River cross-section ecological flow guarantee assessment method and system based on incoming water frequency-destroying depth constraint

Abstract

The application discloses a river section ecological flow guarantee evaluation method and system based on water frequency and damage depth constraints, which comprises collecting historical long series and runoff data of the evaluation period of the river section, and constructing a river and lake section ecological flow target evaluation database; constructing a theoretical probability distribution curve of the average flow of the driest month in the water diversion period, and extracting the monthly runoff data of the driest month as the input of the curve to obtain the corresponding water frequency; calculating the daily damage depth of the ecological flow target of the river and lake section in the evaluation period, and calculating the corresponding maximum damage depth and cumulative damage depth; constructing a river and lake section ecological flow guarantee judgment function, setting a satisfaction domain of the ecological flow target, calculating the satisfaction value of the ecological flow of the river and lake section in the evaluation period, and evaluating whether the ecological flow target meets the standard. The application improves the accuracy of the evaluation result, and provides a more scientific evaluation technical method for implementing river ecological regulation and strengthening ecological flow guarantee.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of river ecological management, and in particular to a river section ecological flow guarantee evaluation method based on inflow frequency-destruction depth constraint. BACKGROUND

[0002] River ecological flow is an important index for maintaining river ecological function and controlling water resources development intensity, and is also an important basis for overall planning of life, production and ecological water use.

[0003] In China, the determination and guarantee of river ecological flow targets have been fully carried out. The Ministry of Water Resources has issued a list of key rivers and lakes for ecological flow guarantee, and has clearly defined the key rivers and lakes for ecological flow determination and guarantee, organization and implementation, and progress requirements. At present, the Ministry of Water Resources has released ecological flow targets for control sections of four batches of key rivers and lakes across provinces and has clearly defined guarantee requirements. Provincial water administrative departments have determined and issued the ecological flow targets for key rivers and lakes within the province, and have clearly included the guarantee of river and lake ecological flow targets into the annual assessment of the most stringent water resources management system. At present, the guarantee status of key river and lake ecological flow targets has been evaluated in pilot projects. However, due to the obvious seasonal and random characteristics of river runoff processes, how to scientifically and reasonably evaluate the guarantee status of ecological flow targets is a technical problem that restricts river and lake ecological flow management.

[0004] The present application proposes a river section ecological flow guarantee evaluation method and system based on inflow frequency-destruction depth constraint, establishes a judgment function for river section ecological flow guarantee satisfaction based on inflow frequency and destruction depth, clearly defines the satisfaction conditions for river ecological flow target guarantee, and improves the accuracy of evaluation results. SUMMARY

[0005] The present application provides a river section ecological flow guarantee evaluation method based on inflow frequency-destruction depth constraint to solve the above-mentioned problems existing in the prior art. On the other hand, a river section ecological flow guarantee evaluation system based on inflow frequency-destruction depth constraint is provided.

[0006] Technical scheme: The river section ecological flow guarantee evaluation method based on inflow frequency-destruction depth constraint comprises the following steps:

[0007] Step S1, collect historical long series and runoff data of the evaluation period of the river section, and construct a river and lake section ecological flow target evaluation database;

[0008] Step S2, construct a theoretical probability distribution curve of the average flow of the driest month in the division period, and extract the monthly runoff data of the driest month as the input of the curve to calculate the corresponding inflow frequency of the monthly scale runoff in the evaluation period;

[0009] Step S3, based on the measured daily runoff of the river-lake section and the ecological flow target, the daily damage depth of the ecological flow target of the river-lake section in the evaluation period is calculated, and the maximum damage depth and the cumulative damage depth of the ecological flow target of the river-lake section in the evaluation period are calculated;

[0010] Step S4, a river-lake section ecological flow guarantee judgment function is constructed, and a satisfaction domain of the ecological flow target is set. The water frequency corresponding to the monthly scale runoff, the maximum damage depth and the cumulative damage depth in the evaluation period are input into the river-lake section ecological flow guarantee judgment function, the satisfaction value of the ecological flow of the river-lake section in the evaluation period is calculated, and whether the ecological flow target is up to standard is evaluated.

[0011] According to an aspect of the present application, the step S1 is further:

[0012] Step S11, collecting runoff data of the river section in the evaluation period, including: measured daily runoff data of the section, upstream river channel water taking amount, upstream engineering water storage amount and the like;

[0013] Step S12, based on the measured daily runoff data of the section, the hydrological reduction method is used to reduce the measured runoff, to obtain the natural runoff data of the section in the evaluation period, and a monthly average natural runoff data matrix in the evaluation period is constructed;

[0014] Step S13, collecting historical long series runoff data of the river section, including: historical long series natural monthly average runoff data, historical long series measured monthly average runoff data;

[0015] Step S14, constructing a river-lake section ecological flow target evaluation database, including: evaluation period monthly average natural runoff data matrix, evaluation period daily average measured runoff data, historical long series natural monthly average runoff data, historical long series measured monthly average runoff data, and section ecological flow target.

[0016] According to an aspect of the present application, the step S2 is further:

[0017] Step S21, extracting historical long series natural monthly average runoff data, constructing a river-lake section historical long series monthly scale natural runoff data matrix, and using a Pearson P-III type curve to establish a theoretical probability distribution curve of the average flow of the driest month in the diversion period; Step S22, inputting the monthly runoff data of the driest month into the theoretical probability distribution curve of the average flow of the driest month in the diversion period, and finding the water frequency corresponding to the monthly scale runoff in the evaluation period.

[0018] According to an aspect of the present application, the step S21 is further:

[0019] Step S21a, extracting historical long series of natural monthly runoff data from the river and lake section ecological flow target evaluation database, constructing a historical long series of monthly natural runoff data matrix of the river and lake section, extracting the runoff period driest month runoff data from the historical long series of monthly natural runoff data matrix of the river and lake section, and constructing a driest month runoff series; Step S21b, fitting the driest month runoff series data with a Pearson III curve to obtain a theoretical probability distribution curve of the average flow of the driest month of the river and lake section.

[0020] According to an aspect of the present application, the step S3 is further:

[0021] Step S31, based on the measured daily runoff data of the river and lake section and the ecological flow target, the daily damage depth of the ecological flow target of the evaluation period of the river and lake section is calculated;

[0022] Step S32, based on the daily damage depth of the ecological flow target of the river and lake section, the maximum damage depth and the cumulative damage depth of the ecological flow target of the evaluation period of the river and lake section are respectively calculated.

