Reservoir water level regulation method, device, equipment and medium in arid and semiarid regions

Abstract

This application discloses a method, apparatus, equipment, and medium for reservoir water level regulation in arid and semi-arid regions. The method includes: constructing a flood hydrograph corresponding to the target flood season based on hydrological sequence data calculated from hydrological, meteorological, and snowmelt sequence data; determining the target regulation duration based on the flood hydrograph with a reference peak flow; determining the water level reduction based on the reference peak flow and the target regulation duration; determining the water level rise based on the required water replenishment and the duration of the target flood season; determining the flood control water level range based on the water level reduction and rise; and adjusting the water level of the target reservoir based on the current forecast precipitation and the flood control water level range. This application utilizes a regulation strategy that dynamically calculates the reservoir's flood control range based on current hydrological, meteorological, and snowmelt factors, and dynamically adjusts the reservoir water level in conjunction with forecast precipitation. Compared to regulation schemes based on fixed flood control water levels, this improves the reservoir water level regulation effectiveness.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of water resource utilization technology, and in particular to a method, device, equipment and medium for regulating reservoir water level in arid and semi-arid regions. Background Technology

[0002] Arid and semi-arid regions generally face the dual challenges of water shortages during the dry season and floods during the flood season. To alleviate these difficulties, accurately identifying the current flood season stage and implementing targeted regulation of reservoir water levels based on this stage is crucial. However, current technologies rely on fuzzy mathematics or empirical statistics to define the flood season stage, failing to capture the spatiotemporal distribution characteristics of rainfall-snowmelt combined floods in arid areas. This leads to biases in the definition of the flood season stage, affecting its accuracy. Furthermore, existing methods for regulating reservoir water levels rely solely on fixed flood control limits, which cannot respond to the sudden and complex nature of floods in arid and semi-arid regions. This makes it difficult to prevent insufficient reservoir capacity during floods, leading to flood control risks, or insufficient water storage during non-flood seasons, resulting in wasted water resources and unsatisfactory regulation effects. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and medium for regulating reservoir water levels in arid and semi-arid regions, which can effectively improve the regulation effect of reservoir water levels.

[0004] In a first aspect, embodiments of this application provide a method for regulating reservoir water levels in arid and semi-arid regions, including:

[0005] Collect hydrological sequence data, meteorological sequence data, and snowmelt sequence data associated with the target reservoir, and calculate the FSAI index based on the hydrological sequence data, the meteorological sequence data, and the snowmelt sequence data, wherein the FSAI index is used to indicate the current flood season risk status index;

[0006] The target flood season is determined based on the FSAI index, wherein the target flood season is the current flood season stage, and the flood season stage is the pre-flood season, the main flood season, or the post-flood season;

[0007] Based on the hydrological sequence data, a flood process line corresponding to the target flood season is constructed, and the target control duration is determined based on the flood process line. The flood process line is used to indicate the mapping relationship between flow and time throughout the entire process of flood rise, peak and recede. The flood process line corresponds to a reference flood peak flow.

[0008] The water level reduction is determined based on the reference peak flow and the target regulation duration, and the water level increase is determined based on the required water replenishment during the target flood season and the duration of the target flood season.

[0009] The flood control water level range is determined based on the water level decrease and the water level increase, wherein the flood control water level range includes a first upper limit value and a first lower limit value;

[0010] Based on the current forecast precipitation and the flood control water level range, the water level of the target reservoir is adjusted, wherein the forecast precipitation represents the average precipitation of the watershed associated with the target reservoir within a preset time period.

[0011] In some embodiments, calculating the FSAI index based on the hydrological series data, the meteorological series data, and the snowmelt series data includes:

[0012] Data preprocessing is performed on the hydrological sequence data, the meteorological sequence data, and the snowmelt sequence data;

[0013] The membership degree is calculated based on the current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation, wherein the current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation are all determined based on meteorological sequence data after data preprocessing;

[0014] The concentration index is calculated by converting the peak flow and corresponding occurrence time in the hydrological sequence data into polar coordinate vectors.

[0015] Based on the Euclidean distance algorithm, the similarity index between the runoff value in the hydrological sequence data, the precipitation in the meteorological sequence data, and the snowmelt amount in the snowmelt sequence data is calculated;

[0016] Assign a first weight to the membership degree, a second weight to the concentration index, and a third weight to the similarity index, wherein the sum of the first weight, the second weight, and the third weight is 1;

[0017] The FSAI index is obtained by summing the product of the first weight and the membership degree, the product of the second weight and the concentration index, and the product of the third weight and the similarity index.

[0018] In some embodiments, determining the target flood season based on the FSAI index includes:

[0019] When the number of consecutive days corresponding to the FSAI index being greater than the first index threshold is greater than a preset number of days threshold, the target flood season is determined to be the main flood season.

[0020] When the FSAI index is greater than or equal to the second index threshold and less than the first index threshold, and the current time is before the main flood season, the target flood season is determined to be the pre-flood season;

[0021] When the FSAI index is greater than or equal to the second index threshold and less than the first index threshold, and the current time is after the main flood season, the target flood season is determined to be the post-flood season.

[0022] In some embodiments, the hydrological sequence data includes multiple samples, with different samples corresponding to different time periods. Constructing a flood process curve corresponding to the target flood season based on the hydrological sequence data includes:

[0023] The hydrological sequence data are sequentially calibrated for mean, coefficient of variation, and skewness to obtain the first intermediate data after calibration.

