A reservoir flood season water level dynamic multi-objective optimization control system and method

Abstract

The present application relates to the technical field of reservoir optimization scheduling, and particularly relates to a reservoir flood season water level dynamic multi-objective optimization control system and method. The method comprises the following steps: obtaining real-time monitoring data of the reservoir; extracting current water level state information according to the real-time monitoring data of the reservoir; establishing a reservoir water balance basic model based on the current water level state information; analyzing the pipeline water supply flow variation characteristics according to the reservoir water balance basic model; identifying the response lag time of the pipeline fluid inertia according to the pipeline water supply flow variation characteristics; predicting the water supply pipeline pressure fluctuation risk based on the response lag time; and determining the water level regulation advance according to the water supply pipeline pressure fluctuation risk and the response lag time. The present application adopts a multi-objective optimization model, can balance between the flood control safety margin and the water supply guarantee degree, and ensures that the reservoir can not only cope with the flood risk in the flood season, but also stably meet the water supply demand.

Description

Technical Field

[0001] This invention relates to the field of reservoir optimization scheduling technology, and in particular to a reservoir flood season water level dynamic multi-objective optimization control system and method. Background Technology

[0002] The flood season refers to the period when river levels rise periodically due to seasonal rainfall, ice melt, and snowmelt within the basin. For reservoirs, the flood season is a period of high flood risk, requiring particularly strengthened management and scheduling. Dynamic multi-objective optimization control of reservoir water levels during the flood season refers to the dynamic adjustment of reservoir water levels through scientific scheduling strategies and optimization methods, comprehensively considering the needs of flood control, power generation, water supply, and irrigation.

[0003] However, traditional multi-objective optimization control methods for reservoir flood season water level dynamics often suffer from the following problems: Feedback lag caused by pipeline inertia. In urban water supply reservoirs, water needs to be distributed to water plants via long-distance pipelines. When the pipeline filling time is long, the optimization model assumes instantaneous water supply response and does not model pipeline fluid inertia. This makes it prone to water hammer-dispatch resonance during sudden changes in water demand, leading to oscillations in the influent flow to the water distribution plant. In cascade reservoir systems, flood waves from downstream dam failures will propagate upstream. Existing optimization models only consider downstream flood propagation, but the negative wave velocity caused by dam failure can reach 1.5 times that of the positive wave. Existing systems lack the capability to calculate such reverse waves. Summary of the Invention

[0004] Therefore, it is necessary for the present invention to provide a reservoir flood season water level dynamic multi-objective optimization control system and method to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a multi-objective optimization control method for reservoir flood season water level dynamics includes the following steps:

[0006] Step S1: Obtain real-time monitoring data of the reservoir; extract current water level status information based on the real-time monitoring data of the reservoir; establish a basic model of reservoir water balance based on the current water level status information;

[0007] Step S2: Analyze the characteristics of pipeline water supply flow variation based on the reservoir water balance basic model; identify the response lag time of pipeline fluid inertia based on the characteristics of pipeline water supply flow variation; predict the risk of water supply pipeline pressure fluctuation based on the response lag time.

[0008] Step S3: Determine the water level control lead time based on the water supply pipeline pressure fluctuation risk and response lag time; formulate an early intervention strategy for water level control based on the water level control lead time; calculate the target water level control execution sequence based on the early intervention strategy for water level control.

[0009] Step S4: Construct a water level control gradient smoothing curve based on the target water level control execution sequence; monitor the probability of water hammer effect in the pipeline based on the water level control gradient smoothing curve; adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate according to the probability of water hammer effect in the pipeline, and obtain the reservoir outflow control parameters.

[0010] Step S5: Assess the flood control safety margin of the reservoir during the flood season based on the reservoir outflow control parameters, and implement the adjustment of the reservoir gate opening based on the dynamic balance decision of the flood control safety margin of the reservoir during the flood season, so as to realize the dynamic multi-objective optimization control of the reservoir water level during the flood season.

[0011] This invention also provides a reservoir flood season water level dynamic multi-objective optimization control system, characterized in that it is used to execute the reservoir flood season water level dynamic multi-objective optimization control method described above, the reservoir flood season water level dynamic multi-objective optimization control system comprising:

[0012] The water balance analysis module is used to acquire real-time monitoring data of the reservoir; extract current water level status information based on the real-time monitoring data; and establish a basic water balance model of the reservoir based on the current water level status information.

[0013] The pipeline pressure risk assessment module is used to analyze the characteristics of pipeline water supply flow variation based on the reservoir water balance model; identify the response lag time of pipeline fluid inertia based on the characteristics of pipeline water supply flow variation; and predict the pressure fluctuation risk of water supply pipelines based on the response lag time.

[0014] The water level control prediction module is used to determine the water level control lead time based on the risk of water supply pipeline pressure fluctuations and response lag time; formulate water level control early intervention strategies based on the water level control lead time; and calculate the target water level control execution sequence based on the water level control early intervention strategies.

[0015] The water hammer effect control module is used to construct a water level control gradient smoothing curve based on the target water level control execution sequence; monitor the probability of water hammer effect occurrence in the pipeline based on the water level control gradient smoothing curve; and adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate according to the probability of water hammer effect occurrence, so as to obtain the reservoir outflow control parameters.

[0016] The flood control dynamic optimization control module is used to assess the flood control safety margin of the reservoir during the flood season based on the reservoir outflow control parameters, and to execute the adjustment of the reservoir gate opening based on the dynamic balance decision of the flood control safety margin of the reservoir during the flood season, so as to realize the dynamic multi-objective optimization control of the reservoir water level during the flood season.

[0017] This invention effectively improves the safety, regulation efficiency, and water supply security of reservoirs during the flood season through precise water level control, real-time monitoring, and multi-objective optimized control. First, the method ensures precise control of water level changes in complex flood conditions by progressively extrapolating the target water level regulation execution sequence, avoiding the risks of excessive storage or release. By refining the water level changes, gate opening adjustments, and water level regulation rates at each time point, the reservoir's regulation system can respond more accurately to sudden rainfall and flow changes, ensuring a balance between flood control objectives and water supply needs. Second, by predicting reservoir water level change trends and comparing them with flood season warning lines, the method identifies flood control safety margins in advance, enabling reservoir managers to take effective preventative measures before floods occur, avoiding the risk of over-storage due to excessively high water levels. Furthermore, by combining meteorological data and inflow information to predict future flood conditions, this method not only enhances the reservoir's flood forecasting capabilities but also provides a more scientific basis for reservoir operation. This allows the reservoir to flexibly adjust its control strategies under different flood season scenarios, ensuring rapid and effective water level reduction and mitigation of flood risks during floods. Regarding the monitoring of water hammer effects in pipelines, this method effectively monitors and reduces the frequency of water hammer by real-time adjustment and optimization of reservoir outflow control parameters, preventing damage to pipeline facilities caused by pipeline pressure waves and water flow impacts resulting from rapid changes in water flow. By calculating changes in water flow velocity, pressure wave propagation characteristics, and the risk of water hammer effects, the method can adjust reservoir outflow control parameters in real time, ensuring the stability and safety of the pipeline system. This control measure not only improves the efficiency of reservoir outflow but also prevents sudden damage to the pipeline system, providing strong protection for the long-term operation of the reservoir. In addition, through the application of a multi-objective optimization model, this method can dynamically balance the two important objectives of flood control safety margin and water supply security. By comprehensively assessing reservoir storage capacity and water supply security, this method ensures that the reservoir can guarantee the safety of water level regulation during the flood season while maintaining its water supply capacity after floods. This balancing mechanism avoids the problems associated with previous single-objective control methods, such as overemphasizing flood control safety while neglecting water supply demand, or focusing too much on water supply while ignoring flood prevention. By dynamically adjusting reservoir outflow control parameters, the method automatically optimizes the reservoir's regulation strategy based on actual flood conditions at different times, enabling the reservoir to effectively cope with floods while maintaining stable water supply to ensure downstream water demand. This dynamic multi-objective optimization control method not only improves the accuracy of reservoir scheduling but also enhances the reservoir's adaptability to extreme flood conditions. It combines advanced forecasting technology, real-time data monitoring, and intelligent optimization algorithms, enabling the reservoir to respond quickly in complex environments. Furthermore, by reducing safety hazards such as overflow and seepage caused by improper water level regulation, the method lowers the risk of dam failure and protects the lives and property of people downstream.In practical applications, with the accumulation of data from multiple flood seasons and continuous model optimization, the reservoir's scheduling capacity and safety will be continuously improved, ensuring the reservoir's long-term sustainability. In summary, the above methods, through multi-dimensional regulation and optimization, not only enhance the reservoir's flood control capacity but also ensure the continuity and stability of water supply, playing a crucial role in dynamic water level regulation during the flood season. By precisely controlling water levels, scientifically predicting flood conditions, monitoring water hammer effects, and dynamically optimizing outflow control parameters, the reservoir can flexibly adjust its regulation strategies in the face of complex and ever-changing flood season environments, maximizing the safety and water supply needs of the reservoir and its surrounding areas. Attached Figure Description

[0018] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0019] Figure 1 This is a flowchart illustrating the steps of a multi-objective optimization control method for dynamic water level during the flood season of a reservoir, as described in this invention.

[0020] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0021] Figure 3 for Figure 1 A detailed flowchart of step S3. Detailed Implementation

[0022] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for dynamic multi-objective optimization control of reservoir water level during the flood season, the method comprising the following steps:

[0026] Step S1: Obtain real-time monitoring data of the reservoir; extract current water level status information based on the real-time monitoring data of the reservoir; establish a basic model of reservoir water balance based on the current water level status information;

[0027] This invention provides real-time access to monitoring equipment deployed in different areas of a reservoir, including but not limited to water level gauges, rain gauges, flow meters, and evaporation sensors, to acquire real-time reservoir monitoring data. This data includes the current reservoir water level, surface evaporation, inflow, and rainfall. Current water level status information, such as water level height, rate of change, and the correspondence between water level and inflow, is extracted. Based on this information, the water level change trend is obtained through a water balance equation—that is, subtracting outflow and evaporation losses from inflow per unit time—and a basic water balance model is established. This model uses differential relationships to describe the process of water volume change over time. For example, in a typical application scenario, real-time data is updated every minute. When the inflow is 500 cubic meters per second, the evaporation is 5 millimeters per day, and the current water level is 120 meters, the cumulative change is used to simulate the water level change trend, laying the foundation for subsequent predictive analysis.

