Method and apparatus for determining gradation of riverbed coarsening levels

Abstract

The embodiment of the invention provides a method and device for determining riverbed coarsening level assignment, electronic equipment, a computer readable storage medium and a computer program product. The method for determining the layering of the riverbed coarsening layers comprises the following steps: acquiring an initial water and sediment condition of a target river reach; wherein the riverbed of the target river reach is a sand-doped pebble riverbed; based on the initial water and sediment conditions, by means of a preset riverbed coarsening layer gradation calculation rule, riverbed coarsening layer gradation when the target river reach reaches the limit scouring condition is determined; wherein the limit scouring condition is that the sediment scouring thickness and the sediment deposition thickness of the riverbed reach dynamic balance; and pushing the riverbed coarsening level to a user terminal. By means of the method, the response relation between riverbed scouring coarsening and conversion of bed sand, bed load and suspended load can be represented, and the calculation precision of the natural wide grading riverbed is improved.

Description

Technical Field

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

[0002] The composition of bed sediment gradation in the riverbed downstream of a dam is a key indicator of the degree of riverbed coarsening, and is of engineering significance for maintaining navigable depths, ensuring the safety of water intake projects, and assessing river channel stability. After the reservoir was completed and put into operation, the outflow was nearly clear, providing dynamic conditions for scouring the surface sediment. However, the sediment gradation in a pebble-sand riverbed is broad, with a wide range of particle sizes. The mechanical and kinematic properties of different particle sizes vary significantly, making the regeneration of the pebble-sand riverbed downstream of the dam complex.

[0003] Regarding the calculation of the gradation of the gravel-sand riverbed downstream of the dam, existing technologies primarily employ proportional amplification and probabilistic statistical methods. However, the proportional amplification method can lead to the downward movement of the bed sand. While the probabilistic statistical method offers reasonable accuracy when applied to indoor flume tests, it has limitations when applied to natural rivers. Summary of the Invention

[0004] 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 the gradation of a riverbed coarsening layer.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for determining a riverbed coarsening layer gradation 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 riverbed coarsening layer gradation calculation rule, determining the riverbed coarsening layer gradation of the target river section when the extreme scouring condition is reached; wherein the extreme scouring condition is that the sediment scouring thickness and sediment deposition thickness of the riverbed reach a dynamic balance; and pushing the riverbed coarsening layer gradation to a user terminal.

[0006] According to a second aspect of an embodiment of the present disclosure, a device for determining the gradation of a riverbed coarsening layer 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 coarsening layer gradation calculation unit, configured to: based on the initial water and sediment conditions, use a preset riverbed coarsening layer gradation calculation rule to determine the riverbed coarsening layer gradation of the target river section when the extreme scouring condition is reached; wherein the extreme scouring condition is that the sediment scouring thickness and sediment deposition thickness of the riverbed reach a dynamic balance; an information push unit, configured to: push the riverbed coarsening layer gradation to a user terminal.

[0007] 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 grading of a riverbed coarsening layer as described in the present disclosure.

[0008] 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 gradation of a riverbed coarsening layer described in the present disclosure.

[0009] 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 gradation of a riverbed coarsening layer described in the present disclosure is implemented.

[0010] In summary, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure, on the one hand, uses the active layer sediment balance principle and the Markov sediment transfer probability matrix to establish an active layer sediment balance model. After summing the particle size groups, the gradation is strictly normalized, which can to a certain extent make up for the deficiency of the existing technology that ignores the process of bed sand suspension, suspension and sedimentation. Among them, the "active layer sediment balance model" can quantify the adjustment of bed sand gradation under any number of iterative steps during the riverbed coarsening process, intuitively reflect the mutual constraint mechanism between scouring and coarsening, and can characterize the response relationship between riverbed scouring and coarsening and the transformation of bed sand, bed load and suspended load. On the other hand, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure, by considering the process of bed sand suspension, bed load sedimentation and suspended load sedimentation, can increase the applicability and reliability of the bed sand coarsening model in natural wide-graded riverbeds and indoor flume tests, thereby improving the calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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.