[0024] The first intermediate data is normalized to obtain the second intermediate data, wherein the second intermediate data includes the peak flow corresponding to each sample;

[0025] Determine the reference number of times that the peak flow exceeds a preset flow threshold among all the flood peak flows, and divide the reference number by the total number of samples to obtain the exceedance probability, wherein the exceedance probability is associated with the reference peak flow;

[0026] Obtain typical flood hydrographs and typical peak flow rates;

[0027] Divide the reference peak flow by the typical peak flow to obtain an intermediate value, and multiply the intermediate value by the typical flood hydrograph to obtain the flood hydrograph corresponding to the target flood season.

[0028] In some embodiments, determining the target control duration based on the flood hydrograph includes:

[0029] Determine the reference time at which the peak value of the flood in the flood process curve is located, and calculate the first reference duration between the current time and the reference time;

[0030] The precipitation forecast period, information transmission time, decision-making time, and reservoir gate operation time are determined. The information transmission time, decision-making time, and reservoir gate operation time are summed to obtain a first intermediate value. The precipitation forecast period is then subtracted from the first intermediate value to obtain a second reference duration. The information transmission time represents the total time from the acquisition of hydrological and meteorological forecast information to its transmission to the dispatch center and completion of verification. The precipitation forecast period represents the expected duration between the current time and the forecast precipitation time. The decision-making time represents the preset time for the dispatch center to complete the dispatch decision after receiving the hydrological and meteorological forecast information.

[0031] When both the first reference duration and the second reference duration are greater than or equal to a preset duration threshold, the minimum value between the first reference duration and the second reference duration is determined as the target control duration.

[0032] If only one of the first reference duration and the second reference duration is greater than or equal to the preset duration threshold, the reference duration that is greater than or equal to the preset duration threshold is determined as the target control duration.

[0033] In some embodiments, adjusting the water level of the target reservoir based on the current forecast precipitation and the flood control limit water level range includes:

[0034] Divide the difference between the first upper limit value and the first lower limit value by 2 to obtain the second intermediate value, and sum the second intermediate value and the first lower limit value to obtain the second lower limit value;

[0035] A target water level range is formed based on the second lower limit and the first upper limit;

[0036] If the current forecast precipitation is greater than or equal to the first precipitation threshold, the water level of the target reservoir will be lowered to the first lower limit, and the pre-release scheduling mode will be activated.

[0037] If the current forecast precipitation is greater than the second precipitation threshold and less than the first precipitation threshold, the water level of the target reservoir will be controlled within the target water level range.

[0038] If the current forecast precipitation is less than or equal to the second precipitation threshold, the water level of the target reservoir will be raised to the first upper limit value.

[0039] In some embodiments, the operation of mean calibration of the hydrological sequence data is associated with a first calibration coefficient, the first lower limit is associated with a first flood control risk coefficient, and the first upper limit is associated with a first water storage benefit coefficient. After adjusting the water level of the target reservoir based on the current forecast precipitation and the flood control limit water level range, the method further includes:

[0040] Determine the predicted and measured water levels of the target reservoir during the same time period, as well as the predicted and measured flow rates of the target watershed associated with the target reservoir during the same time period;

[0041] The relative error of water level prediction is calculated based on the predicted water level, the measured water level, the first upper limit value, and the first lower limit value.

[0042] The relative error of flow prediction is calculated based on the predicted flow, the measured flow, and the reference peak flow.

[0043] When the relative error of water level prediction is greater than a first error threshold, or the relative error of flow prediction is greater than a second error threshold, the first flood control risk coefficient is adjusted based on the relative error of water level prediction to obtain a second flood control risk coefficient, the first water storage benefit coefficient is adjusted based on the relative error of water level prediction to obtain a second water storage benefit coefficient, and the first calibration coefficient is adjusted based on the relative error of flow prediction to obtain a second calibration coefficient.

[0044] Secondly, embodiments of this application provide a control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the reservoir water level regulation method for arid and semi-arid regions as described in the first aspect.

[0045] Thirdly, embodiments of this application also provide an electronic device, including the control device of the second aspect.

[0046] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for executing the reservoir water level control method for arid and semi-arid regions as described in the first aspect.

[0047] This application provides a method, apparatus, equipment, and medium for reservoir water level regulation in arid and semi-arid regions. The method includes: collecting hydrological sequence data, meteorological sequence data, and snowmelt sequence data associated with a target reservoir; calculating the FSAI index based on the hydrological sequence data, the meteorological sequence data, and the snowmelt sequence data, wherein the FSAI index is used to indicate the current flood season risk status index; determining the target flood season based on the FSAI index, wherein the target flood season is the current flood season stage, and the flood season stage is the pre-flood season, the main flood season, or the post-flood season; constructing a flood process curve corresponding to the target flood season based on the hydrological sequence data; and determining the target regulation duration based on the flood process curve, wherein... The flood hydrograph is used to indicate the mapping relationship between flow rate and time throughout the entire process of a flood with a preset return period, from rise to peak to recession. The flood hydrograph corresponds to a reference peak flow rate. The water level reduction is determined based on the reference peak flow rate and the target regulation duration. The water level rise is determined based on the required water replenishment during the target flood season and the duration of the target flood season. The flood limit water level range is determined based on the water level reduction and the water level rise, wherein the flood limit water level range includes a first upper limit value and a first lower limit value. The water level of the target reservoir is adjusted based on the current forecast precipitation and the flood limit water level range, wherein the forecast precipitation represents the average precipitation of the basin associated with the target reservoir within a preset time period. According to the solution provided in the embodiments of this application, the flood control limit range of the reservoir can be dynamically calculated based on the actual factors such as current hydrology, meteorology, and snowmelt, and the reservoir water level can be dynamically adjusted in combination with the forecast precipitation. Compared with the existing solution that only regulates the water level based on a fixed flood control limit, this solution can effectively improve the regulation effect of the reservoir water level. Attached Figure Description