[0023] According to an aspect of the present application, the step S4 is further:

[0024] Step S41, constructing a river and lake section ecological flow guarantee judgment function, the constraint condition is the inflow frequency and the damage depth;

[0025] Step S42, setting a satisfaction domain of the ecological flow target meeting;

[0026] Step S43, extracting the inflow frequency, the maximum damage depth and the cumulative damage depth corresponding to the monthly runoff of the evaluation period, inputting the river and lake section ecological flow guarantee judgment function, and calculating the satisfaction value of the ecological flow of the evaluation period of the river and lake section;

[0027] Step S44, judging the satisfaction value of the ecological flow of the evaluation period of the river and lake section and the satisfaction domain of the ecological flow target meeting, and obtaining the ecological flow target meeting condition.

[0028] According to another aspect of the present application, a river section ecological flow guarantee evaluation system based on inflow frequency-damage depth constraint is provided, comprising:

[0029] At least one processor; and

[0030] The memory is in communication connection with the at least one processor; wherein,

[0031] The memory stores instructions executable by the processor, and the instructions are executed by the processor to implement the river section ecological flow guarantee evaluation method based on the inflow frequency-damage depth constraint of any one of the above technical solutions.

[0032] Beneficial effects: the river section ecological flow guarantee evaluation method based on the inflow frequency-disruption depth constraint can effectively avoid the bias of the existing evaluation method for the evaluation of natural extreme dry period, accurately identify the ecological flow disruption caused by human factors, and improve the accuracy of river section ecological flow guarantee condition judgment by about 10%, thereby providing a more scientific evaluation technical method for implementing ecological flow quantification and lean management. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of the present application.

[0034] Figure 2 is a flowchart of step S1 of the present application.

[0035] Figure 3 is a flowchart of step S2 of the present application.

[0036] Figure 4 is a flowchart of step S3 of the present application.

[0037] Figure 5 is a flowchart of step S4 of the present application. DETAILED DESCRIPTION

[0038] As shown in Figure 1 , the following technical solutions are proposed. According to one aspect of the present application, a river section ecological flow guarantee evaluation method based on inflow frequency-disruption depth constraint is provided, characterized in that it comprises the following steps:

[0039] Step S1, collecting historical long series and runoff data of the evaluation period of the river section, and constructing a river and lake section ecological flow target evaluation database;

[0040] Step S2, constructing a theoretical probability distribution curve of the average flow of the driest month of the division period, and extracting the monthly runoff data of the driest month as the input of the curve to calculate the corresponding inflow frequency of the monthly runoff of the evaluation period;

[0041] Step S3, calculating the daily disruption depth of the ecological flow target of the evaluation period of the river and lake section based on the measured daily runoff of the river and lake section and the ecological flow target, and calculating the maximum disruption depth and cumulative disruption depth of the ecological flow target of the evaluation period of the river and lake section;

[0042] Step S4, constructing a river and lake section ecological flow guarantee judgment function, setting a satisfaction domain of the ecological flow target, inputting the inflow frequency, maximum disruption depth and cumulative disruption depth corresponding to the monthly runoff of the evaluation period into the river and lake section ecological flow guarantee judgment function, calculating the satisfaction value of the ecological flow of the evaluation period of the river and lake section, and evaluating whether the ecological flow target meets the standard.

[0043] The application realizes a technical breakthrough that cannot be achieved by a traditional single index evaluation method by coupling the incoming water frequency with the damage depth.

[0044] Specifically, the traditional ecological flow evaluation method usually adopts a fixed threshold for compliance judgment, and there is a one-size-fits-all evaluation problem, that is, the same evaluation standard is adopted regardless of the change of the hydrological year type, which leads to the technical defects of loose standard in wet years and strict requirement in dry years. i <90) requires that the damage depth must be zero, reflecting the concept of strict protection; in normal years (90≤P i <97) allows a certain damage depth but decreases with the increase of frequency; in dry years (97≤P i <100) focuses on controlling the cumulative damage depth, reflecting the bottom line thinking, which effectively solves the unreasonable problem caused by the fixed threshold evaluation and realizes the intelligent matching of the evaluation standard and the hydrological condition.

[0045] The technical scheme of the application also solves the technical problem of spatio-temporal scale fusion in ecological flow evaluation. The incoming water frequency reflects the interannual hydrological variability, which is a statistical characteristic of the macro time scale. The damage depth is calculated based on daily runoff data and reflects the flow gap condition of the fine time scale. The traditional method cannot effectively integrate information of different time scales. The application realizes the comprehensive judgment of different time scales from daily scale accurate calculation to annual scale macro evaluation by constructing a frequency-depth coupling judgment function, so that the evaluation result can reflect the influence of short-term fluctuations and the change of long-term trend, and the scientificity and accuracy of the evaluation result are significantly improved.

[0046] According to one aspect of the application, the step S1 is further:

[0047] Step S11, collecting runoff data of the evaluation period of the river section, including: measured daily runoff data of the section, upstream water intake and use amount, upstream engineering storage amount and the like;

[0048] Step S12, based on the measured daily runoff data of the section, the measured runoff is restored by using the hydrological restoration method to obtain the natural runoff data of the section in the evaluation period, and a monthly average natural runoff data matrix of the evaluation period is constructed;

[0049] Step S13, collecting historical long series runoff data of the river section, including: historical long series natural monthly average runoff data, historical long series measured monthly average runoff data;

[0050] Step S14, constructing a river and lake section ecological flow target evaluation database, including: an evaluation period monthly natural runoff data matrix, an evaluation period daily measured runoff data, a historical long series of monthly natural runoff data, a historical long series of measured monthly runoff data, and a section ecological flow target.

[0051] A historical long series of monthly natural runoff data matrix R k ×12, k≧30, an evaluation period monthly runoff RN i and a measured daily runoff RM i ,n, a section ecological flow target EF i wherein i=1:12 is a month sequence number; n=1:31 is a day sequence number within a month.

[0052] In the embodiment, the historical long series of monthly natural runoff data matrix R k ×12 should be no less than 30 years, that is, k≧30.

[0053] According to an aspect of the present application, the step S2 is further:

[0054] Step S21, extracting a historical long series of natural monthly runoff data, constructing a river and lake section historical long series of monthly natural runoff data matrix, and establishing a theoretical probability distribution curve of a minimum monthly average flow in a diversion period by using a Pearson P-III type curve; in the embodiment, by analyzing historical long series of flow data, a period with relatively stable inflow and prominent water demand contradiction is determined and delimited as the diversion period.

[0055] Step S22, inputting the minimum monthly runoff data into the theoretical probability distribution curve of the minimum monthly average flow in the diversion period, and searching for an inflow frequency corresponding to the evaluation period monthly runoff.