[0028] Step S2: Analyze the characteristics of pipeline water supply flow variation based on the reservoir water balance basic model; identify the response lag time of pipeline fluid inertia based on the characteristics of pipeline water supply flow variation; predict the risk of water supply pipeline pressure fluctuation based on the response lag time.

[0029] This invention, based on the reservoir water balance model established in step S1, analyzes the water supply flow variation characteristics of major pipelines (such as water diversion tunnels and flood discharge culverts) in the water supply system. This is primarily achieved through time-series analysis of historical flow variation curves, extracting statistical characteristics of their frequency, amplitude, and rate of change. For example, in application scenarios, if the peak flow fluctuation of a pipeline reaches 20% within 24 hours, the variation characteristics are considered significant. Furthermore, the response lag time of the pipeline fluid inertia is identified. This lag time is the time delay between the issuance of an instruction and a significant response to the flow change due to the large water mass and high velocity change inertia. The inertial lag time can be obtained by fitting the actual flow change curve after applying a small control instruction. For example, in a typical scenario, the response lag time is approximately 3 to 7 minutes. Based on this, and using the current lag time data combined with the flow change amplitude, the risk range for abnormal pressure fluctuations in the water supply pipeline is predicted. Risk prediction can be comprehensively judged based on the flow mutation rate, lag time, and water hammer model. If a flow surge exceeding 15% is detected within 5 minutes, it is judged as a high-risk pressure fluctuation.

[0030] Step S3: Determine the water level control lead time based on the water supply pipeline pressure fluctuation risk and response lag time; formulate an early intervention strategy for water level control based on the water level control lead time; calculate the target water level control execution sequence based on the early intervention strategy for water level control.

[0031] Based on the pressure fluctuation risk and fluid response lag time of the water supply pipeline analyzed in step S2, this embodiment of the invention further determines the advance amount for water level control. The advance amount for water level control refers to the amount of time required to issue a pre-instruction based on the fluid response characteristics of the pipeline before the formal water level control. This amount of time is usually set based on the maximum lag time plus a system safety margin (generally 10%-20% redundancy). For example, if the lag time is measured to be 5 minutes in an application, the advance amount can be set to 6 minutes. Subsequently, an advance intervention strategy for water level control is formulated, that is, based on factors such as predicted load changes and forecasted rainfall intensity, a small-amplitude adjustment instruction is issued 6 minutes in advance to avoid water hammer caused by sudden control. Finally, based on the advance intervention strategy, combined with the current water level, the rate of change of the target water level, the minimum adjustable step size, and other conditions, the target water level control execution sequence is calculated. The execution sequence includes the control start time, control amplitude, and control duration. For example, for the requirement of a target water level drop of 0.5 meters, the execution sequence may include a fine-tuning operation of reducing the water level by 5 centimeters every 10 minutes for 3 consecutive hours.

[0032] Step S4: Construct a water level control gradient smoothing curve based on the target water level control execution sequence; monitor the probability of water hammer effect in the pipeline based on the water level control gradient smoothing curve; adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate according to the probability of water hammer effect in the pipeline, and obtain the reservoir outflow control parameters.

[0033] Based on the target water level control execution sequence calculated in step S3, this embodiment of the invention constructs a water level control gradient smoothing curve. This smoothing curve ensures a smooth water level change rate during control by interpolating the water level change points in the control sequence and correcting for the continuity of the first derivative. This avoids abrupt changes. For example, cubic spline interpolation is used to make the water level change curve continuous in time without sharp turns. Based on the constructed gradient smoothing curve, combined with the flow change characteristic model and fluid inertial response parameters, the probability of water hammer effect in the water supply pipeline is monitored in real time. Water hammer effect refers to the pressure shock caused by a sharp change in water flow velocity. The monitoring method can collect the pipeline pressure change rate. When the change rate exceeds a set threshold (e.g., 10% change per second), it is judged as high risk. If a high risk of water hammer is detected, resonance risk analysis is performed based on the relationship between the current pressure fluctuation frequency and the control command frequency. By adjusting the amplitude, rhythm, or distribution of interpolation nodes of the water level control command, the pressure wave frequency is ensured to be far away from the control signal frequency to avoid resonance. Thus, the reservoir outflow control parameters suitable for the actual working conditions of the reservoir and pipeline are finally determined, such as the optimal opening ratio and timing of the floodgate.

[0034] Step S5: Assess the flood control safety margin of the reservoir during the flood season based on the reservoir outflow control parameters, and implement the adjustment of the reservoir gate opening based on the dynamic balance decision of the flood control safety margin of the reservoir during the flood season, so as to realize the dynamic multi-objective optimization control of the reservoir water level during the flood season.

[0035] Based on the reservoir outflow control parameters determined in step S4, this embodiment of the invention further assesses the flood control safety margin of the reservoir during the flood season. The safety margin is defined as the dynamic margin between the current reservoir capacity, the predicted upstream inflow, and the downstream discharge capacity. The assessment method employs dynamic simulation to simulate water level changes under different rainfall scenarios over the next 24 hours, updating the safety margin indicators in real time. For example, if a safety warning line is set at 135 meters, and the simulated maximum water level does not exceed 134 meters and the discharge flow is controlled within the downstream safe flow limit, the reservoir is considered safe. If the safety margin is insufficient, the gate opening is dynamically adjusted based on real-time monitoring and simulation results. Combined with the predicted arrival time of the flood peak, some reservoir capacity is released first, while ensuring that no flooding occurs downstream. In specific applications, for example, if a rainstorm with a peak flow of 800 cubic meters per second is expected within the next 3 hours, the gate opening can be increased to 30% 2 hours before the rainstorm to free up sufficient reservoir capacity in advance. The entire water level regulation process realizes dynamic multi-objective optimization control of the reservoir's water level during the flood season, taking into account the objectives of flood control safety, downstream protection, and rational utilization of water resources.

[0036] Preferably, step S1 includes the following steps:

[0037] Step S11: Collect raw monitoring data periodically by using water level, flow rate, and rainfall sensors deployed at the reservoir inlet, the middle of the reservoir area, and the spillway, and upload the data to the data concentrator.

[0038] This invention provides the data foundation for a dynamic multi-objective optimization control method for reservoir water levels during the flood season. Firstly, sensors are deployed in key areas of the reservoir. Ultrasonic water level gauges and electromagnetic flow meters are installed at the main inlet to measure the inflow water level and flow rate. A set of radar water level gauges is deployed in the central part of the reservoir as a primary control water level reference. Automatic rain gauges are also deployed to monitor local rainfall in the reservoir area. Flow meters and water level gauges are installed at the spillway to monitor flood discharge in real time. All sensor equipment is selected as industrial-grade equipment with IP68 protection rating and capable of withstanding high temperature and humidity environments. To ensure continuous data acquisition, the sensors are set to a 5-minute acquisition cycle. Each acquisition includes a timestamp, measured value, and status identifier. Each sensor transmits the raw monitoring data to a data concentrator located at the reservoir management center periodically via a LoRa wireless communication module or a 4G gateway. The data concentrator has breakpoint resume and local caching functions to ensure data integrity and provides a reserved interface for subsequent data preprocessing.

[0039] Step S12: Process the raw monitoring data and perform outlier detection to obtain real-time monitoring data of the reservoir;

[0040] In this embodiment of the invention, the raw monitoring data uploaded to the data concentrator in step S11 is first standardized in terms of data structure. For example, water level, flow rate, and rainfall data are uniformly converted into data forms labeled with UTC time standard and in meters, cubic meters per second, and millimeters. Then, outlier detection is carried out. The methods used include physical boundary verification (e.g., the normal range of water level is limited to between 100 meters and 160 meters above sea level), time continuity verification (e.g., the water level change between any two consecutive samples does not exceed 0.5 meters), and the moving average test in statistical methods (the mean and standard deviation are calculated with a 30-minute moving window, and if a single value exceeds 3 times the standard deviation, it is judged as an anomaly). For the discovered abnormal data, it is removed, linearly interpolated, or retained and marked according to different situations. Finally, a complete reservoir real-time monitoring dataset with outlier cleansing is formed, which lays the foundation for subsequent extraction of water level status information.

[0041] Step S13: Extract real-time reservoir monitoring data, inflow rate, outflow rate, and rainfall from the real-time reservoir monitoring data to obtain the current water level status information;

[0042] This invention utilizes the real-time reservoir monitoring data obtained in step S12 to further extract key indicators, namely, the reservoir's current water level, inflow rate, outflow rate, and rainfall information. The water level is determined by the reading of a radar-type water level gauge in the central part of the reservoir as the primary reference data. The inflow rate is directly measured by an electromagnetic flowmeter at the inlet, the outflow rate is provided by a spillway flowmeter, and the rainfall is calculated by averaging the cumulative rainfall over 5 minutes from multiple automatic rain gauges deployed in the reservoir area. To ensure consistency, all data is aligned to a standardized format by time. For example, in an application scenario, if at 10:00 AM on July 10, 2025, the water level is 145.6 meters, the inflow rate is 320 cubic meters per second, the outflow rate is 180 cubic meters per second, and the rainfall is 1.2 millimeters, these data are uniformly compiled into the "Current Water Level Status Information" record for further analysis.

[0043] Step S14: Calculate the rate of water level change based on real-time reservoir monitoring data and the changes in water level and flow over several time periods, and determine whether the trend is rising or falling, thereby obtaining water level trend data;

[0044] Based on the current water level status information extracted in step S13, this embodiment of the invention selects 3 hours, 6 hours, and 12 hours as characteristic time periods to dynamically calculate the water level change rate. Specifically, the difference between the water level at the beginning and end of each time period is taken and divided by the corresponding time period length to obtain the water level change rate per unit time. At the same time, the water level change trend is determined according to the positive or negative sign of the rate. For example, if the water level rises from 145.6 meters to 146.2 meters within a 3-hour time period, the change rate is 0.2 meters per hour, which is determined to be an upward trend. If the water level drops, it is determined to be a downward trend. If the change is less than a preset threshold (such as ±0.05 meters / hour), the water level is considered to remain stable. Finally, water level trend data including the water level change rate and trend status at different time scales are obtained, providing a basis for evaluating the future dynamic changes of the reservoir water level and formulating optimized scheduling strategies.

