Simple scheduling method for small reservoir
By establishing a dynamic relationship model and a real-time water level-reservoir volume relationship model, dynamically adjusting the runoff coefficient, the problem of oversimplification of runoff coefficient determination in the existing technology is solved, and more accurate rain encapsulation capacity calculation and reservoir scheduling decisions are achieved.
Patent Information
- Application Number
- CN202510277635.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-10
- Publication Date
- 2025-06-27
AI Technical Summary
When calculating the rain-sucking capacity of small reservoirs, the determination of runoff coefficient is too simplified and fails to effectively reflect the comprehensive impact of multiple complex factors, resulting in deviations in the calculation results and affecting the scientific nature of reservoir scheduling decisions.
By collecting the basic data of the reservoir, meteorological data and soil and vegetation data around the reservoir, a dynamic relationship model between the runoff coefficient and each influencing factor is established, and combined with the real-time water level-reservoir relationship model, the value of the runoff coefficient is dynamically adjusted to more accurately reflect the actual runoff situation.
The comprehensive effect of more accurately reflecting multiple factors when calculating rain absorption capacity is achieved, overcoming the problem of too one-sided determination of runoff coefficients in the prior art, and improving the scientificity and accuracy of reservoir scheduling decisions.
Smart Images
Figure CN120218653A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of water conservancy projects, and particularly to a simple scheduling method for small reservoirs. Background Art
[0002] In the field of water conservancy projects, the scientific scheduling of small reservoirs is crucial for ensuring the rational utilization of regional water resources, flood control and disaster reduction, and ecological environment stability. There are numerous small reservoirs widely distributed across various regions, and their scheduling management is directly related to the economic development and people's livelihood safety in the surrounding areas.
[0003] In terms of the calculation of rainfall interception capacity, there are obvious deficiencies in existing methods. When calculating the rainfall interception capacity, the runoff coefficient is a key parameter. Some current schemes only obtain the runoff coefficient based on the antecedent precipitation index of the reservoir's watershed. However, the runoff coefficient is actually affected by a variety of complex factors, including soil type, vegetation cover, land use pattern, etc. Different soil types have significant differences in water permeability and water holding capacity, which will significantly affect the proportion of precipitation converted into runoff; vegetation cover not only plays a role in intercepting precipitation, but also indirectly affects runoff formation by influencing the soil structure; the change in land use pattern, such as the increase in construction land during the urbanization process, will reduce the infiltration capacity of the ground surface, thereby changing the runoff coefficient.
[0004] Moreover, these influencing factors are not fixed, but show dynamic changes with seasons, years, etc. For example, in different seasons, the growth conditions of vegetation are different, the vegetation coverage and root water absorption capacity change, and the runoff coefficient will also change accordingly. Determining the runoff coefficient only based on the antecedent precipitation index is too one-sided in this simplified way, and it is difficult to accurately reflect the actual runoff situation, thus resulting in deviations in the calculation results of the rainfall interception capacity and affecting the scientific nature of reservoir scheduling decisions. Summary of the Invention
[0005] The purpose of the present invention is to provide a simple scheduling method for small reservoirs to solve the problems raised in the above background art. In order to achieve the above technical features, the purpose of the present invention is realized as follows:
[0006] A simple scheduling method for small reservoirs includes the following steps:
[0007] Step S1: Collect basic data of the reservoir, meteorological data, and soil and vegetation data around the reservoir;
[0008] Step S2: Analyze various factors affecting the runoff coefficient, establish a dynamic relationship model between the runoff coefficient and each influencing factor based on the collected data, and verify and optimize this model;
[0009] Step S3: Modify the water level - storage capacity curve based on the reservoir operation and topographic data, and establish a real - time water level - storage capacity relationship model by combining real - time water level observation data;
[0010] Step S4: Calculate the current runoff coefficient according to real - time data, and calculate the rainwater storage capacity of the reservoir at different water levels by combining the real - time water level - storage capacity relationship model;
[0011] Step S5: Determine the input and output variables of the fuzzy logic model, establish a fuzzy rule base, and obtain the scheduling decision result through the fuzzy inference mechanism;
[0012] Step S6: Formulate and implement a scheduling plan according to the decision result, monitor and feedback the parameters of the reservoir in real - time, and adjust and optimize the scheduling plan according to the feedback result.