[0048] Figure 1 This is a flowchart of the steps of a reservoir water level regulation method in arid and semi-arid regions provided in one embodiment of this application;

[0049] Figure 2 This is a structural diagram of a control device provided in another embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0052] Arid and semi-arid regions generally face the dual challenges of water shortages during the dry season and floods during the flood season. To alleviate these difficulties, accurately identifying the current flood season stage and implementing targeted regulation of reservoir water levels based on this stage is crucial. However, current technologies rely on fuzzy mathematics or empirical statistics to define the flood season stage, failing to capture the spatiotemporal distribution characteristics of rainfall-snowmelt combined floods in arid areas. This leads to biases in the definition of the flood season stage, affecting its accuracy. Furthermore, existing methods for regulating reservoir water levels rely solely on fixed flood control limits, which cannot respond to the sudden and complex nature of floods in arid and semi-arid regions. This makes it difficult to prevent insufficient reservoir capacity during floods, leading to flood control risks, or insufficient water storage during non-flood seasons, resulting in wasted water resources and unsatisfactory regulation effects.

[0053] To address the aforementioned problems, this application provides a method, apparatus, equipment, and medium for reservoir water level regulation in arid and semi-arid regions. The method includes: collecting hydrological sequence data, meteorological sequence data, and snowmelt sequence data associated with a target reservoir; calculating the FSAI index based on the hydrological sequence data, meteorological sequence data, and snowmelt sequence data, wherein the FSAI index is used to indicate the current flood season risk status index; determining the target flood season based on the FSAI index, wherein the target flood season is the current flood season stage, and the flood season stage is the pre-flood season, the main flood season, or the post-flood season; constructing a flood hydrograph corresponding to the target flood season based on the hydrological sequence data; and determining the target regulation duration based on the flood hydrograph. The flood process curve is used to indicate the mapping relationship between flow rate and time during the entire process of flooding from rise to peak and recede within a preset return period. The flood process curve corresponds to a reference peak flow rate. The water level reduction is determined based on the reference peak flow rate and the target regulation duration. The water level increase is determined based on the required water replenishment during the target flood season and the duration of the target flood season. The flood limit water level range is determined based on the water level reduction and the water level increase. The flood limit water level range includes a first upper limit value and a first lower limit value. The water level of the target reservoir is adjusted based on the current forecast precipitation and the flood limit water level range. The forecast precipitation represents the average precipitation of the basin associated with the target reservoir within a preset time period. According to the solution provided in the embodiments of this application, the flood control limit range of the reservoir can be dynamically calculated based on the actual factors such as current hydrology, meteorology, and snowmelt, and the reservoir water level can be dynamically adjusted in combination with the forecast precipitation. Compared with the existing solution that only regulates the water level based on a fixed flood control limit, this solution can effectively improve the regulation effect of the reservoir water level.

[0054] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0055] refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a reservoir water level control method in arid and semi-arid regions according to an embodiment of this application. This application provides a reservoir water level control method in arid and semi-arid regions, which includes, but is not limited to, the following steps:

[0056] Step S10: Collect hydrological sequence data, meteorological sequence data and snowmelt sequence data associated with the target reservoir, and calculate the FSAI index based on the hydrological sequence data, meteorological sequence data and snowmelt sequence data. The FSAI index is used to indicate the current flood season risk status index.

[0057] It should be noted that the hydrological sequence data collected in this embodiment are long-term data of the target reservoir and the hydrological stations in the basin associated with the target reservoir. The time span of the hydrological sequence data is greater than or equal to 30 years. The hydrological sequence data includes daily runoff data and hourly peak flow data (including measured flow, peak occurrence time, and flood duration). The meteorological sequence data includes global precipitation products (such as MSWEP V2) and daily average temperature of regional meteorological stations. The snowmelt sequence data includes snow cover and glacier snowmelt data. The time span of the snowmelt sequence data is greater than or equal to 10 years.

[0058] It is understood that by collecting hydrological, meteorological, and snowmelt sequence data associated with the target reservoir, this embodiment can provide an effective data foundation for subsequently determining the target flood season, flood process line, and dynamic water level control strategy of the target reservoir.

[0059] Specifically, in some embodiments, Figure 1 Step S10, which calculates the FSAI index based on hydrological, meteorological, and snowmelt sequence data, includes, but is not limited to, the following steps:

[0060] Step S11: Perform data preprocessing on hydrological sequence data, meteorological sequence data, and snowmelt sequence data;

[0061] Step S12: Calculate the membership degree based on the current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation. The current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation are all determined based on the meteorological sequence data after data preprocessing.

[0062] Step S13: Convert the peak flow and corresponding occurrence time in the hydrological sequence data into polar coordinate vectors and calculate the concentration index.

[0063] Step S14: Based on the Euclidean distance algorithm, calculate the similarity index between runoff values ​​in hydrological sequence data, precipitation in meteorological sequence data, and snowmelt amount in snowmelt sequence data;

[0064] Step S15: Assign a first weight to the membership degree, a second weight to the concentration index, and a third weight to the similarity index, wherein the sum of the first weight, the second weight, and the third weight is 1.