[0045] Step S15: Calculate the water storage coefficient, evaporation loss rate, and leakage loss rate based on the water level trend data to obtain the water balance model parameters;

[0046] Based on the water level trend data obtained in step S14, this embodiment of the invention further combines inflow, outflow, and rainfall information to calculate key parameters of the water balance model. The storage coefficient refers to the water level change caused by a unit change in flow rate, which can be obtained by statistically analyzing the ratio of unit inflow to water level change over several time periods. For example, if the total inflow is 6900 cubic meters within 6 hours and the water level rises by 0.3 meters, then the storage coefficient is 1 meter for every 23000 cubic meters of inflow. The evaporation loss rate is based on... The calculations are based on daily evaporation data from meteorological monitoring and the reservoir surface area. For example, if the daily evaporation monitored by the evaporation pan is 7 mm and the reservoir surface area is 5 square kilometers, then the daily evaporation is 35,000 cubic meters, which is converted into the reservoir capacity loss rate. The leakage loss rate is estimated by balancing and comparing the historical water level changes with the inflow and outflow rates and rainfall. For example, if the estimated leakage loss for a certain period is 0.01% of the reservoir capacity per day, a complete set of parameters for water storage coefficient, evaporation loss rate, and leakage loss rate is finally formed, providing support for dynamic water volume simulation.

[0047] Step S16: Construct a basic water balance model for the reservoir based on the current water level status information and the parameters of the water balance model.

[0048] This invention, based on the current water level information extracted in step S13 and the water balance model parameters calculated in step S15, constructs a basic reservoir water balance model using the principle of water balance. The specific modeling method is as follows: Using the initial water volume of the reservoir as a basis, the water volume increment within each time step (e.g., 5 minutes) is considered. This increment is obtained by subtracting the sum of outflow, evaporation loss, and leakage loss from the sum of inflow and rainfall. The reservoir capacity change is updated by continuously superimposing the water volume increments from each time step, and the real-time water level change is calculated simultaneously. In a scenario where the current water level is 145.6 meters and the initial reservoir capacity is 20 million cubic meters, if within 5 minutes the inflow is 1600 cubic meters, the outflow is 900 cubic meters, rainfall contributes 120 cubic meters, and evaporation and seepage result in a loss of 10 cubic meters, then the net increase in water volume in this step is 810 cubic meters. After the update, the reservoir capacity is 20,000,810 cubic meters, and the corresponding water level rises slightly. This ultimately forms a basic water balance model that can dynamically reflect the changes in water volume and water level trends during the flood season, laying a solid data and model foundation for subsequent multi-objective optimization and control.

[0049] Preferably, step S2, analyzing the characteristics of pipeline water supply flow variation based on the reservoir water balance model, includes:

[0050] Based on the reservoir water balance basic model, the theoretical water supply flow sequence is generated by inputting the pre-acquired historical and real-time water level prediction sequences and outflow control parameters.

[0051] The theoretical water supply flow sequence is compared with the inflow from the reservoir in real-time monitoring data, and the flow error sequence between the two at the same time point is calculated.

[0052] The mean, variance, and maximum deviation of the flow error sequence were calculated, and the prediction accuracy of the reservoir water balance basic model and the systematic offset during pipeline water conveyance were evaluated.

[0053] The rate of change of flow rate is calculated based on a fixed time window in the theoretical water supply flow rate sequence to obtain the pipeline water supply flow rate change sequence;

[0054] Generate flow change characteristic curves based on the pipeline water supply flow rate change sequence;

[0055] Extract key inflection points and their corresponding times from the flow change characteristic curve, and evaluate the effective change amount of each inflection point by combining systematic offset;

[0056] The effective changes before and after each inflection point are integrated with the flow rate change characteristic curve to form the pipeline water supply flow rate change characteristics.

[0057] Based on the aforementioned reservoir water balance model, this invention first inputs pre-acquired historical water level prediction sequences, real-time water level prediction sequences, and outflow control parameters for simulation calculation. The historical water level prediction sequences can be obtained by modeling the daily water level change trends during the same flood season over the past three years. The real-time water level prediction sequences are calculated by comprehensively considering the water level changes over the past three days and the forecasted rainfall. The outflow control parameters include the gate opening scheduling scheme and the maximum outflow capacity of the spillway. Through the water balance model, at each simulation time step, the theoretical water supply flow is calculated based on the dynamic balance relationship between inflow, evaporation, leakage, and outflow. This is the optimal discharge or supply flow under the current predicted water level conditions, thereby generating a theoretical water supply flow sequence. This sequence is generally recorded in hourly units for subsequent comparison and characteristic extraction. Subsequently, the generated theoretical water supply flow sequence is compared with the inflow flow sequence in the aforementioned real-time monitoring data time-by-time. The difference between the theoretical water supply flow and the actual inflow flow at each time point is calculated to obtain a flow error sequence, the unit of which is usually cubic meters per second. A positive flow error value indicates that the water supply is greater than the inflow, with a potential risk of water level drop; a negative error value indicates insufficient water supply, with a risk of water accumulation. This process is automated using a Python script, and the error results are stored for subsequent statistical analysis. For the obtained flow error sequence, the mean, variance, and maximum deviation are further calculated over the entire sequence. The mean reflects the overall trend of supply and demand differences, the variance measures the magnitude of error fluctuations, and the maximum deviation is used to identify extreme anomalies. In the mean statistics, if the mean deviates from zero by more than ±5% of the threshold, it is determined that there is a systematic shift in the water balance model, requiring further calibration. These statistics can be used to assess the prediction accuracy of the reservoir water balance basic model under the current flood season conditions, and at the same time, to preliminarily determine whether there are unmodeled losses in the pipeline water transmission system, such as hidden leakage or abnormal enhanced evaporation, to ensure that subsequent adjustments to control parameters are more targeted. Next, the flow rate is calculated by sliding a fixed time window (e.g., 6 hours) across the theoretical water supply flow sequence. This rate of change is calculated by subtracting the initial flow from the final flow and then dividing by the window width. The unit of the rate of change is cubic meters per second per hour. This rate of change reflects the dynamic adjustment characteristics of the water supply strategy. For example, increasing or decreasing the outflow before or after heavy rainfall will show significant fluctuations in the rate of change. Based on the obtained pipeline water supply flow rate change sequence, the Lowesel weighted regression method is used for curve smoothing, generating a continuous flow change characteristic curve that removes high-frequency noise interference. The Lowesel method can eliminate random disturbances caused by measurement errors while preserving the main trend, making subsequent inflection point extraction and trend analysis more accurate and reliable. The characteristic curve is generally presented graphically to visually demonstrate the changes in water supply flow regulation during the flood season.From the smoothed flow change characteristic curve, an inflection point detection algorithm based on the first derivative change is further employed to identify all local maxima, minima, and inflection point locations, and the time point corresponding to each inflection point is recorded. Subsequently, combined with the previously statistically obtained systematic offset, the change at each inflection point is effectively corrected. That is, if the systematic offset is positive, the change at the inflection point should be reduced by the offset mean, and vice versa, ensuring that the final extracted key change features reflect the actual supply and demand regulation effect rather than the impact of systematic errors. The effective change information before and after each inflection point is integrated with the overall flow change characteristic curve to form a complete set of pipeline water supply flow change characteristic parameters. This characteristic set includes the inflection point location, corresponding time, inflection point change amplitude, direction of change trend before and after (rising or falling), and duration of change, etc., which are ultimately used as input for the subsequent dynamic multi-objective optimization scheduling model of water level, supporting the balanced regulation of the three objectives of flood season water level safety, water supply demand, and flood discharge risk.

[0058] Preferably, the response lag time for identifying the fluid inertia of the pipeline based on the characteristics of changes in pipeline water supply flow rate in step S2 includes:

[0059] For the flow change process curves of each segment in the pipeline water supply flow change characteristics, mark the time when the flow adjustment command is issued and the time when the actual flow begins to change, and calculate the time difference to form an initial response delay sequence;

[0060] Fit the flow rate change curves of each segment to determine the time difference between the moment when the flow rate change rate reaches its peak and the moment when the command is issued, and form a peak response delay sequence;

[0061] Calculate the time required for the flow rate change curve in the pipeline water supply flow rate change characteristics to stabilize from the start of change, and form a time series of the steady process.

[0062] Based on the initial response delay sequence, peak response delay sequence, and steady-state process time series, grouped statistics based on different flow change amplitudes are performed to obtain lag characteristic statistics.

[0063] The response lag time of pipeline fluid inertia is determined based on statistical data of lag characteristics.

[0064] In this embodiment of the invention, for each segment of the flow change process curve, the corresponding control and scheduling log is first traced back to extract the issuance time of each flow adjustment command. These command times usually come from the operation records of the scheduling system, including manual operation or automatic control system commands, such as adjusting the opening of the floodgate, starting or stopping the pumping station, etc. Then, in the flow change curve, the first time point when the actual flow begins to change continuously is identified, usually by detecting that the flow change rate exceeds a preset small threshold (such as a change of more than 0.1 cubic meters per second per hour) within three consecutive time steps. The time difference between the issuance time of each pair of commands and the start time of the actual flow change is calculated to form an initial response delay sequence. This sequence is used to reflect the time lag characteristics between the system receiving the command and generating an observable response. Next, for each of the above-mentioned flow rate change process curves, a piecewise fitting method is adopted. Local polynomial fitting is performed on each curve segment, usually using a second-order polynomial to smoothly and accurately capture the trend of flow rate change. By taking the derivative of the fitted curve, the curve of flow rate change over time is calculated, and the moment when the flow rate change reaches its maximum value during each change process is further identified, i.e., the time point of the most drastic change. Subsequently, the time difference between the peak change time and the corresponding flow adjustment command issuance time is calculated to form a peak response delay sequence. This sequence reflects the time required for the system to reach its maximum response speed after the scheduling command is issued, and is an important indicator of the inertial response of the fluid system. The analysis continues for each flow change curve to identify the time elapsed from the start of flow change to near-stability. Specifically, after finding the initial point of change in the curve, the flow change is tracked. When the flow change amplitude is lower than a set threshold (e.g., less than 0.05 cubic meters per second per hour) for five consecutive time steps, it is determined to have reached a stable state, where the flow change is approximately constant. The time difference between the start of flow change and the moment of determination of stability is calculated to obtain the stabilization process time for each flow change segment, and these are summarized to form a stabilization process time series. This time series is used to evaluate the time characteristics required for the pipeline system to reach a new stable state when facing different amplitude flow adjustments. Subsequently, the obtained initial response delay sequence, peak response delay sequence, and steady-state time series were grouped and statistically analyzed according to the magnitude of flow change. Specifically, the flow change was divided into three intervals based on the absolute value of the change: small change (less than 5 cubic meters per second), medium change (5 to 15 cubic meters per second), and large change (greater than 15 cubic meters per second). Within each interval, the mean, standard deviation, and maximum value of each lag indicator (initial response delay, peak response delay, and steady-state time) were calculated to depict the statistical characteristics of the system response under different flow control magnitudes. This process used the Pandas library in Python for data grouping and statistics, and the results were visualized in the form of histograms or box plots to facilitate the identification of abnormal lags or phenomena with significantly different lag characteristics under specific magnitude changes.Based on the aforementioned statistical data on lag characteristics, the comprehensive response lag time of pipeline fluid inertia is determined. The specific method is as follows: First, in the small, medium, and large change groups, the weighted average of the mean initial response delay and the mean peak response delay is calculated respectively. The weights are set according to the actual occurrence frequency to reflect the impact of the frequency of various changes in actual operation. Second, combined with the median of the stabilization process time, the total lag time required from the issuance of the command to the basic stabilization of the system under a typical flow adjustment scenario is comprehensively evaluated. Finally, the comprehensive lag time under the small change scenario is used as the minimum response limit, the comprehensive lag time under the large change scenario is used as the maximum response limit, and the lag time under the medium change scenario is used as the reference setting for the optimized control model, which is used to consider the advance compensation of the timing of the control command issuance in the subsequent dynamic multi-objective optimization scheduling of water level.