[0012] Figure 1 is a flow chart of a method for determining the gradation of a riverbed coarsening layer provided by an exemplary embodiment of the present disclosure;

[0013] 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;

[0014] Figure 3 This disclosure Figure 1 An exemplary flow chart of a method for determining the gradation of a riverbed coarsening layer provided in an embodiment;

[0015] Figure 4 This disclosure Figure 1 Another exemplary flow chart of a method for determining the gradation of a riverbed coarsening layer provided in an embodiment;

[0016] Figure 5 This disclosure Figure 1 Another exemplary flow chart of a method for determining the gradation of a riverbed coarsening layer provided in an embodiment;

[0017] Figure 6 This 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;

[0018] Figure 7 is a flow chart of a method for determining the gradation of a riverbed coarsening layer provided by another exemplary embodiment of the present disclosure;

[0019] Figure 8 1 is a schematic diagram of a comparison curve of a calculated coarsening layer gradation calculated using the method for determining the riverbed coarsening layer gradation provided in an exemplary scenario of the present disclosure, and a measured coarsening layer gradation and an initial bed sand gradation;

[0020] Figure 9 1 is a schematic diagram of a comparison curve of a calculated coarsening layer gradation calculated using the method for determining the riverbed coarsening layer gradation provided in an embodiment of the present disclosure, and a measured coarsening layer gradation and an initial bed sand gradation in another exemplary scenario of the present disclosure;

[0021] Figure 101 is a schematic structural diagram of an apparatus for determining the gradation of a riverbed coarsening layer provided by an exemplary embodiment of the present disclosure;

[0022] Figure 11 It is a structural diagram of an application embodiment of the electronic device disclosed in the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Summary of the invention concept:

[0035] After repeated research, the inventors of the present disclosure found that the causes of the problems in the prior art are:

[0036] First, when applying the scaling-up method, it is usually assumed that there is a critical starting particle size D under certain water flow conditions. c , making it greater than D c The coarse particles stay on the bed surface during the coarsening process, while those smaller than D c The fine particles are completely or partially washed down. However, its application method does not take into account the pulsation effect of water flow and sediment, which leads to the critical starting particle size D c Larger than the maximum particle size D of bed sand max When the bed sand is washed down,

[0037] Second, the probabilistic statistical method considers the randomness of sediment movement, calculates the probability of sediment initiation at each level, and combines the active layer sediment balance principle to calculate the changes in bed sediment gradation, reflecting the mutual constraints between scour and coarsening. However, because this method ignores the processes of bed sediment suspension, bed load deposition, and suspended load deposition, its calculation accuracy is acceptable when applied to indoor flume tests, but it has limitations when applied to natural rivers.

[0038] In view of this, the inventors of the present disclosure consider providing a scheme for determining the gradation of the riverbed coarsening layer. In the process of calculating the gradation of the riverbed coarsening layer, the scheme will take into account the pulsating effect of water flow and sediment, and at the same time take into account the factors of bed sand suspension, bed load sedimentation and suspended load sedimentation, thereby solving the technical problems existing in the prior art to a certain extent.

[0039] The following combination Figures 1 to 11 The embodiment describes the implementation details of the solution for determining the gradation of the riverbed coarsening layer disclosed in the present invention.

[0040] Exemplary Methods

[0041] Figure 1 The present invention provides a flow chart of a method for determining the gradation of a riverbed coarsening layer, according to an exemplary embodiment of the present invention. The method can be executed on a server, which may include but is not limited to a server or a cloud computing platform.

[0042] Specifically, refer to Figure 1 The method for determining the gradation of the riverbed coarsening layer includes:

[0043] S110. Obtaining initial water and sediment conditions of the target river section.

[0044] 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).

[0045] 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.

