A method for predicting the survival of drifting fish eggs in flooded vegetation.

Abstract

This application discloses a method for predicting the survival of drifting fish eggs in flooded vegetation, relating to the field of biological monitoring technology. The method includes: determining the study area and the target to be predicted; acquiring fish egg parameter variation curves, vegetation characteristic parameters, and other influencing parameters; determining the input parameters for the current cycle; and calculating the survival prediction model for drifting fish eggs using a model that calculates the settling velocity of each fish egg, calculates coordinates based on the settling velocity, determines the fish eggs that have settled to the riverbed, and determines whether the settled fish eggs are resuspended; updating the number of suspended fish eggs; and outputting the fish egg survival prediction result based on the ratio of the number of suspended fish eggs to the number of eggs laid. The parameters for the next cycle are updated, and the calculation is repeated until the drifting time is greater than or equal to the hatching time threshold. This application systematically analyzes water flow characteristics, vegetation characteristics, and fish egg biological characteristics, enabling more accurate prediction of fish egg movement and survival, and providing data support for ecological protection.

Description

Technical Field

[0001] This application relates to the field of biological monitoring technology, and in particular to a method for predicting the survival of drifting fish eggs in flooded vegetation. Background Technology

[0002] In aquatic ecosystems, the reproduction and survival of fish are closely related to the drifting environment of their eggs. Drifting fish eggs are suspended, transported, and dispersed under hydrodynamic conditions to avoid the risks of death, sterility, or predation caused by sinking to the bottom. Therefore, suitable water flow and aquatic ecological environment are crucial for the survival of fish eggs.

[0003] Currently, most studies analyze the spatiotemporal variations of fish egg concentration by numerically solving convection-diffusion equations to reveal their movement patterns. However, these models often fail to accurately depict the trajectory of fish eggs and do not fully consider the changes in physical and biological characteristics caused by the growth and development of fish eggs during drifting. Furthermore, the abundance of large-scale submerged vegetation in rivers and lakes globally is showing a gradual increasing trend. This submerged vegetation provides complex habitats for fish eggs, influencing not only their drift paths but also providing shelter and nutrients, thus affecting their survival and development. Related technologies typically neglect the role of vegetation factors in the drifting and settling of fish eggs. Previous studies have shown that fish eggs drifting in water are affected by various factors, including water flow velocity, vegetation density, vegetation submersion degree, and the physicochemical properties of the water body.

[0004] Therefore, there is currently a lack of a systematic analysis that comprehensively considers water flow characteristics, vegetation distribution, and the biological characteristics of fish eggs.

[0005] This will help to more accurately predict the movement and survival of fish eggs. Such comprehensive multi-factor research not only enhances our understanding of fish ecology but also provides an important theoretical foundation for water resource management and ecological protection. Summary of the Invention

[0006] This application provides a method for predicting the survival of drifting fish eggs in submerged vegetation currents, addressing the shortcomings of the aforementioned related technologies. This application can simulate different fish egg and vegetation characteristics to predict the early movement of fish eggs in the water after fertilization, as well as the suspension and settling behavior of fish eggs under the combined influence of vegetation and water flow, thus more accurately predicting the early survival of drifting fish eggs. The technical solution is as follows:

[0007] In a first aspect, embodiments of this application provide a method for predicting the survival of drifting fish eggs in flooded vegetation, comprising:

[0008] The study area and the target to be predicted are determined, the change curve of fish egg parameters corresponding to the fish egg type of the target to be predicted is obtained, and the vegetation characteristic parameters and other influencing parameters in the study area are obtained; wherein, the target to be predicted includes fish eggs with a corresponding number of spawning;

[0009] Determine the fish egg parameters for each fish egg in the current cycle, and input the fish egg parameters, vegetation characteristic parameters, and other influencing parameters into the drifting fish egg survival prediction model. The calculation process of the drifting fish egg survival prediction model includes:

[0010] Calculate the settling velocity of each fish egg;

[0011] The position coordinates of each fish egg are calculated based on the sinking velocity of each fish egg;

[0012] Based on the location coordinates of each fish egg, determine the fish eggs that have sunk to the riverbed, and determine whether the fish eggs that have sunk to the riverbed have been resuspended, and update the number of suspended fish eggs.

[0013] The survival prediction result of fish eggs is output based on the ratio of the number of suspended fish eggs to the number of eggs laid.

[0014] Update the fish egg parameters for the next cycle and proceed to the step of inputting the drifting fish egg survival prediction model until the drifting time is greater than or equal to the hatching time threshold.

[0015] The parameters of the fish eggs include: egg location, egg diameter, and egg density.

