Method for determining riverbed limit depth-of-impact

Abstract

The embodiment of the invention provides a method and device for determining the limit depth of stroke of a riverbed, electronic equipment, a computer readable storage medium and a computer program product. The method for determining the riverbed limit stroke depth comprises the steps that the initial water and sediment condition of a target river reach is obtained; based on the initial water and sediment conditions, determining the riverbed limit depth of the target river reach when the target river reach reaches a preset balance condition by utilizing a preset limit depth of stroke calculation rule; wherein the preset balance condition is that the sediment scouring thickness and the sediment deposition thickness of the riverbed reach dynamic balance; and pushing the limit depth of the riverbed to a user terminal. By utilizing the method disclosed by the embodiment of the invention, the dynamic process of'formation-damage-reconstruction 'of the coarsened protective layer can be reflected by calculating the ultimate scouring condition of the damage of the protective layer on the surface of the riverbed and the transportation of the deep bed sand, so that the applicability and the reliability of the scheme in a natural river and / or water tank test can be improved, and the calculation precision can also be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of river dynamics technology, and in particular to a method, device, electronic device, computer-readable storage medium, and computer program product for determining a riverbed limit scour depth. Background Art

[0002] In the field of water conservancy projects, when a reservoir is built and put into operation, the water and sediment conditions of the river channel downstream of the reservoir will change significantly. Specifically, due to the water storage of the reservoir, the downstream water flow of the river channel is in a serious subsaturated state, which provides dynamic conditions for the scouring of the surface sediment on the riverbed. With the long-term scouring of the downstream water flow, the pebble and sand riverbed gradually forms a relatively stable coarsening protective layer. However, if the flow rate of the downstream water flow increases (for example, the reservoir discharges floods), the scouring capacity of the water flow increases exponentially, and the coarsening layer that has been formed may be destroyed; in this case, if the deep bed sand gradation is finer, it may cause a large amount of lower bed sand to be transported, resulting in strong scouring, thereby affecting the stability of the downstream river channel.

[0003] The riverbed coarsening process described in the above scenario involves factors such as the destruction of the riverbed surface protective layer and the participation of deep bed sand in scouring. Among them, due to the dynamic changes in factors such as water flow conditions and riverbed heterogeneity, the complexity of the extreme scouring process of the pebble and sand riverbed downstream of the dam will increase.

[0004] In the prior art, the solutions to the extreme scouring of the gravel-and-sand riverbed downstream of the dam mainly include two solutions: the "constant method" and the "constant-non-constant-constant method". Specifically, the "constant method" means that after one-step calculation, the protective layer gradation and the extreme scouring depth after the extreme scouring can be obtained. However, this type of method ignores the mutual constraint relationship between bed sand scouring and coarsening, and does not reflect the response relationship between the extreme scouring results and the sediment scouring and deposition process. The "constant-non-constant-constant method" means that the riverbed coarsening is regarded as a multi-layer scouring, and the water flow and sediment conditions are regarded as constant conditions in each step of the calculation process. After the scouring, the water flow changes and sediment grading information are dynamically adjusted, and used as the starting conditions for the next scouring step. This type of method often combines probability and statistical theory to calculate the probability of sediment initiation, which not only reflects the randomness of water flow and sediment conditions, but also reflects the restrictive relationship between bed sand scouring and coarsening. However, it ignores the processes of bed sand suspension, suspension and sedimentation, and does not reflect the impact of sediment scouring and deposition changes on extreme scouring. It also rarely considers the extreme scouring conditions after the surface and subsurface bed sands are mixed after the coarsening layer on the riverbed surface is destroyed. Summary of the Invention

[0005] In order to solve the above technical problems, the solution of the present disclosure is proposed. The embodiments of the present disclosure provide a method, apparatus, electronic device, computer-readable storage medium, and computer program product for determining a riverbed limit scour depth.

[0006] According to a first aspect of an embodiment of the present disclosure, a method for determining a riverbed limit scour depth is provided, wherein the method comprises: obtaining initial water and sediment conditions of a target river section; wherein the riverbed of the target river section is a pebble and sand riverbed; based on the initial water and sediment conditions, using a preset limit scour depth calculation rule, determining the riverbed limit scour depth of the target river section when a preset equilibrium condition is reached; wherein the preset equilibrium condition is that the sediment scour thickness and sediment deposition thickness of the riverbed reach a dynamic equilibrium; and pushing the riverbed limit scour depth to a user terminal.

[0007] According to a second aspect of an embodiment of the present disclosure, a device for determining a riverbed limit scour depth is provided, wherein the device comprises: a data acquisition unit configured to: acquire initial water and sediment conditions of a target river section; wherein the riverbed of the target river section is a pebble and sand riverbed; a riverbed scour depth calculation unit configured to: based on the initial water and sediment conditions, use a preset limit scour depth calculation rule to determine the riverbed limit scour depth of the target river section when a preset equilibrium condition is reached; wherein the preset equilibrium condition is that the sediment scour thickness and sediment deposition thickness of the riverbed reach a dynamic equilibrium; an information push unit configured to: push the riverbed limit scour depth to a user terminal.

[0008] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for reading the executable instructions from the memory and executing the instructions to implement the method for determining the limiting depth of a riverbed described in the present disclosure.

[0009] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the storage medium stores a computer program for executing the method for determining the limiting scour depth of a riverbed described in the present disclosure.

[0010] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the limiting scour depth of a riverbed as described in the present disclosure is implemented.

[0011] In summary, the method for determining the ultimate scour depth of a riverbed provided by the embodiments of the present disclosure, on the one hand, utilizes the active layer sediment balance principle and the Markov sediment transfer probability matrix to establish an active layer sediment balance model. The gradation of each particle size group is strictly normalized after summation, which can, to a certain extent, make up for the deficiency of the existing technology that ignores the process of bed sediment suspension, suspension and sedimentation. Among them, the "active layer sediment balance model" can quantify the adjustment of bed sediment gradation at any number of iterations during the riverbed coarsening process, intuitively reflect the mutual constraint mechanism between scour and coarsening, and can characterize the response relationship between riverbed scour and coarsening and the transformation of bed sediment, bed load, and suspended load. On the other hand, by calculating the ultimate scour situation in which the riverbed surface protective layer is destroyed and the deep bed sediment participates in the transport, it can reflect the dynamic process of "formation-destruction-reconstruction" of the coarsening protective layer, thereby not only increasing the applicability and reliability of the scheme in natural river and / or flume tests, but also improving the calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other purposes, features, and advantages of the present disclosure will become more apparent through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and are not intended to limit the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0013] Figure 1 is a flow chart of a method for determining a riverbed limit scour depth provided by an exemplary embodiment of the present disclosure;

[0014] Figure 2 This is a schematic diagram of the replenishment of surface bed sand by deep bed sand in the bed sand scouring and coarsening process according to an exemplary embodiment of the present disclosure, based on the active layer sand balance principle;

[0015] Figure 3 This disclosure Figure 1 An exemplary flow chart of a method for determining a riverbed limit scour depth provided in an embodiment;

[0016] Figure 4 This disclosure Figure 1 Another exemplary flow chart of a method for determining a riverbed limit scour depth provided in an embodiment;

[0017] Figure 5 This disclosure Figure 1 Another exemplary flow chart of a method for determining a riverbed limit scour depth provided in an embodiment;

[0018] Figure 6This is a schematic diagram of the exchange process of suspended load, bed load and bed sand (with corresponding transfer probabilities marked) during the coarsening process of a pebble-sand riverbed provided by an exemplary embodiment of the present disclosure;