[0065] Preferably, the risk prediction of water supply pipeline pressure fluctuation based on response lag time in step S2 includes:

[0066] A time correlation analysis was conducted between the response lag time of the pipeline fluid inertia and the pre-acquired historical pressure fluctuation events to establish a preliminary correspondence between the lag time and pressure fluctuations.

[0067] A model of fluid momentum change in the pipeline is established based on response lag time and pipeline water supply flow variation characteristics to calculate theoretical pressure peak.

[0068] Calculate the pipeline pressure-bearing capacity and natural vibration frequency of the pipeline system based on the pre-acquired pipeline geometric parameters and material properties;

[0069] By comparing the theoretical peak pressure with the pipeline's pressure-bearing capacity, a pressure over-limit risk index is generated.

[0070] The pressure wave resonance risk index is assessed by combining the preliminary correspondence with the matching degree between the natural vibration frequency and the response lag time.

[0071] Predict the pressure fluctuation risk of water supply pipelines based on the pressure over-limit risk index and the pressure wave resonance risk index.

[0072] Based on the aforementioned determination of the response lag time of pipeline fluid inertia, this embodiment of the invention further performs a time correlation analysis between the response lag time and pre-collected historical pressure fluctuation events. Specifically, it first compiles historical pressure anomaly events that occurred during reservoir scheduling operations over the past three flood seasons, including pressure monitoring records triggered by pump station start-ups and shutdowns, rapid sluice gate closures, etc. These records contain the time of pressure anomaly occurrence and the magnitude of pressure change. Then, it compares the flow adjustment command time before each historical pressure fluctuation event with the known response lag time to retrospectively determine whether the event might have been caused by a sudden change in flow velocity due to the lag effect. By setting a maximum tolerance deviation window (e.g., 5 seconds), it determines whether there is a temporal correspondence. Finally, it statistically analyzes the frequency and proportion of pressure fluctuations triggered in different lag time intervals, initially establishing a correspondence between lag time and pressure fluctuation events, providing a basis for subsequent pressure fluctuation risk assessment. Based on the above time correlation analysis results, pressure data sequences from various pipeline monitoring points were further extracted from the reservoir's real-time monitoring system. Reservoirs typically have multiple pressure monitoring points along the main water supply pipeline, such as inlets, main sections, branch points, and pump station outlets, with a sampling frequency generally once per second. After data extraction, a sliding window statistical method (e.g., 30 seconds per window with a step size of 5 seconds) was used to calculate characteristic quantities such as the pressure mean, standard deviation, and maximum rate of change within each window. In addition, to characterize the intensity of pressure fluctuations, a pressure fluctuation amplitude index was introduced, which is the ratio of the maximum pressure change per unit time to the initial value. The time points of abrupt changes were also recorded to form a pressure fluctuation characteristic dataset, laying the data foundation for subsequent coupling calculations with the fluid momentum change model. Based on the extracted flow rate change characteristics and response lag time information, a fluid momentum change model within the pipeline is established to calculate the theoretical peak pressure. In practice, firstly, according to the control volume method, the fluid within the pipeline is considered as a one-dimensional unsteady flow system, taking into account the pipeline length, cross-sectional changes, and frictional losses. In the momentum change equation, the momentum increment equals the pressure difference multiplied by the pipeline cross-sectional area minus the frictional resistance term, plus the water mass multiplied by the velocity change rate multiplied by the time step. Under the existing flow rate change curve and response lag time correction, the instantaneous velocity change rate is calculated and substituted into the above momentum balance relationship to deduce the instantaneous pressure change. The cumulative maximum pressure increment, superimposed on the initial pressure, is the theoretical peak pressure. Commonly used parameters in this step include a pipeline diameter of 1 meter, an initial velocity of 2 meters per second, a fluid density of 1000 kg per cubic meter, and a pipeline roughness of 0.01 meters.After calculating the theoretical peak pressure based on the momentum change model, the pipeline's geometric parameters (such as diameter, wall thickness, and length) and material properties (such as elastic modulus and yield strength) are extracted by combining the pipeline design drawings provided by the reservoir management department. The maximum pressure-bearing capacity of the pipeline is calculated using the thin-walled cylinder theory, i.e., the maximum allowable internal pressure equals the material yield strength multiplied by the wall thickness and then divided by the pipeline radius, while adjusting for temperature influence coefficients and aging reduction coefficients. In addition, based on wave theory, the natural vibration frequency of the pipeline is calculated as follows: the natural frequency of the pipeline equals the pressure wave propagation velocity divided by the pipeline length, where the pressure wave velocity can be calculated jointly based on the material elastic modulus and the water volume modulus. For actual parameters such as a pipeline wall thickness of 20 mm, a yield strength of 350 MPa, and a pipe length of 500 m, the natural frequency is initially estimated to be around 20 Hz. Subsequently, the theoretical pressure peak obtained in the previous step is compared with the pipeline's pressure-bearing capacity to calculate the pressure over-limit risk index. The specific method is as follows: subtract the pressure-bearing limit pressure from the theoretical pressure peak. If the result is positive, there is an over-pressure risk. The risk index is defined as the ratio of the pressure over-limit to the pressure-bearing capacity and is normalized to between 0 and 1. If the theoretical pressure peak is less than the pressure-bearing capacity, the over-limit risk index is set to zero, indicating that there is no direct over-pressure risk. For example, if the pressure-bearing limit of a certain pipeline section is 2.5 MPa and the theoretical pressure peak is 2.8 MPa, then the over-limit risk index is (2.8-2.5) divided by 2.5, which is 0.12, indicating that there is a moderate degree of over-limit risk, which requires dispatch attention. Furthermore, utilizing the previously established preliminary correspondence between lag time and historical pressure fluctuations, and combining it with the known natural vibration frequency of the pipeline and the response lag time, the potential pressure wave resonance phenomenon is assessed, and a pressure wave resonance risk index is calculated accordingly. The specific method is as follows: if the lag time is close to an integer multiple of the natural vibration frequency (e.g., a 20 Hz natural frequency corresponds to a period of 0.05 seconds, then 5 times the period is 0.25 seconds), and if the lag time is within ±10% (i.e., 0.225 seconds to 0.275 seconds), then a potential resonance risk is identified. The resonance risk index is set according to the degree of matching: a perfect match is recorded as 1, a partial match as 0.5, and a large deviation as 0. For example, if the response lag time of a certain adjustment operation is 0.26 seconds and the natural frequency is 20 Hz, then the resonance risk index is set to 0.5, indicating a certain risk of resonance induction.Finally, based on the pressure over-limit risk index and the pressure wave resonance risk index, the pressure fluctuation risk of the water supply pipeline under specific scheduling operations is comprehensively assessed. The specific method is as follows: the pressure over-limit risk index and the resonance risk index are assigned weights respectively, with typical values ​​of 0.7 for pressure over-limit and 0.3 for resonance risk, and the weighted sum is used to obtain a comprehensive risk score; the risk level is distinguished according to the score, such as a score less than 0.3 as low risk, 0.3 to 0.6 as medium risk, and more than 0.6 as high risk; in practical applications, when a high risk is predicted, the execution time of the corresponding scheduling command will be automatically delayed in the dynamic multi-objective optimization control strategy, or the flow change amplitude will be adjusted, in order to avoid the risk of structural damage to the pipeline system caused by pressure fluctuations and ensure the safety and system stability of water level scheduling during the flood season.

[0073] Preferably, step S3 includes the following steps:

[0074] Step S31: Set the initial range of water level control advance amount corresponding to different risk levels based on the water supply pipeline pressure fluctuation risk and response lag time.

[0075] After analyzing the pressure fluctuation risk and response lag time of the water supply pipeline, this embodiment of the invention formulates preliminary response strategies for water level control based on different risk levels. First, based on the predicted pressure fluctuation risk index and response lag time, initial ranges of water level control lead times are set for different risk levels. The water level control lead time refers to the time advance before a significant water level change is likely to occur. For example, when the pressure fluctuation risk index is less than 0.3 and the response lag time is less than 5 minutes, the water level control lead time is set between 10 and 20 minutes; when the risk index is between 0.3 and 0.6 and the lag time is between 5 and 15 minutes, the lead time range is set between 20 and 40 minutes; when the risk index is higher than 0.6 and the lag time exceeds 15 minutes, the lead time range is set between 40 minutes and 1 hour. This tiered setting ensures that the response timing of water level control is reasonably arranged under different risk scenarios, avoiding situations where pipeline overpressure or vibration out of control occurs due to excessively delayed response.

[0076] Step S32: Based on the current water level status information, screen the applicability of the initial value range of water level control advance to obtain the initial value of water level control advance.