[0016] In one alternative to the first aspect, the calculation of the settling velocity of each fish egg includes:

[0017] Based on the other influencing parameters, the fluid density is obtained, and the diameter and density of each fish egg in the current cycle are obtained. The settling velocity of each fish egg is then calculated using the formula:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] in, The settling velocity of the fish eggs. It is the acceleration due to gravity. This represents the drag coefficient of the fish eggs. Let Reynolds number be the number of fish eggs. The diameter of the fish egg. Fish egg density; The density of the water body, Let be the kinematic viscosity of the water, where and Both are functions of water temperature.

[0023] In one alternative to the first aspect, the position coordinates include the horizontal and vertical coordinates of the fish egg;

[0024] The process of updating the position coordinates of each fish egg based on its settling velocity includes:

[0025] Calculate the vertical coordinates of each fish egg using the following formula:

[0026] ;

[0027] Calculate the horizontal coordinate of each fish egg using the following formula:

[0028] ;

[0029] in, The longitudinal velocity of the water body. The vertical turbulent diffusion coefficient is... These are random numbers selected from a normal distribution with a mean of 0 and a standard deviation of 1. The time step for each period is t, where t is the start time of the current period. The horizontal coordinate of the starting time of the current cycle. The horizontal coordinate of the end time of the current cycle. The vertical coordinate of the starting time of the current cycle is given. The vertical coordinate is the end time of the current cycle.

[0030] In one alternative embodiment of the first aspect, the longitudinal velocity of the water body is a piecewise function related to the vertical coordinate. The function corresponding to each range is determined based on the range in which the vertical coordinate values ​​fall, and the longitudinal velocity of the water body is calculated, including:

[0031] If the value of the vertical coordinate is less than the water depth but greater than the first depth threshold, then the fish eggs are determined to be located in the upper layer of water flow within the vegetation zone. The longitudinal flow velocity of the water body is then calculated using the following formula:

[0032] ;

[0033] If the value of the vertical coordinate is less than the first depth threshold and greater than the second depth threshold, then the fish eggs are determined to be located in the exchange zone, and the longitudinal flow velocity of the water body is calculated using the following formula:

[0034] ;

[0035] If the value of the vertical coordinate is less than or equal to the second depth threshold, then the fish eggs are determined to be located in the wake zone, and the longitudinal velocity of the water body is calculated using the following formula:

[0036] ;

[0037] Wherein, the first depth threshold is greater than the second depth threshold, and the first depth threshold is the vegetation height. The second depth threshold is , The length of the shear vortex penetrating into the vegetation. , Let be the area of ​​the vegetation front per unit volume of water, and 'a' be used to characterize vegetation density. The number of plants per square meter. The diameter of the vegetation; For hydraulic gradient, coefficient roughness height Displacement height , To submerge vegetation frictional flow velocity , The longitudinal velocity of the water in the wake region. Kármán constant .

[0038] In one alternative embodiment of the first aspect, after updating the position coordinates of each fish egg based on the settling velocity of each fish egg, the process specifically includes:

[0039] If the calculated vertical coordinates are greater than the water depth h, the fish eggs are considered to be located outside the water surface, and the vertical coordinates of the fish eggs are updated using the following formula:

[0040] ;

[0041] When the calculated vertical coordinate is less than 0, the fish egg position is represented as being below the riverbed, and the fish egg position is updated to be sinking to the riverbed;

[0042] in, To update the previously calculated vertical coordinates, These are the updated vertical coordinates.

[0043] In one alternative embodiment of the first aspect, after updating the position coordinates of each fish egg based on the settling velocity of each fish egg, the method further includes:

[0044] Based on the position coordinates of each fish egg, determine the fish eggs that have sunk to the riverbed and the fish eggs that are suspended.

[0045] Determine whether the fish eggs that have settled to the riverbed are resuspended, and calculate the uplift rate of each fish egg that has settled to the riverbed.

[0046] For each fish egg that sinks to the riverbed, generate a random value randomly drawn from a uniform distribution (0,1);

[0047] If the random value is less than or equal to the product of the rise rate and the time step, it is determined that the fish eggs that have settled to the riverbed will be resuspended.

[0048] Update the state of each fish egg to get the updated number of suspended fish eggs.

[0049] In one alternative embodiment of the first aspect, the method further includes:

[0050] Determine the time step using the formula:

[0051] ;

[0052] in, where h is the time step and h is the water depth. The vertical turbulent diffusion coefficient is z, where z is the ordinate. The settling velocity of the fish eggs.

[0053] Secondly, embodiments of this application also provide a device for predicting the survival of drifting fish eggs in flooded vegetation, comprising:

[0054] The parameter acquisition unit is used to determine the area to be studied and the target to be predicted, and is also used to acquire the fish egg parameter variation curve of the fish egg type corresponding to the target to be predicted, and to acquire vegetation characteristic parameters and other influencing parameters within the area to be studied; wherein, the target to be predicted includes fish eggs with a corresponding number of spawning;

[0055] The parameter acquisition unit is also used to determine the fish egg parameters for each fish egg in the current cycle, and input the fish egg parameters, the vegetation characteristic parameters, and the other influencing parameters as input parameters into the drifting fish egg survival prediction model of the calculation unit. The calculation process of the calculation unit using the drifting fish egg survival prediction model includes:

[0056] Calculate the settling velocity of each fish egg;

[0057] The position coordinates of each fish egg are updated based on the settling velocity of each fish egg;

[0058] Based on the location coordinates of each fish egg, determine the fish eggs that have sunk to the riverbed, and determine whether the fish eggs that have sunk to the riverbed have been resuspended, and update the number of suspended fish eggs.