[0019] Figure 7 is a flow chart of a method for determining a riverbed limit scour depth provided by another exemplary embodiment of the present disclosure;

[0020] Figure 8 1 is a schematic diagram of the curves of the surface bed sediment gradation and the subsurface bed sediment gradation of the riverbed downstream of Xiangjiaba in an exemplary scenario embodiment of the present disclosure;

[0021] Figure 9 This is a schematic diagram of a coarsening level pairing comparison curve of a typical section downstream of Xiangjiaba under different water flow rates, calculated using the method for determining the ultimate scour depth of a riverbed provided in an exemplary scenario of the present disclosure;

[0022] Figure 10 This is a schematic diagram of a comparison curve of the ultimate scour depth of a typical section downstream of Xiangjiaba under different water flow rates, calculated using the method for determining the ultimate scour depth of a riverbed provided in an exemplary scenario of the present disclosure;

[0023] Figure 11 1 is a schematic structural diagram of a device for determining a riverbed limit scour depth provided by an exemplary embodiment of the present disclosure;

[0024] Figure 12 It is a structural diagram of an application embodiment of the electronic device disclosed in the present invention. DETAILED DESCRIPTION

[0025] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0026] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0027] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, and do not represent any specific technical meanings, nor do they indicate a necessary logical order between them.

[0028] It should also be understood that in the embodiments of the present disclosure, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0029] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0030] In addition, the term "and / or" in this disclosure is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this disclosure generally indicates that the related objects are in an "or" relationship.

[0031] It should also be understood that the description of the various embodiments in this disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0032] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0033] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0034] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0035] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0036] The following combination Figures 1 to 12 The embodiment describes the implementation details of the solution for determining the gradation of the riverbed coarsening layer disclosed in the present invention.

[0037] Exemplary Methods

[0038] Figure 1 The figure is a flow chart of a method for determining the maximum scour depth of a riverbed provided by an exemplary embodiment of the present disclosure. The method can be executed on a server, wherein the server may include but is not limited to a server or a cloud computing platform.

[0039] Specifically, refer to Figure 1 The method for determining the maximum scour depth of a riverbed comprises:

[0040] S110. Obtaining initial water and sediment conditions of the target river section.

[0041] Wherein, the riverbed of the target river section is a pebble-sand riverbed. Figure 2 Under the action of continuous water flow scouring, the active layer elevation of the pebble-sand riverbed continues to decrease (i.e., the bed surface drops), and the deep sediment also continuously replenishes the thickness of the surface bed sand during the scouring process (i.e., the amount of sand is replenished).

[0042] Optionally, as the executor of step S110, the server can "acquire" the initial water and sediment conditions of the target river section through any feasible means. For example, the server can communicate with the data storage service to obtain the initial water and sediment conditions of the target river section. It should be noted that this disclosure does not limit the communication method, and for example, wireless communication may include but is not limited to wireless communication, such as mobile networks, Wi-Fi, etc.

[0043] Optionally, the initial water and sediment conditions include an initial value of average water depth, an initial value of average flow velocity, an initial value of riverbed surface sediment gradation, an initial value of riverbed subsurface sediment gradation, riverbed surface sediment thickness, and an average gradient.

[0044] The average gradient is a constant value representing the slope of the target river section; larger values ​​indicate steeper slopes and faster water flow. The physical meaning of bed sediment gradation refers to the particle size distribution characteristics of the riverbed surface sediment particles and their spatial distribution patterns. For more details, please refer to the diagrams and figures in the following specific scenario examples for a better understanding; we will not elaborate on them here.

[0045] It should be noted that, generally, for the same target river section, under the condition of maintaining a certain flow rate, the product of the initial average water depth and the initial average flow velocity is a constant value. The subsequent embodiments, which involve determining the flow velocity after determining the water depth, are based on this principle. If the flow rate changes (for example, switching from flow rate Q1 to flow rate Q2), the product of the initial average water depth and the initial average flow velocity will also change accordingly.

[0046] In addition, it should be noted that, in the direction perpendicular to the riverbed, the riverbed surface is located above the riverbed subsurface, that is, the riverbed subsurface will only be exposed after the riverbed surface is washed away. The internal hierarchical structure of the riverbed surface and the riverbed subsurface can include the following: Figure 2 The active layer and deep layer are shown in the figure. After the active layer water flow removes the sediment N1, according to the sediment balance principle of the active layer, the deep layer will theoretically adaptively replenish the sediment N1 to the active layer.

[0047] S120. Based on the initial water and sediment conditions, using a preset limit scour depth calculation rule, determine the limit scour depth of the riverbed of the target river section when the preset equilibrium condition is reached.

[0048] The preset equilibrium condition is that the thickness of sediment scouring and sediment deposition on the riverbed reaches a dynamic equilibrium.

[0049] As an optional embodiment, refer to Figure 3 , step S120 includes the following steps:

[0050] S1210: Determine the sediment particle size grouping of the riverbed in the target river section based on the initial value of the riverbed surface sediment gradation.

[0051] As an optional example, since the initial values ​​for the surface bed sediment gradation of the target river section can roughly reflect the riverbed sediment particle size distribution, the particle size groupings in the initial values ​​for the surface bed sediment gradation can be used as the sediment particle size groupings for the riverbed in the target river section. For ease of understanding, the initial values ​​for the surface bed sediment gradation can be exemplarily presented in Table 1.

[0052] Table 1 Example of initial values ​​of riverbed surface sand gradation

[0053]

[0054] S1220. Based on the initial value of the average water depth, the initial value of the average flow velocity, the initial value of the riverbed surface sediment gradation, and the average gradient, a preset sediment transfer probability quantification rule is used to determine the Markov transition probability matrix of each group of sediment particle sizes under the current water and sediment conditions.

[0055] The Markov transition probability matrix includes transition probabilities corresponding to the three movement forms of the sediment, namely, bed sediment, bed load, and suspended load.

[0056] Optionally, the transition probabilities P corresponding to the three sediment movement forms of bed sand (recorded as state 1 or form 1), bed load (recorded as state 2 or form 2), and suspended load (recorded as state 3 or form 3) can be defined as follows: ij (P ij represents the probability of transitioning from form i to form j; i=j=1, 2, 3). Specifically, refer to Figure 6 , P 11 represents the probability of sediment maintaining the bed sand state; P 12 represents the probability of sediment transferring from the bed sand state to the bed load state; P 13 It represents the probability of sediment transferring from bed sand state to suspended load state. 21 represents the probability of sediment transferring from the bed load state to the bed sand state; P 22 It represents the probability that sediment remains in the bed load state; P 23 P represents the probability of sediment transferring from bed load to suspended load; 31 represents the probability of sediment transferring from the suspended matter state to the bed sand state; P 32 P represents the probability of sediment remaining in the suspended load state and transferring to the bed load state; 33It represents the probability that sediment remains in a suspended state.

[0057] Furthermore, the sediment transfer probability matrix can be defined as follows:

[0058]

[0059] Where, l represents the particle size group number, represents the sediment transfer probability matrix corresponding to the nth iteration; represents the probability of sediment transitioning from form i to form j in the nth iteration, for example It represents the probability that sediment is converted from bed sand to bed load in the nth iteration.

[0060] Here, the "iteration" refers to iterative execution of step S1220 to step S1230. The complete process will be described below and will not be repeated here.

[0061] As an alternative example, refer to Figure 4 , step S1220 may include the following steps:

[0062] S12210. Use the basic sediment transfer probability quantification rules to determine the probability of no stopping, starting, suspension, and suspension of each group of sediment particle sizes.