[0056] According to an aspect of the present application, the step S21 is further:

[0057] Step S21a, extracting the historical long series of natural monthly runoff data from the river and lake section ecological flow target evaluation database, constructing a river and lake section historical long series of monthly natural runoff data matrix, extracting diversion period minimum monthly runoff data from the river and lake section historical long series of monthly natural runoff data matrix, and constructing a minimum monthly runoff series; step S21b, performing curve fitting on the minimum monthly runoff series data by using a Pearson III type curve, and obtaining a river and lake section minimum monthly average flow theoretical probability distribution curve.

[0058] For the river section historical long series of restored monthly runoff data matrix R k ×12, diversion period minimum monthly runoff data is selected to construct a minimum monthly runoff series R kminX1, according to the SL44 “Code for Design Flood Calculation of Water Conservancy and Hydropower Engineering”, the Pearson III curve is used to fit the series data of the monthly runoff in the driest month, and the theoretical probability distribution curve of the average monthly runoff in the river section is obtained. The monthly runoff RN in the evaluation period is searched in the frequency distribution curve of the flow theory. i The corresponding inflow frequency P i , where p∈[0, 100].

[0059] According to one aspect of the present application, step S22 is further:

[0060] Step S22a: constructing a daily scale flow frequency distribution;

[0061] Read the historical long series of daily average runoff data;

[0062] Frequency analysis is performed on the daily runoff data in each month respectively;

[0063] The kernel density estimation method is used to obtain the daily flow probability density function f_daily(x);

[0064] Step S22b: calculating the frequency drift index;

[0065] Read the daily average runoff data in the evaluation period;

[0066] Calculate the daily instantaneous frequency P_daily in the month;

[0067] Construct the frequency standard deviation σ_P=std(P_daily);

[0068] Calculate the frequency skewness Sk_P=skewness(P_daily);

[0069] Get the frequency drift feature vector [σ_P, Sk_P, max(P_daily)-min(P_daily)];

[0070] Step S22c: generating a compensated comprehensive frequency.

[0071] Read the monthly scale inflow frequency P_i;

[0072] Based on the frequency drift feature, calculate the compensation coefficient C_drift=1+k×σ_P×sign(Sk_P);

[0073] Get the drift-compensated inflow frequency P_compensated=P_i×C_drift.

[0074] According to one aspect of the present application, the step S3 is further:

[0075] Step S31, based on the measured daily runoff data of the river and lake section and the ecological flow target, the daily damage depth of the ecological flow target of the river and lake section in the evaluation period is calculated;

[0076] When RM i，n > EF i , D n = 0;

[0077] When RM i，n ≤ EF i , D n = (1-RM i，n / EF i )×100;

[0078] In the formula, Dn is the damage depth of the ecological flow target of the river section on the nth day;

[0079] Dn represents the degree of measured flow not reaching the ecological target, the larger the value, the more serious the flow shortage. When the measured flow is sufficient, that is, RMi,n> EFi, the damage depth is 0, and there is no need to calculate; when the measured flow is insufficient, the gap ratio is calculated by (1-measured flow / target flow), and then multiplied by 100 to convert to percentage, which directly reflects the damage degree.

[0080] In some embodiments, specifically:

[0081] The ecological flow target of a river section A is 50m 3 / s, and the collected measured flow is 60m 3 / s. Since 60m 3 / s> 50m 3 / s, the ecological flow damage depth is 0;

[0082] In another embodiment, the collected measured flow is 30m 3 / s, 30m 3 / s< 50m 3 / s, D n = (1-30 / 50)×100 = (1-0.6)×100 = 0.4×100 = 40%, the ecological flow damage depth of the day is 40%, that is, the measured flow is 40% lower than the ecological target, and there is a flow shortage.

[0083] Step S32, based on the daily damage depth of the ecological flow target of the river and lake section, the maximum damage depth and the cumulative damage depth of the ecological flow target of the river and lake section in the evaluation period are calculated respectively.

[0084] D max = max(D1, D2, D3, …, D n );

[0085] Dsum =D1+D2+D3+……+D n ;

[0086] In the formula, D max is the maximum damage depth of the ecological flow target of the river section in the evaluation period, and D sum is the cumulative damage depth of the ecological flow target of the river section in the evaluation period.

[0087] In an embodiment, specifically:

[0088] For a river section B, the evaluation period is from July 1 to July 5, and the calculated Dn values of the 5 days are 20%, 0%, 35%, 10%, and 0% in turn;

[0089] The data is substituted into the formula to calculate the maximum damage depth, max(20%, 0%, 35%, 10%, 0%) = 35%, and the maximum damage depth of the ecological flow in the evaluation period is 35%, which occurs on July 3;

[0090] The cumulative damage depth D sum =D1+D2+D3+D4+D5=20%+0%+35%+10%+0%=65%, and the cumulative damage depth of the ecological flow in the evaluation period is 65%, that is, the total of the non-compliance degree of the measured flow in the 5 days is 65%.

[0091] Further, the application effectively solves the trade-off problem between short-term impact and long-term stress of the ecological system through the dual constraints of the maximum damage depth and the cumulative damage depth. The ecological system is easily subject to acute damage of short-term severe impact and chronic damage due to long-term continuous stress, and a single index cannot prevent both risks. The dual constraint mechanism designed by the application prevents acute damage by controlling the maximum damage depth and prevents chronic damage by controlling the cumulative damage depth, thereby achieving all-round protection of the ecological system.

[0092] According to an aspect of the application, step S3 can also be:

[0093] Step S31: constructing an ecological system memory decay function;

[0094] Read historical ecological monitoring data (including biodiversity index, water quality parameters, etc.), and analyze the ecological response curve under different damage-recovery cycles;

[0095] An exponential decay model is used to construct the memory function M(t) = exp(-t / τ), where τ is an ecological recovery time constant;

[0096] Through historical data fitting, a classified ecological recovery time constant τ_i (i represents different damage depth levels) is obtained;

[0097] Step S32: Calculate the daily damage depth considering memory effect;

[0098] Read the measured daily runoff data RM_(i,n) and the ecological flow target EF_i;

[0099] Calculate the instantaneous damage depth: when RM_(i,n)≤EF_i, D_instant=(1-RM_(i,n) / EF_i)×100;

[0100] Read the historical damage depth sequence D_(n-1), D_(n-2)...D_(n-k) of the previous k days;

[0101] Calculate the memory weighted damage depth D_memory=Σ(D_(n-j)×M(j)), j from 1 to k;

[0102] Get the dynamic damage depth D_n=D_instant+α×D_memory, where α is the memory influence coefficient;

[0103] Step S33: Calculate the dynamic maximum damage depth and the cumulative damage depth;

[0104] Based on the dynamic damage depth sequence D_n, extract the maximum value to get the dynamic maximum damage depth D_max_dynamic;

[0105] Calculate the cumulative value to get the dynamic cumulative damage depth D_sum_dynamic. According to one aspect of the present application, it further includes:

[0106] Step S34: Ecological mutation point identification and nonlinear damage assessment.