[0059] The analysis unit is used to output a fish egg survival prediction result based on the ratio of the number of suspended fish eggs to the number of eggs laid;

[0060] The parameter acquisition unit is also used to update the fish egg parameters for the next cycle, and then proceed to the step of the drifting fish egg survival prediction model of the input calculation unit until the drifting time is greater than or equal to the hatching time threshold.

[0061] The parameters of the fish eggs include: egg location, egg diameter, and egg density.

[0062] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect or any implementation thereof of the embodiments of this application.

[0063] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiments of this application or any implementation thereof.

[0064] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0065] This application provides a method for predicting the survival of drifting fish eggs in submerged vegetation flow. Based on vegetation characteristic parameters and other influencing parameters within the study area containing submerged vegetation, and considering the parameters of drifting fish eggs, as well as factors such as resuspension and abortion caused by turbulent vegetation flow, the method simulates the movement trajectory of drifting fish eggs in the river channel. It constructs a fish egg survival prediction model applicable to different vegetation conditions and fish egg biological characteristics, accurately simulating and predicting the number of surviving fish eggs at hatching time. Furthermore, this application also clarifies the influence of vegetation characteristic parameters, such as relative submergence and vegetation density, on the survival score, providing a predictive model for the dynamic changes in fish egg survival scores during vegetation succession.

[0066] This application's embodiments systematically analyze water flow characteristics, vegetation characteristics, and the biological characteristics of fish eggs, which will help to more accurately predict the movement and survival of fish eggs. This comprehensive study of multiple factors not only enhances our understanding of fish ecology but also provides an important theoretical foundation for water resource management and ecological protection.

[0067] Furthermore, based on the fish egg survival prediction results determined in the embodiments of this application, early fish resources can be supplemented when the number of surviving fish eggs is less than a certain value. Specifically, with the goal of increasing the fish egg survival score, the ecological flow required for fish protection can be quantitatively assessed, and a fish habitat protection and regulation strategy based on hydrodynamic mechanisms can be derived, providing data support for river ecological restoration. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is a flowchart illustrating a method for predicting the survival of drifting fish eggs in flooded vegetation, provided in an embodiment of this application.

[0070] Figure 2 This is a schematic diagram showing the survival score distribution of a method for predicting the survival of drifting fish eggs in flooded vegetation, provided in an embodiment of this application.

[0071] Figure 3 This is a schematic diagram showing the survival score distribution of a method for predicting the survival of drifting fish eggs in flooded vegetation, provided in an embodiment of this application.

[0072] Figure 4 This is a schematic diagram showing the survival score distribution of a method for predicting the survival of drifting fish eggs in flooded vegetation, provided in an embodiment of this application.

[0073] Figure 5 This is a schematic diagram showing the survival score distribution of a method for predicting the survival of drifting fish eggs in flooded vegetation, provided in an embodiment of this application.

[0074] Figure 6 This is a schematic diagram of the structure of a drifting fish egg survival prediction device applied to submerged vegetation water flow provided in an embodiment of this application;

[0075] Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.

[0078] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.

[0079] The present application will now be described in detail with reference to specific embodiments.

[0080] Next, combine Figure 1 This paper introduces a method for predicting the survival of drifting fish eggs in flooded vegetation, provided by embodiments of this application. For details, please refer to... Figure 1 , Figure 1 A flowchart illustrating a method provided in an embodiment of this application is shown. Figure 1 As shown, the method includes the following steps:

[0081] S101, determine the area to be studied and the target to be predicted, obtain the fish egg parameter change curve corresponding to the fish egg type of the target to be predicted, and obtain the vegetation characteristic parameters and other influencing parameters within the area to be studied.

[0082] S102, determine the fish egg parameters for each fish egg in the current cycle, and input the fish egg parameters, the vegetation characteristic parameters, and the other influencing parameters into the drifting fish egg survival prediction model.

[0083] The calculation process of the drifting fish egg survival prediction model includes the following steps:

[0084] S103, calculate the sinking velocity of each fish egg.

[0085] S104, calculate the position coordinates of each fish egg based on the sinking velocity of each fish egg.

[0086] S105, determine the fish eggs that have sunk to the riverbed based on the position coordinates of each fish egg, determine whether the fish eggs that have sunk to the riverbed have been resuspended, and update the number of suspended fish eggs.