[0013] In the said Step S1, the basic data of the reservoir include the detailed topographic information of the reservoir, historical water levels, storage capacities, and inflow - outflow water volume data; the meteorological data include the real - time meteorological data of the area where the reservoir is located, future meteorological forecast information, and historical meteorological data of the basin where the reservoir is located; the soil and vegetation data around the reservoir include the type, texture, and porosity of the soil, as well as the coverage type and growth status of the vegetation.
[0014] In the said Step S2, the multiple regression analysis method is used to establish a regression equation between the runoff coefficient and each influencing factor. The form of the regression equation is:
[0015] α = α0 + α1×H + α2×V + α3×P
[0016] where α is the runoff coefficient, H is the soil moisture, V is the vegetation coverage, P is the antecedent precipitation index, and α0, α1, α2, and α3 are regression coefficients.
[0017] In the said Step S3, analyze the reservoir sedimentation and bottom deformation conditions according to the historical operation data of the reservoir and the results of regular topographic surveys, and thus modify the water level - storage capacity curve in real - time; combine the real - time water level observation data and the modified water level - storage capacity curve, and establish a real - time water level - storage capacity relationship model through numerical simulation methods.
[0018] In the said Step S4, the calculation formula for the rainwater storage capacity is:
[0019]
[0020] where P 纳 is the rainwater storage capacity, V 控 is the storage capacity corresponding to the control water level, V 实时 is the real - time storage capacity of the reservoir, F is the area of the basin above the reservoir, and α is the real - time runoff coefficient.
[0021] In step S5, the input variables are the real-time water level of the reservoir, the real-time rainwater intake capacity, and the precipitation amount in the weather forecast, and the output variable is the reservoir operation decision, including whether to pre-discharge flood and the amount of water for pre-discharging flood.
[0022] In step S5, the fuzzy rule base is established based on the operation experience and expert knowledge of the reservoir; in the fuzzy inference process, a fuzzy inference algorithm is adopted to calculate the membership function of the output variable according to the membership functions of the input variables and the fuzzy rules, and then the specific operation decision value is obtained through the defuzzification method.
[0023] In step S6, when the decision result is that pre-discharging flood is required, the parameters for pre-discharging flood are determined. The flood discharge parameters include but are not limited to time and flow rate, and the flood discharge facilities are operated for flood discharge.
[0024] The parameters of the reservoir are monitored in real time. The reservoir parameters include but are not limited to the water level and water volume, as well as the water level change of the downstream river channel.
[0025] The monitored reservoir data is fed back into the operation model for comparison and analysis with the expected results. If the actual water level change does not match the expectation or adverse effects occur downstream, the flow rate of pre-discharging flood is adjusted in time or the flood discharge is stopped.
[0026] Advantages of the present invention:
[0027] 1. The present invention utilizes historical data and on-site monitoring data to analyze the runoff generation laws of different soil types under different precipitation intensities, and combines the seasonal changes of vegetation coverage and the long-term evolution of land use patterns to construct a runoff coefficient calculation model that can reflect the comprehensive effects of multiple factors in real time. In this way, when calculating the rainwater intake capacity, the model can dynamically adjust the value of the runoff coefficient according to the real-time data of various influencing factors actually monitored, so as to more accurately reflect the actual runoff situation, overcoming the one-sidedness problem of the prior art that determines the runoff coefficient based on a single factor.
[0028] 2. In the simulation process of the present invention, factors such as the terrain of the reservoir and hydraulic structures are accurately incorporated into the model. By establishing a high-precision water flow model, the flow pattern of water in the reservoir can be accurately simulated, including complex phenomena such as turbulent flow and backflow. Based on the simulation results, more realistic water flow parameters can be obtained, and then the rainwater intake capacity can be accurately calculated, no longer relying on simple ideal assumptions, effectively solving the problem of deviation in calculation results caused by the inconsistency between the assumptions and the actual situation in the prior art, and making the calculation of the rainwater intake capacity closer to the actual operation status of the reservoir. Description of the Drawings
[0029] The present invention will be further described below in conjunction with the drawings and embodiments.
[0030] Figure 1Schematic flow chart of a simple scheduling method for small reservoirs provided by an embodiment of the present invention; Detailed implementation manners
[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0032] Refer to the attached Figure 1 , the present invention provides a technical solution:
[0033] A simple scheduling method for small reservoirs, comprising the following steps:
[0034] Step S1, collecting basic data of the reservoir, meteorological data, and soil and vegetation data around the reservoir;
[0035] The basic data of the reservoir includes detailed topographic information of the reservoir, historical water levels, storage capacities, and inflow and outflow data; the meteorological data includes real-time meteorological data of the area where the reservoir is located, future meteorological forecast information, and historical meteorological data of the basin where the reservoir is located; the soil and vegetation data around the reservoir includes the type, texture, and porosity of the soil, as well as the coverage type and growth status of the vegetation.
