Bed-forming flow estimation method and equipment based on river characteristics

Abstract

The present invention provides a method and device for estimating riverbed flow based on river characteristics. The method comprises steps 1 to 7. The present invention enables the gradual screening of calculation models and automatic parameter calibration and optimization for the selected models. This reduces the reliance of previous methods on manual experience, improves engineering efficiency, and meets the needs of engineering practice.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of river management engineering, and in particular to a method and device for estimating river bed flow based on river characteristics. Background Art

[0002] Bed-forming flow is a crucial parameter in alluvial river management planning, water-related engineering design, and river ecological restoration. It is widely used in flood control, navigation, and other engineering fields. The magnitude of bed-forming flow is closely related to river characteristics. For rivers in arid regions and gravel rivers with large bed sediments, bed-forming flow has a recurrence period greater than one year. In contrast, for rivers in humid regions with well-vegetated basins and rivers with fine sediments, bed-forming flow often has a recurrence period of less than one year. Regardless of river type, bed-forming flow must be determined in conjunction with long-term hydrological and sediment observation data.

[0003] Currently, the engineering community generally believes that bed-forming flow is the flow range with the highest sediment transport efficiency, and its bed-forming effect is equivalent to that of a multi-year flow process. For alluvial balanced rivers, when the in-channel flow reaches the bed-forming flow, the water level is essentially flush with the floodplain. However, in practical applications, the determination of bed-forming flow relies heavily on experience in two aspects. First, there are multiple calculation models. Different models, such as graphical and analytical methods, are used for different water and sediment inflow conditions, such as arid and humid regions, and balanced and non-equilibrium rivers. The applicability of these models varies greatly, and can even produce conflicting results. Second, parameter selection is subjective and arbitrary. Steps such as selecting the number of flow levels, flow interval division, and the selection of the empirical frequency curve line shape rely largely on experience. If the flow interval division is not properly implemented, the true water and sediment transport characteristics may not be reflected, and the bed-forming flow cannot be determined. Therefore, the development of a bed-forming flow estimation method and equipment based on river characteristics that can effectively overcome the shortcomings of the above-mentioned related technologies has become a technical problem that needs to be urgently addressed in the industry. Summary of the Invention

[0004] In response to the above-mentioned problems existing in the prior art, an embodiment of the present invention provides a method and device for estimating bed-forming flow based on river characteristics.

[0005] In a first aspect, an embodiment of the present invention provides a bed-forming flow estimation method based on river characteristics, including: step 1, water-sediment sequence consistency test and flow interval division method selection, including: step 1.1 taking the daily average flow Q and sediment transport rate Qs observation data of the hydrological station M years long sequence to test whether the water-sediment sequence is consistent. If not, the premise is not established; step 1.2 based on the premise in step 1.1, for the daily average flow sequence, statistics the average flow of m consecutive days and the corresponding flow variation amplitude ΔQ of the adjacent m days m , dot painting Relationship, using the formula Perform linear fitting, where k is the first parameter; Step 1.3: Based on the premise of step 1.1, if the corresponding daily average water level Z series data is available, plot the Q-Z relationship using the formula Q = a(Z-Z0) b Perform nonlinear fitting, where a is the second parameter, b is the third parameter, and Z0 is the fourth parameter; Step 1.4, based on the premise of step 1.1, divide the daily average flow data into N1 flow intervals in equal intervals from small to large, ensuring that each interval has more than 2 data, and use the arithmetic mean of the left and right endpoints of the interval as the interval representative flow value Q i , i = 1 ~ N1, and calculate the average sediment transport rate of each interval and standard deviation where n i is the number of flows in the ith interval, Qs j is the sediment transport rate corresponding to the flow in the ith interval, and the dot plot is σ(Qs i )~Q i , i=1~N1, using the formula σ(Qs)=cQ d Fit the relationship between the two, where c is the fifth parameter and d is the sixth parameter; if k < 0.01 in step 1.2, b < 2 in step 1.3, and d < 1.5 in step 1.4 are satisfied in step 1.5, then divide the flow sequence into flow intervals according to arithmetic equal intervals, and the interval size is initially selected as the average value of the flow variation amplitude of adjacent m days in step 1.2 The corresponding interval number N2 is The flow value of the interval represents the arithmetic mean of the left and right endpoints of the interval; Step 1.6 If the conditions in Step 1.5 are not met, the flow sequence is divided into flow intervals at equal logarithmic intervals, and the number of intervals is initially adopted. Calculate, the range of the i-th interval is The flow rate value of the corresponding interval is the geometric mean of the left and right endpoints of the interval, that is, Step 2. Selection of flow frequency curve and evaluation of fitting effect of multi-year flow series probability density, including: Step 2.1 According to the N2 flow intervals divided in step 1, count the flow frequency D in each interval i , and calculate the probability density of each level interval Where D is the total number of days in the daily average flow sequence, dQ i is the length of the i-th interval; Step 2.2 is based on the calculated p(Q i ) and Q i, draw the empirical frequency curve p(Q)~Q, check the continuity of the curve and make corresponding adjustments: if the curve is smooth and continuous, keep the number and spacing of intervals unchanged; if the number of curve discontinuities exceeds 10% of the total number of intervals N2, it is necessary to appropriately reduce the number of intervals and repeat the statistics in step 2.1; if the number of curve discontinuities is less than 10% of the total number of intervals N2, the discontinuous interval and the adjacent subsequent interval can be merged into one interval, and the corresponding interval representative value and probability density are recalculated; step 2.3 selects a suitable theoretical distribution function for fitting the empirical frequency curve, and calculates the corresponding goodness of fit Where p(Q i )、f(Q i ) are the measured value and the calculated value of the fitting function of the probability density of the i-th level flow, The measured probability density logarithm of each level of flow is averaged, n is the flow level, and there may be three situations for empirical frequency curve fitting: i. The curve is smooth and continuous without turning points, and the overall empirical frequency curve is fitted with a goodness of fit LNSE>0.9; ii. The curve is obviously segmented between low water flow and medium flood flow, and the curve is fitted for the medium flood flow part with a goodness of fit LNSE>0.9; iii. In other cases, if there are multiple theoretical functions that meet situation i or ii, the best one is selected based on the maximum LNSE value; Step 3. Multi-year average sediment transport rate curve fitting and effect evaluation, including: Step 3.1 According to the flow interval adjusted in step 2.2, the average sediment transport rate in each interval is calculated And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q; Step 3.2 uses the power function Qs=αQ for the sediment transport rate curve β Perform fitting, where α is the seventh parameter and β is the eighth parameter, and calculate the corresponding goodness of fit Where Cov(·,·) represents covariance, Var(·) represents variance, and there are three possible cases of sediment transport rate curve fitting: I. The curve is smooth, monotonous, and has no turning points. The power function is directly used for fitting. The goodness of fit R 2 ≥0.9, II. The curve turns in the low water flow area, and a power function is fitted for the part above the turning point of the curve. The goodness of fit R 2 ≥0.9, III. Other cases; Step 4. Priority judgment of the bed-forming flow solution method: If both the flow frequency curve and the sediment transport rate curve can be fitted with a good theoretical function, that is, case i or ii in step 2.3 and case I or II in step 3.2 appear at the same time, then the analytical method in step 6 is preferred to solve the bed-forming flow, otherwise the graphical method in step 5 is preferred to solve the bed-forming flow; Step 5. The graphical method for solving the bed-forming flow includes: Step 5.1 According to the flow interval adjusted in step 2.2, calculate the total sediment transport in each interval Used to characterize the geomorphic work under each flow level; Step 5.2 calculates Φ i With Q i , draw the geomorphic work curve Φ~Q, and determine the bed-forming flow: if the maximum peak of the curve is prominent, take the flow corresponding to the maximum peak as the bed-forming flow; if the curve has two peaks of equal magnitude under different flow levels, select the larger flow level as the bed-forming flow; if the curve has two peaks of equal magnitude under similar flow levels, take the average flow of the two as the bed-forming flow; if the curve has multiple peaks of equal magnitude, it is necessary to redraw the geomorphic work curve by reducing the number of intervals until the above situation occurs; Step 6. Analytical method to solve the bed-forming flow, including: Step 6.1 Use the theoretical frequency distribution function f(Q) and the sediment transport rate power function Qs=αQ β , we can get that under the arithmetic interval, the size of the geomorphic work Φ is proportional to Q β f(Q), in the logarithmic interval, the size of the geomorphic work Φ is proportional to Q β+1 f(Q); Step 6.2 for Q β f(Q) or Q β+1 Take the derivative of f(Q) and take the zero point of its derivative value as the analytical solution of the bed flow Q e Step 7. Comprehensive evaluation of bed-forming flow: The characteristic flows that appear in the above steps are: ① If the frequency distribution of medium flood flow in step 2 obeys a piecewise power function, the inflection point of the curve is a1; ② If the sediment transport rate curve in step 3 turns under large flow, the turning point is Q t , ③ the bed-forming flow value solved by the graphical method in step 5, ④ the bed-forming flow value solved by the analytical method in step 6 if it can be solved; take ③ value as the standard of bed-forming flow, if ① value is close to ③ value, it means that the bed-forming flow is dominated by the hydrological frequency characteristics, if ② value is close to ③ value, it means that the flat beach flow and the bed-forming flow are similar, and the river channel is in a quasi-equilibrium state, if ④ value is close to ③ value, it means that the selected theoretical distribution function is in good agreement with the actual situation, and use analytical formula to analyze the influence of hydrological and sediment transport parameters on bed-forming flow.