[0077] In this embodiment of the invention, after setting the initial value range for water level control advance measures at different risk levels, applicability screening is performed based on the real-time water level status of the reservoir to determine the specific initial value for water level control advance measures. During the applicability screening process, data on the current water level height, water level change trend (rate of rise or fall), current water storage, and flood discharge capacity are collected in real time. If the current water level is less than 1 meter from the flood control limit and the water level rise rate is greater than 10 centimeters per hour, a smaller advance value within the initial value range is preferentially selected to accelerate the control response. If the current water level is greater than 2 meters from the flood control limit and the water level rise rate is less than 5 centimeters per hour, a larger advance value within the initial value range can be selected to reduce energy consumption and operating costs caused by frequent control. For example, at a certain moment, if the current water level is 95% of the reservoir's upper limit and the rise rate is 15 centimeters per hour, then within the aforementioned high-risk range of 40 minutes to 1 hour, a 45-minute initial advance value is selected. Through applicability screening, the water level control strategy is made more consistent with the actual operating status of the reservoir.

[0078] Step S33: Establish a candidate set of water level control early intervention strategies based on the initial value of the water level control advance amount, wherein the candidate set of water level control early intervention strategies includes the timing of control initiation, the target water level for control, and the duration of control.

[0079] In this embodiment of the invention, after determining the initial value of the advance timing for water level regulation, a candidate set of early intervention strategies for water level regulation is further established based on this initial value. The candidate set of intervention strategies includes the timing of regulation initiation (i.e., how many minutes after the current moment to begin regulation), the target water level (i.e., the expected water level to be lowered or controlled through this intervention), and the duration of regulation (i.e., how long it takes to complete this intervention). In specific operation, multiple sets of strategies are set around the initial value. For example, based on an initial value of 45 minutes, strategies are set to initiate 40 minutes, 45 minutes, and 50 minutes in advance. For the target water level, different targets are set for a decrease of 0.5 meters, 0.8 meters, and 1.0 meter from the current water level. The duration of regulation is calculated within a reasonable range based on the pump drainage capacity or flood discharge flow, such as 1 hour, 1.5 hours, and 2 hours. This yields nine different combinations of candidate strategies, forming the candidate set of intervention strategies. This step ensures a sufficiently rich selection of strategies for different operational needs, facilitating subsequent optimization and screening.

[0080] Step S34: Screen the candidate set of water level control early intervention strategies based on the matching of water level change rate and flow rate change rate, eliminate strategies whose change rate exceeds the system response capability range, and select strategies with high water level control smoothness and small pressure fluctuation as water level control early intervention strategies.

[0081] This invention employs a matching screening method based on the rate of change of water level and the rate of change of flow rate to eliminate strategies whose rate of change exceeds the system's response capability. The rate of change of water level refers to the height of water level change per unit time, while the rate of change of flow rate is the amount of change in inflow and outflow per unit time. During screening, the design limits of the reservoir discharge system, such as a maximum allowable discharge rate of 200 cubic meters per second and a maximum allowable rate of water level drop of 30 centimeters per hour, are first used as thresholds for system response capability. The rate of change corresponding to each strategy in the candidate set is compared with these thresholds, and strategies that cause excessively rapid water level drops or excessive discharge are eliminated. For example, if a strategy requires a drop of 1 meter per hour, the calculated rate of change of water level is 1 meter per hour, far exceeding the allowable value, and should be eliminated. Furthermore, among the remaining strategies, those with smoother water level control processes and smaller pressure fluctuations are prioritized. These strategies are ranked according to the smoothness index of the water level change curve and the predicted amplitude of water supply pressure fluctuations, ultimately determining strategies with high smoothness and small pressure fluctuations as early intervention strategies for water level control. This screening process ensures both the safety of regulation and the stability of system operation.

[0082] Step S35: Calculate the target water level control execution sequence based on the water level control early intervention strategy.

[0083] In this embodiment of the invention, after determining the final water level control intervention strategy, the target water level control execution sequence is calculated based on this strategy. Specifically, the execution instructions are refined into minute-level or higher resolution instructions based on the control initiation timing, target water level, and control duration, combined with real-time water supply flow and flood discharge equipment operating capacity. For example, if the strategy is to initiate control in 45 minutes, with a target water level drop of 0.8 meters and a duration of 1.5 hours, then with a time step of 5 minutes, corresponding flood discharge flow change instructions are planned based on the flood discharge valve opening adjustment curve and pump station drainage capacity, forming an execution sequence. The execution sequence clearly indicates the required gate opening ratio, pump station start / stop status, and other control actions at each time node, and dynamically calculates water level change predictions, adjusting the execution sequence in real time to cope with sudden changes in inflow, ensuring that the water level change process is stable and controlled, and that pressure fluctuations remain within a safe range. The final target water level control execution sequence can be directly used as input instructions for the reservoir scheduling system to guide actual flood season water level control operations.

[0084] Preferably, step S35 includes the following steps:

[0085] Step S351: Divide the target control period into control time periods based on the control initiation timing and control duration in the water level control early intervention strategy, and generate a set of control time nodes;

[0086] In this embodiment of the invention, after determining the early intervention strategy for water level regulation, the target regulation period is first divided according to the timing and duration of regulation initiation, and a set of regulation time nodes is generated. Specifically, assuming the current time is 8:00 AM, and the early intervention strategy for water level regulation requires initiation at 8:45 AM and a duration of 1.5 hours, the regulation period is from 8:45 AM to 10:15 AM. Then, the entire regulation period is divided with a fixed time step, for example, choosing 5 minutes as the step size. That is, a time node is generated every 5 minutes from 8:45 AM, 8:50 AM, 8:55 AM, all the way to 10:15 AM, forming the set of regulation time nodes. The set of regulation time nodes refers to the set of time points arranged at predetermined intervals, which is the basis for subsequent water level changes and the formulation of control commands. For example, the set generated in this example is {8:45, 8:50, 8:55, 9:00, …, 10:15}, a total of 22 nodes. The granularity (step size) can be flexibly set according to the response speed of reservoir regulation and the system regulation capacity. If higher control accuracy is required, a smaller step size, such as 1 minute, can be used.

[0087] Step S352: Calculate the corresponding target water level value for each control time node in the control time node set, taking into account the target water level change, and form a time-water level comparison table;

[0088] In this embodiment of the invention, after obtaining the set of control time nodes, the target water level value corresponding to each control time node is calculated based on the target water level change, forming a time-water level comparison table. Specifically, the total water level change is calculated based on the initial water level, target water level, and total control duration. This change is then evenly or according to a weighted trend and distributed to each time node. For example, assuming the water level before control begins is 135.0 meters, the target water level is 134.2 meters, the total drop is 0.8 meters, and the control lasts for 1.5 hours (90 minutes), divided into 22 time nodes, then the water level needs to drop by approximately 0.036 meters every 5 minutes on average. Based on this result, the change is accumulated sequentially according to time to determine the target water level value for each node, forming a time-water level comparison table such as {8:45-134.964 meters, 8:50-134.928 meters, 8:55-134.892 meters, ..., 10:15-134.2 meters}. If the regulation requires nonlinear changes, such as slow changes in the initial stage, accelerated changes in the middle stage, and slow changes in the final stage, a weighted allocation method can be used. For example, the water level can be reduced by 20% in the first 30% of the time, by 60% in the middle 40% of the time, and by 20% in the last 30% of the time, to further improve the rationality and safety of water level control.

[0089] Step S353: Calculate the water level change and rate of change between adjacent time points based on the time-water level comparison table to form a water level change sequence;

[0090] Based on the aforementioned time-water level comparison table, this embodiment of the invention further calculates the water level change and rate of change between adjacent time nodes, forming a water level change sequence. Specifically, for each pair of adjacent time nodes, the water level value of the next node is subtracted from the water level value of the previous node to obtain the water level change. Then, the change is divided by the time interval (e.g., 5 minutes) to obtain the water level change rate. For example, from 8:45 to 8:50, if the water level drops from 134.964 meters to 134.928 meters, the change is -0.036 meters, and the rate of change is -0.036 meters / 5 minutes, or -0.0072 meters / minute. All adjacent nodes are processed sequentially to form a complete water level change sequence, such as [-0.036 meters, -0.036 meters, -0.036 meters, ...]. The water level change sequence reflects the expected change at each time step and is the basis for calculating the gate opening adjustment in subsequent control operations, ensuring that the control actions in each time period can accurately match the target water level change.

[0091] Step S354: Calculate the corresponding gate opening adjustment amount based on the water level change sequence and the target water level, and form a gate opening execution command sequence;

[0092] This invention calculates the corresponding gate opening adjustment based on the water level change sequence and the overall target water level, forming a gate opening execution command sequence. Specifically, the operation first determines the required water discharge per unit water level change based on the relationship curve between reservoir gates and outflow (usually derived from measured data or system calibration, such as the functional relationship between opening percentage and flow rate). Then, considering the current effective reservoir area, the corresponding flood discharge volume per unit water level drop is calculated, and this is converted into the required gate opening change based on the flood discharge equipment's flow capacity. For example, if the effective reservoir area is 1 square kilometer (1 million square meters), a water level drop of 0.036 meters requires the discharge of 36,000 cubic meters of water. If a single gate at 50% opening has a flood discharge capacity of 10,000 cubic meters per hour, then 3,000 cubic meters need to be discharged within 5 minutes, requiring the opening to be adjusted to approximately 15% (solved by searching or interpolating curves). This method calculates the gate opening adjustment for each time point, forming an opening execution command sequence, such as {8:45-15%, 8:50-15%, 8:55-15%, ...}. This sequence ensures that each water level change is accompanied by a corresponding opening adjustment, achieving precise and controllable dynamic regulation.

[0093] Step S355: Generate the target water level control execution sequence based on the gate opening command sequence, water level change sequence, and control time node set.

[0094] This invention, after generating a gate opening execution command sequence, combines a water level change sequence and a set of control time nodes to ultimately generate a target water level control execution sequence. Specifically, at each control time node, the gate opening command, water level change, and time information are packaged into a control command unit, such as "At 8:45, adjust the gate opening to 15%, with an expected water level change of -0.036 meters." All command units are then arranged sequentially to form a complete target water level control execution sequence. The execution sequence not only includes operational commands but can also include monitoring and feedback requirements. For example, real-time monitoring of water level changes is performed within 5 minutes after each operation. If the change deviates from the expected value by more than 10%, an adjustment command is automatically triggered to improve the robustness and safety of actual operation. Ultimately, this sequence serves as the direct input to the reservoir scheduling system, guiding the automatic control equipment to gradually complete the optimized control of water levels during the flood season, achieving dynamic balance control of multiple objectives: flood control, water supply, and ecology.