[0107] Step S34a: Construct the damage rate index;

[0108] Read the dynamic damage depth sequence D_n;

[0109] Calculate the daily damage rate V_n=|D_n-D_(n-1)|;

[0110] Construct the moving average of damage rate V_avg=(V_n+V_(n-1)+V_(n-2)) / 3;

[0111] Get the damage rate sequence V_series;

[0112] Step S34b: Identify the ecological mutation point;

[0113] Set the mutation threshold V_critical (based on the type of ecological system);

[0114] When V_n>V_critical and D_n>50%, mark it as a potential mutation point;

[0115] Sliding window method is adopted to detect whether there is mutation feature within 3 consecutive days;

[0116] Output the ecological mutation point set T_points and the corresponding mutation severity S_i;

[0117] Step S34c: Calculate the nonlinear weighted damage depth;

[0118] For the mutation point, apply the nonlinear amplification function D_adjusted=D_n×(1+β×exp(V_n / V_critical));

[0119] For the non-mutation point, keep the original damage depth;

[0120] Get the nonlinear adjusted damage depth sequence D_nonlinear.

[0121] According to an aspect of the present application, the step S4 is further:

[0122] Step S41, construct the river and lake section ecological flow guarantee judgment function, the constraint condition is the water frequency and the damage depth; when 0 i <90, D max =0;

[0123] When 90≦P i <97, D max <(-P i 2 +1409P i / 7-70110 / 7)∩D sum <(-P i 2 +1909P i / 7-115110 / 7);

[0124] When 97≦P i <100, D sum ≦600;

[0125] In the formula, P i is the water frequency, D max is the maximum damage depth of the ecological flow target of the river section in the evaluation period, and D sum is the cumulative damage depth of the ecological flow target of the river section in the evaluation period.

[0126] In the constraint condition design of normal water years, the quadratic polynomial function D max <(-P i ²+1409P iThe nonlinear relationship between the maximum damage depth and the incoming water frequency is established, and the nonlinear constraint fully considers the nonlinear response characteristics of the ecosystem to hydrological changes, and embodies the professional and technical characteristics of ecological hydrology.

[0127] The most critical non-obviousness of the present application is the hierarchical design of the constraint logic.

[0128] Step S42, setting a satisfaction domain of the ecological flow target meeting;

[0129] Step S43, extracting the incoming water frequency, the maximum damage depth and the cumulative damage depth corresponding to the monthly scale runoff in the evaluation period to input the ecological flow guarantee judgment function of the river and lake section, and calculating the satisfaction value of the ecological flow of the river and lake section in the evaluation period;

[0130] Step S44, judging the satisfaction value of the ecological flow of the river and lake section in the evaluation period and the satisfaction domain of the ecological flow target meeting, and obtaining the ecological flow target meeting condition.

[0131] In the prior art, the maximum value control and the cumulative value control are usually regarded as two independent management strategies, and few technical solutions organically couple the two, and the non-obviousness of the present application is that different index combination strategies are adopted under different hydrological conditions, the zero tolerance strategy is adopted in the wet year, the double constraint strategy is adopted in the normal year, and the single cumulative constraint strategy is adopted in the dry year.

[0132] In an embodiment, specifically:

[0133] The ecological flow target meeting condition of the research area A section in 2017 is selected as data, an ecological base flow target evaluation database of the research area A section is constructed, including a monthly scale natural runoff data matrix of 1956-2016, R61x12, monthly scale runoff RN of 2017 i , and measured daily scale runoff RM i,n , the section ecological flow target, 1.16m 3 / s from December to the next March, and 13.86m 3 / s;

[0134] Based on the monthly-scale natural runoff data matrix from 1956 to 2016, the theoretical probability distribution curves of the average flow in the driest month during the freezing period and the water-bearing period at section A in the study area were established using Pearson P-III type curves, and the inflow frequency P from January to December was calculated. i ;

[0135] Based on the measured daily runoff RM at section A in the study area in 2017 i,n Calculate the daily destruction depth D of the ecological flow target at section A in the study area in 2017. n Furthermore, the maximum damage depth D of the river cross-section ecological flow target during the assessment period was calculated. max and cumulative damage depth D sum ;

[0136] Based on the monthly inflow frequency P of section A in the study area in 2017 i -Depth of destruction D max &D sum A constrained ecological flow guarantee judgment function is used to determine the ecological flow target satisfaction domain and assess the achievement of the ecological flow target at section A in the study area in 2017. The maximum damage depth D in October 2017 is also considered. max =28.3%, exceeding the satisfaction range under a water inflow frequency of 72.13%; the cumulative damage depth D in November sum =788.6%, exceeding the satisfaction range under 98.00% water inflow frequency, indicating that the ecological flow targets for section A in the study area for October and November 2017 were not met.

[0137] The technical solution of this invention has significant engineering practical value, solving key technical problems in existing ecological flow management. In practical applications, traditional assessment methods often face the dilemma of overly strict standards leading to widespread non-compliance and overly lenient standards losing their protective significance, affecting the effective implementation of ecological flow management policies. This invention establishes a hierarchical risk control system, realizing differentiated management under different hydrological conditions: zero tolerance level is suitable for periods of abundant water, limited tolerance level is suitable for periods of moderate water, and bottom-line level is suitable for periods of water scarcity. This hierarchical control mechanism not only ensures the seriousness of ecological protection but also considers the objective constraints of water resource conditions, providing scientific and reasonable technical support for water resource management departments and has important value for promotion and application.

[0138] In one embodiment, specifically:

[0139] For the evaluation period of 2016-2020, the daily runoff data of the section was collected, the complete records were obtained from the local water conservancy department and multiple monitoring stations, and the upstream river water use data covering industrial, agricultural and domestic water use were collected. For the upstream water storage, the daily changes were obtained through the operation records of reservoirs, dams and other water conservancy projects;

[0140] The measured runoff was reduced by hydrological reduction method, and the influence of human activities such as upstream water taking and engineering water storage change was deducted to obtain the natural runoff data of the section in the evaluation period. The daily natural runoff data was averaged on a monthly scale to construct the monthly natural runoff data matrix of the evaluation period. The long series of runoff data of the river section from 1990 to 2015 was collected, including the measured monthly runoff data and the natural monthly runoff data of the section, and the river and lake section ecological flow target evaluation database was constructed, including the evaluation period monthly natural runoff data matrix, the evaluation period daily measured runoff data, the long series of natural monthly runoff data, the long series of measured monthly runoff data and the ecological flow target value of the section;