[0063] Optionally, step S12210 may be implemented in any feasible manner. For example, the following calculation formulas (1) to (10) may be used to calculate the probability of not stopping, the probability of starting, the probability of suspension, and the probability of suspension for each group of sediment particle sizes.

[0064]

[0065]

[0066] in, Indicates the longitudinal bottom velocity of the water flow; B 4,l represents the sediment suspension condition; ε 0,l represents the probability that the sediment of the lth particle size will not stop under the current water and sand conditions; ε 1,l represents the starting probability of the sediment of group l under the current water and sediment conditions; ε 4,i represents the suspension probability of the lth group of particle size sediment under the current water and sediment conditions; β l represents the probability of suspension of sediment of the first group of particle sizes under the current water and sediment conditions; g represents the acceleration of gravity; R represents the hydraulic radius, which is equal to the average water depth under the current water and sediment conditions by default; J represents the average gradient; u represents the average flow velocity of the target river section under the current water and sediment conditions; u * Indicates friction flow velocity; D lrepresents the particle size of the first group of sediment; H represents the average water depth of the target river section under the current water and sediment conditions; V b,c,0,l Indicates the stopping flow velocity represented by the bottom velocity of the water flow; V b,c,1,l Indicates the starting flow rate represented by the water bottom velocity; It represents the upper limit of the critical vertical instantaneous velocity for loosening of sediment particles; Indicates the lower limit of the critical vertical instantaneous velocity for loosening of sediment particles; ω 0,l Indicates the starting speed parameter when there is no adhesion of sediment particles and no additional pressure of film water; ω 1,l represents the sediment starting characteristic velocity; ω l represents the sediment settling velocity of the first group of particle size sediment; C represents the calculation parameter, which is 1.2; γ s Indicates the bulk density of sediment particles, with a value of 25970N / m 3 ; γ represents the water flow density, with a value of 9800N / m 3 ; dt represents the integral differential element with respect to time.

[0067] S12220. Based on the non-stop probability, starting probability, suspension probability, and suspension probability of each group of sediment particle sizes, a predefined sediment transfer probability matrix is ​​used to determine a Markov transfer probability matrix that matches each group of sediment particle sizes.

[0068] Optionally, step S12220 can be implemented in any feasible manner.

[0069] For example, in the first step, based on the non-stop probability, starting probability, suspension probability and suspension probability of each group of particle size sediment, the transition probability of each group of particle size sediment maintaining the bed sand state, the transition probability of transferring from the bed sand state to the bed load state, and the transition probability of transferring from the bed sand state to the suspended load state can be calculated respectively; the transition probability of each group of particle size sediment transferring from the bed load state to the bed sand state, the transition probability of maintaining the bed load state, and the transition probability of each group of particle size sediment transferring from the suspended load state to the bed sand state, the transition probability of each group of particle size sediment transferring from the suspended load state to the bed sand state, the transition probability of each group of particle size sediment transferring from the suspended load state to the bed load state, and the transition probability of each group of particle size sediment transferring from the suspended load state to the bed sand state, the transition probability of the suspended load state to the bed load state, and the transition probability of each group of particle size sediment transferring In the second step, the transition probability of each group of particle size sediments maintaining the bed sand state, the transition probability of transferring from the bed sand state to the bed load state, and the transition probability of transferring from the bed sand state to the suspended load state, the transition probability of each group of particle size sediments transferring from the bed load state to the bed sand state, the transition probability of maintaining the bed load state, and the transition probability of each group of particle size sediments transferring from the suspended load state to the bed sand state, the transition probability of transferring from the suspended load state to the bed sand state, and the transition probability of each group of particle size sediments maintaining the suspended load state can be substituted into the predefined sediment transition probability matrix to obtain the Markov transition probability matrix matching each group of particle size sediments as shown in the following calculation formula (11):

[0070]

[0071] Here, the superscript n represents the number of iterations.

[0072] in addition, represents the probability of the lth group of particle size sediments remaining in the bed sand state at the nth iteration; represents the probability of the lth group of particle size sediments transferring from the bed sand state to the bed load state at the nth iteration; It represents the probability of the lth group of particle size sediments transferring from the bed sand state to the suspended load state at the nth iteration; represents the probability of the lth group of particle size sediments transitioning from the bed load state to the bed sand state at the nth iteration; It represents the probability of the lth group of particle size sediments remaining in the bed load state at the nth iteration; It represents the probability of the lth group of sediment particles transferring from the bed load state to the suspended load state at the nth iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended matter state to the bed sand state at the nth iteration; It represents the probability of the lth group of sediment particles transferring from the suspended load state to the bed load state at the nth iteration; It represents the probability that the sediment of the lth particle size group remains in the suspended state at the nth iteration.

[0073] It should be noted that the above transition probability matrix It is a non-periodic irreducible discrete Markov chain, and the sum of the transition probabilities in each row is strictly normalized.

[0074] S1230. Based on the Markov transition probability matrix of each group of sediment particle sizes, and using the active layer sediment balance rule, calculate the current water and sediment parameters of the target river section under the current water and sediment conditions.

[0075] The current water and sediment parameters include the current bed sediment gradation, the current bed load gradation, the current suspended load gradation, the current average water depth, the current average flow velocity, and the average gradient.

[0076] As an alternative example, refer to Figure 5 , step S1230 may include the following steps:

[0077] S12310. Based on the Markov transition probability matrix of each group of sediment particle sizes and the current water and sediment conditions, and using a preset scouring and silting depth calculation rule, determine the water flow parameters of the target river section under the current water and sediment conditions.

[0078] The water flow parameters include average water depth, average flow velocity and average gradient.

[0079] Optionally, step S12310 can be implemented in any available manner.

[0080] For example, first, the total scouring and silting depth under the current water and sediment conditions can be calculated using the following calculation formulas (12) to (16).

[0081]

[0082] Where l represents the sediment particle size group number, which is a positive integer; max represents the maximum number of the sediment particle size group, which is a positive integer; ΔH (n-1) represents the total scouring and silting depth obtained after the n-1th iteration; It represents the bed sand gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed sand state to the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the bed sand state to the suspended load state after the n-1th iteration; It represents the bed load gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the suspended sediment gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended matter state to the bed sand state after the n-1th iteration; E (n-1) It represents the thickness of the active layer of bed sand after the n-1th iteration; m represents the thickness coefficient of the siltation layer.

[0083] It should be explained that the active layer sediment balance principle means that: assuming that the thickness of the active layer is H at the n-1th iteration (i.e., the simulated water flow scours the riverbed active layer for the n-1th time), (n-1) , the scouring depth after this scouring is ΔH (n-1) ; then refer to Figure 2 At the beginning of the nth iteration, the active layer is replenished by the original bed sand of the lower layer, that is, the deep layer replenishes the upper layer by ΔH (n-1) Thickness of the original bed sand; after a complete scouring and silting process, it is considered that the obtained sediment gradation is evenly distributed in the thickness of the active layer.

[0084] Secondly, based on the total scour and sediment depth, the average water depth of the target river section under the current water and sediment conditions can be determined. Specifically, the average water depth of the target river section under the current water and sediment conditions can be obtained by summing the total sedimentation depth (where a positive total sedimentation depth indicates scour and a negative total sedimentation depth indicates sedimentation) with the average water depth under the current conditions (i.e., the average water depth calculated in the previous iteration).

[0085] Finally, the average flow velocity of the target river section under the current water and sediment conditions can be determined based on the average water depth under the current water and sediment conditions. Specifically, as described in the previous embodiment, "usually, for the same target river section, the product of the initial average water depth and the initial average flow velocity is a constant." Based on this, once the average water depth is determined, the flow velocity can also be determined accordingly.