[0046] 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 bed sediment grading, and an average gradient. The average gradient is a fixed value determined based on the target river section; this indicator represents the slope of the river surface. The larger the value, the greater the slope and the faster the water flow rate. The physical meaning of the bed sediment grading refers to: the particle size distribution characteristics of the sediment particles on the surface of the riverbed and their spatial combination patterns. For details, please refer to the diagrams and figures of the specific scenario examples below for a better understanding, which will not be described here.

[0047] It should be noted that, usually for the same target river section, the product of the initial value of the average water depth and the initial value of the average flow velocity is a constant, and the subsequent embodiments involve determining the flow velocity after determining the water depth, which is based on this principle.

[0048] S120 , based on the initial water and sediment conditions, using a preset riverbed coarsening layer gradation calculation rule, determining the riverbed coarsening layer gradation of the target river section when the extreme scour condition is reached.

[0049] The extreme scouring condition is that the sediment scouring thickness and sediment deposition thickness of the riverbed reach a dynamic balance.

[0050] As an optional embodiment, refer to Figure 3 , step S120 includes the following steps:

[0051] S1210: Determine the sediment particle size grouping of the riverbed in the target river section based on the initial value of the bed sediment gradation.

[0052] As an optional example, since the initial bed sediment gradation values ​​for the target river section can roughly reflect the riverbed sediment particle size distribution, the particle size groups in the initial bed sediment gradation values ​​can be used as the riverbed sediment particle size groups for the target river section. For ease of understanding, the initial bed sediment gradation values ​​can be exemplarily presented in Table 1.

[0053] Table 1 Example of initial values ​​of bed sand gradation

[0054]

[0055] S1220. Based on the initial value of the average water depth, the initial value of the average flow velocity, the initial value of the bed 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.

[0056] 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.

[0057] 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: jk (P jk represents the probability of transitioning from form j to form k; j = k = 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; 33 It represents the probability that sediment remains in a suspended state.

[0058] Furthermore, the sediment transfer probability matrix can be defined as follows:

[0059]

[0060] Where i 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 j to form k in the nth iteration, for example It represents the probability that sediment is converted from bed sand to bed load in the nth iteration.

[0061] 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.

[0062] As an alternative example, refer to Figure 4 , step S1220 may include the following steps:

[0063] 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.

[0064] Optionally, step S12210 may be implemented in any feasible manner. For example, the following calculation formulas (1) to (13) 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.

[0065]

[0066] V b,c,0,i ≈0.916ω 0,i (6)

[0067]

[0068] Among them, ε 0,i represents the probability that the sediment of the i-th particle size group will not stop under the current water and sand conditions; ε 1,i represents the starting probability of the sediment of group i under the current water and sediment conditions; ε 4,i represents the suspension probability of the sediment of group i under the current water and sediment conditions; β i represents the probability of suspension of sediment of the i-th group of particle size 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 i represents the particle size of the sediment of group i; H represents the average water depth of the target river section under the current water and sediment conditions; V b,c,0,i Indicates the stopping flow velocity represented by the bottom velocity of the water flow; V b,,c,1,i 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; ω0,i Indicates the starting speed parameter when there is no adhesion of sediment particles and no additional pressure of film water; ω 1,i represents the sediment starting characteristic velocity; ε 51,i represents the calculation parameter; ε 52,i Indicates calculation parameters; w i represents the sediment settling velocity of the i-th 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 integration variable.

[0069] 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.

[0070] Optionally, step S12220 can be implemented in any feasible manner. For example, in the first step, based on the non-stopping probability, starting probability, suspension probability, and suspension-rising probability of each group of sediment particle sizes, the transition probability of each group of sediment particle sizes remaining in the bed sand state, the transition probability of the bed sand state transitioning to the bed load state, and the transition probability of the bed sand state transitioning to the suspended load state can be calculated; the transition probability of each group of sediment particle sizes 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 bed load state transitioning to the suspended load state can be calculated; and the transition probability of each group of sediment particle sizes transitioning 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 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 the suspended load state to the suspended load state can be calculated. 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 transferring from the bed load state to the suspended load state, as well as 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 load state, and the transition probability of 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 (14):

[0071]