[0087] S106, based on the ratio of the number of suspended fish eggs to the number of eggs laid, output the fish egg survival prediction result.

[0088] If the drifting time of the fish eggs in the current cycle has not reached the hatching time threshold, proceed with step S107:

[0089] S107, Update fish egg parameters for the next cycle;

[0090] Proceed to step S102, which involves inputting the drifting fish egg survival prediction model, until the drifting time is greater than or equal to the hatching time threshold.

[0091] Specifically, in S101, any river section, wetland, or other drifting fish spawning ground can be selected as the study area. The study area should cover the entire drifting interval from the fish spawning site to the hatching of the fish eggs.

[0092] Among these methods, a certain type of fish can be identified as the research target, and the spawning location and corresponding number of eggs can be obtained. The target to be predicted is the total number of fish eggs corresponding to the number of eggs spawned. The hatching time can be determined based on the type of fish eggs.

[0093] Understandably, after spawning, the fish eggs are in the process of growth, and the change curves of fish egg parameters can be determined, including egg location, egg diameter, and egg density. Among these, egg density and egg diameter will change over time.

[0094] ;

[0095] ;

[0096] In the formula, , , This is a dimensionless constant, and its specific value depends on the type of fish eggs, as detailed in Table 1.

[0097] In some embodiments, to accurately compare the density and settling velocity of different types of fish eggs, the calculated fish egg density can be standardized to a specific temperature (typically 22°C) using the following formula:

[0098] ;

[0099] In the formula, For ambient temperature, For reference temperature; The correction factor for temperature adjustment can be 0.20646.

[0100] Table 1. Values ​​of dimensionless constants

[0101]

[0102] In some embodiments, vegetation characteristic parameters include vegetation height and density, which can be taken as the average value of vegetation height and density in the entire area under study. Other influencing parameters are external abiotic factors, that is, all environmental factors other than biotic factors, such as water temperature, water flow, and water width. In general, the average value of vegetation characteristic parameters for the entire watershed, as well as the average value of each type of other influencing parameters, can be taken.

[0103] Optionally, the distribution of vegetation characteristics and / or other influencing parameters in the area under study can be obtained, and the vegetation characteristic-related parameters and / or other influencing parameters can be updated in real time according to the location of fish eggs during the calculation of each period. This application does not limit this aspect.

[0104] In some embodiments, in S102, the calculation of the first cycle can be performed from the spawning location of the fish eggs as the starting point. The initial parameters of the first cycle can be determined, including the fish egg parameters of each fish egg in the current cycle, as well as the vegetation characteristic parameters and other influencing parameters determined in S101. The initial parameters are used as input to the drifting fish egg survival prediction model, and the specific process of calculation is performed through the drifting fish egg survival prediction model.

[0105] In some embodiments, in S103, the settling velocity of the fish eggs depends on the fluid density, drift duration, and fish egg diameter. The fluid density can be obtained based on the other influencing parameters. The drift duration of each fish egg can be obtained according to the current cycle. The fish egg diameter can be determined by the fish egg parameters in step S102. The settling velocity of each fish egg can be calculated using an iterative equation for the settling velocity of spherical particles in a static fluid.

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] in, The settling velocity of the fish eggs. It is the acceleration due to gravity. This represents the drag coefficient of the fish eggs. Let Reynolds number be the number of fish eggs. The diameter of the fish egg. Fish egg density; The density of the water body, Let be the kinematic viscosity of the water, where and Both are functions of water temperature.

[0111] In some embodiments, in S104, the position coordinates include the horizontal and vertical coordinates of the fish egg. These coordinates can be calculated based on the settling velocity obtained in S103. The horizontal and vertical coordinates can be understood as the positional change of a single fish egg within a time step corresponding to one cycle. Specifically, the horizontal and vertical displacements of the fish egg can be simulated using the random displacement module of the drifting fish egg survival prediction model, including:

[0112] Calculate the vertical coordinates of each fish egg using the following formula:

[0113] ;

[0114] Calculate the horizontal coordinate of each fish egg using the following formula:

[0115] ;

[0116] in, The longitudinal velocity of the water body. The vertical turbulent diffusion coefficient is... These are random numbers selected from a normal distribution with a mean of 0 and a standard deviation of 1. The time step for each period is t, where t is the start time of the current period. The horizontal coordinate of the starting time of the current cycle. The horizontal coordinate of the end time of the current cycle. The vertical coordinate of the starting time of the current cycle is given. The vertical coordinate is the end time of the current cycle.