[0086] S12320. For each group of sediment particle sizes, based on the Markov transition probability matrix of the sediment particle size group, using the active layer sediment balance equation group, solve the current water and sediment parameter subset of the sediment particle size group under the current water and sediment conditions.

[0087] The current water-sediment parameter subset includes bed sediment gradation, bed load gradation, and suspended load gradation corresponding to the group of sediment particle sizes.

[0088] Optionally, step S12320 can be implemented in any available manner. For example, the current water and sediment parameter subset of the group of particle sizes under the current water and sediment conditions can be solved using equations (17) to (32).

[0089]

[0090]

[0091] ΔH=∑ΔH (n-1) (32)

[0092] Among them, W l (n-1) W represents the weight of the sediment particles of the lth group after the n-1th iteration; (n-1) represents the total amount of sand in the active layer of bed sand after the n-1th iteration; γ s Indicates sediment bulk density; E (n-1) represents the thickness of the active layer of bed sand after the n-1th iteration; It represents the bed sand gradation of the lth group of particle size sediment obtained after the nth iteration; It represents the bed load gradation of the lth group of particle size sediment obtained after the n-1th iteration; represents the porosity of the bed sand after the n-1th iteration; It represents the amount of bed sediment of group l converted into bed load and suspended sediment after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed sand state to the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the bed sand state to the suspended load state after the n-1th iteration; It represents the sum of the sedimentation amount of the lth group of bed load and the lth group of suspended load after the n-1th iteration; m represents the sedimentation layer thickness coefficient; It represents the bed load gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the suspended sediment gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended matter state to the bed sand state after the n-1th iteration; It represents the amount of bed sand replenished in the first group of particle sizes after the n-1th iteration; ΔH (n-1) represents the total scouring and silting depth obtained after the n-1th iteration; represents the initial value of the riverbed surface sand gradation of the first group of particle size; represents the initial value of the riverbed subsurface sand gradation of the first group of particle size; It represents the bed load gradation of the lth group of particle size sediment obtained after the nth iteration; It represents the suspended sediment gradation of the lth group of particle size sediment obtained after the nth iteration; It represents the probability that the sediment of group l remains in the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended load state to the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the bed load state to the suspended load state after the n-1th iteration; It represents the probability of the lth group of particle size sediment remaining in the suspended state after the n-1th iteration; ΔH (n-1) represents the total scouring depth after the n-1th iteration; ΔH represents the scouring depth of the riverbed after the n-1th iteration.

[0093] It should be noted that the bed sand coarsening process is closely related to the sediment scouring and deposition process. As scouring progresses, the water flow and sediment conditions are dynamically adjusted, which is manifested in the continuous weakening of the hydrodynamic conditions and the continuous coarsening of the bed sand. These two processes restrict each other and are ultimately reflected in the exchange process of different sediment movement forms. Based on this, the above embodiment of the present disclosure introduces the above calculation formulas (17) to (32) to establish a bed sand scouring and coarsening calculation model based on the active layer sand balance principle. The model fully considers the bed sand suspension, bed load and suspended sediment deposition process; that is, the above embodiment of the present disclosure, while considering the bed sand suspension process, also focuses on the sediment deposition process. It makes up for the shortcomings of the existing technology that ignores the bed sand suspension, bed load and suspended sediment deposition process, and presents the response relationship between bed sand scouring and coarsening and the mutual transformation of bed sand, bed load and suspended sediment in a quantifiable way.

[0094] S12330. Determine the current water and sediment parameters of the target river section under current water and sediment conditions based on the water flow parameters and the current water and sediment parameter subset corresponding to each group of particle size sediment.

[0095] As mentioned above, the current water and sediment parameter subset includes the bed sediment gradation, bed load gradation, and suspended load gradation corresponding to the set of particle size sediments. The water flow parameters include average water depth, average flow velocity, and average gradient.

[0096] It should be noted that, due to the iterative execution of steps S1220 to S1230, the "current water and sand conditions" mentioned here refer to the water and sand parameters obtained in the previous round of iterative calculation, which serve as the initial conditions for this round of iterative calculation.

[0097] Specifically, S12330 can be implemented by any available means. For example, the "current water and sediment parameter subset corresponding to each particle size group" can be integrated and statistically analyzed to obtain bed sediment gradation, bed load gradation, and suspended load gradation similar to the format in Table 1. The average water depth and average flow rate can be obtained from the water flow parameters of the current iteration. The average gradient can generally be assumed to remain unchanged for the same target river section and can be directly obtained from the initial water and sediment conditions.

[0098] S1240. Based on the numerical comparison result between the riverbed scour depth and the thickness of the riverbed surface sand, iteratively execute steps S1220 to S1230 until the preset equilibrium condition is met, thereby obtaining the riverbed limit scour depth of the target river section when the preset equilibrium condition is met.

[0099] Wherein, during the iterative process, the current water and sediment parameters obtained by the iterative calculation in the current round are used as the initial conditions for the next round of iterative calculation.

[0100] Optionally, the numerical comparison result between the riverbed scouring depth and the riverbed surface sand thickness may include two cases: specifically, case 1, the riverbed scouring depth is less than the riverbed surface sand thickness; and case 2, the riverbed scouring depth is greater than or equal to the riverbed surface sand thickness.

[0101] Optionally, according to situation one, step S1240 can be implemented as follows: in response to the riverbed scouring depth being less than the riverbed surface sand thickness, continue to use the current water and sand parameters obtained in the current round of iterative calculation as the initial conditions for the next round of iterative calculation, execute steps S1220 to S1230 until the preset equilibrium condition is met, and obtain the riverbed limit scouring depth of the target river section when the preset equilibrium condition is reached.

[0102] Optionally, according to the second scenario, step S1240 may be implemented as follows: in response to the first determination that the riverbed scour depth is greater than or equal to the riverbed surface sediment thickness, using the revised water-sediment parameters as the initial conditions for the next round of iterative calculation, iteratively executing steps S1220 to S1230 until the preset equilibrium condition is satisfied, thereby obtaining the riverbed limit scour depth for the target river section when the preset equilibrium condition is satisfied. The revised water-sediment parameters are obtained by replacing the current sediment gradation in the current water-sediment parameters calculated in the current iterative round with the initial value of the riverbed subsurface sediment gradation.

[0103] What needs to be explained here is that, Figure 7 The “judgment condition ①” is the above-mentioned “numerical comparison result between the riverbed depth and the riverbed surface sand thickness”.

[0104] When the judgment result is case 1, that is, the riverbed depth is less than the thickness of the riverbed surface sand, it means that the riverbed surface sand has not been completely washed away by the water flow and there is still some residual sand. Figure 7 The branch shown as "Surface Bed Sand Replenishment" continues to iterate.

[0105] When the judgment result is case 2, that is, the riverbed depth is greater than or equal to the thickness of the riverbed surface sand, it means that the riverbed surface sand has been completely washed away by the water flow and there is no residue. In this case, the iterative process will enter Figure 7 The branch shown as "Deep Bed Sediment Replenishment" continues the iteration.

[0106] It should be emphasized here that the "switching action" performed based on the "judgment condition ①" is only performed when it is judged for the first time that the riverbed scouring depth is greater than or equal to the thickness of the riverbed surface bed sand. Because in the real physical world, in the scenario of enhanced water flow (that is, the current water flow is greater than the water flow that forms the initial value of the riverbed surface bed sand grading), the scouring of the riverbed bed sand by water flow is an irreversible process. Therefore, there is no situation of switching back to "case one" from "case two". Based on this, as long as it is judged for the first time that the riverbed scouring depth is greater than or equal to the thickness of the riverbed surface bed sand, "the current bed sand grading in the current water and sand parameters calculated in the current iteration round is replaced with the initial value of the riverbed sub-surface bed sand grading" as the initial condition for the next round of iterative calculation, and subsequent iterative processes will all choose Figure 7 The branch shown as "Deep Bed Sand Replenishment" continues.