[0141] Based on the long series of monthly natural runoff data matrix of the river section, the Pearson III curve was used to establish the water period, and the average flow probability distribution curve of the driest month from November to the next February was established. The driest month runoff data was extracted from the long series of reduced monthly runoff data matrix to construct the driest month runoff series, which was specifically: in the historical data from 1990 to 2015, the driest month runoff value of the water period was extracted every year to form a series containing 26 data. The Pearson III curve was fitted to the series data, and the mean, coefficient of variation and skewness coefficient were adjusted to make the curve best fit the data distribution characteristics to obtain the theoretical frequency distribution curve of the average flow of the driest month. The driest month runoff data in the evaluation period was extracted and input into the curve to find the corresponding inflow frequency. For example, after the driest month runoff data of 2018 was input, the corresponding inflow frequency was Y2% calculated by the software;

[0142] According to the measured daily runoff of the river and lake section and the ecological flow target, the daily damage depth in the evaluation period was calculated. If the measured daily runoff is less than the ecological flow target value, the daily damage depth is the ecological flow target value minus the measured runoff. Otherwise, it is 0. Based on the daily damage depth, the maximum damage depth and the cumulative damage depth in the evaluation period were calculated;

[0143] The ecological flow guarantee judgment function of the river and lake section is constructed, and the weighted summation method is adopted, and the function form is: the satisfaction value = a2x water frequency + b2x maximum damage depth + c2x cumulative damage depth, a2, b2, and c2 are weight coefficients determined according to the ecological importance and flow characteristics of the river, and the satisfaction domain of the ecological flow target is set to 5, 6, such as when the satisfaction value is less than the threshold value 5, it is up to standard, and when it is greater than or equal to the threshold value 6, it is seriously out of standard. The water frequency, maximum damage depth and cumulative damage depth calculated are input into the function to calculate the satisfaction value, and the satisfaction value is less than the threshold value 5, and it is determined that the ecological flow target of the river section in the evaluation period from 2016 to 2020 is up to standard.

[0144] At the same time, the traditional method is used to divide the whole year into multiple water periods and low water periods, and the 10%-20% of the multi-year average flow is selected as the river ecological environmental water demand in the low water period, and the 30%-40% of the multi-year average flow is selected in the high water period. Based on the runoff data of the river section from 1990 to 2015, the multi-year average flow is calculated;

[0145] The measured runoff of each month in the evaluation period is compared with the calculated ecological flow value of the corresponding month, if the measured runoff of a month is greater than or equal to the calculated ecological flow value, the month is up to standard; otherwise, it is not up to standard. The proportion of the number of months up to standard to the total number of months is taken as the ecological flow up to standard rate of the section. According to the statistics, the number of months up to standard is 35, the total number of months is 60 months, and the up to standard rate is 35 / 60x100%=58.33%. When the up to standard rate is greater than or equal to the threshold value, it is determined that the ecological flow is up to standard; otherwise, it is not up to standard. In this case, because the up to standard rate is 58.33%, which is less than the threshold value 80%, it is determined that the ecological flow of the river section in the evaluation period is not up to standard.

[0146] The method determines that the ecological flow target of the river section in the evaluation period is up to standard, while the traditional method determines that it is not up to standard. This is because the traditional method only determines the ecological flow based on the multi-year average flow according to a fixed proportion, without considering the water frequency and the damage depth when the flow is lower than the ecological flow target. In the river, although the flow of some months is lower than the calculated value of the traditional method, from the overall water frequency, it does not reach the degree of affecting the ecological flow guarantee, and the damage depth of insufficient flow is within the acceptable range. The method considers these factors comprehensively and obtains a more realistic result;

[0147] The method uses long historical data to construct the theoretical probability distribution curve of the average flow of the driest month, accurately calculates the inflow frequency, and quantifies the damage depth, fully reflecting the ecological flow guarantee condition, which is different from the traditional method of simply dividing the proportion according to the average annual flow to determine the ecological flow, which does not fully exploit the flow frequency information in the historical data and does not quantitatively analyze the extent of flow failure. For example, during the evaluation period, the flow of some months is slightly lower than the calculated value, but the duration is short, the deviation is small, and the impact on the ecological system is limited. The traditional method cannot reflect this situation, but the method can be considered comprehensively through damage depth calculation.

[0148] The ecological flow guarantee judgment function constructed by the method comprehensively considers the inflow frequency and damage depth, which is more consistent with the actual situation of the ecological system affected by the flow. The stability of the ecological system depends not only on whether the flow reaches a certain fixed value, but also on the inflow frequency and the severity of the flow shortage. The method can accurately reflect the situation that the flow does not reach the standard in some period, but the inflow frequency is normal, and the damage depth does not cause substantial damage to the ecology. The evaluation of the actual situation of the ecological system is more accurate, and it provides a more scientific basis for water resources management and ecological protection decision-making.

[0149] According to one aspect of the present application, it also includes:

[0150] Step S5: Dynamic optimization of ecological flow target based on evaluation feedback.

[0151] Step S51: Constructing an ecological health-flow response database;

[0152] Collecting contemporaneous ecological monitoring data (fish, benthic organisms, water quality, etc.);

[0153] Reading the measured daily runoff data and the original ecological flow target EF_i;

[0154] Calculate the flow satisfaction rate R_satisfy=count(RM>EF) / total days;

[0155] Constructing an ecological health index-flow satisfaction rate correspondence database;

[0156] Step S52: Identifying target deviation patterns;

[0157] When the ecological health index is greater than the threshold value and R_satisfy is less than 0.8, it is marked as the target is too strict;

[0158] When the ecological health index is less than the threshold value and R_satisfy is greater than 0.9, it is marked as the target is too loose;

[0159] Statistically analyze the deviation patterns of the target for 3 consecutive months to obtain a sequence of target deviation types;

[0160] Step S53: Calculate the target correction coefficient;

[0161] Based on the Bayesian updating principle, the prior distribution P(EF| ecological health) is constructed;

[0162] The measured ecological response data is input, and the posterior distribution is updated;

[0163] The correction coefficient K_correct = E[EF_posterior] / EF_prior is calculated;

[0164] The corrected ecological flow target EF_corrected = EF_i x K_correct is obtained;

[0165] Step S54: update the ecological flow guarantee judgment.

[0166] Read the corrected ecological flow target EF_corrected;

[0167] Recalculate the damage depth based on the new target;

[0168] Input P_compensated, D_max_dynamic, D_sum_dynamic into the judgment function;

[0169] Get the final evaluation result considering the dynamic optimization of the target.

[0170] According to another aspect of the present application, in order to solve the problem that the instantaneous damage depth cannot reflect the cumulative damage of the ecological system, the time memory effect is introduced to make the evaluation of the damage depth more consistent with the ecological principle.