[0072] Wherein, the superscript n represents the number of iterations. In addition, represents the transition probability of the sediment of group i maintaining the bed sand state at the nth iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the bed load state at the n-th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the suspended load state at the n-th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed load state to the bed sand state at the n-th iteration; It represents the transfer probability of the sediment of group i maintaining the bed load state at the nth iteration; It represents the probability of the i-th group of sediment particles transferring from the bed load state to the suspended load state at the n-th iteration; represents the probability of the i-th group of sediment particles transitioning from the suspended matter state to the bed sediment state at the n-th iteration; It represents the probability of the i-th group of sediment particles transferring from the suspended load state to the bed load state at the n-th iteration; It represents the probability that the sediment of the i-th particle size group remains in the suspended state at the n-th 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 (15) to (19).

[0081]

[0082] Where i represents the sediment particle size grouping number, which is a positive integer; max represents the maximum number of the sediment particle size grouping, 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 i-th group of particle size sediment obtained after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the bed load state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the suspended load state after the n-1th iteration; It represents the bed load gradation of the sediment of group i obtained after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the suspended sediment gradation of the i-th group of particle size sediment obtained after the n-1th iteration; represents the probability of the i-th group of particle size sediments transferring from the suspended matter state to the bed sand state after the n-1th iteration; E (n-1) represents the thickness of the active layer of bed sand after the n-1th iteration; E (n-1) It is determined based on the average water depth in the current water and sand conditions and the initial value of the average water depth in the initial water and sand conditions; m represents the silt layer thickness coefficient.

[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 original bed sand.

[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 size sediment under the current water and sediment conditions can be solved using equations (20) to (31);

[0089]

[0090]

[0091] Among them, W i represents the weight of sediment particles of group i after the n-1th iteration; γ s Indicates sediment bulk density; E (n -1) represents the thickness of the bed sand active layer after the n-1th iteration; L represents the length of the bed sand active layer after the n-1th iteration; B represents the width of the bed sand active layer after the n-1th iteration; It represents the bed sand gradation of the i-th group of particle size sediment obtained after the n-1th iteration; G represents the bed sand gradation of the i-th group of particle size sediment obtained after the n-th iteration; i It represents the amount of bed sediment of group i converted into bed load and suspended sediment after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the bed load state after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the suspended load state after the n-1th iteration; S i It represents the sum of the sedimentation of the bed load of the i-th particle size group and the sedimentation of the suspended load of the i-th particle size group after the n-1th iteration; It represents the bed load gradation of the sediment of group i obtained after the n-1th iteration; It represents the suspended sediment gradation of the i-th group of particle size sediment obtained after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transitioning from the suspended matter state to the bed sand state after the n-1th iteration; represents the amount of bed sand replenished in the i-th particle size group 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 bed sand gradation of the i-th group of particle size; It represents the bed load gradation of the sediment of group i obtained after the nth iteration; It represents the suspended sediment gradation of the i-th group of particle size sediment obtained after the n-th iteration; It represents the probability that the sediment of group i remains in the bed load state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transitioning from the suspended load state to the bed load state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transferring from the bed load state to the suspended load state after the n-1th iteration; It represents the probability that the sediment of the i-th particle size group remains in the suspended state after the n-1th iteration.

[0092] It should be noted that, since scouring and coarsening are mutually constrained processes, as scouring proceeds, the hydrodynamic conditions gradually weaken, and the sediment particles that have started to move may be converted into static bed sand. Therefore, the above embodiment of the present disclosure introduces the above calculation formulas (19) to (30) 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] S1240. Iteratively execute steps S1220 to S1230 until the extreme scour condition is met, and obtain the riverbed coarsening layer gradation of the target river section when the extreme scour condition is met.

[0098] In the iterative process, the current water and sediment parameters obtained in this round of iteration are used as the initial conditions for the next round of iteration.