[0107] To understand the above iterative process, refer to Figure 7 The process of the “method for determining the maximum scour depth of a riverbed” provided in the above embodiment (executing entity is, for example, a cloud computing service platform) is summarized as follows:

[0108] Reference Figure 7 First, input the initial water flow Q1 and sediment conditions. By calculating the Markov sediment transfer probability matrix, the coarsening layer gradation and scouring depth after this scouring can be obtained. The bed sediment gradation at this time is the stable coarsening layer gradation under this flow rate. Based on this bed sediment gradation, gradually increase the hydrodynamic condition Qn. When the calculated scouring depth increases significantly and the coarsening layer gradation becomes significantly coarser, it can be considered that the protective layer formed at Q1 is destroyed and a new coarsening protective layer is formed under the flow rate Qn. The calculated scouring depth and coarsening layer gradation at this time are the extreme scouring conditions under the Qn condition. In addition, during the calculation process, it is necessary to judge the dynamic changes in the scouring depth. When the scouring depth is greater than the thickness of the surface bed sediment, the deep bed sediment gradation must be used to supplement the surface bed sediment accordingly.

[0109] Specifically, the following steps may be included:

[0110] Step 1) obtains the initial water and sediment conditions of the target river section. These initial water and sediment conditions include the initial average water depth, the initial average flow velocity, the initial riverbed surface sediment gradation, the riverbed surface sediment thickness, and the average gradient. The average gradient is a constant value representing the slope of the target river section.

[0111] The first calculation corresponds to the initial state, and the bed sediment gradation corresponding to this initial state can also be called the "initial bed sediment gradation." Since clear water is flowing downstream at this time, the initial water and sediment conditions do not include the bed load gradation and suspended load gradation. However, during the iterative process (i.e., simulating the movement and suspension of sediment caused by water erosion on the riverbed), these two indicators are calculated and generated. That is, starting from the second iteration, the initial conditions of each calculation will include the bed load gradation and suspended load gradation.

[0112] For example, Figure 7 As shown, in the case of the current iteration round n-1, the parameters included in the initial conditions are equal to the current average water depth H (n-1) , current average flow rate U (n-1) 、Current bed sand gradation P (n-1) , average comparison J; also includes the current bed load gradation P b (n-1) , Current suspended load gradation P s (n-1) .

[0113] Step 2) First, the thickness of the bed sand active layer can be determined based on the average water depth in the current water and sand conditions and the initial average water depth in the initial water and sand conditions. For example, after the n-1th iteration, the thickness of the bed sand active layer can be expressed as E (n-1) .

[0114] Then, based on the calculation formulas (1) to (10) in the above embodiment, the Markov transition probability matrix matching each group of sediment particle size can be calculated using the initial water and sediment conditions, for example: Figure 7 The transition probability matrix corresponding to the n-1th iteration is shown

[0115] Afterwards, the sediment scouring thickness, sediment deposition thickness and scouring depth on the active layer of the riverbed of the target river section can be determined according to the calculation formulas (12) to (16) in the above embodiment. For example, after the nth iteration AΔH (n) Finally, the coarsening layer gradation (i.e., bed sediment gradation), bed load gradation, suspended load gradation, average water depth, average flow velocity and other parameters under the current iteration round can be calculated according to the calculation formulas (17) to (32) in the above embodiment.

[0116] Step 3) Execute judgment condition ①ΔH (n)≥ E sur , that is, to determine whether the cumulative scouring depth is greater than or equal to the thickness of the riverbed surface sand.

[0117] When the judgment result is case 1, that is, the riverbed depth is less than the thickness of the riverbed surface sand, it means that the riverbed surface sand has not been completely washed away by the water flow and there is still some residual sand. Figure 7 The branch shown as "Surface Bed Sand Replenishment" continues to iterate.

[0118] When the judgment result is case 2, that is, the riverbed depth is greater than or equal to the thickness of the riverbed surface sand, it means that the riverbed surface sand has been completely washed away by the water flow and there is no residue. In this case, the iterative process will enter Figure 7 The branch shown as "Deep Bed Sediment Replenishment" continues the iteration.

[0119] It should be emphasized here that the "switching action" performed based on the "judgment condition ①" is only performed when it is judged for the first time that the riverbed scouring depth is greater than or equal to the thickness of the riverbed surface bed sand. Because in the real physical world, in the scenario of enhanced water flow (that is, the current water flow is greater than the water flow that forms the initial value of the riverbed surface bed sand grading), the scouring of the riverbed bed sand by water flow is an irreversible process. Therefore, there is no situation of switching back to "case one" from "case two". Based on this, as long as it is judged for the first time that the riverbed scouring depth is greater than or equal to the thickness of the riverbed surface bed sand, "the current bed sand grading in the current water and sand parameters calculated in the current iteration round is replaced with the initial value of the riverbed sub-surface bed sand grading" as the initial condition for the next round of iterative calculation, and subsequent iterative processes will all choose Figure 7 The branch shown as "Deep Bed Sand Replenishment" continues.

[0120] Step 4) Execute judgment condition ②ΔH1 (n) ≈ΔH2 (n) , that is, to determine whether the sediment scouring thickness is close to the sediment deposition thickness (that is, whether a dynamic balance is achieved).

[0121] Optionally, a thickness difference between the sediment scour thickness and the sediment accumulation thickness can be calculated; in response to the thickness difference being less than a preset error threshold, it can be determined that the sediment scour thickness and sediment accumulation thickness of the riverbed have reached dynamic equilibrium. The present disclosure does not limit the specific value or range of the preset error threshold, which can be set according to actual needs.

[0122] Furthermore, if the thickness difference is not less than the preset error threshold, the next round of iteration is executed, i.e., steps 2) to 4 are iteratively executed. Specifically, the "bed sediment gradation, bed load gradation, suspended load gradation, average water depth, average flow velocity, and average gradient" obtained in the current round are used as the initial conditions for the next round of iteration; that is, Figure 7 The "water flow and sediment conditions in this step serve as the initial conditions for the next step" shown.

[0123] If the thickness difference is less than the preset error threshold, the iteration loop is exited. The current cumulative scouring depth is used as the riverbed limit scouring depth of the target river section when the preset equilibrium condition is reached. For example, Figure 7 As shown in the figure, the current iteration round is the nth iteration, and the riverbed limit depth is recorded as ΣΔH.

[0124] At the same time, the current coarsening layer gradation, the current bedload gradation, and the current suspended load gradation can also be used as the coarsening layer gradation, bedload gradation, and suspended load gradation of the target river section when the preset equilibrium condition is reached. Figure 7 , the current iteration round is the nth iteration, and the coarsening level is denoted as P (n) , bed load gradation is recorded as P b (n) And the suspended sediment gradation is recorded as P s (n) .

[0125] S130: Push the riverbed maximum depth to the user terminal.

[0126] The present disclosure does not limit the type of user terminal. For example, it can be a mobile smart terminal or a terminal device dedicated to the field of water conservancy projects. The mobile smart terminal may include, but is not limited to, a smartphone or a tablet computer. Here, because the method provided in the embodiments of the present disclosure is executed on the server side, the obtained riverbed limit impulse is pushed to the user terminal, allowing the user to easily access it in a timely manner, thereby improving the user experience.