[0171] The specific scheme is as follows:

[0172] Step one: calculate the daily dynamic damage depth;

[0173] The step of calculating the daily damage depth of the ecological flow target of the river and lake section in the evaluation period introduces the time memory effect of the ecological system, and optimizes the calculation of the daily damage depth to the calculation of the daily dynamic damage depth; The maximum damage depth and the cumulative damage depth are calculated based on the sequence of the daily dynamic damage depth.

[0174] In this embodiment, the ecological system is like a sponge with memory. After a drought (flow damage), even if the water quantity is restored, the health status of the ecological system will not recover instantly, but will need a process. The damage of the previous day will be remembered and will affect the status of the day. Therefore, the dynamic damage depth is used instead of the instantaneous damage depth, which can more accurately depict the cumulative effect.

[0175] Step two: quantify the time memory effect;

[0176] The ecosystem time memory effect is quantified by constructing an ecosystem memory decay function.

[0177] Specifically, the decay function describes the process by which the impact of historical destructive events fades over time. Preferably, an exponential decay model M(j)=exp(-j / τ) is adopted, where the ecological recovery time constant τ is a key parameter. It is fitted according to the type of river ecosystem and historical monitoring data. For example, for rivers with strong recovery capacity, the value of τ is small (e.g., 7 days), indicating that the memory fades quickly; for fragile rivers, the value of τ is large (e.g., 30 days), indicating that the memory lasts for a long time.

[0178] Step 3: Combine calculation of daily dynamic damage depth.

[0179] The calculation of the daily dynamic damage depth includes: calculating the instantaneous damage depth based on the measured daily average runoff data and the cross-sectional ecological flow target; calculating the memory-weighted damage depth based on the historical daily dynamic damage depth sequence and the ecosystem memory decay function; and combining the instantaneous damage depth and the memory-weighted damage depth to obtain the daily dynamic damage depth for that day.

[0180] To illustrate this with a specific calculation example: Suppose the ecological flow target EF of a certain river cross-section is... i 50m 3 / s, the ecological restoration time constant τ is 10 days, the memory impact coefficient α is 0.5, the memory backtracking days k is set to 3 days, and the daily dynamic damage depths for the first three days are known to be: D n－1,dynamic =30%,D n－2,dynamic =10%,D n－3,dynamic =0%, on day n, the measured daily average runoff data R is obtained. Mi,n 30m 3 / s.

[0181] Calculate the instantaneous damage depth D n,instant Due to RM i,n =30≤EF i =50, so D n,instant =(1-30 / 50)×100=40%;

[0182] Calculate the memory-weighted destruction depth D n,memory :

[0183] First, calculate the memory decay function value M(1) for each historical moment.

[0184] M(1)=exp(-1 / 10)≈0.905M(2)=exp(-2 / 10)≈0.819M(3)=exp(-3 / 10)≈0.741;

[0185] Then the memory weighted damage depth D is calculated n,memory

[0186] D n,memory =∑ j=1 3 (D n－j,dynamic M(j));

[0187] D n,memory =(30%×0.905)+(10%×0.819)+(0%×0.741)=27.15%+8.19%+0%=35.34%,

[0188] The daily dynamic damage depth D is obtained by combination n,dynamic

[0189] D n,dynamic =D n,instant +α D n,memory =40%+0.5×35.34%=40%+17.67%=57.67%。

[0190] It can be seen that although the instantaneous damage of the day is only 40%, but due to the memory of the previous damage has not faded away, the actual dynamic damage depth evaluated by the system is as high as 57.67%, which more truly reflects the composite pressure borne by the ecological system, and this dynamic damage depth sequence will replace the original instantaneous damage depth sequence, which is used for the calculation of the maximum damage depth and the cumulative damage depth, so that the basis of the whole evaluation system is more scientific.

[0191] According to one aspect of the present application, in order to solve the problem that the dynamic damage depth cannot effectively measure the impact damage caused by short-term and severe flow reduction to the ecological system.

[0192] The following scheme is provided:

[0193] Step one: introducing nonlinear weighting;

[0194] The calculation of the daily dynamic damage depth further includes: identifying an ecological mutation point, and nonlinearly weighting the daily dynamic damage depth to obtain a nonlinearly adjusted damage depth.

[0195] In this embodiment, the ecological system is not only sensitive to the amount of damage, but also sensitive to the speed of damage. Gentle flow reduction may give the ecological system some adaptation time, while short-term and severe flow reduction (such as emergency discharge stop of a reservoir) may cause sudden ecological disasters such as fish stranded, therefore, this step identifies such mutations and applies a nonlinear punitive weight to the damage depth of the corresponding date, so that it is more significantly reflected in the evaluation result.

[0196] Step two: identifying an ecological mutation point;​​

[0197] The identifying the ecological mutation point comprises: based on the sequence of daily dynamic damage depth, obtaining an inter-daily damage rate; comparing the inter-daily damage rate with a preset mutation threshold to determine the ecological mutation point.

[0198] Continuing the calculation result of the above embodiment, it is assumed that a sequence of daily dynamic damage depth has been obtained.

[0199] For example, the dynamic damage depth D n－1,dynamic of the (n-1)th day is 5%, and the dynamic damage depth D n,dynamic of the nth day is 57.67%;

[0200] The inter-daily damage rate Vn=|D n,dynamic -D n－1,dynamic |=|57.67%-5%|=52.67% / day is calculated.

[0201] The mutation threshold V critical of the river is set to 30% / day, which can be obtained by statistical analysis of the flow rate change rate when historical ecological disaster events occur. Since the calculated Vn=52.67% / day is much greater than V critical =30% / day, it is determined that the nth day is an ecological mutation point.

[0202] Step three: implementing nonlinear weighting.

[0203] The nonlinear weighting specifically comprises: for the day determined to be an ecological mutation point, a nonlinear amplification function is used to adjust the daily dynamic damage depth, and the input of the nonlinear amplification function includes the inter-daily damage rate.

[0204] In this embodiment, an exponential nonlinear amplification function is used, and the nonlinear amplification coefficient β=0.8 is set.

[0205] Since the nth day is determined to be an ecological mutation point, the dynamic damage depth thereof needs to be adjusted: D n,adjusted =D n,dynamic (1+β exp(Vn / V critical )).

[0206] Substituting the numerical value: D n,adjusted =57.67% (1+0.8 exp(52.67 / 30)).

[0207] D n,adjusted =57.67% (1+0.8 exp(1.756)).

[0208] D n,adjusted =57.67% (1+0.8 5.789).

[0209] D n,adjusted =57.67% (1+4.631)=57.67% 5.631≈324.7%.