[0036] Step S2, analyzing various factors affecting the runoff coefficient, establishing a dynamic relationship model between the runoff coefficient and each influencing factor based on the collected data, and verifying and optimizing the model;
[0037] Using the multiple regression analysis method to establish a regression equation between the runoff coefficient and each influencing factor, and the form of the regression equation is:
[0038] α = α0 + α1×H + α2×V + α3×P
[0039] where α is the runoff coefficient, H is the soil moisture, V is the vegetation coverage, P is the antecedent precipitation, and α0, α1, α2, and α3 are regression coefficients.
[0040] Step S3, correcting the water level-storage capacity curve according to the reservoir operation and topographic data, and establishing a real-time water level-storage capacity relationship model in combination with real-time water level observation data;
[0041] Analyze the reservoir sedimentation and bottom deformation conditions based on the historical operation data of the reservoir and the results of regular topographic surveys, and thus correct the water level-storage capacity curve in real time; combine the real-time water level observation data and the corrected water level-storage capacity curve, and establish a real-time water level-storage capacity relationship model through numerical simulation methods.
[0042] Step S4: Calculate the current runoff coefficient based on real-time data, and calculate the rainwater storage capacity of the reservoir at different water levels in combination with the real-time water level-storage capacity relationship model.
[0043] The calculation formula for the rainwater storage capacity is as follows:
[0044]
[0045] Where P 纳 is the rainwater storage capacity, V 控 is the storage capacity corresponding to the control water level, V 实时 is the real-time storage capacity of the reservoir, F is the catchment area above the reservoir, and α is the real-time runoff coefficient.
[0046] Step S5: Determine the input and output variables of the fuzzy logic model, establish a fuzzy rule base, and obtain the scheduling decision result through the fuzzy inference mechanism.
[0047] The input variables are the real-time water level of the reservoir, the real-time rainwater storage capacity, and the precipitation in the weather forecast. The output variable is the scheduling decision of the reservoir, including whether to pre-discharge flood and the amount of pre-discharged flood; the fuzzy rule base is established based on the operation experience and expert knowledge of the reservoir; in the fuzzy inference process, a fuzzy inference algorithm is used to calculate the membership function of the output variable according to the membership functions of the input variables and the fuzzy rules, and then the specific scheduling decision value is obtained through the defuzzification method.
[0048] Step S6: Formulate and implement the scheduling plan according to the decision result, monitor and feedback relevant parameters in real time, and adjust and optimize the scheduling plan according to the feedback result.
[0049] When the decision result is that pre-discharge flood is required, determine the pre-discharge flood parameters. The flood discharge parameters include but are not limited to time and flow rate, and operate the flood discharge facilities to discharge flood; monitor the parameters of the reservoir in real time. The reservoir parameters include but are not limited to water level and water volume, as well as the water level change of the downstream river channel, and feed the monitoring data back into the scheduling model for comparison and analysis with the expected results; if the actual water level change does not match the expectation or adverse effects occur downstream, adjust the pre-discharge flood flow rate or stop discharging flood in time.
[0050] In a specific embodiment, data on soil moisture H, vegetation coverage V, antecedent precipitation index P, and runoff coefficient α are obtained from the monitoring stations of each reservoir. Clean the data to remove obviously incorrect or missing data records. Divide the data into a training set (80%) and a test set (20%).
[0051] Considering that the runoff coefficient may be comprehensively affected by multiple factors, a multiple linear regression model is selected. Its general form is α = α0 + α1×H + α2×V + α3×P + ∈, where ∈ is the error term.
[0052] Using the training set data, the regression coefficients α0, α1, α2, and α3 are estimated by the least squares method. The goal of the least squares method is to minimize the sum of the squared errors between the predicted values and the actual values, that is where α i is the actual runoff coefficient, is the runoff coefficient predicted by the regression model, and n is the number of data points in the training set.
[0053] The prediction error of the model is calculated using the test set data. Commonly used evaluation metrics include the mean squared error (MSE), the root mean squared error (RMSE), and the coefficient of determination (R 2 ).