[0095] Preferably, step S5 includes the following steps:

[0096] Step S41: Based on the target water level control execution sequence, extract the target water level value, gate opening degree and execution time point at each time point to obtain preliminary water level control data;

[0097] After formulating the target water level control execution sequence, this embodiment of the invention first needs to extract the corresponding target water level value, gate opening degree, and execution time point at each time node to obtain preliminary water level control data. Specifically, the previously generated control execution sequence is parsed line by line. Each control instruction unit contains three data items: "time point, target water level value, and gate opening degree." These data are extracted in chronological order and stored in a unified format, for example, in a table. The first column is the execution time (e.g., 8:45, 8:50, etc.), the second column is the corresponding target water level value (e.g., 134.964 meters, 134.928 meters, etc.), and the third column is the gate opening percentage (e.g., 15%, 15%, etc.). This preliminary water level control data is the foundational data set for subsequent calculations of trends, smoothing control actions, and evaluating water hammer effects. The "target water level value" represents the reservoir water level the system aims to achieve at that time point, and the "gate opening degree" is the percentage of the gate opening set by the system to achieve water level control.

[0098] Step S42: Calculate the rate of change sequence of water level and gate opening based on the preliminary water level control data;

[0099] This invention, based on the extracted preliminary water level control data, further calculates the rate of change sequence of water level and gate opening. Specifically, for the target water level value, a difference is performed between two adjacent time points: the subsequent water level value is subtracted from the previous water level value, and then divided by the interval between the two time points (e.g., 5 minutes) to obtain the water level change rate, typically in meters per minute. Similarly, for the gate opening, the difference between adjacent nodes is performed and divided by the time interval to obtain the gate opening change rate, in percentages per minute. For example, if the water level is 134.964 meters at 8:45 and 134.928 meters at 8:50, with a time interval of 5 minutes, the water level change rate is (-0.036 meters) / 5 minutes, or -0.0072 meters per minute. If the gate opening is 15% at 8:45 and still 15% at 8:50, the gate opening change rate is 0, indicating no change in gate opening. This process generates two rate-of-change sequences, which respectively describe the water level change trend and the gate adjustment trend, providing a basis for the next step of gradient curve construction.

[0100] Step S43: Construct the initial water level regulation gradient curve using the rate of change sequence;

[0101] This invention utilizes the water level change rate sequence and gate opening change rate sequence obtained in the previous step to construct an initial water level control gradient curve. Specifically, time is used as the abscissa, and the water level change rate or gate opening change rate as the ordinate. The change rate points for each time step are plotted sequentially, and then all points are connected by a broken line to form a preliminary gradient curve. The gradient curve reflects the rate of change during water level control; a high change rate indicates a large and rapid adjustment, while a low change rate indicates a gradual adjustment. For example, in certain periods, to rapidly lower the water level to cope with sudden flood peaks, the gradient curve will show a steep drop; while when the water level approaches the target, to avoid overshoot, the change rate approaches zero, making the curve flat. This initial gradient curve is an important foundation for subsequent smoothing processing and optimization of control commands.

[0102] Step S44: Smooth the initial water level control gradient curve to eliminate abrupt changes and discontinuities, forming a smoothed water level control gradient curve.

[0103] This invention further smooths the initial water level control gradient curve to eliminate abrupt changes and discontinuities, forming a smoothed water level control gradient curve. Specifically, a moving average method or cubic spline interpolation is used to process the gradient curve. Taking moving average as an example, a sliding window, such as three time steps, can be set. The rate of change at each time point is taken as the average of the rates of change at the time points before and after it, plus the rate of change itself, to smooth out sharp abrupt changes and avoid drastic fluctuations in water level during control. In application, it was found that, for example, if the water level change rate suddenly increases from -0.0072 m / min to -0.02 m / min at 8:55, after smoothing, it can be adjusted to -0.012 m / min, making the adjustment transition more natural. The resulting smoothed water level control gradient curve ensures continuous and stable control actions, reducing system instability caused by frequent and rapid adjustments, especially reducing the impact on downstream river channels.

[0104] Step S45: Monitor the probability of water hammer effect in the pipeline based on the water level control gradient smoothing curve;

[0105] After obtaining the smoothed curve of the water level control gradient, this embodiment of the invention needs to monitor the probability of water hammer effect in the pipeline based on this curve. Water hammer effect refers to the pressure fluctuation phenomenon caused in a water pipeline due to excessively rapid gate opening and closing speed or sudden flow changes, which may cause pipeline damage or system failure. In specific implementation, the corresponding flow rate change rate is first calculated based on the water level change rate in each time period of the smoothed curve. Combined with parameters such as the length, diameter, material, and water density of the pipeline system, empirical formulas or simulation models (such as the simplified Joukowsky formula) are used to calculate the possible instantaneous pressure increase. If the flow rate change rate is too large in a certain time period, causing the pressure wave amplitude to exceed the safety threshold (such as 80% of the pipeline's pressure resistance limit), then a high risk of water hammer is determined for that time period. For example, if the predicted water hammer pressure increase is 0.8 MPa in a 5-minute time step, while the maximum pipeline pressure resistance is 1 MPa, then this change requires special attention. Through full-time scanning, the probability distribution of water hammer effect occurrence for each time step is generated.

[0106] Step S46: Adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate based on the probability of water hammer effect in the pipeline, and obtain the reservoir outflow control parameters.

[0107] After obtaining the smoothed curve of the water level control gradient, this embodiment of the invention needs to monitor the probability of water hammer effect in the pipeline based on this curve. Water hammer effect refers to the pressure fluctuation phenomenon caused in a water pipeline due to excessively rapid gate opening and closing speed or sudden flow changes, which may cause pipeline damage or system failure. In specific implementation, the corresponding flow rate change rate is first calculated based on the water level change rate in each time period of the smoothed curve. Combined with parameters such as the length, diameter, material, and water density of the pipeline system, empirical formulas or simulation models (such as the simplified Joukowsky formula) are used to calculate the possible instantaneous pressure increase. If the flow rate change rate is too large in a certain time period, causing the pressure wave amplitude to exceed the safety threshold (such as 80% of the pipeline's pressure resistance limit), then a high risk of water hammer is determined for that time period. For example, if the predicted water hammer pressure increase is 0.8 MPa in a 5-minute time step, while the maximum pipeline pressure resistance is 1 MPa, then this change requires special attention. Through full-time scanning, the probability distribution of water hammer effect occurrence for each time step is generated.

[0108] Of particular importance, step S45 includes the following steps:

[0109] Step S451: Calculate the changes in water flow velocity and pressure wave propagation characteristics in the pipeline based on the water level control gradient smoothing curve and the pre-acquired pipeline parameters;

[0110] In this embodiment of the invention, after generating the water level control gradient smoothing curve, the next step is to calculate the changes in water flow velocity and pressure wave propagation characteristics within the pipe based on this curve and pre-acquired pipe parameters. Specifically, it is first necessary to obtain the pipe's geometric parameters (such as pipe diameter, length, and material properties) and the fluid's physical parameters (such as water density and viscosity). Then, based on the water level changes during the water level control process, the changes in water flow velocity within the pipe are calculated. During this process, the water flow velocity can be derived using flow rate calculation formulas based on the pipe, for example, by calculating the water flow velocity at each time point through the relationship between water flow rate and pipe cross-sectional area. Simultaneously, combining fluid mechanics principles, the influence of water flow velocity changes on pressure wave propagation is further analyzed to obtain the pressure wave propagation characteristics at different time periods, including parameters such as wave velocity and amplitude. These are fundamental data for evaluating the water hammer effect.

[0111] Step S452: Establish a mathematical model of the water hammer effect in the pipeline based on the changes in water flow velocity and the propagation characteristics of pressure waves.

[0112] After obtaining the changes in water flow velocity and pressure wave propagation characteristics within the pipeline, this invention requires the establishment of a mathematical model of the pipeline water hammer effect. Specifically, a pipeline-based water hammer effect model is used, combining changes in water flow velocity and pipeline parameters. The Joukowsky formula from fluid dynamics or a specific water hammer effect calculation model is employed to describe how pressure waves are generated within the pipeline when drastic changes in water flow occur during water level control. This mathematical model considers pipeline characteristics such as length, diameter, and wall roughness, and calculates the propagation speed and fluctuation range of the pressure wave by measuring the instantaneous pressure and velocity changes of the fluid. The mathematical model allows for a quantitative assessment of the intensity of the water hammer effect and the determination of the potential peak pressure wave values ​​at different time points, which is crucial for subsequent control optimization.

[0113] Step S453: Calculate the peak water hammer pressure and occurrence frequency under the current water level control gradient smoothing curve using the pipeline water hammer effect mathematical model to obtain water hammer effect risk data.

[0114] This invention, based on a mathematical model of water hammer effect in pipelines, further utilizes this model to calculate the peak water hammer pressure and frequency of occurrence under the current water level control gradient smoothing curve, thereby obtaining water hammer effect risk data. In specific implementation, based on the established water hammer effect model and current water level control information, the sudden changes in water flow at each time point are simulated, and the peak water hammer pressure and corresponding frequency of occurrence are calculated. For example, if the water level changes significantly and the gate opening changes rapidly during a certain period, it may cause a sudden acceleration of water flow in the pipeline, resulting in a large pressure wave. Through simulation or numerical calculation, the peak pressure (usually in megapascals) and the frequency of water hammer occurrence (times / hour) are determined, and the risk of water hammer effect that the pipeline may encounter is assessed based on this data. The risk assessment results can provide parameter basis for subsequent control, ensuring the stability of system operation.

[0115] Step S454: Calculate the probability of water hammer effect occurring in the pipeline based on water hammer effect risk data and pipeline pressure bearing capacity.

[0116] This invention, in its embodiments, calculates the probability of water hammer effects occurring in pipelines based on water hammer risk data and pipeline pressure-bearing capacity. Specifically, it compares the pipeline's maximum pressure-bearing capacity (such as pressure resistance limit and pipeline material strength) with the peak water hammer pressure to determine the probability of water hammer occurring at different time points. If the calculated peak water hammer pressure exceeds the pipeline's pressure-bearing capacity, the probability of water hammer occurring during that period is high; conversely, it is low. Furthermore, it is necessary to comprehensively consider factors such as pipeline material, age, and flow velocity changes, and further optimize the calculation of the probability of water hammer effects using statistical methods and Monte Carlo simulations to obtain a numerical result reflecting the water hammer risk, which guides subsequent adjustments to control parameters.