[0210] As can be seen, the original dynamic damage depth of 57.67% is nonlinearly amplified to an extremely high value of 324.7% after considering its extremely rapid abrupt change. This does not represent the actual flow gap ratio, but rather a risk-weighted assessment indicator. This indicator ensures that such short-term, severe shocks dominate the calculation of subsequent maximum and cumulative damage depths, thereby ensuring that such high-risk events are effectively captured and warned by the assessment system. For non-abrupt points, the damage depth remains unchanged, i.e., D. n,adjusted =D n,dynamic Finally, this non-linearly adjusted sequence of damage depths is used for the final evaluation.

[0211] According to one aspect of this application, in order to address the problem that using only monthly average runoff to calculate inflow frequency may mask the ecological risks caused by drastic fluctuations in daily runoff within a month, the following solution is provided.

[0212] Step 1: Introduce frequency drift compensation;

[0213] The step of calculating the inflow frequency corresponding to the monthly runoff during the assessment period further includes: analyzing the frequency drift characteristics based on the daily runoff data within the month; and compensating the inflow frequency according to the frequency drift characteristics to obtain the drift-compensated inflow frequency.

[0214] In this embodiment, a month with normal average runoff may contain a combination of prolonged drought in the early stage and brief flood in the later stage. Such drastic fluctuations have a much greater impact on the ecosystem than a month with stable water volume. Therefore, a frequency drift compensation mechanism is introduced to correct the inflow frequency Pi so that it can reflect the unevenness and risk within the month.

[0215] Step 2: Quantize frequency drift characteristics;

[0216] The analysis of frequency drift characteristics includes: performing frequency analysis on daily runoff data within the month to obtain the instantaneous frequency for each day within the month; and constructing the frequency standard deviation and frequency skewness as the frequency drift characteristics based on the instantaneous frequency sequence.

[0217] To illustrate with a specific calculation example: Suppose that in July of a certain year (the assessment period, which lasts for 31 days), the water inflow frequency Pi calculated based on the historical long-term monthly average runoff is 85% (which is a dry year), and now it is necessary to perform drift compensation.

[0218] Obtaining daily scale data and calculating instantaneous frequency: Collect all the measured daily runoff data from July 1 to July 31 of the year. Based on the daily runoff data of all July in the long series of history, the daily flow probability distribution curve of July is established. The daily runoff data of the 31 days in the evaluation period is input into the curve one by one, and a sequence {P daily,1, P daily,2, ...,P daily,31} containing 31 instantaneous frequency values is obtained.

[0219] Constructing frequency drift characteristics: statistical analysis is performed on the sequence of 31 instantaneous frequency values, assuming that the calculated frequency standard deviation σP=15.0 is large, indicating that the daily flow fluctuates violently within the month, and the frequency skewness SkP=1.2 is a positive skewness, indicating that the flow is lower than the monthly average (the instantaneous frequency is high, i.e. dry) most of the time, but a few days of extreme high flow events have raised the monthly average;

[0220] Calculating the incoming water frequency after drift compensation: set the sensitivity coefficient k drift of the frequency drift compensation to 0.01, which is used to adjust the intensity of compensation, and calculate the compensation coefficient C drift =1+k drift σ P sign(Sk P ), C drift =1+0.01 15.0 sign(1.2)=1+0.15 1=1.15, and the incoming water frequency P compensated after drift compensation is calculated as P i =C drift P compensated =85%1.15=97.75%.

[0221] As can be seen, the original monthly incoming water frequency of 85% is corrected to 97.75%, which more scientifically reflects the true hydrological regime of the month: although the monthly average flow corresponds to a frequency of 85%, due to the large fluctuation of the monthly flow and the dry days, the actual ecological stress degree is equivalent to that of an extreme dry month with an incoming water frequency of 97.75%, and this drift-compensated incoming water frequency will be used as a more accurate input to provide the subsequent ecological flow guarantee judgment function.

[0222] As a preferred and more advanced implementation, the present scheme aims to solve the fundamental problem that the ecological flow target itself may have deviation, by introducing real ecological response data to build a self-adaptive closed-loop correction and evaluation system.

[0223] Step one: introducing a feedback correction mechanism;

[0224] The method further comprises: feeding back and correcting the section ecological flow target in the section ecological flow target evaluation database based on real ecological monitoring data, to generate a corrected ecological flow target.

[0225] In the embodiment, it is acknowledged that any preset ecological flow target may have uncertainty, therefore, a feedback loop is introduced to use the real expression of the downstream ecosystem (i.e. ecological monitoring data) to check and correct the upstream set target index (ecological flow target) in reverse, so that the evaluation system has the ability of self-learning and optimization.

[0226] Step two: build a response relationship and identify bias;

[0227] The feedback correction comprises: collecting synchronous ecological monitoring data and measured daily runoff data; building an ecological health-flow response database, and identifying a bias mode of the section ecological flow target from the database.

[0228] Specifically, for an evaluation year, two types of data are collected synchronously:

[0229] Physical data: measured daily runoff data RM i,n ;

[0230] Biological data: ecological monitoring data such as population density of key fish species, benthic animal diversity index, etc., which are integrated into an ecological health index (value 0-100, the higher the better);

[0231] Then a database is built and a bias mode is identified, for example, for July: the original ecological flow target EF7 is 50m 3 / s, the calculated flow satisfaction rate (i.e. the number of days when RM 7,n >EF7) of this month is 95%, but the ecological monitoring data of the same period shows that the ecological health index is only 40 points (lower than the health threshold of 60 points), at this time, a bias mode is identified: the target is too loose, that is, although the flow target is basically met, the ecological system is still in an unhealthy state, which shows that the original target is too low and is not enough to provide real ecological protection.

[0232] Step three: calculate and generate a corrected target;

[0233] The generation of the corrected ecological flow target comprises: based on the bias mode, a target correction coefficient is calculated by using the Bayes updating principle; the target correction coefficient is applied to the original section ecological flow target to obtain the corrected ecological flow target.

[0234] Continuing the above example, the original target EF7=50m 3The identified target loose bias pattern and its corresponding ecological health index (40 points) are regarded as new evidence, and through the principle of Bayesian update, the evidence will make us believe that the real required target should be higher. After calculation, the expected value of the posterior distribution E[EF posterior ] is 65 m 3 / s, and the target correction coefficient K correct is calculated as E[EF posterior ] / EF prior = 65 / 50 = 1.3, and the corrected ecological flow target EF 7,corrected = EF7K correct = 50 1.3 = 65 m 3 / s. Thus, through real ecological feedback, the ecological flow target in July is dynamically corrected from 50 m 3 / s to 65 m 3 / s.

[0235] Step four: perform the final comprehensive evaluation.

[0236] The river and lake section ecological flow guarantee judgment function in the step S4, the input is: the drift compensated incoming water frequency; and the maximum damage depth and cumulative damage depth calculated based on the corrected ecological flow target.