[0127] In summary, the method for determining the ultimate scour depth of a riverbed provided by the embodiments of the present disclosure, on the one hand, utilizes the active layer sediment balance principle and the Markov sediment transfer probability matrix to establish an active layer sediment balance model. After summing up each particle size group, the gradation is strictly normalized, which can, to a certain extent, make up for the deficiency of the prior art in ignoring the process of bed sediment suspension, suspension and sedimentation. Among them, the "active layer sediment balance model" can quantify the adjustment of bed sediment gradation under any number of iterations during the riverbed coarsening process, intuitively reflect the mutual constraint mechanism between scour and coarsening, and can characterize the response relationship between riverbed scour and coarsening and the transformation of bed sediment, bed load and suspended load. On the other hand, by calculating the ultimate scour situation in which the riverbed surface protective layer is destroyed and the deep bed sediment participates in the transport, it can reflect the dynamic process of "formation-destruction-reconstruction" of the coarsening protective layer, thereby not only increasing the applicability and reliability of the scheme in natural rivers and / or flume tests, but also improving the calculation accuracy.

[0128] Application scenario examples:

[0129] The following summarizes the method for determining the maximum scour depth of a riverbed in the above embodiment of the present disclosure with reference to specific scenario examples.

[0130] After the Xiangjiaba Hydropower Station was completed and put into operation, the flow of water and sediment downstream changed significantly, the amount of sediment downstream decreased significantly, and the discharge of clear water caused the gravel and sand riverbed downstream of the dam to coarsen until a stable protective layer was formed. After long-term water erosion, the riverbed downstream of Xiangjiaba has been basically stable in recent years. 3 / s, there is basically no obvious scouring and incision in the river channel below this level. However, under the scouring of interannual floods, the existing surface protective layer may be destroyed and the riverbed may be further coarsened.

[0131] In this implementation, the bed sediment gradation data of Xiangjiaba Hydropower Station before dam construction were collected, including the surface bed sediment gradation and the deep bed sediment gradation below 1.0m, such as Figure 8 As shown. Through physical model tests, it was determined that the representative flow rates were 15000m 3 / s、20000m 3 / s and 28200m 3 / s, of which 28200m 3 / s represents a 20-year flood at the Xiangjiaba dam site. The JY16 section, 2.1 km downstream of the Xiangjiaba Hydropower Station, was selected for calculation. The corresponding flow conditions are shown in Table 2. Using the "method for determining the ultimate scour depth of a riverbed" provided in the previous embodiment of this disclosure, the non-steady process of "formation, destruction, and reconstruction" of the riverbed coarsening layer downstream of the Xiangjiaba dam was calculated under the aforementioned three-level flow scour.

[0132] Table 2 Initial water flow conditions

[0133]

[0134] ①When the flushing flow Q=15000m 3 / s, the calculated scour depth is 0.6m, and the obtained coarsening layer gradation is shown in Table 3, which corresponds to the scour flow Q = 15000m 3 / s stable coarsening layer gradation.

[0135] Table 3 Coarsening layer gradation (15000m 3 / s)

[0136]

[0137] ②Use flushing flow Q=20000m 3 / s is scoured on the basis of the gradation in Table 3, and the calculated scour depth is 0.2m, and the scour amplitude is small, indicating that when the scour flow Q = 20000m 3 / s, the coarsening layer remains stable and is not completely destroyed. The coarsening layer gradation corresponding to this situation is shown in Table 4, which is slightly coarsened compared to the gradation shown in Table 3.

[0138] Table 4 Coarsening layer gradation (20000m 3 / s)

[0139]

[0140] ③Use flushing flow Q=28200m 3 / s is scoured on the basis of the gradation in Table 3, and the calculated scour depth is 2.1m. The scour amplitude is very obvious, indicating that when the scour flow Q = 28200m 3 / s erosion caused the riverbed to be severely eroded and cut.

[0141] There are two reasons for this phenomenon: 1) High flow rates destroy the surface coarsening layer, subjecting the bed sand below the surface to the direct effects of high flow rates, resulting in a large amount of sediment transport; 2) As scouring progresses, surface sediment gradually moves downward, and subsurface fine sediment participates in the coarsening process, resulting in a significant increase in the scouring amplitude. The corresponding coarsening layer gradation is shown in Table 5.

[0142] Table 5 Coarsening layer gradation (28200m3 / s)

[0143]

[0144] Based on the calculation of different flushing flow conditions, the gradation of the coarsening layer under different flow levels can be obtained as follows: Figure 9 As shown in Figure 2, the scour depth changes as Figure 10 shown.

[0145] Depend on Figure 9 It can be clearly seen that the coarsening layer is formed from a small flow rate (Q = 15000m 3 / s) scour formation, large flow (Q = 20000m 3 / s, Q=28200m 3 / s) The process of destruction and reconstruction. Figure 10 , at flushing flow Q = 15000m 3 / s, the scouring depth is 0.6m, and a stable coarsening protective layer is formed; when the scouring flow rate Q = 20000m 3 / s acts on the protective layer, the effect of continuing to flush downward is not obvious; at a flushing flow rate of 28200m 3 / s scouring, the protective layer was destroyed, and the fine-grained sediment in the subsurface layer of the riverbed also participated in the scouring and coarsening process, and on this basis was scoured downward again by 2.1m.

[0146] As described above, based on the above specific scenario examples, it is verified that the method for determining the ultimate scouring depth of the riverbed provided by the embodiment of the present disclosure can reflect the dynamic process of "formation-destruction-reconstruction" of the coarsening protective layer by calculating the ultimate scouring conditions of the destruction of the riverbed surface protective layer and the participation of deep bed sand in transport. Therefore, it has applicability and reliability, and can maintain a certain level of calculation accuracy.

[0147] Exemplary devices

[0148] It should be understood that the aforementioned embodiment of the present invention regarding the method for determining the riverbed limit scouring depth can also be similarly applied to the following device for determining the gradation of the riverbed coarsening layer with similar expansion; for the sake of simplicity, it is not described in detail.

[0149] Figure 11 It is a schematic diagram of the structure of an apparatus for determining the gradation of a riverbed coarsening layer provided by an exemplary embodiment of the present disclosure.

[0150] Reference Figure 11 , a device for determining the ultimate scour depth of a riverbed, comprising: a data acquisition unit 110, configured to: acquire initial water and sediment conditions of a target river section; wherein the riverbed of the target river section is a pebble-and-sand riverbed; a riverbed scour depth calculation unit 120, configured to: based on the initial water and sediment conditions, use a preset ultimate scour depth calculation rule to determine the ultimate scour depth of the riverbed of the target river section when a preset equilibrium condition is reached; wherein the preset equilibrium condition is that the sediment scour thickness and sediment deposition thickness of the riverbed reach a dynamic equilibrium; an information push unit 130, configured to: push the ultimate scour depth of the riverbed to a user terminal.

[0151] In summary, the method for determining the ultimate scour depth of a riverbed provided by the embodiments of the present disclosure, on the one hand, utilizes the active layer sediment balance principle and the Markov sediment transfer probability matrix to establish an active layer sediment balance model. After summing up each particle size group, the gradation is strictly normalized, which can, to a certain extent, make up for the deficiency of the prior art in ignoring the process of bed sediment suspension, suspension and sedimentation. Among them, the "active layer sediment balance model" can quantify the adjustment of bed sediment gradation under any number of iterations during the riverbed coarsening process, intuitively reflect the mutual constraint mechanism between scour and coarsening, and can characterize the response relationship between riverbed scour and coarsening and the transformation of bed sediment, bed load and suspended load. On the other hand, by calculating the ultimate scour situation in which the riverbed surface protective layer is destroyed and the deep bed sediment participates in the transport, it can reflect the dynamic process of "formation-destruction-reconstruction" of the coarsening protective layer, thereby not only increasing the applicability and reliability of the scheme in natural rivers and / or flume tests, but also improving the calculation accuracy.