[0099] To understand the above iterative process, refer to Figure 7 The process of the “method for determining the gradation of the riverbed coarsening layer” provided in the above embodiment (executing entity is, for example, a cloud computing service platform) is summarized as follows:

[0100] Step 1) can obtain the initial water and sediment conditions of the target river section. The initial water and sediment conditions include the initial average water depth, the initial average flow velocity, the initial bed sediment gradation, and the average gradient; the average gradient is a fixed value determined based on the target river section.

[0101] Step 2) 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) .

[0102] Then, based on the calculation formulas (1) to (13) in the above embodiment, the initial water and sediment conditions can be used to calculate the Markov transition probability matrix matching each group of sediment particle sizes, for example: Figure 7 The transition probability matrix corresponding to the n-1th iteration is shown

[0103] Step 3) The sediment scouring thickness and sediment deposition thickness on the active layer of the riverbed of the target river section can be determined according to the calculation formulas (14) to (18) in the above embodiment. For example, the sediment scouring thickness after the n-1th iteration is recorded as The thickness of sediment deposition is recorded as

[0104] Step 4) can calculate the scour depth, coarsening layer gradation (i.e., bed sand gradation), bed load gradation, and suspended load gradation in the current iteration according to the calculation formulas (19) to (30) in the above embodiment. For example, referring to Figure 7 The scour depth when the current iteration is the nth iteration is recorded as H (n) , the coarsening level is denoted as P (n) (i.e., bed sand gradation), bed load gradation is denoted as P b (n) And the suspended sediment gradation is recorded as P s (n) .

[0105] Step 5) Determine whether the scour thickness and the sedimentation thickness on the active layer of the riverbed of the target river section are sufficiently close to each other in the current iteration round (ie, whether dynamic equilibrium is achieved).

[0106] 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.

[0107] 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 5 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.

[0108] If the thickness difference is less than the preset error threshold, the iteration loop is exited. The current bed sand gradation is used as the riverbed coarsening layer gradation of the target river section when the extreme scour condition is reached. For example, Figure 7 As shown, the current iteration round is the nth iteration and the coarsening level is denoted as P( n) .

[0109] At the same time, the accumulated value of the current bed load gradation, the current suspended load gradation, and the average water depth can also be used as the bed load gradation, suspended load gradation, and total scouring depth of the target river section when the extreme scouring condition is reached. Figure 7 The bed load gradation in the case of the current iteration round being the nth iteration is denoted as P b (n) The suspended sediment gradation in the case of the current iteration round being the nth iteration is denoted as P s(n) ; The total scour depth when the current iteration round is the nth iteration is recorded as ΣH.

[0110] S130: Push the riverbed coarsening layer gradation to the user terminal.

[0111] This disclosure does not limit the type of user terminal. For example, it can be a mobile smart terminal or a terminal device specifically used in the field of water conservancy projects. The mobile smart terminal may include, but is not limited to, a smartphone or tablet computer. Since the method provided in the embodiments of this disclosure is executed on the server side, the obtained riverbed coarsening layer gradation is pushed to the user terminal, allowing the user to access it in a timely manner, thereby improving the user experience.

[0112] In summary, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure, on the one hand, uses the active layer sediment balance principle and the Markov sediment transfer probability matrix to establish an active layer sediment balance model. After summing the particle size groups, the gradation is strictly normalized, which can to a certain extent make up for the deficiency of the existing technology that ignores the process of bed sand suspension, suspension and sedimentation. Among them, the "active layer sediment balance model" can quantify the adjustment of bed sand gradation under any number of iterative steps during the riverbed coarsening process, intuitively reflect the mutual constraint mechanism between scouring and coarsening, and can characterize the response relationship between riverbed scouring and coarsening and the transformation of bed sand, bed load and suspended load. On the other hand, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure, by considering the process of bed sand suspension, bed load sedimentation and suspended load sedimentation, can increase the applicability and reliability of the bed sand coarsening model in natural wide-graded riverbeds and indoor water tank tests, thereby improving the calculation accuracy of the model.

[0113] Application scenario examples:

[0114] The following summarizes the method for determining the gradation of the riverbed coarsening layer in the above embodiment of the present disclosure with reference to specific scenario examples.