[0065] Step S16: Sum the product of the first weight and membership degree, the product of the second weight and concentration index, and the product of the third weight and similarity index to obtain the FSAI index.

[0066] It should be noted that this embodiment does not limit the specific methods of data preprocessing for hydrological, meteorological, and snowmelt sequence data. It can involve using conventional interpolation to fill in missing data, correcting outliers using outlier identification criteria, and standardizing the hydrological sequence data. The standardization of the hydrological sequence data is achieved according to the following formula:

[0067] ;

[0068] in, The original hydrological sequence data, For standardized hydrological sequence data, The mean of the hydrological series data. denoted as the standard deviation of the hydrological sequence data.

[0069] It should be noted that in this embodiment, the membership degree is calculated based on the current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation, using the following formula:

[0070] ;

[0071] in, x This refers to daily precipitation. μ The mean of the sequence. σ Standard deviation F fuzzy This represents the degree of membership.

[0072] It should be noted that the concentration index is calculated by converting the peak flow and corresponding occurrence time in the hydrological series data into polar coordinate vectors, and then calculating the concentration index using the following formula:

[0073] ;

[0074] in, F vector It is a concentration index. Q i The first in the hydrological sequence data i Second flood peak flow, θ i This is the angle conversion value corresponding to the time.

[0075] It should be noted that in this embodiment, the FSAI index is obtained by summing the product of the first weight and the membership degree, the product of the second weight and the concentration index, and the product of the third weight and the similarity index, and is calculated according to the following formula:

[0076] ;

[0077] Wherein, FSAI is the FSAI index. ω 1 is the first weight. ω 2 is the second weight. ω 3 is the third weight. F cluster The similarity index, F cluster The value range of is [0,1]. ω 1+ ω 2+ ω 3=1, in this embodiment ω 1 = 0.35 ω 2 = 0.35, ω 3 = 0.3.

[0078] Step S20: Determine the target flood season based on the FSAI index, where the target flood season is the current flood season stage, and the flood season stage is the pre-flood season, the main flood season, or the post-flood season.

[0079] Specifically, in some embodiments, Figure 1 Step S20 includes, but is not limited to, the following steps:

[0080] Step S21: When the number of consecutive days corresponding to the FSAI index being greater than the first index threshold is greater than the preset number of days threshold, the target flood season is determined to be the main flood season.

[0081] Step S22: When the FSAI index is greater than or equal to the second index threshold and less than the first index threshold, and the current time is before the main flood season, the target flood season is determined as the pre-flood season.

[0082] Step S23: When the FSAI index is greater than or equal to the second index threshold and less than the first index threshold, and the current time is after the main flood season, the target flood season is determined to be the post-flood season.

[0083] Specifically, in this embodiment, the first index threshold is set to 0.6, and the second index threshold is set to 0.3. It can be understood that in this embodiment, when FSAI ≥ 0.6 and persists for more than 10 days, the target flood season is determined to be the main flood season; before the main flood season, and with the currently calculated FSAI ≥ 0.3 and FSAI < 0.6, the target flood season is determined to be the pre-flood season; after the main flood season, and with the currently calculated FSAI ≥ 0.3 and FSAI < 0.6, the target flood season is determined to be the post-flood season. Since different flood seasons correspond to different flood causes and peak flood characteristics, determining the target flood season (pre-flood season, main flood season, or post-flood season) based on the FSAI index can provide effective support for subsequently determining differentiated scheduling strategies.

[0084] Step S30: Construct the flood hydrograph corresponding to the target flood season based on hydrological sequence data, and determine the target control duration based on the flood hydrograph. The flood hydrograph is used to indicate the mapping relationship between flow and time throughout the entire process of flood rise, peak and recede. The flood hydrograph corresponds to a reference flood peak flow.

[0085] It is understood that the flood hydrograph constructed in this embodiment is used to indicate the entire flood flow curve. This flood hydrograph can provide effective support for subsequently determining the water level regulation strategy of the target reservoir, on the one hand, for calculating the water level reduction Δ. On the one hand, it provides data support, and on the other hand, it can guide the pre-release scheduling rhythm through the temporal characteristics of the flood process line (such as the rate of rise and fall of water and the duration), and at the same time quantify the total flood demand under specific risks, so as to provide quantitative support for balancing flood control capacity and water storage benefits.

[0086] Specifically, in some embodiments, Figure 1 Step S30, which involves constructing the flood hydrograph corresponding to the target flood season based on hydrological sequence data, includes, but is not limited to, the following steps:

[0087] Step S31: Perform mean calibration, coefficient of variation calibration, and skewness coefficient calibration on the hydrological sequence data in sequence to obtain the first intermediate data after calibration.

[0088] Step S32: Normalize the first intermediate data to obtain the second intermediate data, wherein the second intermediate data includes the peak flow corresponding to each sample;

[0089] Step S33: Determine the reference number of times the peak flow exceeds the preset flow threshold among all the peak flows, and divide the reference number by the total number of samples to obtain the exceedance probability, wherein the exceedance probability is associated with the reference peak flow.

[0090] Step S34: Obtain typical flood hydrograph and typical peak flow;

[0091] Step S35: Divide the reference flood peak flow by the typical flood peak flow to obtain an intermediate value. Multiply the intermediate value by the typical flood hydrograph to obtain the flood hydrograph corresponding to the target flood season.