[0006] Based on the content of the above method embodiment, the bed-forming flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 1.1, the method for judging whether the water and sediment sequence is consistent is as follows: first, calculate the one-year autocorrelation coefficient R(365) of the daily average flow sequence, where Q(t) and Q(t+365) are the average daily flow rates on the tth and (t+365th)th day in the sequence respectively. If R(365)>0.7, it means that the water flow sequence is consistent; secondly, take the average annual flow rate of the past years The average annual sediment transport rate Through standardization Convert to and draw and A linear trend line that changes over time; if the slope of the trend line is within ±0.05, it means that the water-sediment sequence is consistent; if the first and second conditions are met at the same time, it means that the water-sediment sequence is consistent.

[0007] Based on the content of the above method embodiment, the river bed flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 1.2, the method for determining the value of m is: linearly regressing the flow sequence Q(t) with the flow sequence Q(t+m-1) lagged by m-1 days, and determining their correlation coefficient R 2 Value, denoted as R 2 (m), m value starts from 2, if R 2 (2) If it is less than 0.98, m is set to 2. Otherwise, the value of m increases gradually until R 2 (m)≥0.98>R 2 (m+1), determine the final value of m.

[0008] Based on the content of the above method embodiment, the bed-forming flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 1.4, the method for determining the value of N1 is as follows: first, the flow interval length is set to 0.25S, where S is the standard deviation of the flow sequence, and the number of intervals is determined. Determine whether there are less than 2 flow data in each interval. Intervals with less than 2 data are determined to be too few data intervals: if the interval with too few data exceeds 10% of the total number of intervals N1, then double the interval length, that is, 0.5S, and re-count. If the interval with too few data is less than 10% of the total number of intervals N1, appropriately reduce the number of 2 to 4 intervals and re-count. Repeat the above operation until there are no less than 2 flow data in each interval; in step 1.4, the power function fitting generally reflects the relationship between σ(Qs) and Q, and screens out data scatter points that deviate from the power function, so that the power function fitting correlation coefficient is above 0.7.

[0009] Based on the contents of the above method embodiments, the river characteristics-based bed-forming flow estimation method provided in the embodiments of the present invention, in step 2.3, the low water flow and the medium flood flow are divided, with the flow with a cumulative frequency of 50% or the average flow as the boundary, or based on the empirical frequency distribution characteristics, the turning point of the frequency curve is selected as the dividing flow; the selection of the dividing flow must ensure that the subsequently calculated bed-forming flow value is within the study range, otherwise the division is re-performed.

[0010] Based on the content of the above method embodiment, the bed-forming flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 3.2, the sediment transport rate curve turns at low water flow or high water flow, and its turning point Q tThe identification method is: using the power function Qs = αQ β When fitting the sediment transport rate curve, the data scatter points that deviate from the power function are gradually screened out from both ends of the curve until the correlation coefficient R of the power function fitting is 2 The value is the largest.

[0011] In a second aspect, an embodiment of the present invention provides a device for estimating bed-forming flow based on river characteristics, including: a first main module for implementing step 1, water-sediment sequence consistency test and flow interval division method selection, including: step 1.1 taking the M-year long sequence daily average flow Q and sediment transport rate Qs observation data of the hydrological station to test whether the water-sediment sequence is consistent. If not, the premise does not hold; step 1.2 based on the premise in step 1.1, for the daily average flow sequence, statistics the average flow of m consecutive days and the corresponding flow variation amplitude ΔQ of the adjacent m days m , dot painting Relationship, using the formula Perform linear fitting, where k is the first parameter; Step 1.3: Based on the premise of step 1.1, if the corresponding daily average water level Z series data is available, plot the Q-Z relationship using the formula Q = a(Z-Z0) b Perform nonlinear fitting, where a is the second parameter, b is the third parameter, and Z0 is the fourth parameter; Step 1.4, based on the premise of step 1.1, divide the daily average flow data into N1 flow intervals in equal intervals from small to large, ensuring that each interval has more than 2 data, and use the arithmetic mean of the left and right endpoints of the interval as the interval representative flow value Q i , i = 1 ~ N1, and calculate the average sediment transport rate of each interval and standard deviation where n i is the number of flows in the ith interval, Qs j is the sediment transport rate corresponding to the flow in the ith interval, and the dot plot is σ(Qs i )~Q i , i=1~N1, using the formula σ(Qs)=cQ d Fit the relationship between the two, where c is the fifth parameter and d is the sixth parameter; if k < 0.01 in step 1.2, b < 2 in step 1.3, and d < 1.5 in step 1.4 are satisfied in step 1.5, then divide the flow sequence into flow intervals according to arithmetic equal intervals, and the interval size is initially selected as the average value of the flow variation amplitude of adjacent m days in step 1.2 Number of corresponding intervals The flow value of the interval represents the arithmetic mean of the left and right endpoints of the interval; Step 1.6 If the conditions in Step 1.5 are not met, the flow sequence is divided into flow intervals at equal logarithmic intervals, and the number of intervals is initially adopted. Calculate, the range of the i-th interval is The flow rate value of the corresponding interval is the geometric mean of the left and right endpoints of the interval, that is, The second main module is used to implement step 2. The flow frequency curve selection and the multi-year flow series probability density fitting effect evaluation include: step 2.1 according to the N2 flow intervals divided in step 1, the flow frequency D in each interval is counted. i , and calculate the probability density of each level interval Where D is the total number of days in the daily average flow sequence, dQ i is the length of the i-th interval; Step 2.2 is based on the calculated p(Q i ) and Q i , draw the empirical frequency curve p(Q)~Q, check the continuity of the curve and make corresponding adjustments: if the curve is smooth and continuous, keep the number and spacing of intervals unchanged; if the number of curve discontinuities exceeds 10% of the total number of intervals N2, it is necessary to appropriately reduce the number of intervals and repeat the statistics in step 2.1; if the number of curve discontinuities is less than 10% of the total number of intervals N2, the discontinuous interval and the adjacent subsequent interval can be merged into one interval, and the corresponding interval representative value and probability density are recalculated; step 2.3 selects a suitable theoretical distribution function for fitting the empirical frequency curve, and calculates the corresponding goodness of fit Where p(Q i )、f(Q i ) are the measured value and the calculated value of the fitting function of the probability density of the i-th level flow, The measured probability density logarithm of each level of flow is averaged, n is the flow level, and there may be three situations in the empirical frequency curve fitting: i. The curve is smooth and continuous without turning points, and the fitting is performed on the overall empirical frequency curve, with a goodness of fit LNSE>0.9; ii. The curve is obviously segmented between low water flow and medium flood flow, and the curve is fitted for the medium flood flow part, with a goodness of fit LNSE>0.9; iii. In other cases, if there are multiple theoretical functions that meet situation i or ii, the best one is selected based on the maximum LNSE value; the third main module is used to implement step 3. Multi-year average sediment transport rate curve fitting and effect evaluation, including: step 3.1 According to the flow interval adjusted in step 2.2, the average sediment transport rate in each interval is calculated. And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q; Step 3.2 uses the power function Qs=αQ for the sediment transport rate curve β Perform fitting, where α is the seventh parameter and β is the eighth parameter, and calculate the corresponding goodness of fit Where Cov(·,·) represents covariance, Var(·) represents variance, and there are three possible cases of sediment transport rate curve fitting: I. The curve is smooth, monotonous, and has no turning points. The power function is directly used for fitting. The goodness of fit R2 ≥0.9, II. The curve turns in the low water flow area, and a power function is fitted for the part above the turning point of the curve. The goodness of fit R 2 ≥0.9, III. Other cases; The fourth main module is used to implement the priority judgment of the bed-forming flow solution method in step 4: If both the flow frequency curve and the sediment transport rate curve can be fitted with a good theoretical function, that is, case i or ii in step 2.3 and case I or II in step 3.2 appear at the same time, then the analytical method in step 6 is used to solve the bed-forming flow, otherwise the graphical method in step 5 is recommended to solve the bed-forming flow; The fifth main module is used to implement step 5. The graphical method for solving the bed-forming flow includes: Step 5.1 Count the total sediment transport in each interval according to the flow interval adjusted in step 2.2 Used to characterize the geomorphic work under each flow level; Step 5.2 calculates Φ i and