[0115] Scenario Example 1

[0116] Scenario 1 involves measuring the coarsening of the bed sediment in the Huangjiagang-Guanghua section of the Han River before and after the completion of the Danjiangkou Reservoir. Specifically, the natural bed sediment gradation data for this section in 1960 are shown in Table 2. By 1978, a stable protective layer of coarsened bed sediment had formed in the Huangjiagang-Guanghua section, and its gradation information is shown in Table 3.

[0117] Table 2 Measured bed sand gradation in the Huanghua section in 1960

[0118]

[0119] Table 3 Measured bed sand gradation in the Huanghua section in 1978

[0120]

[0121] Select the maximum flow rate of 10000m in the measured data 3 / s is used as the test flow (occurring no more than 5 days per year on average). In 1960, the corresponding average velocity was 2.64 m / s, the average water depth was 2.84 m, and the average gradient J was 3.51×10 -4 The average gradient J remains unchanged during the calculation process. Based on the above initial conditions, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure is used to perform iterative calculations, and the following can be obtained: Figure 8 The calculation results are shown.

[0122] Reference Figure 8 It can be seen that the calculated coarsening layer gradation is close to the measured coarsening layer gradation. Overall, the calculated value is consistent with the measured value. Comparing the characteristic particle size D of the bed sand gradation 35 、D 50 、D 84 , average particle size D m The difference between the calculated value and the measured value is shown in Table 4. As can be seen from Table 4, in each particle size range, the results calculated using the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure are relatively close to the measured values.

[0123] From this we can see that Figure 8 Table 4 and Table 4 comprehensively reflect the reliability of the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure.

[0124] Table 4 Comparison of characteristic particle size values ​​(measured data)

[0125]

[0126] Scenario Example 2

[0127] Scenario 2 involves using flume test data from related technologies to verify the "method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure." A total of 17 groups of tests were conducted on 8 different bed sand gradations, and the results of four groups of tests, Run5C, Run6C, Run7C, and Run8C, were selected to verify the calculation accuracy. The calculation results are as follows: Figure 9 shown.

[0128] Reference Figure 9 It can be seen that for the four groups of test conditions, the coarsening layer gradation results calculated by the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure are relatively close to the measured values.

[0129] Table 5 shows the calculation results of the characteristic particle size. It can be seen that the calculated value is very close to the measured value, which further proves the reliability of the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure.

[0130] Table 5 Comparison of characteristic particle size values ​​(water tank test data)

[0131]

[0132] Exemplary devices

[0133] It should be understood that the method for determining the gradation of the riverbed coarsening layer in the aforementioned embodiment of this document can also be similarly applied to the following apparatus for determining the gradation of the riverbed coarsening layer with similar extension; for the sake of simplicity, it is not described in detail.

[0134] Figure 10 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.

[0135] Reference Figure 10 The device includes: a data acquisition unit 110, configured to: acquire the initial water and sediment conditions of the target river section; wherein the riverbed of the target river section is a pebble-and-sand riverbed; a riverbed coarsening layer gradation calculation unit 120, configured to: based on the initial water and sediment conditions, use a preset riverbed coarsening layer gradation calculation rule to determine the riverbed coarsening layer gradation of the target river section when the extreme scouring condition is reached; wherein the extreme scouring condition is that the sediment scouring thickness and sediment deposition thickness of the riverbed reach a dynamic balance; an information push unit 130, configured to: push the riverbed coarsening layer gradation to a user terminal.

[0136] In summary, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure, on the one hand, uses the active layer sediment balance principle and the Markov sediment transfer probability matrix to establish an active layer sediment balance model. After summing the particle size groups, the gradation is strictly normalized, which can to a certain extent make up for the deficiency of the existing technology that ignores the process of bed sand suspension, suspension and sedimentation. Among them, the "active layer sediment balance model" can quantify the adjustment of bed sand gradation under any number of iterative steps during the riverbed coarsening process, intuitively reflect the mutual constraint mechanism between scouring and coarsening, and can characterize the response relationship between riverbed scouring and coarsening and the transformation of bed sand, bed load and suspended load. On the other hand, the method for determining the gradation of the riverbed coarsening layer provided by the embodiment of the present disclosure, by considering the process of bed sand suspension, bed load sedimentation and suspended load sedimentation, can increase the applicability and reliability of the bed sand coarsening model in natural wide-graded riverbeds and indoor water tank tests, thereby improving the calculation accuracy of the model.