[0117] In the above embodiments, the longitudinal flow velocity of the water body is a piecewise function related to the vertical coordinate. The function corresponding to each range is determined based on the range in which the vertical coordinate values ​​fall, and the longitudinal flow velocity of the water body is calculated, including:

[0118] If the value of the vertical coordinate is less than the water depth but greater than the first depth threshold, then the fish eggs are determined to be located in the upper layer of the vegetation zone. The average flow velocity distribution in the upper layer of the vegetation zone follows a logarithmic distribution. The longitudinal flow velocity of the water body is calculated using the following formula:

[0119] ,

[0120] If the value of the vertical coordinate is less than the first depth threshold and greater than the second depth threshold, then the fish eggs are determined to be located in the exchange zone. The flow is driven by vegetation resistance and turbulent stress, and the velocity exhibits an exponential distribution. The longitudinal velocity of the water body is calculated using the following formula:

[0121] , ;

[0122] If the value of the vertical coordinate is less than or equal to the second depth threshold, it is determined that the fish eggs are located in the wake region. At this time, the average flow velocity is approximately uniformly distributed. Since the vegetation resistance is much greater than the riverbed resistance, the momentum equation is usually simplified to the balance between the pressure gradient and the vegetation resistance. The longitudinal velocity of the water body is calculated using the following formula:

[0123] , ;

[0124] Wherein, the first depth threshold is greater than the second depth threshold, and the first depth threshold is the vegetation height. The second depth threshold is , The length of the shear vortex penetrating into the vegetation. , Let be the area of ​​the vegetation front per unit volume of water, and 'a' be used to characterize vegetation density. The number of plants per square meter. The diameter of the vegetation; For hydraulic gradient, coefficient roughness height Displacement height , To submerge vegetation frictional flow velocity , The longitudinal velocity of the water in the wake region. is the Kármán constant.

[0125] In some embodiments, in S104, the turbulent diffusion coefficient of the shear eddy can also be determined based on the different areas where the fish eggs are located in the water body. Specifically, it includes:

[0126] When fish eggs are located in the wake region, and when vegetation density is high The turbulent diffusion coefficient of the shear eddy is approximately uniformly distributed.

[0127] ;

[0128] When fish eggs are located in the upper layer of water flow above vegetation, the transport of the water in this upper layer is mainly affected by shear eddies. The turbulent diffusion coefficient varies with the size of the shear eddies and the velocity difference between the average velocity of the vegetation and the upper water flow. The turbulence intensity reaches its maximum at the top of the vegetation and then gradually decreases from the top of the vegetation to the water surface. For different vegetation densities, the expression for the turbulence diffusion coefficient at the top of the vegetation is:

[0129] ;

[0130] in, The vertical turbulent diffusion coefficient is approximately equal to the vegetation height. Since the vertical turbulent diffusion coefficient is approximately linearly distributed in the exchange zone and the upper layer of water flow above the vegetation, after determining the vertical diffusion coefficient in the wake zone and the maximum value at the top of the vegetation, the vertical turbulent diffusion coefficient at the water surface is assumed to be zero, and the vertical distribution of the vertical turbulent diffusion coefficient can be obtained.

[0131] In some embodiments, the water surface can be used as the upper boundary and the riverbed as the lower boundary. This is because, in reality, when fish eggs float, they will not exceed the upper boundary corresponding to the water surface, and when they settle, they will not exceed the lower boundary corresponding to the riverbed.

[0132] Therefore, boundary conditions can be set for the upper boundary corresponding to the water surface and the lower boundary corresponding to the riverbed, specifically including:

[0133] If the calculated vertical coordinates are greater than the water depth h, the fish eggs are considered to be located outside the water surface, and the vertical coordinates of the fish eggs are updated using the following formula:

[0134] ;

[0135] When the calculated vertical coordinate is less than 0, the fish egg position is represented as being below the riverbed, and the fish egg position is updated to be sinking to the riverbed;

[0136] in, To update the previously calculated vertical coordinates, These are the updated vertical coordinates.

[0137] Understandably, by using the location coordinates of the fish eggs and the fish egg locations from the previous cycle, it is easy to update and obtain the fish egg locations for the current cycle.

[0138] In some embodiments, the vertical coordinate of the water surface can be set to h and the vertical coordinate of the riverbed to 0, making it easy to determine whether the vertical coordinates calculated in S104 need to be adjusted based on the vertical coordinates.

[0139] Furthermore, after updating the position coordinates of each fish egg based on its settling velocity, S104 also includes:

[0140] The fish eggs that have sunk to the riverbed are determined based on the position coordinates of each fish egg, as well as the fish eggs that are suspended in the water.

[0141] Determine whether the fish eggs that have settled to the riverbed are resuspended, and calculate the uplift rate of each fish egg that has settled to the riverbed. .

[0142] For each fish egg that sinks to the riverbed, generate a random value randomly drawn from a uniform distribution (0,1). If the random value is less than or equal to the rise rate and the time step The product of, i.e. If the fish eggs that have settled to the riverbed are resuspended, then it is determined that the fish eggs will remain settled to the riverbed, that is, they will be deposited on the riverbed.