[0012] Q i , draw the geomorphic work curve Φ~Q, and determine the bed-forming flow: if the maximum peak of the curve is prominent, then take the flow corresponding to the maximum peak as the bed-forming flow; if the curve has two peaks of equal magnitude under different flow levels, select the larger flow level as the bed-forming flow; if the curve has two peaks of equal magnitude under similar flow levels, then take the average flow of the two as the bed-forming flow; if the curve has multiple peaks of equal magnitude, then it is necessary to redraw the geomorphic work curve by reducing the number of intervals until the above situation occurs; the sixth main module is used to implement step 6. The analytical method is used to solve the bed-forming flow, including: step 6.1 using the theoretical frequency distribution function f(Q) and the sediment transport rate power function Qs=αQ β , we can get that under the arithmetic interval, the size of the geomorphic work Φ is proportional to Q β f(Q), in the logarithmic interval, the size of the geomorphic work Φ is proportional to Q β+1 f(Q); Step 6.2 for Q β f(Q) or Q β+1 Take the derivative of f(Q) and take the zero point of its derivative value as the analytical solution of the bed flow Q e The seventh main module is used to implement step 7. Comprehensive evaluation of bed-forming flow: The characteristic flows that appear in the above steps are: ① If the frequency distribution of medium flood flow in step 2 obeys a piecewise power function, the inflection point of the curve is a1; ② If the sediment transport rate curve in step 3 turns under large flow, the turning point is Q t, ③ the bed-forming flow value solved by the graphical method in step 5, ④ the bed-forming flow value solved by the analytical method in step 6 if it can be solved; take ③ value as the standard of bed-forming flow, if ① value is close to ③ value, it means that the bed-forming flow is dominated by the hydrological frequency characteristics, if ② value is close to ③ value, it means that the flat beach flow and the bed-forming flow are similar, and the river channel is in a quasi-equilibrium state, if ④ value is close to ③ value, it means that the selected theoretical distribution function is in good agreement with the actual situation, and use analytical formula to analyze the influence of hydrological and sediment transport parameters on bed-forming flow.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0014] at least one processor; and

[0015] at least one memory in communication with the processor, wherein:

[0016] The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the river characteristic-based bed-forming flow estimation method provided by any one of the various implementations of the first aspect.

[0017] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute the river characteristic-based bed-forming flow estimation method provided by any one of the various implementation methods of the first aspect.

[0018] The river-characteristic-based bed-forming flow estimation method and device provided in the embodiments of the present invention can gradually realize the layer-by-layer screening of calculation models, and realize automatic parameter calibration and parameter optimization for the screened calculation models, thereby reducing the dependence of previous technical methods on manual experience, improving engineering efficiency, and meeting the needs of engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flow chart of a method for estimating bed-forming flow based on river characteristics provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the structure of a device for estimating bed-forming flow based on river characteristics provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of flow rate variation effects under different flow rates provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of the effect of varying sediment transport rates under different flow rates provided in an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of the fitting effect of a common theoretical distribution on a frequency curve provided in an embodiment of the present invention;

[0026] Figure 7 A schematic diagram of the fitting effect of the multi-year average sediment transport rate curve provided in an embodiment of the present invention;

[0027] Figure 8 A schematic diagram of the geomorphic work curve involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention can be arbitrarily combined with each other to form a feasible technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0029] The embodiment of the present invention provides a method for estimating bed-forming flow based on river characteristics, see Figure 1 The method includes: step 1, water-sediment sequence consistency test and flow interval division method selection, including: step 1.1 taking the daily average flow Q and sediment transport rate Qs observation data of the hydrological station M years long sequence to test whether the water-sediment sequence is consistent. If not, the premise is not established; step 1.2 based on the premise of step 1.1, for the daily average flow sequence, statistics the average flow of consecutive m days and the corresponding flow variation amplitude ΔQ of the adjacent m days m , dot painting Relationship, using the formula Perform linear fitting, where k is the first parameter; Step 1.3: Based on the premise of step 1.1, if the corresponding daily average water level Z series data is available, plot the Q-Z relationship using the formula Q = a(Z-Z0) b Perform nonlinear fitting, where a is the second parameter, b is the third parameter, and Z0 is the fourth parameter; Step 1.4, based on the premise of step 1.1, divide the daily average flow data into N1 flow intervals in equal intervals from small to large, ensuring that each interval has more than 2 data, and use the arithmetic mean of the left and right endpoints of the interval as the interval representative flow value Q i , i = 1 ~ N1, and calculate the average sediment transport rate of each interval and standard deviation where n i is the number of flows in the ith interval, Qs j is the sediment transport rate corresponding to the flow in the ith interval, and the dot plot is σ(Qs i )~Q i , i=1~N1, using the formula σ(Qs)=cQ d Fit the relationship between the two, where c is the fifth parameter and d is the sixth parameter; if k < 0.01 in step 1.2, b < 2 in step 1.3, and d < 1.5 in step 1.4 are satisfied in step 1.5, then divide the flow sequence into flow intervals according to arithmetic equal intervals, and the interval size is initially selected as the average value of the flow variation amplitude of adjacent m days in step 1.2 The corresponding interval number N2 is The flow value of the interval represents the arithmetic mean of the left and right endpoints of the interval; Step 1.6 If the conditions in Step 1.5 are not met, the flow sequence is divided into flow intervals at equal logarithmic intervals, and the number of intervals is initially adopted. Calculate, the range of the i-th interval is The flow rate value of the corresponding interval is the geometric mean of the left and right endpoints of the interval, that is, Step 2. Selection of flow frequency curve and evaluation of fitting effect of multi-year flow series probability density, including: Step 2.1 According to the N2 flow intervals divided in step 1, count the flow frequency D in each interval i , and calculate the probability density of each level interval Where D is the total number of days in the daily average flow sequence, dQ i is the length of the i-th interval; Step 2.2 is based on the calculated p(Q i ) and Q i, draw the empirical frequency curve p(Q)~Q, check the continuity of the curve and make corresponding adjustments: if the curve is smooth and continuous, keep the number and spacing of intervals unchanged; if the number of curve discontinuities exceeds 10% of the total number of intervals N2, it is necessary to appropriately reduce the number of intervals and repeat the statistics in step 2.1; if the number of curve discontinuities is less than 10% of the total number of intervals N2, the discontinuous interval and the adjacent subsequent interval can be merged into one interval, and the corresponding interval representative value and probability density are recalculated; step 2.3 selects a suitable theoretical distribution function for fitting the empirical frequency curve, and calculates the corresponding goodness of fit Where p(Q i )、f(Q i ) are the measured value and the calculated value of the fitting function of the probability density of the i-th level flow, The measured probability density logarithm of each level of flow is averaged, n is the flow level, and there may be three situations for empirical frequency curve fitting: i. The curve is smooth and continuous without turning points, and the overall empirical frequency curve is fitted with a goodness of fit LNSE>0.9; ii. The curve is obviously segmented between low water flow and medium flood flow, and the curve is fitted for the medium flood flow part with a goodness of fit LNSE>0.9; iii. In other cases, if there are multiple theoretical functions that meet situation i or ii, the best one is selected based on the maximum LNSE value; Step 3. Multi-year average sediment transport rate curve fitting and effect evaluation, including: Step 3.1 According to the flow interval adjusted in step 2.2, the average sediment transport rate in each interval is calculated And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q; Step 3.2 uses the power function Qs=αQ for the sediment transport rate curve β Perform fitting, where α is the seventh parameter and β is the eighth parameter, and calculate the corresponding goodness of fit Where Cov(·,·) represents covariance, Var(·) represents variance, and there are three possible cases of sediment transport rate curve fitting: I. The curve is smooth, monotonous, and has no turning points. The power function is directly used for fitting. The goodness of fit R 2 ≥0.9, II. The curve turns in the low water flow area, and a power function is fitted for the part above the turning point of the curve. The goodness of fit R 2 ≥0.9, III. Other cases; Step 4. Priority judgment of the bed-forming flow solution method: If both the flow frequency curve and the sediment transport rate curve can be fitted with a good theoretical function, that is, case i or ii in step 2.3 and case I or II in step 3.2 appear at the same time, the analytical method in step 6 is preferred to solve the bed-forming flow, otherwise the graphical method in step 5 is preferred to solve the bed-forming flow; Step 5. The graphical method for solving the bed-forming flow includes: Step 5.1 According to the flow interval adjusted in step 2.2, statistics of each