[0092] Understandably, before calculating the flood hydrograph, the hydrological sequence data needs to be calibrated sequentially with mean, coefficient of variation, and skewness coefficient to obtain the first intermediate data after calibration. This can improve the parameter stability and regional adaptability under extreme flood scenarios, and provide effective support for ensuring the subsequent reservoir water level regulation effect.

[0093] Specifically, hydrological sequence data includes long-series data and short-series data. In this embodiment, the mean calibration operation for hydrological sequence data is implemented according to the following formula:

[0094] ;

[0095] in, The mean of the hydrological series data after calibration. This represents the raw mean of a short-series data point, spanning 10 years. This is the raw mean of a long-series data point, with a time span greater than or equal to 30 years. The first calibration coefficient is determined based on the length ratio of the long sequence data to the short sequence data.

[0096] In this embodiment, the operation of calibrating the coefficient of variation of hydrological series data is achieved according to the following formula:

[0097] ;

[0098] in, The calibrated coefficient of variation. The coefficient of variation is the original coefficient of variation for short sequence data.

[0099] In this embodiment, the skewness coefficient calibration operation for hydrological sequence data is achieved according to the following formula:

[0100] ;

[0101] in, The skewness coefficients after calibration. The original skewness coefficients for short sequence data.

[0102] Specifically, in this embodiment, the normalization process for the first intermediate data to obtain the second intermediate data is applied to the peak flow rate in the water level sequence data. The normalization operation is calculated according to the following formula:

[0103] ;

[0104] Among them, For the first i Normalized peak flow for each station The original peak flow rate, This represents the average peak flow rate for all stations within the region.

[0105] Specifically, the exceedance probability in this embodiment is calculated according to the following formula:

[0106] ;

[0107] in, To exceed probability, Q For the peak flow in the hydrological series data,Q des Here, m represents the preset flow rate threshold. Q Greater than Q des Number of times, n This represents the total number of samples. It is understood that the exceedance probability in this embodiment quantifies the rarity of the current peak flow. The reciprocal of the exceedance probability is the flood return period (e.g., P=1% corresponds to a return period of 100 years), and any flood return period corresponds to a reference peak flow. The smaller the P-value, the rarer the flood. The larger the probability of exceedance, the more effective the data foundation can be for constructing the current flood process curve.

[0108] Specifically, in this embodiment, the flood hydrograph corresponding to the target flood season is calculated according to the following formula:

[0109] ;

[0110] in, The flood hydrograph corresponding to the target flood season is in t Flow rate at any moment For typical flood hydrographs in t Flow rate at any moment This represents a typical peak flood flow. It can be seen that the flood hydrograph corresponding to the target flood season in this embodiment is determined first based on the calculated exceedance probability to determine the flood risk level; the lower the exceedance probability, the higher the risk level. The larger the value, the more the temporal characteristics of a typical flood are combined with the peak flow under the currently determined flood risk level using the frequency amplification method to construct the current flood hydrograph. In this way, the constructed flood hydrograph can retain the temporal characteristics of a typical flood, ensuring its rationality.

[0111] Specifically, in some embodiments, Figure 1 Step S30, which involves determining the target control duration based on the flood hydrograph, includes, but is not limited to, the following steps:

[0112] Step S36: Determine the reference time of the peak value in the flood hydrograph, and calculate the first reference duration between the current time and the reference time;

[0113] Step S37: Determine the precipitation forecast period, information transmission time, decision-making time, and reservoir gate operation time. Sum the information transmission time, decision-making time, and reservoir gate operation time to obtain a first intermediate value. Subtract the first intermediate value from the precipitation forecast period to obtain a second reference duration. Here, the information transmission time represents the total time from the acquisition of hydrological and meteorological forecast information to its transmission to the dispatch center and completion of verification. The precipitation forecast period represents the expected duration between the current time and the forecast precipitation time. The decision-making time represents the preset time for the dispatch center to complete the dispatch decision after receiving the hydrological and meteorological forecast information.

[0114] Step S38: When both the first reference duration and the second reference duration are greater than or equal to the preset duration threshold, the minimum value between the first reference duration and the second reference duration is taken as the target control duration.

[0115] Step S39: If only one of the first reference duration and the second reference duration is greater than or equal to the preset duration threshold, the reference duration that is greater than or equal to the preset duration threshold is determined as the target control duration.

[0116] It should be noted that, in this embodiment, the second reference duration is calculated according to the following formula;

[0117] ;

[0118] in, This is the second reference duration. For the precipitation forecast period, The time taken for information transmission is 0.5 h to 1 h for wired transmission and 1 h to 1.5 h for wireless transmission. The decision-making time is calculated by summing the standard time for management processes with the average of historical case statistics. The reservoir gate operation time refers to the total time from the issuance of the operation command to the gate reaching the target opening degree and the pre-discharge flow stabilizing. The determination method is as follows: the sum of the measured mechanical action time of the gate and the stable flow monitoring time is used. When there is no measured data, 1 hour to 1.5 hours is taken for medium-sized gates and 1.5 hours to 2 hours is taken for large gates.

[0119] Specifically, the second reference duration is the core time window for ensuring the effectiveness of pre-release scheduling. It refers to the effective time remaining after deducting the time required for information transmission, decision-making, and gate operation from the time a reliable precipitation forecast is obtained, which can be used for pre-release to empty the reservoir. The first reference duration is the time between the current moment and the reference time where the flood peak occurs in the flood process curve. The preset duration threshold is 2 hours. In this embodiment, the target control duration is constrained to be greater than the preset duration threshold. If the control duration is too short, the pre-release scheduling will not be effective. The first and second reference durations are used as candidate control durations. If both reference durations are greater than the preset duration threshold, the one with the smallest value between the first and second reference durations is selected as the target control duration.