[0143] Update the state of each fish egg to get the updated number of suspended fish eggs.

[0144] Specifically, the rate of increase The calculation process includes:

[0145] Particle resuspension and near-bed turbulent kinetic energy This is relevant. The turbulent kinetic energy of water flow in vegetated channels is partly generated by the riverbed and partly by the vegetation, as shown by the following formula:

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] Among them, the shear stress of the riverbed surface Riverbed surface friction coefficient ; The average flow velocity of the river channel; The average flow rate of the river channel; The river is wide; For water depth; The average flow velocity in the vegetated area; The average flow velocity in the upper layer of vegetation; This is an empirical coefficient representing the turbulent stress at the top of the vegetation; for rigid vegetation, ; Scale factor ; The mean turbulence length scale; stem Reynolds number. Solid volume fraction within vegetation zone ;

[0153] The dimensionless erosion rate of the bed surface can be expressed by the following formula:

[0154] ；

[0155] Among them, coefficient , ; , Dry density; =, ; Dimensionless kinetic energy , Dimensionless critical turbulence intensity ; Dimensionless critical shear stress Diameter of fish eggs without size .

[0156] The weight of fish eggs that rise from the bed surface per unit time is Apply the formula:

[0157] ;

[0158] The thickness of the eroded surface per unit time (bed erosion rate) was further calculated:

[0159] ;

[0160] The resuspension probability of deposited particles is determined by the uplift rate of a single particle leaving the bed surface, which is calculated using the following formula:

[0161] .

[0162] It should be noted that drifting fish eggs are suspended, transported, and dispersed under the action of hydrodynamics. If they sink to the riverbed, they are more likely to die, become sterile, or be preyed upon. Therefore, the survival status of fish eggs is determined by the ratio of the number of fish eggs that remain suspended in the water to the total number of eggs laid.

[0163] In some embodiments, in S106, the fish egg survival prediction result can be output based on the ratio of the number of suspended fish eggs to the number of eggs laid, and in S107, the fish egg parameters for the next cycle can be updated based on the fish egg parameter change curve after the calculation of one cycle is completed.

[0164] This can be understood as follows: In S107, at the end of the previous cycle, the diameter and density of the fish eggs at the end of the previous cycle can be determined based on the end time of the previous cycle and the corresponding type of fish egg parameter change curve. The position of each fish egg can be determined based on the position coordinates of each fish egg calculated in the previous cycle. Therefore, the position, diameter, and density of the fish eggs at the end time of the previous cycle can be used as input parameters for the next cycle. This data is then transferred to S102 to input into the drifting fish egg survival prediction model to continue the calculation for the next cycle.

[0165] Specifically, based on step S106, the survival prediction results for the corresponding fish eggs can be output for each cycle. A chart can be generated with each cycle as the x-axis and the ratio of suspended fish eggs to the number of eggs laid as the y-axis, thus visually displaying the survival changes across multiple cycles up to the fish egg hatching time threshold. Alternatively, the drift distance can be used as the x-axis and the relative submergence depth (i.e., the submergence depth of the fish eggs in the water) as the y-axis. The submergence depth determines the position of the fish eggs in the water, reflecting the survival distribution of fish eggs at different locations during the drift process. Specifically, the survival score can be determined based on a mapping table between the ratio of suspended fish eggs to the number of eggs laid and the survival score. Figures 2-3 As shown, the survival score of fish eggs changes along the flow under different flow conditions. Red indicates a higher survival score of fish eggs. As the fish eggs transition from the red area to the blue area, the survival score of fish eggs gradually decreases. The present application does not limit the form of the fish egg survival prediction results.

[0166] In some embodiments, a cross-section at a specific drifting distance can also be selected based on vegetation height. The simulation results are illustrated in the diagram below, with the horizontal axis representing the area and the vertical axis representing the vegetation density (a). Figures 4-5 As shown, Figure 4 The example uses a location where the drifting distance is 25m as the cross-section. Figure 5 An example was taken at a location with a drift distance of 50m as a cross-section, which reflects the distribution of the survival rate of fish eggs at the corresponding cross-section as affected by vegetation density and vegetation height.

[0167] The following are apparatus embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0168] Please see below. Figure 6This is a schematic diagram of a device for predicting the survival of drifting fish eggs in flooded vegetation, provided as an exemplary embodiment of this application. This device can be implemented as all or part of a terminal through software, hardware, or a combination of both, or it can be integrated as an independent module on a server. The device for predicting the survival of drifting fish eggs in flooded vegetation according to this embodiment can be applied to a terminal or the cloud. The device 60 includes a parameter acquisition unit 601, a calculation unit 602, and an analysis unit 603, wherein:

[0169] The parameter acquisition unit 601 is used to determine the area to be studied and the target to be predicted, and is also used to acquire the fish egg parameter variation curve of the fish egg type corresponding to the target to be predicted, and to acquire vegetation characteristic parameters and other influencing parameters within the area to be studied; wherein, the target to be predicted includes fish eggs with a corresponding number of eggs laid;