[0030] Total sediment transport within the interval Used to characterize the geomorphic work at each flow level; Step 5.2

[0031] According to the calculation of Φ i With Q i , draw the geomorphic work curve Φ~Q, and determine the bed-forming flow: if the maximum peak of the curve is prominent, take the flow corresponding to the maximum peak as the bed-forming flow; if the curve has two peaks of equal magnitude under different flow levels, select the larger flow level as the bed-forming flow; if the curve has two peaks of equal magnitude under similar flow levels, take the average flow of the two as the bed-forming flow; if the curve has multiple peaks of equal magnitude, it is necessary to redraw the geomorphic work curve by reducing the number of intervals until the above situation occurs; Step 6. Analytical method to solve the bed-forming flow, including: Step 6.1 Use the theoretical frequency distribution function f(Q) and the sediment transport rate power function Qs=αQ β , we can get that under the arithmetic interval, the size of the geomorphic work Φ is proportional to Q β f(Q), in the logarithmic interval, the size of the geomorphic work Φ is proportional to Q β+1 f(Q); Step 6.2 for Q β f(Q) or Q β+1 Take the derivative of f(Q) and take the zero point of its derivative value as the analytical solution of the bed flow Q e Step 7. Comprehensive evaluation of bed-forming flow: The characteristic flows that appear in the above steps are: ① If the frequency distribution of medium flood flow in step 2 obeys a piecewise power function, the inflection point of the curve is a1; ② If the sediment transport rate curve in step 3 turns under large flow, the turning point is Q t , ③ the bed-forming flow value solved by the graphical method in step 5, ④ the bed-forming flow value solved by the analytical method in step 6 if it can be solved; take ③ value as the standard of bed-forming flow, if ① value is close to ③ value, it means that the bed-forming flow is dominated by the hydrological frequency characteristics, if ② value is close to ③ value, it means that the flat beach flow and the bed-forming flow are similar, and the river channel is in a quasi-equilibrium state, if ④ value is close to ③ value, it means that the selected theoretical distribution function is in good agreement with the actual situation, and use analytical formula to analyze the influence of hydrological and sediment transport parameters on bed-forming flow.

[0032] Based on the content of the above method embodiment, as an optional embodiment, the bed-forming flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 1.1, the method for judging whether the water and sediment sequence is consistent is as follows: first, calculate the one-year autocorrelation coefficient R(365) of the daily average flow sequence, where Q(t) and Q(t+365) are the average daily flow rates on the tth and (t+365th)th day in the sequence respectively. If R(365)>0.7, it means that the water flow sequence is consistent; secondly, take the average annual flow rate of the past years The average annual sediment transport rate Through standardization Convert to and draw and Change over time

[0033] If the slope of the trend line is within ±0.05, it means that the water-sediment sequence is consistent; if the first and second conditions are met at the same time, it means that the water-sediment sequence is consistent.

[0034] Based on the content of the above method embodiment, as an optional embodiment, the river bed flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 1.2, the method for determining the value of m is: linearly regressing the flow sequence Q(t) with the flow sequence Q(t+m-1) lagged by m-1 days, and determining their correlation coefficient R 2 Value, denoted as R 2 (m), m value starts from 2, if R 2 (2) If it is less than 0.98, m is set to 2. Otherwise, the value of m increases gradually until R 2 (m)≥0.98>R 2 (m+1), determine the final value of m.

[0035] Based on the content of the above method embodiment, as an optional embodiment, the bed-forming flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 1.4, the method for determining the value of N1 is: first, the flow interval length is set to 0.25S, S is the standard deviation of the flow sequence, and the number of intervals is determined. Determine whether there are less than 2 flow data in each interval. Intervals with less than 2 data are determined to be too few data intervals: if the interval with too few data exceeds 10% of the total number of intervals N1, then double the interval length, that is, 0.5S, and re-count. If the interval with too few data is less than 10% of the total number of intervals N1, appropriately reduce the number of 2 to 4 intervals and re-count. Repeat the above operation until there are no less than 2 flow data in each interval; in step 1.4, the power function fitting generally reflects the relationship between σ(Qs) and Q, and screens out data scatter points that deviate from the power function, so that the power function fitting correlation coefficient is above 0.7.

[0036] Based on the content of the above method embodiment, as an optional embodiment, the river characteristics-based bed-forming flow estimation method provided in the embodiment of the present invention, in step 2.3, the low water flow and the medium flood flow are divided, with the flow with a cumulative frequency of 50% or the average flow as the boundary, or based on the empirical frequency distribution characteristics, the turning point of the frequency curve is selected as the dividing flow; the selection of the dividing flow must ensure that the subsequently calculated bed-forming flow value is within the study range, otherwise the division is performed again.

[0037] Based on the content of the above method embodiment, as an optional embodiment, the bed-forming flow estimation method based on river characteristics provided in the embodiment of the present invention, in step 3.2, the sediment transport rate curve turns at low water flow or high water flow, and its turning point Q t The identification method is: using the power function Qs = αQ β When fitting the sediment transport rate curve, the data scatter points that deviate from the power function are gradually screened out from both ends of the curve until the correlation coefficient R of the power function fitting is 2 The value is the largest.

[0038] The river characteristics-based bed-forming flow estimation method provided by the embodiment of the present invention can gradually realize the layer-by-layer screening of calculation models, and realize automatic parameter calibration and parameter optimization for the screened calculation models, thereby reducing the dependence of previous technical methods on manual experience, improving engineering efficiency, and meeting the needs of engineering practice.

[0039] In another implementation, the daily average flow, sediment transport rate, and water level data of a hydrological station for 49 consecutive years (a total of 17,897 days) were used, and the following steps were included:

[0040] Step 1. Check the consistency of water and sediment sequence and select the flow interval division method:

[0041] Step 1.1 First, use the 49-year long series of daily average flow data of the hydrological station to calculate the one-year autocorrelation coefficient of the daily average flow series. It is greater than the set threshold of 0.7, indicating that the traffic sequence has good consistency. The average annual sediment transport rate Through standardization Convert to and Draw in Excel software and A time series scatter plot was constructed and a linear trend line was fitted. The trend slopes were 0.0011 and -0.0101, respectively, which were within the set threshold of ±0.05, indicating that the water-sediment series had good consistency.

[0042] Step 1.2: First, let m = 2. In Excel, perform a linear regression between the flow sequence Q(t) and the flow sequence Q(t+m-1) lagged by (m-1) days to determine the correlation coefficient R. 2 (2) = 0.9979, and then let m increase in sequence and perform similar operations to determine its correlation coefficient, and obtain R 2 (3) = 0.9922, R 2 (4) = 0.9836, R 2 (5) = 0.9725. According to the setting condition R 2(4)≥0.98>R 2 (5), determine that m is 4. Therefore, the average flow rate for 4 consecutive days is calculated. Plot the corresponding flow variation ΔQ4 over the four consecutive days in Excel software. Relationship (such as Figure 4 ), and adopt the formula Perform linear fitting and determine its parameter k=0.0454.

[0043] Step 1.3 Using the daily average flow and water level data, use the formula Q = a(Z-Z0) in SPSS software b Perform nonlinear fitting and determine the water level-discharge relationship as Q=4.56(Z+8.06) 3.07 , correlation coefficient R 2 The value is 0.99.

[0044] Step 1.4 First, the flow interval length is set to 0.25S (where the standard deviation of the flow sequence S = 155556m 3 / s), number of intervals (Among them, the maximum flow Q max =91800m 3 / s, minimum flow Q min =6300m 3 / s). Then the traffic sequence is divided into 21 intervals from small to large, with an interval length of 4071: [6300, 10371], (10371, 14443], ..., (87729, 91800). Statistics show that the data in each interval is no less than 2, which meets the set requirements. Therefore, N1 is set to 21, and the average value of the left and right endpoints of the interval is used as the representative value Q i , and calculate the average sediment transport rate of each interval and standard deviation Finally, plot σ(Qs i )~Q i , i=1~N1, and adopt the formula σ(Qs)=cQ d Fit the relationship between the two, such as Figure 5 By removing some data points that obviously deviate from the power function relationship, we finally get the relationship σ(Qs)=2×10 -6 Q 2.13 , the correlation coefficient is 0.7571, which meets the set requirements.