[0054] The calculation formula for MSE is where m is the number of data points in the test set. RMSE is the square root of MSE, that is, the calculation formula for it is R 2 The calculation formula for it is is the average value of the actual runoff coefficient. The quality of the model is judged according to the evaluation metrics. If the model performance is not ideal, it is possible to consider increasing data, adjusting the model, or introducing other variables.
[0055] At a certain moment, it is necessary to evaluate the rainfall intake capacity of a small reservoir in order to formulate a reasonable operation plan. Given the current water level, storage capacity information of the reservoir, as well as the basin area and real-time runoff coefficient.
[0056] The storage capacity V corresponding to the control water level 控 : Obtained through the design documents or historical data of the reservoir, which is the water storage capacity of the reservoir at a specific control water level.
[0057] The real-time storage capacity V of the reservoir 实时 : Calculated from the water level observation data of the reservoir and the established water level-storage capacity relationship model.
[0058] The basin area F above the reservoir: Determined through geographic information system (GIS) data or field measurements.
[0059] The real-time runoff coefficient α: Calculated through the runoff coefficient regression equation established previously, combined with the current soil moisture, vegetation coverage, and antecedent precipitation.
[0060] The concept of rainfall intake capacity refers to the amount of rainfall that the reservoir can accept in its current state. Considering from the perspective of water balance, assuming the rainfall is P 纳 , and the formed runoff is Q, then Q = P 纳 ×F×α (the runoff is equal to the rainfall multiplied by the basin area multiplied by the runoff coefficient). The runoff that the reservoir can accommodate is equal to the difference between the storage capacity corresponding to the control water level and the real-time storage capacity, that is, Q = V 控-V 实时 Real-time. By combining the above two equations, P is obtained 纳 ×F×α = V 控 -V 实时 , thus deriving the calculation formula for rainwater intake capacity
[0061] Suppose an accurate modeling of the water level - storage capacity relationship is carried out for a small reservoir with an irregular shape. The specific steps are as follows:
[0062] Step 1: Use GIS data or on-site measurement to obtain the topographic data of the reservoir. Divide the planar area of the reservoir into a grid of i rows and j columns, and the area of each grid is ΔA ij . The size of the grid is determined according to the complexity of the terrain and the requirements of calculation accuracy.
[0063] Step 2: For each grid, determine its bottom elevation according to the topographic data When the water level of the reservoir is h. The water depth of each grid
[0064] Step 3: When the water storage volume V of each grid ij = ΔA ij ×d ij , the total storage capacity of the reservoir where m and n are the number of rows and columns of the grid respectively.
[0065] Step 4: By changing the water level h, repeat Steps 2 and 3 to calculate the storage capacity V at different water levels, and fit the calculated water level and storage capacity data to obtain the water level - storage capacity relationship model. For example, use polynomial fitting V = α0 + α1h + α2h 2 +…α k h k , where, α0, α1, …, α k are fitting coefficients, and k is the degree of the polynomial.
[0066] During the reservoir operation process, it is necessary to comprehensively consider multiple factors such as real-time water level, real-time rainwater intake capacity, and precipitation in meteorological forecasts to make reasonable operation decisions, such as whether to pre-discharge flood and the amount of pre-discharge flood.
[0067] Suppose the normal water level of the reservoir is [h min ,h max = [50, 100] m. Divide the water level into three fuzzy sets: low water level, medium water level, high water level. For the low water level fuzzy set, set h mid = 60 m.
[0068] Specifically, define the membership function of the low water level L fuzzy set as:
[0069]
[0070] When the real-time water level \(x = 55\) meters, substitute it into the membership function for calculation: That is, the membership degree of the real-time water level \(x = 55\) meters belonging to the low water level fuzzy set is \(0.5\).
[0071] Suppose there is a fuzzy rule: If the real-time water level is high water level and the real-time rainwater intake capacity is low rainwater intake capacity, then the scheduling decision is pre-flood discharge. Given that the membership degree of the real-time water level \(x\) for the high water level is \(\mu_H(x)=0.8\), and the membership degree of the real-time rainwater intake capacity \(y\) for the low rainwater intake capacity is \(\mu_L(y)=0.6\).
[0072] According to the Mamdani reasoning method, the membership degree \(\mu_P\) of the scheduling decision result of pre-flood discharge is \(\min(\mu_H(x),\mu_L(y))\). Substitute the known values to get \(\mu_P=\min(0.8,0.6)=0.6\), that is, the membership degree of the pre-flood discharge scheduling decision result is \(0.6\).