[0120] Step S40: Determine the water level reduction based on the reference flood peak flow and the target regulation duration, and determine the water level increase based on the required water replenishment during the target flood season and the duration of the target flood season.

[0121] Specifically, in this embodiment, the water level reduction is determined based on the reference peak flow and the target control duration, and is calculated according to the following formula:

[0122] ;

[0123] in, The amount of water level drop, Adjust the duration to the target. This is the flood discharge efficiency coefficient. The value range is from 0.7 to 0.9. The target reservoir's capacity-water level coefficient and the amount of water level drop. It is the lower limit of the first water level The core calculation basis, by quantifying the water level drop corresponding to the reservoir capacity that needs to be reserved to cope with the design flood, can ensure To meet flood control and safety requirements.

[0124] Specifically, in this embodiment, the water level rise is determined based on the required water replenishment during the target flood season and the duration of the target flood season, and is calculated according to the following formula:

[0125] ;

[0126] in, For the water level rise, To meet the water replenishment needs during the target flood season, The duration of the target flood season. During the flood season t Reservoir capacity-water level coefficient at any given time, water level rise It is the upper limit of the first water level. The core calculation basis is to quantify the amount of water level that can be raised to meet the water demand at this stage, ensuring... It can take into account both water resource utilization efficiency and efficiency.

[0127] Step S50: Determine the flood control water level range based on the water level decrease and water level increase, wherein the flood control water level range includes a first upper limit value and a first lower limit value.

[0128] It should be noted that the first lower limit value in this embodiment is calculated according to the following formula:

[0129] ;

[0130] in, This is the first lower limit value. The baseline flood control limit water level, α It is the first flood control risk coefficient.

[0131] It should be noted that the first upper limit value in this embodiment is calculated according to the following formula:

[0132] ;

[0133] in, This is the first upper limit value. β This is the first water storage benefit coefficient. That is to say, the flood control water level range is [ H min , H max It changes dynamically with actual factors.

[0134] Step S60: Adjust the water level of the target reservoir based on the current forecast precipitation and flood control limit water level range, wherein the forecast precipitation represents the average precipitation of the watershed associated with the target reservoir within a preset time period.

[0135] It is understood that this embodiment can dynamically calculate the flood control limit range of the reservoir based on current hydrological, meteorological, snowmelt and other actual factors, and dynamically adjust the reservoir water level in combination with the forecast precipitation. Compared with the existing scheme that only regulates the water level based on a fixed flood control limit, it can effectively improve the regulation effect of the reservoir water level.

[0136] Specifically, in some embodiments, Figure 1 Step S60 includes, but is not limited to, the following steps:

[0137] Step S61: Divide the difference between the first upper limit value and the first lower limit value by 2 to obtain the second intermediate value, and calculate the second intermediate value and the first lower limit value to obtain the second lower limit value.

[0138] Step S62: Form a target water level range based on the second lower limit and the first upper limit;

[0139] Step S63: If the current forecast precipitation is greater than or equal to the first precipitation threshold, lower the water level of the target reservoir to the first lower limit and start the pre-release scheduling mode.

[0140] Step S64: If the current forecast precipitation is greater than the second precipitation threshold and less than the first precipitation threshold, control the water level of the target reservoir within the target water level range.

[0141] Step S65: If the current forecast precipitation is less than or equal to the second precipitation threshold, raise the water level of the target reservoir to the first upper limit value.

[0142] Understandably, the predicted precipitation P fore It refers to the average precipitation over a short period of time within the controlled watershed upstream of the target reservoir. It can be calculated by converting meteorological station and remote sensing precipitation product data within the watershed into the average precipitation over the watershed using the Thiessen polygon method.

[0143] Specifically, in this embodiment, the first precipitation threshold is 25 mm, and the second precipitation threshold is 10 mm. When P fore When the water level is ≥25mm, lower the water level to [the appropriate level]. H min And initiate the pre-discharge scheduling mode, the pre-discharge flow corresponding to the pre-discharge scheduling mode. The calculation formula is as follows: Kpre is the pre-discharge coefficient, a key parameter for adapting the pre-discharge flow rate to the reference peak flow rate. It balances flood control reservoir emptying and downstream flood safety, and is determined based on the river's carrying capacity. The value of Kpre ranges from 0.3 to 0.5 to create reservoir capacity in advance to cope with floods. When 10mm < P fore When the water level is <25mm, control the water level at [ H min +( H max - H min ) / 2, H max The target water level range (i.e., the range between target and target water levels) is maintained at a minimum ecological discharge flow to balance flood control and ecological needs; when P fore ≤10mm, raise the water level to H max To maximize water storage benefits and ensure subsequent water demand.

[0144] Specifically, in some embodiments, the operation of mean calibration of hydrological sequence data is associated with a first calibration coefficient, a first lower limit value is associated with a first flood control risk coefficient, and a first upper limit value is associated with a first water storage benefit coefficient. Figure 1 Following step S60, the reservoir water level regulation method for arid and semi-arid regions provided in this application embodiment further includes, but is not limited to, the following steps:

[0145] Step S71: Determine the predicted and measured water levels of the target reservoir during the same time period, as well as the predicted and measured flow rates of the target watershed associated with the target reservoir during the same time period.