[0045] Step 1.5: From the results of steps 1.2, 1.3, and 1.4 above, we can get k = 0.0454, b = 3.07, and d = 2.13. These conditions do not satisfy k < 0.01, b < 2, and d < 1.5, so the flow interval is not divided into equal intervals using arithmetic.

[0046] Step 1.6 Since the conditions of step 1.5 are not met, the flow intervals are divided into logarithmic equal intervals. According to the result of step 1.2, k = 0.0454, the number of intervals is preliminarily determined. The traffic sequence is divided into 61 flow intervals with equal logarithmic intervals from small to large: [6300, 6583], (6583, 6878], …, (87855, 91800]). The corresponding interval represents the geometric mean of the left and right endpoints of the interval: 6440, 6729, …, 89806.

[0047] Step 2. Selection of flow frequency curve and evaluation of the fitting effect of multi-year flow series probability density:

[0048] Step 2.1: Count the flow frequency D in each of the 61 flow intervals divided in step 1.6. i , and calculate the probability density of each level interval

[0049] Step 2.2 According to the calculated p(Q i ) and Q i , draw the empirical frequency curve p(Q)~Q, and find that the curve has good continuity, so there is no need to adjust the number or spacing of intervals.

[0050] Step 2.3 First, SPSS software was used to fit common theoretical distribution functions to the overall empirical frequency curve, including normal distribution N, lognormal distribution LN, three-parameter lognormal distribution LN3, gamma distribution G, Pearson type III distribution P3, logPearson type III distribution LP3, exponential function EXP, piecewise power function BPL, etc. Considering the large difference in the magnitude of random errors under different flood and dry season flows, the logarithmic Nash coefficient (LNSE) was used as the goodness of fit, that is,

[0051] like Figure 6 As shown in the figure, due to the obvious segmented characteristics of the empirical frequency curve, the fitting effects of common theoretical distribution functions are not ideal. The LNSE values corresponding to the normal distribution N, lognormal distribution LN, three-parameter lognormal distribution LN3, gamma distribution G, Pearson type III distribution P3, and logPearson type III distribution LP3 are 0.758, 0.847, 0.731, 0.896, 0.897, and 0.828, respectively, all of which are less than the set threshold of 0.9. Therefore, the peak mutation position on the frequency curve is selected as the boundary flow between low water flow and medium flood flow, and the exponential function EXP and the piecewise power function BPL are used to fit the frequency curve of the medium flood flow. The final calculated goodness of fit of the two are 0.882 and 0.970, respectively. Among them, the piecewise power function is the best match with the empirical frequency distribution, and its expression is: The LNSE value meets the set threshold requirement, which is consistent with situation ii.

[0052] Step 3. Multi-year average sediment transport rate curve fitting and effect evaluation:

[0053] Step 3.1: Calculate the average sediment transport rate in each of the 61 flow intervals divided above. And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q in Excel software.

[0054] Step 3.2: Based on the sediment transport rate curve, use Excel software to adopt the power function Qs = αQ β Perform fitting and calculate the correlation coefficient R 2 Value. Figure 7 As shown in the figure, after the large flow exceeds a certain critical flow, the sediment transport rate no longer increases with the flow, or even decreases, which obviously deviates from the power function law. Therefore, by gradually screening out the data scatter points that obviously deviate from the power function starting from the maximum flow, until the correlation coefficient R of the power function fitting is 2 The value is the largest, which determines the turning flow Q under large flow t =44476m 3 / s, below which the sediment transport rate curve follows Qs=3×10 -7 Q 2.37 , correlation coefficient R 2 The value is 0.9978, which meets the set threshold requirement, that is, it meets the situation III.

[0055] Step 4. Prioritize the bed-forming flow solution method. Based on the results of Steps 2.3 and 3.2, the medium-flood flow frequency curve follows a piecewise power function, but the sediment transport rate curve shows a turning point at high flows, corresponding to Case II + Case III. Therefore, the graphical method is preferred for solving the bed-forming flow.

[0056] Step 5. Calculate the bed-forming flow rate using the graphical method:

[0057] Step 5.1: Calculate the total sediment transport in each of the 61 flow intervals divided above. Used to characterize the geomorphic work at each flow level.

[0058] Step 5.2 Calculate Φ based on i With Q i , draw the geomorphic work curve Φ~Q in Excel and determine the bed-forming flow. Figure 8 As shown, the curve is at a flow rate of 42565m 3 / s and 46472m 3 A peak of similar magnitude appears at 100 m / s, so the average of the two is taken, which is 44519 m / s.3 / s, as the final bed-forming flow rate value.

[0059] Step 6. Because the sediment transport rate curve has an inflection point at high flow rates, this may affect the accuracy of the analytical method for calculating the bed-forming flow rate. To further analyze this impact, we will continue to attempt to calculate the bed-forming flow rate using the analytical method.

[0060] Step 6.1 Using the theoretical frequency distribution function and sediment transport rate power function Qs=3×10 -7 Q 2.37 , we can get that under logarithmic equal interval, the size of the landform work Φ is proportional to

[0061] Step 6.2 Take the derivative and take the zero point of the derivative value as the analytical solution Q of the bed flow rate e The solution is,

[0062] Step 7. Comprehensive evaluation of bed-forming flow. According to the above results, the characteristic flows include: ① The frequency distribution of the medium flood flow in step 2 obeys a piecewise power function, and the inflection point of the curve is a1 = 45000m 3 / s;②The sediment transport rate curve in step 3 turns under large flow, and its turning point Q t =44476m 3 / s; ③ The bed flow rate calculated by the graphical method in step 5 is 44519m 3 / s; ④ The bed flow rate value solved by the analytical method in step 5 is 44491m 3 / s. Taking the value ③ as the standard for bed-forming flow, the difference between the values ① and ③ is only 1.1%, indicating that the bed-forming flow is dominated by the hydrological frequency characteristics; the difference between the values ② and ③ is only 0.1%, indicating that the flatland flow is similar to the bed-forming flow and the river channel is in a quasi-equilibrium state; the difference between the values ④ and ③ is less than 0.1%, that is, in this case, even at a large flow rate exceeding the critical flow Q t The sediment transport rate curve deviates from the power function, but the analytical bed-forming discharge differs only slightly from the graphical method. This indicates, on the one hand, that the selected theoretical distribution function agrees well with the actual results, and on the other hand, that the river channel is in a quasi-equilibrium state, with water and sediment conditions and channel morphology in harmony. The bed-forming effect is most pronounced at flat areas. Furthermore, the analytical expression shows that the magnitude of the bed-forming discharge depends primarily on the inflection point a1 of the piecewise power function, indicating that hydrological frequency is the primary factor in the bed-forming discharge.

[0063] By combining river hydrology and sediment transport characteristics, the rationality of bed-forming flow calculation results is improved, the understanding of river equilibrium state and the dominant factors of bed-forming flow is deepened, and a basis is provided for river management decisions.

[0064] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with processor functions. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention can be encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a device for estimating bed-forming flow based on river characteristics, which is used to execute the method for estimating bed-forming flow based on river characteristics in the above method embodiment. Figure 2 The device includes: a first main module for implementing step 1, water-sediment sequence consistency test and flow interval division method selection, including: step 1.1 taking the daily average flow Q and sediment transport rate Qs observation data of the hydrological station M years long sequence to test whether the water-sediment sequence is consistent. If not, the premise is not established; step 1.2 based on the premise of step 1.1, for the daily average flow sequence, statistics the average flow of consecutive m days and the corresponding flow variation amplitude ΔQ of the adjacent m days m , dot painting Relationship, using the formula Perform linear fitting, where k is the first parameter; Step 1.3: Based on the premise of step 1.1, if the corresponding daily average water level Z series data is available, plot the Q-Z relationship using the formula Q = a(Z-Z0) b Perform nonlinear fitting, where a is the second parameter, b is the third parameter, and Z0 is the fourth parameter; Step 1.4, based on the premise of step 1.1, divide the daily average flow data into N1 flow intervals in equal intervals from small to large, ensuring that each interval has more than 2 data, and use the arithmetic mean of the left and right endpoints of the interval as the interval representative flow value Q i , i = 1 ~ N1, and calculate the average sediment transport rate of each interval and standard deviation where n i is the number of flows in the ith interval, Qs j is the sediment transport rate corresponding to the flow in the ith interval, and the dot plot is σ(Qs i )~Q i , i=1~N1, using the formula σ(Qs)=cQ d Fit the relationship between the two, where c is the fifth parameter and d is the sixth parameter; if k < 0.01 in step 1.2, b < 2 in step 1.3, and d < 1.5 in step 1.4 are satisfied in step 1.5, then divide the flow sequence into flow intervals according to arithmetic equal intervals, and the interval size is initially selected as the average value of the flow variation amplitude of adjacent m days in step 1.2 Number of corresponding intervals The flow value of the interval represents the arithmetic mean of the left and right endpoints of the interval; Step 1.6 If the conditions in Step 1.5 are not met, the flow sequence is divided into flow intervals at equal logarithmic intervals, and the number of intervals is initially adopted. Calculate, the range of the i-th interval is The corresponding interval represents the flow value of the interval

