom and surface sediment concentrations , that is, the lower-layer suspension index can be expressed as: (8) Formula (8) shows that the lower-layer suspension index and are linearly related, and the slope , intercept and the correlation coefficient are marked in Figure 7 . To a certain extent, the ratio of the bottom and surface sediment concentrations reflects the stratification and mixing alternation process within the tidal cycle: the strengthening of stratification can drive the suspended sediment to gather near the bottom layer, the ratio of the bottom and surface sediment concentrations increases, the vertical distribution of suspended sediment becomes uneven, and the value of the lower-layer suspension index increases; the strengthening of mixing can drive the suspended sediment to diffuse to the surface layer, the ratio of the bottom and surface sediment concentrations decreases, the vertical distribution of suspended sediment tends to be uniform, and the value of the lower-layer suspension index decreases. Therefore, the physical meaning expressed by formula (8) is relatively reasonable. For the spring tide during the flood season, the neap tide during the flood season, and the neap tide during the dry season, the linear relationship between the lower-layer suspension index and is significant, and the correlation coefficients all exceed 0.8. The linear relationship during the spring tide in the dry season is weak, and the change of the lower-layer suspension index with is not obvious, and its average value can be approximately taken for substitution, that is , .

[0061] Substituting formula (7) and formula (8) into the above formula (6), the applied unified expression can be obtained: (9) where .

[0062] Based on the above description, the above formula (9) provided by this application was verified using data from different seasons and different tidal types in 2018, and the verification results are as Figure 8 shown. The correlation coefficients between the predicted sediment concentration and the measured sediment concentration in the verification year (2018) were 0.85, 0.78, 0.80, and 0.72 for spring tides and neap tides in the flood season, and spring tides 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 of the verification year and the calibration year are very close, and the correlations are all relatively high, indicating that formula (9) can reasonably predict the sediment concentration distribution.

[0063] Predicting the vertical distribution of suspended sediment in the estuarine turbidity maximum is extremely difficult. Although there are still deviations between some predicted values of formula (9) and the measured values, the prediction accuracy has been significantly improved compared with 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. The water depth, tidal range, and surface sediment concentration can be obtained by buoys. The surface sediment concentration can also be obtained through satellite images, and the bottom sediment concentration can be obtained by bottom-mounted observation instruments. With the above measurement methods, this application can provide the possibility for predicting the long-term suspended sediment in the turbidity maximum of the North Passage.

[0064] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store prediction data on the vertical distribution pattern of suspended sediment in the turbidity maximum. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it can implement a method for predicting the vertical distribution pattern of suspended sediment in the turbidity maximum.

[0065] Those skilled in the art can understand, Figure 9The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0066] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0067] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0068] In an exemplary embodiment, a computer program product may also be provided. This computer program product includes a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0069] 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 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 need to comply with relevant regulations.

[0070] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memory (RRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0071] The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0072] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0073] In this article, specific examples are used to illustrate the principle and implementation of this application. The description of the above embodiments is only to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone, characterized in that Including: Obtain sample data; Based on the Rouse formula, construct a vertical distribution prediction model of suspended sediment based on the sample data; Obtain parameter data of the maximum turbidity zone in the estuary area to be predicted; the parameter data includes water depth, tidal range, surface sediment concentration, and bottom sediment concentration; Using the vertical distribution prediction model of suspended sediment, obtain the sediment concentration at different vertical distances from the water bottom based on the parameter data; Based on the sediment concentration at different vertical distances from the water bottom, obtain the vertical distribution pattern of suspended sediment in the maximum turbidity zone of the estuary area to be predicted.

2. The prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 1, wherein The sample data includes sample water depth, sample sediment concentration, and the vertical distance from the sample sediment concentration position to the water bottom; Based on the Rouse formula, constructing a vertical distribution prediction model of suspended sediment based on the sample data includes: Select a reference sediment concentration from the sample sediment concentration, and use the Rouse formula to construct a two-layer suspended sediment distribution model based on the reference sediment concentration, the sample water depth, and the vertical distance from the sample sediment concentration position to the water bottom; Based on the sample sediment concentration, determine the suspension index of the two-layer suspended sediment distribution model to obtain an updated two-layer suspended sediment distribution model; 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 concentration position to the water bottom. Use the weight coefficient to perform normalization connection on the updated two-layer suspended sediment distribution model to obtain a connection model; Select different exponential factors, and use the connection models corresponding to different exponential factors to obtain the predicted sediment concentration at different times and different vertical distances from the sediment concentration position to the water bottom based on the sample water depth; Determine the correlation coefficient between the predicted sediment concentration at different times and different vertical distances from the sediment concentration position to the water bottom and the sample sediment concentration; Take the weight coefficient corresponding to the maximum correlation coefficient as the final weight coefficient of the connection model to obtain the vertical distribution prediction model of suspended sediment.