[0117] Of particular importance, step S46 includes the following steps:

[0118] Step S461: Analyze the frequency relationship between pipeline pressure waves and control commands based on the probability of pipeline water hammer effect, and identify resonance time nodes;

[0119] This invention analyzes the frequency relationship between pipeline pressure waves and control commands based on the probability of water hammer effects in pipelines, identifying potential resonance time points. Specifically, it first compares the calculated water hammer risk data with the natural frequency of the pipeline (e.g., the vibration frequency of the pipeline system) and the frequency of gate opening changes during water level control. If, during certain periods, the rapid change frequency of the water flow approaches or coincides with the natural frequency of the pipeline, resonance may occur. In this case, the pressure wave will be further amplified due to the resonance effect, potentially leading to pipeline damage or system failure. By analyzing the frequency components of the control commands using Fourier transform and combining this with the natural frequency characteristics of the pipeline, these potential resonance time points are identified, providing a reference for subsequent adjustments to gate opening or water level control rates.

[0120] Step S462: Adjust the gate opening change rate or water level control rate at the corresponding time point according to the resonance time node to obtain the optimized water level control parameter set;

[0121] After identifying the resonance time nodes, this invention requires adjusting the gate opening change rate or water level control rate at the corresponding time points to obtain an optimized water level control parameter set. Specifically, for the identified resonance time nodes, the gate opening change rate is adjusted during these periods to avoid excessive flow velocity changes or rapid water level fluctuations. For example, the gate opening change can be slowed down, or the water level adjustment time can be extended to prevent rapid flow changes from resonating with the pipeline's natural frequency. These adjustments help to smooth the control process and reduce the risk of water hammer. The adjusted parameter set effectively controls the reservoir outflow and prevents system pressure waves from exceeding safe limits.

[0122] Step S463: Based on the optimized water level control parameter set, recalculate the probability of water hammer effect and verify the resonance frequency of pipeline pressure wave and control command to obtain adjustment parameter data.

[0123] This invention, based on an optimized set of water level control parameters, further recalculates the probability of water hammer effects and verifies the resonance frequency relationship between pipeline pressure waves and control commands. Specifically, the peak water hammer pressure and frequency are recalculated according to the adjusted water level control parameters to assess whether resonance risk still exists, and to verify whether the adjusted gate opening and water level change rate effectively eliminate resonance. If, after adjustment, the probability of water hammer effects decreases significantly, and the pipeline pressure waves no longer resonate with the frequency of control commands, the effectiveness of the adjustment can be confirmed. This process ensures that reservoir control operations meet water level targets while avoiding pressure wave damage to the pipeline system.

[0124] Step S464: Generate reservoir outflow control parameters based on the adjusted parameter data.

[0125] Finally, in this embodiment of the invention, reservoir outflow control parameters are generated based on the adjusted parameter data. Specifically, the optimized water level control parameters are combined with relevant data on pipeline pressure-bearing capacity to generate the final outflow control parameters. These parameters include the rate of change of gate opening during the control process, the water level control rate over a given time period, and the pressure wave adjustment limits of the pipeline system. These ultimately generated control parameters ensure efficient and stable water level control during the flood season, while preventing pipeline damage caused by water hammer, thus ensuring the safety and reliability of the entire control process.

[0126] Preferably, step S5 includes the following steps:

[0127] Step S51: Calculate the reservoir outflow rate at different time periods based on the reservoir outflow control parameters, and predict the future water level change trend by combining the reservoir water balance basic model.

[0128] This invention requires calculating the reservoir outflow at different time periods based on the reservoir outflow control parameters. The outflow calculation is based on the actual outflow control system and historical scheduling records of the reservoir. In reservoir regulation, the outflow is a key parameter for reservoir storage capacity and downstream water level control; therefore, outflow calculation typically considers factors such as reservoir water level changes, gate opening, and discharge volume. To obtain the outflow for each time period, it can be estimated using a basic water balance model of the reservoir. This model combines the reservoir's storage capacity, rainfall in the reservoir area, inflow, and existing outflow control data to calculate the actual outflow at each time point. Simultaneously, by combining historical water level data and scheduling strategies, numerical simulation or dynamic simulation methods are used to predict the future trend of reservoir water level changes. This process provides a scientific basis for subsequent reservoir flood season water level regulation, ensuring the successful completion of flood control and water supply tasks.

[0129] Step S52: Compare the future water level change trend with the pre-acquired flood season flood level warning line to calculate the flood control safety margin of the reservoir during the flood season;

[0130] This invention requires comparing future water level trends with pre-determined flood season warning lines to calculate the reservoir's flood control safety margin. Flood control safety margin refers to whether a reservoir has sufficient flood control capacity to withstand potential flood rises at a specific water level. In practice, the flood control safety margin is calculated by comparing the predicted reservoir water level trends with the flood season warning lines. This calculation typically involves two aspects: first, the reservoir water level must be below the warning line to avoid overflow or excessive pressure on the dam; second, based on the predicted water level trends, the remaining controllable water level range of the reservoir is determined to ensure that the reservoir still possesses a certain level of controllability under potential flood conditions. Based on this, the obtained flood control safety margin can be used in subsequent multi-objective optimization models to adjust the reservoir's control parameters.

[0131] Step S53: Based on the rainfall and inflow information in the real-time monitoring data of the reservoir, and in conjunction with meteorological information, predict the future development trend of the flood situation;

[0132] This invention, based on real-time monitoring data of rainfall and inflow from a reservoir, combined with meteorological information, aims to predict future flood trends. In implementation, rainfall data is first acquired through the reservoir's real-time monitoring system. This data comes from meteorological stations in the reservoir's basin and is typically collected hourly. Simultaneously, inflow data within the reservoir's basin needs to be obtained, which can be derived using flow meters and hydrological models. Combined with meteorological forecasts, rainfall and inflow for the next few days can be further predicted, thus estimating the future evolution of the flood situation. Specifically, using rainfall-runoff models (such as the SCS-CN model or the unit hydrological response method), the inflow to the reservoir for the next few days is calculated based on historical rainfall and flow relationships. This data, combined with rainfall forecasts, is used to assess the flood season's development. This information will help reservoir managers promptly grasp the impending flood situation and conduct effective prevention and control measures.

[0133] Step S54: Based on the predicted flood situation, assess the reservoir's water storage capacity and water supply security under the current outflow control parameters, and construct a multi-objective optimization model for reservoir water storage and water supply security.

[0134] This invention, based on the aforementioned flood forecast results, assesses the reservoir's water storage capacity and water supply guarantee capacity under current outflow control parameters. Specifically, it first assesses the reservoir's current water storage status based on predicted future inflow and rainfall data, combined with the reservoir's current water storage capacity. Then, it estimates the water supply guarantee level for the future period by considering historical water level records and water supply demand. If the reservoir's water storage capacity is close to the warning line, or if predicted future water level changes may lead to over-storage, the system will automatically adjust the reservoir's outflow control strategy to prevent excessive water storage and ensure stable water supply capacity. Simultaneously, the system also calculates the reservoir's water supply guarantee level, i.e., whether the reservoir can continuously provide sufficient water supply in the coming days, especially in the event of extreme weather conditions. Based on a multi-objective optimization model of water storage capacity and water supply guarantee level, the system can scientifically assess the reservoir's operational capacity during the future flood season and ensure a balance between flood control and water supply objectives.

[0135] Step S55: Dynamically adjust the reservoir outflow control parameters through a multi-objective optimization model to balance flood control safety margin and water supply security, and convert the adjusted reservoir outflow control parameters into gate opening adjustment commands to execute dynamic multi-objective optimization control of the reservoir's flood season water level.

[0136] This invention, based on a multi-objective optimization model of reservoir storage capacity and water supply security in step S54, dynamically adjusts the reservoir's outflow control parameters. The optimization model calculates the optimal set of reservoir outflow control parameters by considering both flood control safety margin and water supply security. During the optimization process, multi-objective optimization algorithms (such as genetic algorithms and particle swarm optimization) are typically used to weigh different outflow parameters, ensuring that while meeting flood control requirements, the reservoir's water supply security is optimized as much as possible. The optimized outflow control parameters include the rate of change of gate opening and the time distribution of water release. Ultimately, these adjusted parameters are transformed into gate opening adjustment commands, ensuring that the reservoir can dynamically adjust the water level according to actual conditions, avoiding over-storage risks and meeting both flood control and water supply objectives. Through this dynamic adjustment process, the reservoir can flexibly adjust its strategies according to different scenarios during the flood season, ensuring the reservoir's safety and water supply capacity.

[0137] This invention also provides a reservoir flood season water level dynamic multi-objective optimization control system, characterized in that it is used to execute the reservoir flood season water level dynamic multi-objective optimization control method described above, the reservoir flood season water level dynamic multi-objective optimization control system comprising:

[0138] The water balance analysis module is used to acquire real-time monitoring data of the reservoir; extract current water level status information based on the real-time monitoring data; and establish a basic water balance model of the reservoir based on the current water level status information.

[0139] The pipeline pressure risk assessment module is used to analyze the characteristics of pipeline water supply flow variation based on the reservoir water balance model; identify the response lag time of pipeline fluid inertia based on the characteristics of pipeline water supply flow variation; and predict the pressure fluctuation risk of water supply pipelines based on the response lag time.

[0140] The water level control prediction module is used to determine the water level control lead time based on the risk of water supply pipeline pressure fluctuations and response lag time; formulate water level control early intervention strategies based on the water level control lead time; and calculate the target water level control execution sequence based on the water level control early intervention strategies.

[0141] The water hammer effect control module is used to construct a water level control gradient smoothing curve based on the target water level control execution sequence; monitor the probability of water hammer effect occurrence in the pipeline based on the water level control gradient smoothing curve; and adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate according to the probability of water hammer effect occurrence, so as to obtain the reservoir outflow control parameters.

[0142] The flood control dynamic optimization control module is used to assess the flood control safety margin of the reservoir during the flood season based on the reservoir outflow control parameters, and to execute the adjustment of the reservoir gate opening based on the dynamic balance decision of the flood control safety margin of the reservoir during the flood season, so as to realize the dynamic multi-objective optimization control of the reservoir water level during the flood season.