[0065] The geometric mean of the endpoints, i.e. The second main module is used to implement step 2. The flow frequency curve selection and the multi-year flow series probability density fitting effect evaluation include: step 2.1 according to the N2 flow intervals divided in step 1, the flow frequency D in each interval is counted. i , and calculate the probability density of each level interval Where D is the total number of days in the daily average flow sequence, dQ i is the length of the i-th interval; Step 2.2 is based on the calculated p(Q i ) and Q i , draw the empirical frequency curve p(Q)~Q, check the continuity of the curve and make corresponding adjustments: if the curve is smooth and continuous, keep the number and spacing of intervals unchanged; if the number of curve discontinuities exceeds 10% of the total number of intervals N2, it is necessary to appropriately reduce the number of intervals and repeat the statistics in step 2.1; if the number of curve discontinuities is less than 10% of the total number of intervals N2, the discontinuous interval and the adjacent subsequent interval can be merged into one interval, and the corresponding interval representative value and probability density are recalculated; step 2.3 selects a suitable theoretical distribution function for fitting the empirical frequency curve, and calculates the corresponding goodness of fit Where p(Q i )、f(Q i ) are the measured value and the calculated value of the fitting function of the probability density of the i-th level flow, The measured probability density logarithm of each level of flow is averaged, n is the flow level, and there may be three situations in the empirical frequency curve fitting: i. The curve is smooth and continuous without turning points, and the fitting is performed on the overall empirical frequency curve, with a goodness of fit LNSE>0.9; ii. The curve is obviously segmented between low water flow and medium flood flow, and the curve is fitted for the medium flood flow part, with a goodness of fit LNSE>0.9; iii. In other cases, if there are multiple theoretical functions that meet situation i or ii, the best one is selected based on the maximum LNSE value; the third main module is used to implement step 3. Multi-year average sediment transport rate curve fitting and effect evaluation, including: step 3.1 According to the flow interval adjusted in step 2.2, the average sediment transport rate in each interval is calculated. And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q; Step 3.2 uses the power function Qs=αQ for the sediment transport rate curve β Perform fitting, where α is the seventh parameter and β is the eighth parameter, and calculate the corresponding goodness of fit Where Cov(·,·) represents covariance, Var(·) represents variance, and there are three possible cases of sediment transport rate curve fitting: I. The curve is smooth, monotonous, and has no turning points. The power function is directly used for fitting. The goodness of fit R 2 ≥0.9, II. The curve turns in the low water flow area, and a power function is fitted for the part above the turning point of the curve. The goodness of fit R 2 ≥0.9, III. Other cases; The fourth main module is used to implement the priority judgment of the bed-forming flow solution method in step 4: If both the flow frequency curve and the sediment transport rate curve can be fitted with a good theoretical function, that is, case i or ii in step 2.3 and case I or II in step 3.2 appear at the same time, then the analytical method in step 6 is used to solve the bed-forming flow, otherwise the graphical method in step 5 is recommended to solve the bed-forming flow; The fifth main module is used to implement step 5. The graphical method for solving the bed-forming flow includes: Step 5.1 Count the total sediment transport in each interval according to the flow interval adjusted in step 2.2 Used to characterize the geomorphic work under each flow level; Step 5.2 calculates Φ i and

[0066] Q i , draw the geomorphic work curve Φ~Q, and determine the bed-forming flow: if the maximum peak of the curve is prominent, then take the flow corresponding to the maximum peak as the bed-forming flow; if the curve has two peaks of equal magnitude under different flow levels, select the larger flow level as the bed-forming flow; if the curve has two peaks of equal magnitude under similar flow levels, then take the average flow of the two as the bed-forming flow; if the curve has multiple peaks of equal magnitude, then it is necessary to redraw the geomorphic work curve by reducing the number of intervals until the above situation occurs; the sixth main module is used to implement step 6. The analytical method is used to solve the bed-forming flow, including: step 6.1 using the theoretical frequency distribution function f(Q) and the sediment transport rate power function Qs=αQ β , we can get that under the arithmetic interval, the size of the geomorphic work Φ is proportional to Q β f(Q), in the logarithmic interval, the size of the geomorphic work Φ is proportional to Q β+1 f(Q); Step 6.2 for Q β f(Q) or Q β+1 Take the derivative of f(Q) and take the zero point of its derivative value as the analytical solution of the bed flow Q e The seventh main module is used to implement step 7. Comprehensive evaluation of bed-forming flow: The characteristic flows that appear in the above steps are: ① If the frequency distribution of medium flood flow in step 2 obeys a piecewise power function, the inflection point of the curve is a1; ② If the sediment transport rate curve in step 3 turns under large flow, the turning point is Q t, ③ the bed-forming flow value solved by the graphical method in step 5, ④ the bed-forming flow value solved by the analytical method in step 6 if it can be solved; take ③ value as the standard of bed-forming flow, if ① value is close to ③ value, it means that the bed-forming flow is dominated by the hydrological frequency characteristics, if ② value is close to ③ value, it means that the flat beach flow and the bed-forming flow are similar, and the river channel is in a quasi-equilibrium state, if ④ value is close to ③ value, it means that the selected theoretical distribution function is in good agreement with the actual situation, and use analytical formula to analyze the influence of hydrological and sediment transport parameters on bed-forming flow.

[0067] The embodiment of the present invention provides a device for estimating river bed flow based on river characteristics, which uses Figure 2 Several modules in it can gradually realize the layer-by-layer screening of calculation models, and realize automatic parameter calibration and parameter optimization for the screened calculation models, which reduces the dependence of previous technical methods on manual experience, improves engineering efficiency, and meets the needs of engineering practice.

[0068] It should be noted that the device in the device embodiment provided by the present invention can be used to implement the method in the above-mentioned method embodiment as well as the method in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set. The principle is basically the same as the principle of the above-mentioned device embodiment provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned device embodiment, obtain the corresponding technical means and the technical solutions composed of these technical means by combining technical features, and ensure the practicality of the technical solutions, they can improve the device in the above-mentioned device embodiment to obtain the corresponding device class embodiment, thereby obtaining the corresponding device class embodiment for implementing the methods in other method class embodiments. For example:

[0069] Based on the content of the above device embodiment, as an optional embodiment, the river characteristics-based bed-forming flow estimation device provided in the embodiment of the present invention further includes: a first submodule for implementing the method of determining whether the water-sediment sequence is consistent in step 1.1: first, calculating the one-year autocorrelation coefficient R (365) of the daily average flow sequence, where Q(t) and Q(t+365) are the average daily flow rates on the tth and (t+365th)th day in the sequence respectively. If R(365)>0.7, it means that the water flow sequence is consistent; secondly, take the average annual flow rate of the past years The average annual sediment transport rate Through standardization Convert to

[0070] and draw and A linear trend line that changes over time; if the slope of the trend line is within ±0.05, it means that the water-sediment sequence is consistent; if the first and second conditions are met at the same time, it means that the water-sediment sequence is consistent.

[0071] Based on the content of the above device embodiment, as an optional embodiment, the river characteristics-based bed-forming flow estimation device provided in the embodiment of the present invention further includes: a second submodule for implementing the method for determining the value of m in step 1.2: linearly regressing the flow sequence Q(t) with the flow sequence Q(t+m-1) lagged by m-1 days, and determining their correlation coefficient R 2 Value, denoted as R 2 (m), m value starts from 2, if R 2 (2) If it is less than 0.98, m is set to 2. Otherwise, the value of m increases gradually until R 2 (m)≥0.98>R 2 (m+1), determine the final value of m.