[0237] In the last step of the embodiment, the results of all the aforementioned innovations are collected to perform the most comprehensive evaluation;

[0238] The drift-compensated incoming water frequency P compensated , for example, 97.75%;

[0239] The corrected ecological flow target EF corrected , such as 65 m 3 / s in July, is used as the benchmark;

[0240] Based on this new benchmark, the method of the above embodiment is re-applied to calculate the non-linear adjusted damage depth sequence considering time memory and mutation weighting;

[0241] The maximum and cumulative values are extracted from the sequence to obtain the final maximum damage depth and cumulative damage depth.

[0242] The three comprehensive indicators that best reflect the real hydrological pressure and ecological risk are input into the ecological flow guarantee judgment function to obtain the most scientific and robust final evaluation result considering time memory, mutation impact, frequency drift, and target adaptive correction.

[0243] According to another aspect of the present application, a river section ecological flow guarantee evaluation system based on incoming water frequency-erosion depth constraint is provided, characterized in that comprising:

[0244] at least one processor; and

[0245] a memory connected in communication with the at least one processor; wherein,

[0246] the memory stores instructions executable by the processor, the instructions being executed by the processor to implement the river section ecological flow guarantee evaluation method based on the incoming water frequency-erosion depth constraint.

[0247] The above describes the preferred embodiments of the present application, but the present application is not limited to the specific details in the above-described embodiments, and various equivalent transformations can be made to the technical solutions of the present application within the technical concept scope of the present application, and these equivalent transformations all belong to the protection scope of the present application.

Claims

1. A method for assessing the ecological flow guarantee of river cross-section based on the frequency-disruption depth constraint of incoming water, characterized in that, Comprising the following steps: Step S1, collecting river section historical long series and runoff data in the evaluation period, and constructing river and lake section ecological flow target evaluation database; Step S2, constructing the theoretical probability distribution curve of the average flow of the driest month in the diversion period, and extracting the monthly runoff data of the driest month as the input of the curve to calculate the corresponding inflow frequency of the monthly scale runoff in the evaluation period; Step S3, based on the measured daily scale runoff of the river and lake section and the ecological flow target, the daily damage depth of the ecological flow target of the river and lake section in the evaluation period is calculated, and the maximum damage depth and the cumulative damage depth of the ecological flow target of the river and lake section in the evaluation period are calculated; Step S4, constructing the river and lake section ecological flow guarantee judgment function, and setting the satisfaction domain of the ecological flow target, inputting the inflow frequency, the maximum damage depth and the cumulative damage depth corresponding to the monthly scale runoff in the evaluation period into the river and lake section ecological flow guarantee judgment function, calculating the satisfaction value of the river and lake section ecological flow in the evaluation period, and evaluating whether the ecological flow target is up to standard; Step S5, dynamic optimization of ecological flow target based on evaluation feedback; Wherein step S3 is: Step S31, constructing an ecological system memory decay function; Step S32, calculating the daily damage depth considering the memory effect; Step S33, calculating the dynamic maximum damage depth and cumulative damage depth; Step S34, ecological mutation point identification and nonlinear damage evaluation; Wherein step S34 is: step S34a, constructing a damage rate index; Step S34b, identifying the ecological mutation point; Step S34c, calculating the nonlinear weighted damage depth; Wherein step S34c is: For the mutation point, a nonlinear amplification function is applied; For non-mutation points, the original damage depth is maintained; Get the nonlinear adjusted damage depth sequence.

2. The river cross-section ecological flow guarantee assessment method based on incoming water frequency-destroying depth constraint according to claim 1, characterized in that, The step S1 is further: Step S11, collecting runoff data of the river section in the evaluation period, including: measured daily runoff data of the section, upstream river channel water taking amount, and upstream engineering storage data; Step S12, based on the measured daily runoff data of the section, the measured runoff is restored by using hydrological restoration method to obtain natural runoff data of the section in the evaluation period, and a monthly natural runoff data matrix in the evaluation period is constructed; Step S13, collecting historical long series runoff data of the river section, including: historical long series natural monthly average runoff data, and historical long series measured monthly average runoff data; Step S14, constructing the river and lake section ecological flow target evaluation database, including: evaluation period monthly natural runoff data matrix, evaluation period daily measured runoff data, historical long series natural monthly average runoff data, historical long series measured monthly average runoff data, and section ecological flow target.

3. The river cross-section ecological flow guarantee assessment method based on incoming water frequency-destroying depth constraint according to claim 1, characterized in that, The step S2 is further: Step S21, extracting the historical long series natural monthly average runoff data, constructing the river and lake section historical long series monthly scale natural runoff data matrix, and establishing the theoretical probability distribution curve of the average flow of the driest month in the diversion period by using Pearson P-III curve; Step S22, extracting the monthly runoff data of the driest month as the input of the theoretical probability distribution curve of the average flow of the driest month in the diversion period, and finding the corresponding inflow frequency of the monthly scale runoff in the evaluation period.

4. The river cross-section ecological flow guarantee assessment method based on incoming water frequency-destroying depth constraint according to claim 3, characterized in that, The step S21 is further: Step S21a, extracting historical long series of natural monthly runoff data from the river and lake section ecological flow target evaluation database, constructing a historical long series of monthly natural runoff data matrix of the river and lake section, extracting the driest month runoff data from the historical long series of monthly natural runoff data matrix of the river and lake section, and constructing a driest month runoff series; Step S21b, fitting the driest month runoff series data with Pearson III curve to obtain the theoretical probability distribution curve of the driest month average flow of the river and lake section.

5. The river cross-section ecological flow guarantee assessment method based on incoming water frequency-destroying depth constraint according to claim 1, characterized in that, The step S4 is further: Step S41, constructing a river and lake section ecological flow guarantee judgment function, with the constraint conditions being incoming water frequency and damage depth; Step S42, setting a satisfaction domain of ecological flow target compliance; Step S43, inputting the corresponding incoming water frequency, maximum damage depth and cumulative damage depth of the monthly runoff of the evaluation period into the river and lake section ecological flow guarantee judgment function to obtain the satisfaction value of the ecological flow of the river and lake section in the evaluation period; Step S44, judging the satisfaction value of the ecological flow of the river and lake section in the evaluation period with the satisfaction domain of ecological flow target compliance to obtain the compliance situation of the ecological flow target.

6. A river cross-section ecological flow guarantee assessment system based on incoming water frequency-destroying depth constraints, characterized in that, Comprise: At least one processor; And The memory is connected in communication with the at least one processor; wherein, The memory stores instructions executable by the processor, and the instructions are executed by the processor to implement the river section ecological flow guarantee evaluation method based on the incoming water frequency-damage depth constraint according to any one of claims 1 to 5.

Images