[0137] Exemplary electronic devices

[0138] 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 grading of the riverbed coarsening layer described in any of the above embodiments of the present disclosure is implemented.

[0139] Figure 11 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed in the present invention. Figure 11 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.

[0140] like Figure 11 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 a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a 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 coarsening layer grading of the various embodiments of the present disclosure described above and / or other desired functions.

[0141] 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.

[0142] Of course, to simplify, Figure 11 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.

[0143] 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 grading of the riverbed coarsening layer according to various embodiments of the present disclosure described in the above part of this specification.

[0144] 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.

[0145] 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, causes the processor to execute the steps of the method for determining the grading of the riverbed coarsening layer according to various embodiments of the present disclosure described in the above part of this specification.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 gradation of a riverbed coarsening layer, characterized in that: 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-sand riverbed; Based on the initial water and sediment conditions, using a preset riverbed coarsening layer gradation calculation rule, determining the riverbed coarsening layer gradation of the target river section when the ultimate scouring condition is reached; wherein the ultimate scouring condition is that the sediment scouring thickness and sediment deposition thickness of the riverbed reach a dynamic balance; The riverbed coarsening layer gradation is pushed to the user terminal.

2. The method according to claim 1, characterized in that The initial water and sediment conditions include the initial value of average water depth, the initial value of average flow velocity, the initial value of bed sediment gradation, and the average gradient; The average gradient is a constant value determined based on the target river section.

3. The method according to claim 2, characterized in that The method of determining the riverbed coarsening layer gradation of the target river section when the target river section reaches the extreme scour condition based on the initial water and sediment conditions and using a preset riverbed coarsening layer gradation calculation rule comprises: Determining the sediment particle size grouping of the riverbed in the target river section based on the initial value of the bed sediment gradation; Based on the initial average water depth, the initial average flow velocity, the initial bed sediment gradation, and the average gradient, a preset sediment transfer probability quantification rule is used to determine the Markov transfer probability matrix for 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 particle size sediments, 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, and the average gradient; 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 current water and sediment parameters of the target river section under the current water and sediment conditions until the limit scour condition is met, thereby obtaining the riverbed coarsening layer gradation of the target river section under the condition that the limit scour condition is met; In the iterative process, the current water and sediment parameters obtained in this round of iteration are used as the initial conditions for the next round of iteration.

4. 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 bed sand 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, 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.

5. The method according to claim 4, 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; V b,c,0,i ≈0.916ω 0,i Among them, ε 0,i represents the probability that the sediment of the i-th particle size group will not stop under the current water and sand conditions; ε 1,i represents the starting probability of the sediment of group i under the current water and sediment conditions; ε 4,i represents the suspension probability of the sediment of group i under the current water and sediment conditions; β i represents the probability of suspension of sediment of the i-th group of particle size 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 i represents the particle size of the sediment of group i; H represents the average water depth of the target river section under the current water and sediment conditions; V b,c,0,i Indicates the stopping flow velocity represented by the bottom velocity of the water flow; V b,,c,1,i 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,i Indicates the starting speed parameter when there is no adhesion of sediment particles and no additional pressure of film water; ω 1,i represents the sediment starting characteristic velocity; ε 51,i represents the calculation parameter; ε 52,i Indicates calculation parameters; w i represents the sediment settling velocity of the i-th 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 integration variable.