[0072] Based on the content of the above device embodiment, as an optional embodiment, the bed-forming flow estimation device based on river characteristics provided in the embodiment of the present invention further includes: a third submodule for implementing in step 1.4, in step 1.4, the method for taking the value of N1 is: first, let the flow interval length be 0.25S, S is the standard deviation of the flow sequence, and determine the number of intervals Determine whether there are less than 2 flow data in each interval. Intervals with less than 2 data are determined to be too few data intervals: if the interval with too few data exceeds 10% of the total number of intervals N1, then double the interval length, that is, 0.5S, and re-count. If the interval with too few data is less than 10% of the total number of intervals N1, appropriately reduce the number of 2 to 4 intervals and re-count. Repeat the above operation until there are no less than 2 flow data in each interval; in step 1.4, the power function fitting generally reflects the relationship between σ(Qs) and Q, and screens out data scatter points that deviate from the power function, so that the power function fitting correlation coefficient is above 0.7.

[0073] Based on the contents of the above-mentioned device embodiment, as an optional embodiment, the river characteristic-based bed-forming flow estimation device provided in the embodiment of the present invention further includes: a fourth submodule, used to implement the division of low water flow and medium flood flow in step 2.3, with the flow or average flow with a cumulative frequency of 50% as the boundary, or based on the empirical frequency distribution characteristics, selecting the turning point of the frequency curve as the dividing flow; the selection of the dividing flow must ensure that the subsequently calculated bed-forming flow value is within the research range, otherwise re-division is performed.

[0074] Based on the content of the above device embodiment, as an optional embodiment, the river characteristics-based bed-forming flow estimation device provided in the embodiment of the present invention further includes: a fifth submodule for realizing that in step 3.2, the sediment transport rate curve turns at low water flow or high water flow, and its turning point Q t The identification method is: using the power function Qs = αQ β When fitting the sediment transport rate curve, the data scatter points that deviate from the power function are gradually screened out from both ends of the curve until the correlation coefficient R of the power function fitting is 2 The value is the largest.

[0075] The method of the embodiment of the present invention is implemented by electronic devices, so it is necessary to introduce the relevant electronic devices. Based on this purpose, the embodiment of the present invention provides an electronic device, such as Figure 3 As shown, the electronic device includes: at least one processor, a communications interface, at least one memory, and a communications bus, wherein the at least one processor, the communications interface, and the at least one memory communicate with each other via the communications bus. The at least one processor can call logic instructions in the at least one memory to execute all or part of the steps of the methods provided in the aforementioned method embodiments.

[0076] In addition, the logic instructions in the at least one memory mentioned above can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each method embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0077] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0078] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0079] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. Based on this understanding, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or sometimes in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0080] It should be noted that the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, the elements defined by the phrase "comprise..." do not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the elements.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for estimating bed-forming flow based on river characteristics, characterized in that: include: Step 1: Check the consistency of the water-sediment sequence and select the flow interval division method; Step 2: Select the flow frequency curve and evaluate the fitting effect of the probability density of the multi-year flow sequence; Step 3: Fit the multi-year average sediment transport rate curve and evaluate its effect; Step 4: Prioritize the bed-forming flow solution method; Step 5: Use the graphical method to solve the bed-forming flow; Step 6. Calculate the bed-forming flow rate using analytical method; Step 7. Comprehensive evaluation of bed-making flow; Step 2 specifically includes: Step 2.1 According to the N2 flow intervals divided in step 1, count the flow frequency D in each interval i , and calculate the probability density of each level interval Where D is the total number of days in the daily average flow sequence, dQ i is the length of the i-th interval; Step 2.2 is based on the calculated p(Q i ) and Q i , draw the empirical frequency curve p(Q)~Q, check the continuity of the curve and make corresponding adjustments: if the curve is smooth and continuous, keep the number and spacing of intervals unchanged; if the number of curve discontinuities exceeds 10% of the total number of intervals N2, it is necessary to appropriately reduce the number of intervals and repeat the statistics in step 2.1; if the number of curve discontinuities is less than 10% of the total number of intervals N2, the discontinuous interval and the adjacent subsequent interval can be merged into one interval, and the corresponding interval representative value and probability density are recalculated; step 2.3 selects an appropriate theoretical distribution function for fitting the empirical frequency curve and calculates the corresponding goodness of fit: Where p(Q i )、f(Q i ) are the measured value and the calculated value of the fitting function of the probability density of the i-th level flow, The measured probability density logarithm of each level of flow is averaged, where n is the flow level. There are three possible situations for fitting the empirical frequency curve: i. The curve is smooth and continuous without inflection. The overall empirical frequency curve is fitted, and the goodness of fit LNSE is greater than the fourth preset threshold. ii. The curve is clearly segmented between low water flow and medium flood flow. The curve is fitted for the medium flood flow part, and the goodness of fit LNSE is greater than the fourth preset threshold. iii. In other cases, if there are multiple theoretical functions that meet situation i or ii, the best one is selected based on the maximum LNSE value. In step 2.3, the low water flow and medium flood flow are divided by the flow with a cumulative frequency of 50% or the average flow, or the frequency curve inflection point is selected as the dividing flow based on the empirical frequency distribution characteristics. The selection of the dividing flow must ensure that the subsequent calculated bed-forming flow value is within the study range, otherwise the division is re-performed. Step 3 specifically includes: Step 3.1 According to the flow interval adjusted in step 2.2, calculate the average sediment transport rate in each interval And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q; Step 3.2 uses the power function Qs=αQ for the sediment transport rate curve β Perform fitting, where α is the seventh parameter and β is the eighth parameter, and calculate the corresponding goodness of fit Where Cov(·,·) represents covariance, Var(·) represents variance, and there are three possible cases of sediment transport rate curve fitting: I. The curve is smooth, monotonous, and has no turning points. The power function is directly used for fitting. The goodness of fit R 2 Greater than or equal to the fifth preset threshold, II. The curve turns in the low water flow area, and a power function is fitted for the part above the turning point of the curve. The goodness of fit R 2 Greater than or equal to the fifth preset threshold, III. Other cases; in step 3.2, the sediment transport rate curve turns at low water flow or high water flow, and its turning point Q t The identification method is: using the power function Qs = αQ β When fitting the sediment transport rate curve, the data scatter points that deviate from the power function are gradually filtered out from both ends of the curve until the power function goodness of fit R 2 The value is the largest.

2. The method for estimating bed-forming flow based on river characteristics according to claim 1, characterized in that: Step 1 specifically includes: Step 1.1 Take the observation data of the daily average flow Q and sediment transport rate Qs of the hydrological station for a long series of M years to check whether the water and sediment series are consistent. If not, the premise is not established; Step 1.2 Based on the premise in step 1.1, for the daily average flow series, calculate the average flow rate of consecutive m days. and the corresponding flow variation amplitude ΔQ of the adjacent m days m , dot painting Relationship, using the formula Perform linear fitting, where k is the first parameter; Step 1.3: Based on the premise of step 1.1, if the corresponding daily average water level Z series data is available, plot the Q-Z relationship using the formula Q = a(Z-Z0) b Perform nonlinear fitting, where a is the second parameter, b is the third parameter, and Z0 is the fourth parameter; Step 1.4, based on the premise of step 1.1, divide the daily average flow data into N1 flow intervals in equal intervals from small to large, ensuring that each interval has more than 2 data, and use the arithmetic mean of the left and right endpoints of the interval as the interval representative flow value Q i , i = 1 ~ N1, and calculate the average sediment transport rate of each interval and standard deviation where n i is the number of flows in the ith interval, Qs j is the sediment transport rate corresponding to the flow in the ith interval, and the dot plot is σ(Qs i )~Q i , i=1~N1, using the formula σ(Qs)=cQ d Fit the relationship between the two, where c is the fifth parameter and d is the sixth parameter; in step 1.5, if k in step 1.2 is less than the first preset threshold, b in step 1.3 is less than the second preset threshold, and d in step 1.4 is less than the third preset threshold, then divide the flow sequence into flow intervals according to arithmetic equal intervals, and the interval size is initially selected as the average value of the flow variation amplitude of adjacent m days in step 1.2 The corresponding interval number N2 is The flow value of the interval represents the arithmetic mean of the left and right endpoints of the interval; Step 1.6 If the conditions in Step 1.5 are not met, the flow sequence is divided into flow intervals at equal logarithmic intervals, and the number of intervals is initially adopted. Calculate, the range of the i-th interval is The flow rate value of the corresponding interval is the geometric mean of the left and right endpoints of the interval, that is, In step 1.1, the method to determine whether the water and sediment series are consistent is as follows: First, calculate the one-year autocorrelation coefficient R(365) of the daily average flow series, where Q(t) and Q(t+365) are the average daily flow rates on the tth and t+365th days in the sequence respectively; if R(365)>0.7, it means that the water flow sequence is consistent; second, take the average annual flow rate of the past years The average annual sediment transport rate Through standardization Convert to and draw and A linear trend line that changes over time; if the slope of the trend line is within ±0.05, it means that the water and sediment series are consistent; if both the first and second conditions are met, it means that the water and sediment series are consistent; in step 1.2, the value of m is determined by linearly regressing the flow series Q(t) with the flow series lagged m-1 days later, Q(t+m-1), and determining their correlation coefficient R 2 Value, denoted as R 2 (m), m value starts from 2, if R 2 (2) If it is less than 0.98, m is set to 2. Otherwise, the value of m increases gradually until R 2 (m)≥0.98>R 2 (m+1), determine the final value of m; in step 1.4, the method for determining the value of N1 is: first, let the flow interval length be 0.25S, S is the standard deviation of the flow sequence, and determine the number of intervals Determine whether there are less than 2 flow data in each interval. Intervals with less than 2 data are determined to be too few data intervals: if the interval with too few data exceeds 10% of the total number of intervals N1, then double the interval length, that is, 0.5S, and re-count. If the interval with too few data is less than 10% of the total number of intervals N1, then reduce the number of intervals by 2 to 4 and re-count. Repeat the above operation until there are no less than 2 flow data in each interval; in step 1.4, the power function fitting generally reflects the relationship between σ(Qs) and Q, and screens out data scatter points that deviate from the power function, so that the power function fitting correlation coefficient is above 0.