[0146] Step S72: Calculate the relative error of water level prediction based on the predicted water level, the measured water level, the first upper limit value, and the first lower limit value;

[0147] Step S73: Calculate the relative error of flow prediction based on the predicted flow, the measured flow, and the reference peak flow;

[0148] Step S74: When the relative error of water level prediction is greater than the first error threshold, or the relative error of flow prediction is greater than the second error threshold, adjust the first flood control risk coefficient based on the relative error of water level prediction to obtain the second flood control risk coefficient, adjust the first water storage benefit coefficient based on the relative error of water level prediction to obtain the second water storage benefit coefficient, and adjust the first calibration coefficient based on the relative error of flow prediction to obtain the second calibration coefficient.

[0149] Specifically, the relative error of water level prediction in this embodiment is calculated according to the following formula:

[0150] ;

[0151] in, To predict water levels, To measure the water level, This represents the relative error in water level prediction.

[0152] Specifically, the relative error of traffic prediction in this embodiment is calculated according to the following formula:

[0153] ;

[0154] in, To predict water levels, To measure the water level, This represents the relative error in water level prediction.

[0155] Specifically, in this embodiment, the first error threshold is 10%, and the second error threshold is 15%.

[0156] It should be noted that the second flood control risk coefficient is obtained by correcting it according to the following formula:

[0157] ;

[0158] in, The second flood control risk coefficient

[0159] The second water storage benefit coefficient is obtained by correcting it according to the following formula:

[0160] ;

[0161] in, The second water storage benefit coefficient

[0162] The second calibration coefficient is obtained by correcting according to the following formula:

[0163] ;

[0164] in, This is the second calibration coefficient.

[0165] Understandably, after adjusting the water level of the target reservoir, this embodiment quantifies the water level control effect by calculating the relative error of water level prediction and the relative error of flow prediction. When the relative error of water level prediction exceeds a first error threshold, or the relative error of flow prediction exceeds a second error threshold, it indicates that the model parameters deviate from the actual situation and need to be dynamically corrected. Specifically, the correction method is as follows: adjust the first flood control risk coefficient based on the relative error of water level prediction to obtain the second flood control risk coefficient; adjust the first water storage benefit coefficient based on the relative error of water level prediction to obtain the second water storage benefit coefficient; and adjust the first calibration coefficient based on the relative error of flow prediction to obtain the second calibration coefficient. After correcting the first flood control risk coefficient, the first water storage benefit coefficient, and the first calibration coefficient, the accuracy of subsequent water level control strategies can be optimized, forming a long-term closed loop of control-measurement-correction-re-control, ensuring that the water level adjustment always adapts to the actual flood characteristics and effectively guarantees the water level control effect of the target reservoir.

[0166] like Figure 2 As shown, Figure 2 This is a structural diagram of a control device provided in one embodiment of this application. The present invention also provides a control device 200, comprising:

[0167] The processor 210 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0168] The memory 220 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 220 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 220 and called and executed by the processor 210 to execute the reservoir water level control method for arid and semi-arid regions according to the embodiments of this application.

[0169] Input / output interface 230 is used to implement information input and output;

[0170] The communication interface 240 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0171] Bus 250 transmits information between various components of the device (e.g., processor 210, memory 220, input / output interface 230, and communication interface 240);

[0172] The processor 210, memory 220, input / output interface 230 and communication interface 240 are connected to each other within the device via bus 250.

[0173] In addition, this application also provides an electronic device, including the control device 200 described in the above embodiments.

[0174] In addition, this application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described method for regulating reservoir water levels in arid and semi-arid regions.

[0175] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0177] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for regulating reservoir water levels in arid and semi-arid regions, characterized in that, include: Collect hydrological sequence data, meteorological sequence data, and snowmelt sequence data associated with the target reservoir, and calculate the FSAI index based on the hydrological sequence data, the meteorological sequence data, and the snowmelt sequence data, wherein the FSAI index is used to indicate the current flood season risk status index; The target flood season is determined based on the FSAI index, wherein the target flood season is the current flood season stage, and the flood season stage is the pre-flood season, the main flood season, or the post-flood season; Based on the hydrological sequence data, a flood process line corresponding to the target flood season is constructed, and the target control duration is determined based on the flood process line. The flood process line is used to indicate the mapping relationship between flow rate and time throughout the entire process of flood rise, peak flow and receding. The flood process line corresponds to a reference flood peak flow rate. The water level reduction is determined based on the reference peak flow and the target regulation duration, and the water level increase is determined based on the required water replenishment during the target flood season and the duration of the target flood season. The flood control water level range is determined based on the water level decrease and the water level increase, wherein the flood control water level range includes a first upper limit value and a first lower limit value; Based on the current forecast precipitation and the flood control water level range, the water level of the target reservoir is adjusted, wherein the forecast precipitation represents the average precipitation of the watershed associated with the target reservoir within a preset time period. The calculation of the FSAI index based on the hydrological sequence data, the meteorological sequence data, and the snowmelt sequence data includes: Data preprocessing is performed on the hydrological sequence data, the meteorological sequence data, and the snowmelt sequence data; The membership degree is calculated based on the current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation, wherein the current daily precipitation, the average daily precipitation, and the standard deviation of daily precipitation are all determined based on meteorological sequence data after data preprocessing; The concentration index is calculated by converting the peak flow and corresponding occurrence time in the hydrological sequence data into polar coordinate vectors. Based on the Euclidean distance algorithm, the similarity index between the runoff value in the hydrological sequence data, the precipitation in the meteorological sequence data, and the snowmelt amount in the snowmelt sequence data is calculated; A first weight is assigned to the membership degree, a second weight is assigned to the concentration index, and a third weight is assigned to the similarity index, wherein the sum of the first weight, the second weight, and the third weight is 1; The FSAI index is obtained by summing the product of the first weight and the membership degree, the product of the second weight and the concentration index, and the product of the third weight and the similarity index.