[0170] The parameter acquisition unit 601 is further configured to determine the fish egg parameters for each fish egg in the current cycle, and input the fish egg parameters, the vegetation characteristic parameters, and the other influencing parameters as input parameters into the drifting fish egg survival prediction model of the calculation unit 602. The calculation process of the calculation unit 602 using the drifting fish egg survival prediction model includes:

[0171] Calculate the settling velocity of each fish egg;

[0172] The position coordinates of each fish egg are updated based on the settling velocity of each fish egg;

[0173] Based on the location coordinates of each fish egg, determine the fish eggs that have sunk to the riverbed, and determine whether the fish eggs that have sunk to the riverbed have been resuspended, and update the number of suspended fish eggs.

[0174] Analysis unit 603 is used to output fish egg survival prediction results based on the ratio of the number of suspended fish eggs to the number of eggs laid;

[0175] The parameter acquisition unit 601 is also used to update the fish egg parameters for the next cycle and transfer to the step of the drifting fish egg survival prediction model of the input calculation unit 602 until the drifting time is greater than or equal to the hatching time threshold.

[0176] The parameters of the fish eggs include: egg location, egg diameter, and egg density.

[0177] It should be noted that the device 60 provided in the above embodiments, when executing the method for predicting the survival of drifting fish eggs applied to submerged vegetation water flow, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiments and the embodiment of the method for predicting the survival of drifting fish eggs applied to submerged vegetation water flow belong to the same concept, and its implementation process is detailed in the method embodiment, which will not be repeated here.

[0178] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0179] Please see Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0180] like Figure 7 As shown, the electronic device 700 includes a processor 701 and a memory 702.

[0181] In this embodiment, the processor 701 is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor 701 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 701 can be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0182] Processor 701 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake-up state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state.

[0183] Memory 702 may include one or more computer-readable storage media, which may be non-transitory. Memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in memory 702 is used to store at least one instruction, which is executed by processor 701 to implement the method in the embodiments of this application.

[0184] In some embodiments, the electronic device 700 further includes a peripheral device interface 703 and at least one peripheral device 704. The processor 701, memory 702, and peripheral device interface 703 can be connected via a bus or signal line. Each peripheral device 704 can be connected to the peripheral device interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device 704 includes: a display screen, a camera, and audio circuitry. The peripheral device interface 703 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 701 and memory 702.

[0185] In some embodiments of this application, the processor 701, memory 702, and peripheral device interface 703 are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor 701, memory 702, and peripheral device interface 703 can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.

[0186] The electronic device structural block diagram shown in the embodiments of this application does not constitute a limitation on the electronic device 700. The electronic device 700 may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0187] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0188] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting the survival of drifting fish eggs in flooded vegetation, characterized in that, include: The study area and the target to be predicted are determined. The change curve of fish egg parameters corresponding to the fish egg type of the target to be predicted is obtained. The vegetation characteristic parameters and other influencing parameters in the study area are obtained. The target to be predicted includes fish eggs with a corresponding number of eggs laid. The other influencing parameters are all environmental factors other than biological influencing factors. Determine the fish egg parameters for each fish egg in the current cycle, and input the fish egg parameters, vegetation characteristic parameters, and other influencing parameters into the drifting fish egg survival prediction model. The calculation process of the drifting fish egg survival prediction model includes: Calculate the settling velocity of each fish egg; The position coordinates of each fish egg are calculated based on its settling velocity, including its horizontal and vertical coordinates. The formula for calculating the horizontal and vertical coordinates of each fish egg is as follows: ； ； in, The settling velocity of the fish eggs. The longitudinal velocity of the water body. The vertical turbulent diffusion coefficient is calculated based on the different vertical locations of the fish eggs in the water body. These are random numbers selected from a normal distribution with a mean of 0 and a standard deviation of 1. The time step for each period is t, where t is the start time of the current period. The horizontal coordinate of the starting time of the current cycle. The horizontal coordinate of the end time of the current cycle. The vertical coordinate of the starting time of the current cycle is given. The vertical coordinate of the end time of the current cycle; The location coordinates of each fish egg are used to determine which fish eggs have sunk to the riverbed and which are suspended in the water. Determine whether the fish eggs that have settled to the riverbed are resuspended, and calculate the uplift rate of each fish egg that has settled to the riverbed. For each fish egg that sinks to the riverbed, generate a random value randomly drawn from a uniform distribution (0,1); If the random value is less than or equal to the product of the rise rate and the time step, it is determined that the fish eggs that have settled to the riverbed will be resuspended. Update the state of each fish egg to get the updated number of suspended fish eggs; The survival prediction result of fish eggs is output based on the ratio of the number of suspended fish eggs to the number of eggs laid. Update the fish egg parameters for the next cycle and proceed to the step of inputting the drifting fish egg survival prediction model until the drifting time is greater than or equal to the hatching time threshold. The parameters of the fish eggs include: egg location, egg diameter, and egg density.