[0152] Exemplary electronic devices

[0153] In addition, an embodiment of the present disclosure also provides an electronic device, including: a memory for storing a computer program; a processor for executing the computer program stored in the memory, and when the computer program is executed, the method for determining the maximum scour depth of the riverbed described in any of the above embodiments of the present disclosure is implemented.

[0154] Figure 12 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed in the present invention. Figure 12 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be either or both of the first device and the second device, or a standalone device independent of them, and the standalone device may communicate with the first device and the second device to receive collected input signals from them.

[0155] like Figure 12 As shown, the electronic device includes one or more processors and a memory. The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the method for determining the riverbed limit depth and / or other desired functions of the various embodiments of the present disclosure described above.

[0156] In one example, the electronic device may further include an input device and an output device, wherein these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown). Furthermore, the input device may include, for example, a keyboard, a mouse, and the like. The output device may output various information to the outside, including determined distance information, direction information, and the like. The output device may include, for example, a display, a speaker, a printer, a communication network, and remote output devices connected thereto.

[0157] Of course, to simplify, Figure 12 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0158] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for determining the maximum depth of the riverbed according to various embodiments of the present disclosure described in the above part of this specification.

[0159] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0160] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the method for determining the maximum depth of a riverbed according to various embodiments of the present disclosure described in the above part of this specification.

[0161] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0162] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk, etc. Various media that can store program codes.

[0163] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0164] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0165] The block diagrams of the devices, devices, equipment, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0166] The methods and apparatus of the present disclosure may be implemented in many ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers recording media that store programs for executing the methods according to the present disclosure.

[0167] It should also be noted that in the apparatus, device, and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0168] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0169] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for determining the ultimate scour depth of a riverbed, characterized in that: The method comprises: Obtain the initial water and sediment conditions of the target river section; Wherein, the riverbed of the target river section is a pebble-sand riverbed; Based on the initial water and sediment conditions, using a preset limit scour depth calculation rule, determining the riverbed limit scour depth of the target river section when the preset equilibrium condition is reached; The preset equilibrium condition is that the thickness of sediment scour and sediment deposition on the riverbed reaches a dynamic equilibrium; The riverbed limit depth is pushed to the user terminal.

2. The method according to claim 1, characterized in that The initial water and sediment conditions include an initial value of average water depth, an initial value of average flow velocity, an initial value of riverbed surface sediment gradation, an initial value of riverbed subsurface sediment gradation, riverbed surface sediment thickness, and an average gradient; The average gradient is a constant value representing the slope of the target river section.

3. The method according to claim 2, characterized in that Based on the initial water and sediment conditions, the preset limit scour depth calculation rule is used to determine the riverbed limit scour depth of the target river section when the preset equilibrium condition is reached, including: Determining the sediment particle size grouping of the riverbed in the target river section based on the initial value of the riverbed surface sediment gradation; Based on the initial value of the average water depth, the initial value of the average flow velocity, the initial value of the riverbed surface sediment gradation, and the average gradient, a preset sediment transfer probability quantification rule is used to determine the Markov transfer probability matrix of each group of sediment particle sizes under the current water and sediment conditions; wherein the Markov transfer probability matrix includes the transfer probabilities corresponding to the three movement forms of the sediment as bed sediment, bed load, and suspended load; Based on the Markov transition probability matrix of each group of sediment particle sizes, and using the active layer sediment balance rule, the current water and sediment parameters of the target river section under the current water and sediment conditions are calculated; wherein the current water and sediment parameters include the current bed sediment gradation, the current bed load gradation, the current suspended load gradation, the current average water depth, the current average flow velocity, the average gradient, and the riverbed scour depth; Based on the numerical comparison result between the riverbed scour depth and the riverbed surface sediment thickness, iteratively executing the steps of determining the Markov transition probability matrix of each group of sediment particle sizes under the current water and sediment conditions to calculating the current water and sediment parameters of the target river section under the current water and sediment conditions until the preset equilibrium condition is satisfied, thereby obtaining the riverbed limit scour depth of the target river section when the preset equilibrium condition is achieved; Wherein, during the iterative process, the current water and sediment parameters obtained by the iterative calculation in the current round are used as the initial conditions for the next round of iterative calculation.

4. The method according to claim 3, characterized in that Based on the numerical comparison result between the riverbed scour depth and the thickness of the riverbed surface sediment, iteratively executing the step of determining the Markov transition probability matrix of each group of sediment particle sizes under the current water and sediment conditions to the step of calculating the riverbed scour depth of the target river section under the current water and sediment conditions, including: In response to the riverbed scour depth being less than the riverbed surface sediment thickness, continuing to use the current water and sediment parameters obtained in the current round of iterative calculation as initial conditions for the next round of iterative calculation, iteratively executing the steps of determining the Markov transition probability matrix of each group of sediment particle sizes under the current water and sediment conditions to calculating the riverbed scour depth of the target river section under the current water and sediment conditions; In response to a first determination that the riverbed scour depth is greater than or equal to the riverbed surface sediment thickness, using the modified water and sediment parameters as initial conditions for a next round of iterative calculation, iteratively executing the steps of determining a Markov transition probability matrix for each group of sediment particle sizes under current water and sediment conditions to calculating the riverbed scour depth of the target river section under current water and sediment conditions; The modified water and sediment parameters are obtained by replacing the current bed sediment gradation in the current water and sediment parameters calculated in the current iteration round with the initial value of the riverbed subsurface bed sediment gradation.

5. The method according to claim 3, characterized in that: Based on the initial value of the average water depth, the initial value of the average flow velocity, the initial value of the riverbed surface sediment gradation, and the average gradient, a preset sediment transfer probability quantification rule is used to determine the Markov transition probability matrix of each group of sediment particle sizes under the current water and sediment conditions, including: The basic sediment transfer probability quantification rule is used to determine the probability of no stopping, starting, suspension and suspension of each group of sediment particle sizes. Based on the immobility probability, starting probability, suspension probability and suspending probability of each group of sediment particle sizes, a predefined sediment transfer probability matrix is ​​used to determine a Markov transfer probability matrix matching each group of sediment particle sizes.

6. The method according to claim 5, characterized in that The basic sediment transfer probability quantification rule is used to determine the non-stop probability, starting probability, suspension probability, and suspension probability of each group of sediment particle sizes, including: Use the following formula to calculate the probability of no stopping, starting, suspension and suspension of each particle size group of sediment; in, Indicates the longitudinal bottom velocity of the water flow; B 4,l represents the sediment suspension condition; ε 0,l represents the probability that the sediment of the lth particle size will not stop under the current water and sand conditions; ε 1,l represents the starting probability of the sediment of group l under the current water and sediment conditions; ε 4,i represents the suspension probability of the lth group of particle size sediment under the current water and sediment conditions; β l represents the probability of suspension of sediment of the first group of particle sizes under the current water and sediment conditions; g represents the acceleration of gravity; R represents the hydraulic radius, which is equal to the average water depth under the current water and sediment conditions by default; J represents the average gradient; u represents the average flow velocity of the target river section under the current water and sediment conditions; u * Indicates friction flow velocity; D l represents the particle size of the first group of sediment; H represents the average water depth of the target river section under the current water and sediment conditions; V b,c,0,l Indicates the stopping flow velocity represented by the bottom velocity of the water flow; V b,c,1,l Indicates the starting flow rate represented by the water bottom velocity; It represents the upper limit of the critical vertical instantaneous velocity for loosening of sediment particles; Indicates the lower limit of the critical vertical instantaneous velocity for loosening of sediment particles; ω 0,l Indicates the starting speed parameter when there is no adhesion of sediment particles and no additional pressure of film water; ω 1,l represents the sediment starting characteristic velocity; ω l represents the sediment settling velocity of the first group of particle size sediment; C represents the calculation parameter, which is 1.2; γ s Indicates the bulk density of sediment particles, with a value of 25970N / m 3 ; γ represents the water flow density, with a value of 9800N / m 3 ; dt represents the integral differential element with respect to time.