6. The method according to claim 4, 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 A matching each group of particle size sediments as shown in the following calculation formula i (n) ; Wherein, the superscript n represents the number of iterations; represents the transition probability of the sediment of group i maintaining the bed sand state at the nth iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the bed load state at the n-th iteration; β i (n) represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the suspended load state at the n-th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed load state to the bed sand state at the n-th iteration; It represents the transfer probability of the sediment of group i maintaining the bed load state at the nth iteration; It represents the probability of the i-th group of sediment particles transferring from the bed load state to the suspended load state at the n-th iteration; represents the probability of the i-th group of sediment particles transitioning from the suspended matter state to the bed sediment state at the n-th iteration; It represents the probability of the i-th group of sediment particles transferring from the suspended load state to the bed load state at the n-th iteration; It represents the probability that the sediment of the i-th particle size group remains in the suspended state at the n-th iteration.

7. 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.

8. The method according to claim 7, 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 i represents the sediment particle size grouping number, which is a positive integer; max represents the maximum number of the sediment particle size grouping, 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 i-th group of particle size sediment obtained after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the bed load state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the suspended load state after the n-1th iteration; It represents the bed load gradation of the sediment of group i obtained after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed load state to the bed sand state after the n-1th iteration; It represents the suspended sediment gradation of the i-th group of particle size sediment obtained after the n-1th iteration; represents the probability of the i-th group of particle size sediments transferring from the suspended matter state to the bed sand state after the n-1th iteration; E (n-1) represents the thickness of the active layer of bed sand after the n-1th iteration; E (n-1) It is determined based on the average water depth in the current water and sand conditions and the initial value of the average water depth in the initial water and sand conditions; m represents the silt layer thickness coefficient.

9. The method according to claim 7, 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; ΔH1 (n-1) =E (n-1) N1 Among them, W i represents the weight of sediment particles of group i after the n-1th iteration; γ s Indicates sediment bulk density; E (n-1) represents the thickness of the bed sand active layer after the n-1th iteration; L represents the length of the bed sand active layer after the n-1th iteration; B represents the width of the bed sand active layer after the n-1th iteration; It represents the bed sand gradation of the i-th group of particle size sediment obtained after the n-1th iteration; G represents the bed sand gradation of the i-th group of particle size sediment obtained after the n-th iteration; i It represents the amount of bed sediment of group i converted into bed load and suspended sediment after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the bed load state after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed sand state to the suspended load state after the n-1th iteration; S i It represents the sum of the sedimentation of the bed load of the i-th particle size group and the sedimentation of the suspended load of the i-th particle size group after the n-1th iteration; It represents the bed load gradation of the sediment of group i obtained after the n-1th iteration; It represents the suspended sediment gradation of the i-th group of particle size sediment obtained after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the bed load state to the bed sand state after the n-1th iteration; represents the probability of the i-th group of sediment particles transitioning from the suspended matter state to the bed sand state after the n-1th iteration; F i (n-1) represents the amount of bed sand replenished in the i-th particle size group 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 bed sand gradation of the i-th group of particle size; It represents the bed load gradation of the sediment of group i obtained after the nth iteration; It represents the suspended sediment gradation of the i-th group of particle size sediment obtained after the n-th iteration; It represents the probability that the sediment of group i remains in the bed load state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transitioning from the suspended load state to the bed load state after the n-1th iteration; It represents the probability of the i-th group of sediment particles transferring from the bed load state to the suspended load state after the n-1th iteration; It represents the probability that the sediment of the i-th particle size group remains in the suspended state after the n-1th iteration.

10. A device for determining the gradation of a riverbed coarsening layer, characterized in that: The device comprises: The data acquisition unit is configured to: acquire initial water and sediment conditions of a target river section; wherein the riverbed of the target river section is a pebble-sand riverbed; The riverbed coarsening layer gradation calculation unit is configured to: determine the riverbed coarsening layer gradation of the target river section when the extreme scour condition is reached based on the initial water and sediment conditions and using a preset riverbed coarsening layer gradation calculation rule; The extreme scouring condition is that the thickness of sediment scouring and sediment deposition on the riverbed reaches a dynamic balance; The information pushing unit is configured to push the riverbed coarsening layer gradation to the user terminal.

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