7.

3. The method for estimating bed-forming flow based on river characteristics according to claim 1, characterized in that: Step 4 specifically includes: if both the flow-frequency curve and the sediment transport rate curve can be fitted with good theoretical functions, that is, situation i or ii in step 2.3 and situation I or II in step 3.2 occur simultaneously, then the analytical method in step 6 is preferred to solve the bed-forming flow rate; otherwise, the graphical method in step 5 is recommended to solve the bed-forming flow rate.

4. The method for estimating bed-forming flow based on river characteristics according to claim 3, characterized in that: Step 5 specifically includes: Step 5.1 Count the total sediment transport in each interval according to the flow interval adjusted in step 2.2 Used to characterize the geomorphic work under each flow level; Step 5.2 calculates Φ i With Qi, draw the geomorphic work curve Φ~Q to determine the bed-forming flow: if the maximum peak of the curve is prominent, take the flow corresponding to the maximum peak as the bed-forming flow; if the curve has two peaks of similar magnitude under different flow levels, select the larger flow level as the bed-forming flow; if the curve has two peaks of similar magnitude under similar flow levels, take the average flow of the two as the bed-forming flow; if the curve has multiple peaks of similar magnitude, it is necessary to redraw the geomorphic work curve by reducing the number of intervals until the above situation occurs.

5. The method for estimating bed-forming flow based on river characteristics according to claim 4, characterized in that: Step 6 specifically includes: Step 6.1 Using the theoretical frequency distribution function f(Q) and the sediment transport rate power function Qs=αQ β , we can get that under the arithmetic interval, the size of the geomorphic work Φ is proportional to Q β f(Q), in the logarithmic interval, the size of the geomorphic work Φ is proportional to Q β+1 f(Q); Step 6.2 for Q β f(Q) or Q β+1 Take the derivative of f(Q) and take the zero point of its derivative value as the analytical solution of the bed flow Q e .

6. The method for estimating bed-forming flow based on river characteristics according to claim 5, characterized in that: Step 7 specifically includes: the characteristic flows that appear in the above steps are: ① If the frequency distribution of the medium flood flow in step 2 obeys a piecewise power function, the inflection point of the curve is a1; ② If the sediment transport rate curve in step 3 turns under large flow, the turning point is Q t , ③ the bed-forming flow value solved by the graphical method in step 5, ④ the bed-forming flow value solved by the analytical method in step 6 if it can be solved; take ③ value as the standard of bed-forming flow, if ① value is close to ③ value, it means that the bed-forming flow is dominated by the hydrological frequency characteristics, if ② value is close to ③ value, it means that the flat beach flow and the bed-forming flow are similar, and the river channel is in a quasi-equilibrium state, if ④ value is close to ③ value, it means that the selected theoretical distribution function is in good agreement with the actual situation, and use analytical formula to analyze the influence of hydrological and sediment transport parameters on bed-forming flow.

7. A device for estimating bed-forming flow based on river characteristics, characterized in that: include: The first main module is used to implement step 1, checking the consistency of the water and sediment sequence and selecting the flow interval division method; the second main module is used to implement step 2, selecting the flow frequency curve and evaluating the effect of fitting the probability density of the multi-year flow sequence; the third main module is used to implement step 3, fitting the multi-year average sediment transport rate curve and evaluating its effect; the fourth main module is used to implement step 4, determining the priority of the bed-forming flow solution method; the fifth main module is used to implement step 5, solving the bed-forming flow by graphical method; and the sixth main module is used to implement step 6, solving the bed-forming flow by analytical method. The seventh main module is used to implement step 7. Comprehensive evaluation of bed-making flow; Step 2 specifically includes: Step 2.1 According to the N2 flow intervals divided in step 1, count the flow frequency D in each interval i , and calculate the probability density of each level interval Where D is the total number of days in the daily average flow sequence, dQ i is the length of the i-th interval; Step 2.2 is based on the calculated p(Q i ) and Q i , draw the empirical frequency curve p(Q)~Q, check the continuity of the curve and make corresponding adjustments: if the curve is smooth and continuous, keep the number and spacing of intervals unchanged; if the number of curve discontinuities exceeds 10% of the total number of intervals N2, it is necessary to appropriately reduce the number of intervals and repeat the statistics in step 2.1; if the number of curve discontinuities is less than 10% of the total number of intervals N2, the discontinuous interval and the adjacent subsequent interval can be merged into one interval, and the corresponding interval representative value and probability density are recalculated; step 2.3 selects an appropriate theoretical distribution function for fitting the empirical frequency curve and calculates the corresponding goodness of fit: Where p(Q i )、f(Q i ) are the measured value and the calculated value of the fitting function of the probability density of the i-th level flow, The measured probability density logarithm of each level of flow is averaged, where n is the flow level. There are three possible situations for fitting the empirical frequency curve: i. The curve is smooth and continuous without inflection. The overall empirical frequency curve is fitted, and the goodness of fit LNSE is greater than the fourth preset threshold. ii. The curve is clearly segmented between low water flow and medium flood flow. The curve is fitted for the medium flood flow part, and the goodness of fit LNSE is greater than the fourth preset threshold. iii. In other cases, if there are multiple theoretical functions that meet situation i or ii, the best one is selected based on the maximum LNSE value. In step 2.3, the low water flow and medium flood flow are divided by the flow with a cumulative frequency of 50% or the average flow, or the frequency curve inflection point is selected as the dividing flow based on the empirical frequency distribution characteristics. The selection of the dividing flow must ensure that the subsequent calculated bed-forming flow value is within the study range, otherwise the division is re-performed. Step 3 specifically includes: Step 3.1 According to the flow interval adjusted in step 2.2, calculate the average sediment transport rate in each interval And according to the calculation of Qs i With Q i , draw the multi-year average sediment transport rate curve Qs~Q; step 3.2 uses the power function Qs=αQ for the sediment transport rate curve β Perform fitting, where α is the seventh parameter and β is the eighth parameter, and calculate the corresponding goodness of fit Where Cov(·,·) represents covariance, Var(·) represents variance, and there are three possible cases of sediment transport rate curve fitting: I. The curve is smooth, monotonous, and has no turning points. The power function is directly used for fitting. The goodness of fit R 2 Greater than or equal to the fifth preset threshold, II. The curve turns in the low water flow area, and a power function is fitted for the part above the turning point of the curve. The goodness of fit R 2 Greater than or equal to the fifth preset threshold, III. Other cases; in step 3.2, the sediment transport rate curve turns at low water flow or high water flow, and its turning point Q t The identification method is: using the power function Qs = αQ β When fitting the sediment transport rate curve, the data scatter points that deviate from the power function are gradually filtered out from both ends of the curve until the power function goodness of fit R 2 The value is the largest.

8. An electronic device, characterized in that: include: At least one processor, at least one memory and a communication interface; wherein, The processor, memory and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the method of any one of claims 1 to 6.

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