[0073] After the above simulation reasoning, the membership function of the scheduling decision result (pre-flood discharge flow) is obtained The universe of discourse is \([0, 100]\) cubic meters per second and is discretized into The corresponding membership degrees
[0074] According to the centroid method defuzzification formula
[0075] Calculate the numerator:
[0076] Calculate the denominator:
[0077] Get cubic meters per second, that is, the final scheduling decision value (pre-flood discharge flow) is \(41.54\) cubic meters per second.
Claims
1. A simple dispatching method for small reservoirs, characterized in that: The following steps are involved: Step S1: Collecting basic data related to the reservoir, meteorological data, and soil and vegetation data around the reservoir; Step S2: Analyze various factors affecting the runoff coefficient, establish a dynamic relationship model between the runoff coefficient and various influencing factors based on the collected data, and verify and optimize the model; Step S3: modifying the water level-storage capacity curve according to reservoir operation and topographic data, and establishing a real-time water level-storage capacity relationship model in combination with real-time water level observation data; Step S4: Calculate the current runoff coefficient based on the real-time data, and calculate the rain-receiving capacity of the reservoir at different water levels in combination with the real-time water level-reservoir capacity relationship model; Step S5: Determine the input and output variables of the fuzzy logic model, establish a fuzzy rule base, and obtain the scheduling decision result through the fuzzy reasoning mechanism; Step S6: Formulate and implement a dispatching plan based on the decision results, monitor and feedback the reservoir parameters in real time, and adjust and optimize the dispatching plan based on the feedback results.
2. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S1, the basic data related to the reservoir include detailed topographic information, historical water level, storage capacity, and water inflow and outflow data of the reservoir; the meteorological data include real-time meteorological data of the area where the reservoir is located, future meteorological forecast information, and historical meteorological data of the basin where the reservoir is located; the soil and vegetation data around the reservoir include soil type, texture and porosity, as well as vegetation coverage type and growth conditions.
3. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S2, a regression equation of the runoff coefficient and various influencing factors is established by using a multivariate regression analysis method. The regression equation is in the form of: α=α0+α1×H+α2×V+α3×P Among them, α is the runoff coefficient, H is soil moisture, V is vegetation coverage, P is the previous influencing rainfall, and α0, α1, α2 and α3 are regression coefficients.
4. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S3, the reservoir siltation and reservoir bottom deformation are analyzed based on the historical operation data of the reservoir and the regular topographic measurement results, so as to make real-time corrections to the water level-reservoir capacity curve; and a real-time water level-reservoir capacity relationship model is established by combining the real-time water level observation data and the corrected water level-reservoir capacity curve through a numerical simulation method.
5. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S4, the calculation formula of the rain receiving capacity is: Among them, P 纳 is the rain receiving capacity, V 控 To control the reservoir capacity corresponding to the water level, V 实时 is the real-time storage capacity of the reservoir, F is the basin area above the reservoir, and α is the real-time runoff coefficient.
6. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S5, the input variables are the real-time water level of the reservoir, the real-time rainfall receiving capacity, and the precipitation predicted by the weather forecast, and the output variables are the scheduling decision of the reservoir, including whether to pre-discharge the flood and the amount of water to be pre-discharged.
7. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S5, a fuzzy rule base is established based on the operating experience of the reservoir and expert knowledge; in the fuzzy reasoning process, a fuzzy reasoning algorithm is used to calculate the membership function of the output variable based on the membership function of the input variable and the fuzzy rules, and then a specific scheduling decision value is obtained through a defuzzification method.
8. A simple dispatching method for small reservoirs according to claim 1, characterized in that: In step S6, when the decision result is that pre-flood discharge is needed, the parameters of pre-flood discharge are determined, the flood discharge parameters include but are not limited to time and flow, and the flood discharge facilities are operated to discharge the flood.
9. A simple dispatching method for small reservoirs according to claim 8, characterized in that: Real-time monitoring of reservoir parameters, including but not limited to water level and water volume, as well as changes in water level in downstream rivers.
10. A simple dispatching method for small reservoirs according to claim 9, characterized in that: The monitored reservoir data will be fed back into the dispatching model for comparison and analysis with the expected results. If the actual water level changes do not match expectations or adverse effects occur downstream, the pre-discharge flow will be adjusted in a timely manner or the discharge will be stopped.