2. The method for predicting the survival of drifting fish eggs in flooded vegetation flow according to claim 1, characterized in that, The calculation of the sinking velocity of each fish egg includes: Based on the other influencing parameters, the fluid density is obtained, and the diameter and density of each fish egg in the current cycle are obtained. The settling velocity of each fish egg is then calculated using the formula: ； ； ； ； in, It is the acceleration due to gravity. This represents the drag coefficient of the fish eggs. The Reynolds number of fish eggs. The diameter of the fish egg. Fish egg density; The density of the water body, Let be the kinematic viscosity of the water, where and Both are functions of water temperature.

3. The method for predicting the survival of drifting fish eggs in flooded vegetation flow according to claim 1, characterized in that, The longitudinal flow velocity of the water body is a piecewise function related to the vertical coordinate. The function corresponding to each range is determined based on the range in which the vertical coordinate values ​​fall, and the longitudinal flow velocity of the water body is calculated, including: If the value of the vertical coordinate is less than the water depth but greater than the first depth threshold, then the fish eggs are determined to be located in the upper layer of water flow within the vegetation zone. The longitudinal flow velocity of the water body is then calculated using the following formula: ； If the value of the vertical coordinate is less than the first depth threshold and greater than the second depth threshold, then the fish eggs are determined to be located in the exchange zone, and the longitudinal flow velocity of the water body is calculated using the following formula: ； If the value of the vertical coordinate is less than or equal to the second depth threshold, then the fish eggs are determined to be located in the wake zone, and the longitudinal velocity of the water body is calculated using the following formula: ； Wherein, the coordinate at the water surface is h, and h is the water depth; the coordinate at the riverbed surface is 0; the first depth threshold is greater than the second depth threshold, and the first depth threshold is the vegetation height. The second depth threshold is , The length of the shear vortex penetrating into the vegetation. , Let be the area of ​​the vegetation front per unit volume of water, and 'a' be used to characterize vegetation density. The number of plants per square meter. The diameter of the vegetation; For hydraulic gradient, coefficient roughness height Displacement height , To submerge vegetation frictional flow velocity , The longitudinal velocity of the water in the wake region. is the Kármán constant.

4. The method for predicting the survival of drifting fish eggs in flooded vegetation flow according to claim 3, characterized in that, Set boundary conditions for the upper boundary corresponding to the water surface and the lower boundary corresponding to the riverbed, specifically including: If the calculated vertical coordinates are greater than the water depth h, the fish eggs are considered to be located outside the water surface, and the vertical coordinates of the fish eggs are updated using the following formula: ； When the calculated vertical coordinate is less than 0, the fish egg position is represented as being below the riverbed, and the fish egg position is updated to be sinking to the riverbed; in, To update the previously calculated vertical coordinates, These are the updated vertical coordinates.

5. A method for predicting the survival of drifting fish eggs in flooded vegetation flows according to any one of claims 1-4, characterized in that, The method further includes: Determine the time step using the formula: ; in, where h is the time step and h is the water depth. The vertical turbulent diffusion coefficient is z, where z is the ordinate. The settling velocity of the fish eggs.

6. An apparatus for predicting the survival of drifting fish eggs in flooded vegetation currents, based on the method described in any one of claims 1-5, characterized in that, include: The parameter acquisition unit is used to determine the area to be studied and the target to be predicted, and is also used to acquire the fish egg parameter variation curve of the fish egg type corresponding to the target to be predicted, and to acquire vegetation characteristic parameters and other influencing parameters within the area to be studied; wherein, the target to be predicted includes fish eggs with a corresponding number of spawning; The parameter acquisition unit is also used to determine the fish egg parameters for each fish egg in the current cycle, and input the fish egg parameters, the vegetation characteristic parameters, and the other influencing parameters as input parameters into the drifting fish egg survival prediction model of the calculation unit. The calculation process of the calculation unit using the drifting fish egg survival prediction model includes: Calculate the settling velocity of each fish egg; The position coordinates of each fish egg are calculated based on the sinking velocity of each fish egg; Based on the location coordinates of each fish egg, determine the fish eggs that have sunk to the riverbed, and determine whether the fish eggs that have sunk to the riverbed have been resuspended, and update the number of suspended fish eggs. The analysis unit is used to output a fish egg survival prediction result based on the ratio of the number of suspended fish eggs to the number of eggs laid; The parameter acquisition unit is also used to update the fish egg parameters for the next cycle, and then proceed to the step of the drifting fish egg survival prediction model of the input calculation unit until the drifting time is greater than or equal to the hatching time threshold. The parameters of the fish eggs include: egg location, egg diameter, and egg density.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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