3. The prediction method of the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 2, characterized in that, Select a reference sediment concentration from the sample sediment concentration, and use the Rouse formula to construct a two-layer suspended sediment distribution model based on the reference sediment concentration, the sample water depth, and the vertical distance from the sample sediment concentration position to the water bottom, including: Take the surface sediment concentration in the sample sediment concentration as the reference sediment concentration for the upper half water depth, and take the bottom sediment concentration in the sample sediment concentration as the reference sediment concentration for the lower half water depth; the upper half water depth refers to the area where the vertical distance from the sediment concentration position to the water bottom is greater than or equal to a set multiple of the water depth; the lower half water depth refers to the area where the vertical distance from the sediment concentration position to the water bottom is less than a set multiple of the water depth; Use the Rouse formula to construct a two-layer suspended sediment distribution model based on the reference sediment concentration of the upper half water depth, the reference sediment concentration of the lower half water depth, the sample water depth, and the vertical distance from the sample sediment concentration position to the water bottom.

4. The prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 2, characterized in that Based on the sample sediment concentration, determining the suspension index of the two-layer suspended sediment distribution model to obtain an updated two-layer suspended sediment distribution model, including: Take the natural logarithm of the two-layer suspended sediment distribution model, and use the least squares method to fit the suspension index based on the sediment concentration at different positions in the sample sediment concentration at different times; the suspension index includes an upper layer suspension index and a lower layer suspension index; Substitute the suspended sediment index obtained by fitting into the double-layer suspended sediment distribution model to obtain an updated double-layer suspended sediment distribution model.

5. The prediction method of the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 4, wherein The double-layer suspended sediment distribution model is expressed as: ； In the formula, represents the sediment concentration, h represents the water depth, z represents the perpendicular distance from the sediment concentration position to the bottom, represents the surface sediment concentration, represents the bottom sediment concentration, represents the upper suspension index, represents the lower suspension index.

6. The prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 5, characterized in that, Take the natural logarithm of the double-layer suspended sediment distribution model, and use the least squares method to fit the suspended sediment index based on the sediment concentrations at different positions in the sample sediment concentrations at different times, including: Take the natural logarithm of the upper-layer suspended sediment distribution model in the double-layer suspended sediment distribution model, and use the least squares method. Based on the sediment concentrations at different times in the sample sediment concentration z = h -0.5 m, z = 0.8 h and z = 0.6 h the upper-layer suspension index is obtained by fitting the sediment concentrations m represents the length unit meter; Taking the natural logarithm of the lower-layer suspended sediment distribution model in the double-layer suspended sediment distribution model and using the least squares method, based on the sediment concentrations at different times z= 0.4 h , z = 0.2 h and z = 0.5 m, the lower-layer suspension index is fitted 7. The prediction method of the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 2, characterized in that The connection model is expressed as: ； In the formula, represents the sediment concentration, h represents the water depth, z represents the vertical distance from the sediment concentration position to the bottom of the water, represents the surface sediment concentration, represents the bottom sediment concentration, represents the upper layer suspension index, represents the lower layer suspension index, represents the weight coefficient, represents the exponential factor.

8. The prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to claim 2, characterized in that, The vertical distribution prediction model of suspended sediment is expressed as: ； In the formula, represents the sediment concentration, h represents the water depth, z represents the vertical distance from the sediment concentration position to the bottom of the water, represents the surface sediment concentration, represents the bottom sediment concentration, represents the upper suspension index, represents the lower suspension index, represents the exponential factor corresponding to the maximum correlation coefficient between the predicted sediment concentration and the sample sediment concentration at different times and at different vertical distances from the sediment concentration position to the bottom of the water value.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the prediction method for the vertical distribution pattern of suspended sediment in the maximum turbidity zone according to any one of claims 1-8.

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