7. The method according to claim 5, characterized in that The Markov transition probability matrix matching each group of sediment particle sizes is determined based on the non-stop probability, starting probability, suspension probability, and suspension probability of each group of sediment particle sizes, using a predefined sediment transition probability matrix, including: Based on the non-stopping probability, starting probability, suspension probability, and rising suspension probability of the sediment of each particle size group, respectively calculate the transition probability of the sediment of each particle size group maintaining the bed sand state, the transition probability of the sediment of each particle size group transitioning from the bed sand state to the bed load state, and the transition probability of the sediment of each particle size group transitioning from the bed load state to the bed sand state, the transition probability of maintaining the bed load state, and the transition probability of the sediment of each particle size group transitioning from the bed load state to the bed sand state, as well as the transition probability of the sediment of each particle size group transitioning from the suspended load state to the bed sand state, the transition probability of the sediment of each particle size group transitioning from the suspended load state to the bed sand state, the transition probability of the sediment of each particle size group transitioning from the suspended load state to the bed load state, and the transition probability of the sediment of each particle size group remaining in the suspended load state; Substitute the transition probability of each group of particle size sediments maintaining the bed sand state, the transition probability of transferring from the bed sand state to the bed load state, and the transition probability of transferring from the bed sand state to the suspended load state, the transition probability of each group of particle size sediments transferring from the bed load state to the bed sand state, the transition probability of maintaining the bed load state, and the transition probability of each group of particle size sediments transferring from the suspended load state to the bed sand state, the transition probability of transferring from the suspended load state to the bed sand state, and the transition probability of each group of particle size sediments maintaining the suspended load state into the predefined sediment transition probability matrix to obtain the Markov transition probability matrix matching each group of particle size sediments as shown in the following calculation formula Wherein, the superscript n represents the number of iterations; represents the probability of the lth group of particle size sediments remaining in the bed sand state at the nth iteration; represents the probability of the lth group of particle size sediments transferring from the bed sand state to the bed load state at the nth iteration; It represents the probability of the lth group of particle size sediments transferring from the bed sand state to the suspended load state at the nth iteration; represents the probability of the lth group of particle size sediments transitioning from the bed load state to the bed sand state at the nth iteration; It represents the probability of the lth group of particle size sediments remaining in the bed load state at the nth iteration; It represents the probability of the lth group of sediment particles transferring from the bed load state to the suspended load state at the nth iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended matter state to the bed sand state at the nth iteration; It represents the probability of the lth group of sediment particles transferring from the suspended load state to the bed load state at the nth iteration; It represents the probability that the sediment of the lth particle size group remains in the suspended state at the nth iteration.

8. The method according to claim 3, characterized in that: The current water and sediment parameters of the target river section under the current water and sediment conditions are calculated based on the Markov transition probability matrix of each group of particle size sediments and the active layer sediment balance rule, including: Based on the Markov transition probability matrix of each group of sediment particle sizes and the current water and sediment conditions, and using a preset scouring and silting depth calculation rule, the flow parameters of the target river section under the current water and sediment conditions are determined; wherein the flow parameters include average water depth, average flow velocity, and average gradient; For each particle size group, based on the Markov transition probability matrix of the particle size group, the active layer sediment balance equations are used to solve the current water and sediment parameter subset of the particle size group under the current water and sediment conditions; wherein the current water and sediment parameter subset includes the bed sediment gradation, bed load gradation, and suspended load gradation corresponding to the particle size group; Based on the water flow parameters and the current water and sediment parameter subset corresponding to each group of particle size sediment, the current water and sediment parameters of the target river section under the current water and sediment conditions are determined.

9. The method according to claim 8, characterized in that The method of determining the flow parameters of the target river section under the current water and sediment conditions using a preset scouring and silting depth calculation rule based on the Markov transition probability matrix of each group of sediment particle sizes and the current water and sediment conditions includes: Use the following formula to calculate the total scouring and silting depth under current water and sediment conditions; Based on the total scouring and silting depth, determining the average water depth of the target river section under current water and sediment conditions; determining an average flow velocity of the target river section under the current water and sediment conditions based on the average water depth under the current water and sediment conditions; Where l represents the sediment particle size group number, which is a positive integer; max represents the maximum number of the sediment particle size group, which is a positive integer; ΔH (n-1) represents the total scouring and silting depth obtained after the n-1th iteration; It represents the bed sand gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed sand state to the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the bed sand state to the suspended load state after the n-1th iteration; It represents the bed load gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the suspended sediment gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended matter state to the bed sand state after the n-1th iteration; E (n-1) It represents the thickness of the active layer of bed sand after the n-1th iteration; m represents the thickness coefficient of the siltation layer.

10. The method according to claim 8, characterized in that: For each group of sediment particle sizes, based on the Markov transition probability matrix of the sediment particle size group, the active layer sediment balance equation group is used to solve the current water and sediment parameter subset of the sediment particle size group under the current water and sediment conditions, including: Use the following calculation formula to solve the current water and sediment parameter subset of this group of particle size sediment under the current water and sediment conditions; ΔH=SΔH (n-1) in, W represents the weight of the sediment particles of the lth group after the n-1th iteration; (n-1) represents the total amount of sand in the active layer of bed sand after the n-1th iteration; γ s Indicates sediment bulk density; E (n-1) represents the thickness of the active layer of bed sand after the n-1th iteration; It represents the bed sand gradation of the lth group of particle size sediment obtained after the nth iteration; It represents the bed load gradation of the lth group of particle size sediment obtained after the n-1th iteration; represents the porosity of the bed sand after the n-1th iteration; It represents the amount of bed sediment of group l converted into bed load and suspended sediment after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed sand state to the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the bed sand state to the suspended load state after the n-1th iteration; It represents the sum of the sedimentation amount of the lth group of bed load and the lth group of suspended load after the n-1th iteration; m represents the sedimentation layer thickness coefficient; It represents the bed load gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the suspended sediment gradation of the lth group of particle size sediment obtained after the n-1th iteration; It represents the probability of the lth group of particle size sediments transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended matter state to the bed sand state after the n-1th iteration; It represents the amount of bed sand replenished in the first group of particle sizes after the n-1th iteration; ΔH (n-1) represents the total scouring and silting depth obtained after the n-1th iteration; represents the initial value of the riverbed surface sand gradation of the first group of particle size; represents the initial value of the riverbed subsurface sand gradation of the first group of particle size; It represents the bed load gradation of the lth group of particle size sediment obtained after the nth iteration; It represents the suspended sediment gradation of the lth group of particle size sediment obtained after the nth iteration; It represents the probability that the sediment of group l remains in the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the suspended load state to the bed load state after the n-1th iteration; It represents the probability of the lth group of particle size sediments transferring from the bed load state to the suspended load state after the n-1th iteration; It represents the probability of the lth group of particle size sediment remaining in the suspended state after the n-1th iteration; ΔH (n-1) represents the total scouring depth after the n-1th iteration; ΔH represents the scouring depth of the riverbed after the n-1th iteration.

Images