2. The method for regulating reservoir water levels in arid and semi-arid regions according to claim 1, characterized in that, Determining the target flood season based on the FSAI index includes: When the number of consecutive days corresponding to the FSAI index being greater than the first index threshold is greater than a preset number of days threshold, the target flood season is determined to be the main flood season. When the FSAI index is greater than or equal to the second index threshold and less than the first index threshold, and the current time is before the main flood season, the target flood season is determined to be the pre-flood season; When the FSAI index is greater than or equal to the second index threshold and less than the first index threshold, and the current time is after the main flood season, the target flood season is determined to be the post-flood season.

3. The method for regulating reservoir water levels in arid and semi-arid regions according to claim 1, characterized in that, The hydrological sequence data includes multiple samples, each corresponding to a different time period. A flood hydrograph corresponding to the target flood season is constructed based on the hydrological sequence data, including: The hydrological sequence data are sequentially calibrated for mean, coefficient of variation, and skewness to obtain the first intermediate data after calibration. The first intermediate data is normalized to obtain the second intermediate data, wherein the second intermediate data includes the peak flow corresponding to each sample; Determine the reference number of times that the peak flow exceeds a preset flow threshold among all the flood peak flows, and divide the reference number by the total number of samples to obtain the exceedance probability, wherein the exceedance probability is associated with the reference peak flow; Obtain typical flood hydrographs and typical peak flow rates; Divide the reference peak flow by the typical peak flow to obtain an intermediate value, and multiply the intermediate value by the typical flood hydrograph to obtain the flood hydrograph corresponding to the target flood season.

4. The method for regulating reservoir water levels in arid and semi-arid regions according to claim 1, characterized in that, Determining the target control duration based on the aforementioned flood hydrograph includes: Determine the reference time at which the peak value of the flood in the flood process curve is located, and calculate the first reference duration between the current time and the reference time; The precipitation forecast period, information transmission time, decision-making time, and reservoir gate operation time are determined. The information transmission time, decision-making time, and reservoir gate operation time are summed to obtain a first intermediate value. The precipitation forecast period is then subtracted from the first intermediate value to obtain a second reference duration. The information transmission time represents the total time from the acquisition of hydrological and meteorological forecast information to its transmission to the dispatch center and completion of verification. The precipitation forecast period represents the expected duration between the current time and the forecast precipitation time. The decision-making time represents the preset time for the dispatch center to complete the dispatch decision after receiving the hydrological and meteorological forecast information. When both the first reference duration and the second reference duration are greater than or equal to a preset duration threshold, the minimum value between the first reference duration and the second reference duration is determined as the target control duration. If only one of the first reference duration and the second reference duration is greater than or equal to the preset duration threshold, the reference duration that is greater than or equal to the preset duration threshold is determined as the target control duration.

5. The method for regulating reservoir water levels in arid and semi-arid regions according to claim 1, characterized in that, Based on the current forecast precipitation and the aforementioned flood control water level range, the water level of the target reservoir is adjusted, including: Divide the difference between the first upper limit value and the first lower limit value by 2 to obtain the second intermediate value, and sum the second intermediate value and the first lower limit value to obtain the second lower limit value; A target water level range is formed based on the second lower limit and the first upper limit; If the current forecast precipitation is greater than or equal to the first precipitation threshold, the water level of the target reservoir will be lowered to the first lower limit, and the pre-release scheduling mode will be activated. If the current forecast precipitation is greater than the second precipitation threshold and less than the first precipitation threshold, the water level of the target reservoir will be controlled within the target water level range. If the current forecast precipitation is less than or equal to the second precipitation threshold, the water level of the target reservoir will be raised to the first upper limit value.

6. The method for regulating reservoir water levels in arid and semi-arid regions according to claim 3, characterized in that, The operation of mean calibration of the hydrological sequence data is associated with a first calibration coefficient, the first lower limit is associated with a first flood control risk coefficient, and the first upper limit is associated with a first water storage benefit coefficient. After adjusting the water level of the target reservoir based on the current forecast precipitation and the flood control limit water level range, the method further includes: Determine the predicted and measured water levels of the target reservoir during the same time period, as well as the predicted and measured flow rates of the target watershed associated with the target reservoir during the same time period; The relative error of water level prediction is calculated based on the predicted water level, the measured water level, the first upper limit value, and the first lower limit value. The relative error of flow prediction is calculated based on the predicted flow, the measured flow, and the reference peak flow. When the relative error of water level prediction is greater than a first error threshold, or the relative error of flow prediction is greater than a second error threshold, the first flood control risk coefficient is adjusted based on the relative error of water level prediction to obtain a second flood control risk coefficient, the first water storage benefit coefficient is adjusted based on the relative error of water level prediction to obtain a second water storage benefit coefficient, and the first calibration coefficient is adjusted based on the relative error of flow prediction to obtain a second calibration coefficient.

7. A control device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the reservoir water level regulation method for arid and semi-arid regions as described in any one of claims 1 to 6.

8. An electronic device, characterized in that, Includes the control device as described in claim 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the reservoir water level control method for arid and semi-arid regions as described in any one of claims 1 to 6.

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