[0143] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0144] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for dynamic multi-objective optimization control of reservoir water level during the flood season, characterized in that, Includes the following steps: Step S1: Obtain real-time monitoring data of the reservoir; extract current water level status information based on the real-time monitoring data of the reservoir; establish a basic model of reservoir water balance based on the current water level status information; Step S2: Analyze the characteristics of pipeline water supply flow variation based on the reservoir water balance basic model; identify the response lag time of pipeline fluid inertia based on the characteristics of pipeline water supply flow variation; Predicting water supply pipeline pressure fluctuation risk based on response lag time; The response lag time for identifying pipeline fluid inertia based on pipeline water supply flow rate variation characteristics includes: For the flow change process curves of each segment in the pipeline water supply flow change characteristics, mark the time when the flow adjustment command is issued and the time when the actual flow begins to change, and calculate the time difference to form an initial response delay sequence; Fit the flow rate change curves of each segment to determine the time difference between the moment when the flow rate change rate reaches its peak and the moment when the command is issued, and form a peak response delay sequence; Calculate the time required for the flow change characteristic curve in the pipeline water supply flow change characteristics to stabilize from the start of change, and form a time series of the stable process. Based on the initial response delay sequence, peak response delay sequence, and steady-state process time series, grouped statistics based on different flow change amplitudes are performed to obtain lag characteristic statistics. Determine the response lag time of pipeline fluid inertia based on lag characteristic statistical data; The risk of water supply pipeline pressure fluctuations based on response lag time includes: A time correlation analysis was conducted between the response lag time of the pipeline fluid inertia and the pre-acquired historical pressure fluctuation events to establish a preliminary correspondence between the lag time and pressure fluctuations. A model of fluid momentum change in the pipeline is established based on response lag time and pipeline water supply flow variation characteristics to calculate theoretical pressure peak. Calculate the pipeline pressure-bearing capacity and natural vibration frequency of the pipeline system based on the pre-acquired pipeline geometric parameters and material properties; By comparing the theoretical peak pressure with the pipeline's pressure-bearing capacity, a pressure over-limit risk index is generated. The pressure wave resonance risk index is assessed by combining the preliminary correspondence with the matching degree between the natural vibration frequency and the response lag time. Predicting pressure fluctuation risk in water supply pipelines based on pressure over-limit risk index and pressure wave resonance risk index; Step S3: Determine the water level control lead time based on the water supply pipeline pressure fluctuation risk and response lag time; formulate an early intervention strategy for water level control based on the water level control lead time; calculate the target water level control execution sequence based on the early intervention strategy for water level control. Step S4: Construct a water level control gradient smoothing curve based on the target water level control execution sequence; monitor the probability of water hammer effect in the pipeline based on the water level control gradient smoothing curve; adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate according to the probability of water hammer effect in the pipeline, and obtain the reservoir outflow control parameters. Step S5: Assess the flood control safety margin of the reservoir during the flood season based on the reservoir outflow control parameters, and implement the adjustment of the reservoir gate opening based on the dynamic balance decision of the flood control safety margin of the reservoir during the flood season, so as to realize the dynamic multi-objective optimization control of the reservoir water level during the flood season.

2. The method for dynamic multi-objective optimization control of reservoir water level during flood season according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect raw monitoring data periodically by using water level, flow rate, and rainfall sensors deployed at the reservoir inlet, the middle of the reservoir area, and the spillway, and upload the data to the data concentrator. Step S12: Process the raw monitoring data and perform outlier detection to obtain real-time monitoring data of the reservoir; Step S13: Extract real-time reservoir monitoring data, inflow rate, outflow rate, and rainfall from the real-time reservoir monitoring data to obtain the current water level status information; Step S14: Calculate the rate of water level change based on real-time reservoir monitoring data and the changes in water level and flow over several time periods, and determine whether the trend is rising or falling, thereby obtaining water level trend data; Step S15: Calculate the water storage coefficient, evaporation loss rate, and leakage loss rate based on the water level trend data to obtain the water balance model parameters; Step S16: Construct a basic water balance model for the reservoir based on the current water level status information and the parameters of the water balance model.

3. The method for dynamic multi-objective optimization control of reservoir water level during flood season according to claim 2, characterized in that, Step S2, which involves analyzing the characteristics of pipeline water supply flow variation based on the reservoir water balance model, includes: Based on the reservoir water balance basic model, the theoretical water supply flow sequence is generated by inputting the pre-acquired historical and real-time water level prediction sequences and outflow control parameters. The theoretical water supply flow sequence is compared with the inflow from the reservoir in real-time monitoring data, and the flow error sequence between the two at the same time point is calculated. The mean, variance, and maximum deviation of the flow error sequence were calculated, and the prediction accuracy of the reservoir water balance basic model and the systematic offset during pipeline water conveyance were evaluated. The rate of change of flow rate is calculated based on a fixed time window in the theoretical water supply flow rate sequence to obtain the pipeline water supply flow rate change sequence; Generate flow change characteristic curves based on the pipeline water supply flow rate change sequence; Extract key inflection points and their corresponding times from the flow change characteristic curve, and evaluate the effective change amount of each inflection point by combining systematic offset; The effective changes before and after each inflection point are integrated with the flow rate change characteristic curve to form the pipeline water supply flow rate change characteristics.

4. The method for dynamic multi-objective optimization control of reservoir water level during flood season according to claim 3, characterized in that, Step S3 includes the following steps: Step S31: Set the initial range of water level control advance amount corresponding to different risk levels based on the water supply pipeline pressure fluctuation risk and response lag time. Step S32: Based on the current water level status information, screen the applicability of the initial value range of water level control advance to obtain the initial value of water level control advance. Step S33: Establish a candidate set of water level control early intervention strategies based on the initial value of the water level control advance amount, wherein the candidate set of water level control early intervention strategies includes the timing of control initiation, the target water level for control, and the duration of control. Step S34: Screen the candidate set of water level control early intervention strategies based on the matching of water level change rate and flow rate change rate, eliminate strategies whose change rate exceeds the system response capability range, and select strategies with high water level control smoothness and small pressure fluctuation as water level control early intervention strategies. Step S35: Calculate the target water level control execution sequence based on the water level control early intervention strategy.

5. The method for dynamic multi-objective optimization control of reservoir water level during flood season according to claim 4, characterized in that, Step S35 includes the following steps: Step S351: Divide the target control period into control time periods based on the control initiation timing and control duration in the water level control early intervention strategy, and generate a set of control time nodes; Step S352: Calculate the corresponding target water level value for each control time node in the control time node set, taking into account the target water level change, and form a time-water level comparison table; Step S353: Calculate the water level change and rate of change between adjacent time points based on the time-water level comparison table to form a water level change sequence; Step S354: Calculate the corresponding gate opening adjustment amount based on the water level change sequence and the target water level, and form a gate opening execution command sequence; Step S355: Generate the target water level control execution sequence based on the gate opening command sequence, water level change sequence, and control time node set.

6. The method for dynamic multi-objective optimization control of reservoir water level during flood season according to claim 5, characterized in that, Step S5 includes the following steps: Step S41: Based on the target water level control execution sequence, extract the target water level value, gate opening degree and execution time point at each time point to obtain preliminary water level control data; Step S42: Calculate the rate of change sequence of water level and gate opening based on the preliminary water level control data; Step S43: Construct the initial water level regulation gradient curve using the rate of change sequence; Step S44: Smooth the initial water level control gradient curve to eliminate abrupt changes and discontinuities, forming a smoothed water level control gradient curve. Step S45: Monitor the probability of water hammer effect in the pipeline based on the water level control gradient smoothing curve; Step S46: Adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate based on the probability of water hammer effect in the pipeline, and obtain the reservoir outflow control parameters.

7. The method for dynamic multi-objective optimization control of reservoir water level during flood season according to claim 6, characterized in that, Step S5 includes the following steps: Step S51: Calculate the reservoir outflow rate at different time periods based on the reservoir outflow control parameters, and predict the future water level change trend by combining the reservoir water balance basic model. Step S52: Compare the future water level change trend with the pre-acquired flood season flood level warning line to calculate the flood control safety margin of the reservoir during the flood season; Step S53: Based on the rainfall and inflow information in the real-time monitoring data of the reservoir, and in conjunction with meteorological information, predict the future development trend of the flood situation; Step S54: Based on the predicted flood situation, assess the reservoir's water storage capacity and water supply security under the current outflow control parameters, and construct a multi-objective optimization model for reservoir water storage and water supply security. Step S55: Dynamically adjust the reservoir outflow control parameters through a multi-objective optimization model to balance flood control safety margin and water supply security, and convert the adjusted reservoir outflow control parameters into gate opening adjustment commands to execute dynamic multi-objective optimization control of the reservoir's flood season water level.

8. A multi-objective optimization control system for reservoir flood season water level dynamics, characterized in that, For executing the reservoir flood season water level dynamic multi-objective optimization control method as described in claim 1, the reservoir flood season water level dynamic multi-objective optimization control system includes: The water balance analysis module is used to acquire real-time monitoring data of the reservoir; extract current water level status information based on the real-time monitoring data; and establish a basic water balance model of the reservoir based on the current water level status information. The pipeline pressure risk assessment module is used to analyze the characteristics of pipeline water supply flow variation based on the reservoir water balance model; identify the response lag time of pipeline fluid inertia based on the characteristics of pipeline water supply flow variation; and predict the pressure fluctuation risk of water supply pipelines based on the response lag time. The water level control prediction module is used to determine the water level control lead time based on the risk of water supply pipeline pressure fluctuations and response lag time; formulate water level control early intervention strategies based on the water level control lead time; and calculate the target water level control execution sequence based on the water level control early intervention strategies. The water hammer effect control module is used to construct a water level control gradient smoothing curve based on the target water level control execution sequence; monitor the probability of water hammer effect occurrence in the pipeline based on the water level control gradient smoothing curve; and adjust the parameters to ensure that the pipeline pressure wave and the control command do not resonate according to the probability of water hammer effect occurrence, so as to obtain the reservoir outflow control parameters. The flood control dynamic optimization control module is used to assess the flood control safety margin of the reservoir during the flood season based on the reservoir outflow control parameters, and to execute the adjustment of the reservoir gate opening based on the dynamic balance decision of the flood control safety margin of the reservoir during the flood season, so as to realize the dynamic multi-objective optimization control of the reservoir water level during the flood season.

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