Pumped storage equipment state analysis method and device based on big data

Abstract

The invention discloses a pumped storage equipment state analysis method and device based on big data, and relates to the technical field of pumped storage power station big data operation optimization. Initial water pumping parameters are determined according to the needed electricity storage capacity, when rainfall does not occur, the balance relation between the initial water pumping parameters and the water pumping capacity and between the reservoir and the water level of the pumped position is analyzed, and the optimal water pumping speed is calculated according to a constraint formula; the load adaptability is verified by combining equipment operation data, a rainfall-water level coupling influence model is constructed based on meteorological data and a water pumping process, the influence of rainfall on the water level is analyzed, the optimal water pumping speed after rainfall factors are added is calculated, and a strategy is adjusted according to different influences; and a future electric quantity energy storage state prediction model is constructed through the current water pumping speed and the predicted rainfall, and a water pumping strategy is dynamically adjusted.

Description

Technical Field

[0001] This invention relates to the field of big data operation optimization technology for pumped storage power stations, specifically to a method and apparatus for analyzing the status of pumped storage equipment based on big data. Background Technology

[0002] Pumped storage, as an important energy storage and regulation technology in the power system, aims to achieve long-term stable operation while ensuring equipment safety and meeting the grid's energy storage needs. However, current pumped storage equipment status analysis and strategy optimization technologies still have significant limitations and are difficult to adapt to the multi-objective balance requirements under complex operating conditions. Existing technologies focus solely on reservoir water level safety, neglecting ecological constraints at the pumping site. They lack the ability to coordinate and manage both water levels, relying solely on a single parameter from the reservoir to derive the pumping speed. This can easily lead to excessive pumping intensity causing the water level at the pumping site to fall below the ecological baseline, resulting in ecological damage to the aquatic body. Traditional technologies determine the pumping speed by simply combining the required energy storage capacity and the rated power of the motor, without constructing a multi-parameter linkage calculation system. This results in the pumping speed failing to meet the planned energy storage schedule and the constraints of both water levels. Existing technologies assume stable hydrological conditions during pumping, failing to incorporate rainfall factors into strategy optimization. They cannot dynamically adapt to the impact of rainfall. Traditional static strategies, because they do not consider the water level increase caused by rainfall, are prone to causing the reservoir water level to exceed the safe upper limit, or maintaining a high pumping speed despite excessive water replenishment at the pumping site, making dynamic balance impossible. Summary of the Invention

[0003] The purpose of this invention is to provide a method and apparatus for analyzing the status of pumped storage equipment based on big data, so as to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for analyzing the status of pumped storage equipment based on big data, including: Real-time collection of multi-source data from pumped storage power stations, including equipment operation data, reservoir status data, data from the pumped-out areas, data from the storage equipment, and weather forecast data, followed by preprocessing. Based on the required power storage capacity, determine the initial parameters for the pumping process; when there is no rainfall, analyze the balance between the required power storage capacity, the required pumping volume, the reservoir water level, and the water level at the pumping point, and calculate the optimal pumping speed using constraint formulas. By combining equipment operation data, the equipment load adaptability under the current pumping strategy is verified; based on meteorological forecast data and the current pumping process, a rainfall-water level coupling influence model is constructed to analyze the impact of future rainfall on the reservoir water level and the water level at the pumping point; based on the analysis results of the rainfall-water level coupling influence model, the optimal pumping speed after incorporating rainfall factors is calculated; and the pumping strategy is adjusted according to different influence situations. Based on the current pumping rate and predicted rainfall, a model for predicting the future energy storage status is established, and the pumping strategy is dynamically adjusted based on the prediction results.

[0005] In conjunction with the first aspect, in the first implementation of the first aspect of this application, the real-time acquisition of multi-source data from the pumped storage power station, including equipment operation data, reservoir status data, data from the pumped-out area, data from the storage equipment, and weather forecast data, and the preprocessing thereof, includes: The equipment operation data collection includes core status parameters of the reversible unit, such as motor winding temperature, guide bearing vibration value, motor operating power, and pump efficiency curve; reservoir status data collection includes the current water level, upper limit of reservoir safety level, reservoir surface area, and natural discharge per unit time; pumping point data collection includes the current water level, lower limit of ecological water level, surface area, and natural replenishment per unit time; energy storage equipment data collection includes the current stored capacity and energy storage efficiency; meteorological forecast data collection includes the rainfall level, rainfall period, and watershed runoff coefficient within the preset future time; the collected data are time-axis aligned, outliers are removed using the 3σ criterion, missing data is completed using linear interpolation, and power and water level parameters are converted into a standardized format to form a structured dataset.

[0006] In conjunction with the first aspect, in the second implementation of the first aspect of this application, determining the initial parameters of the pumping stage based on the required energy storage includes: The required energy storage difference is calculated based on the target energy storage capacity and the current energy storage capacity collected in real time by the energy storage equipment; the initial water level difference is calculated based on the current water level of the reservoir and the current water level at the pumping point. Based on the principle of energy conversion, combined with energy storage efficiency, gravitational acceleration, initial water level difference, and the planned pumping time set according to the grid dispatch requirements, the initial pumping volume is calculated. Set initial power parameters, match the initial operating power of the motor according to the initial pumping volume and pump efficiency curve, so that the initial operating power does not exceed the limit of the rated power of the motor, and record the pumping time and allowable power fluctuation range corresponding to the initial parameters.

[0007] In conjunction with the first aspect, in the third implementation of the first aspect of this application, when there is no rainfall, the balance relationship between the required power storage, the required pumping volume, the reservoir water level, and the water level at the pumping point is analyzed, and the optimal pumping speed is calculated using constraint formulas, including: The actual measured volume and the volume of regular columns were obtained from the reservoir side using drone mapping and sonar scanning. The ratio of the actual measured volume to the volume of regular columns was used as a correction factor for irregularity. The actual usable volume and the volume of the regular column at the pumped-out point are obtained from the measured data at the hydrological station. The ratio of the actual usable volume to the volume of the regular column is used as the irregularity correction coefficient. Collect the correlation curve of pumping speed and power consumption per unit time for reversible units, and collect the correlation curve of pumping speed and natural replenishment per unit time at the pumped point; obtain the planned completion time of the required energy storage capacity in the power grid dispatch instructions. The unit is hours. The formula for calculating the total pumping volume required to complete the required energy storage is: ; in, For the required total pumping volume, To store the required amount of electricity, It is the acceleration due to gravity. The initial water level difference. For energy storage efficiency; Analyze the pattern of water level changes in the reservoir during the pumping process, combined with Establish the logic for reservoir water level changes, using the following formula: ; in, This represents the reservoir water level at the end of pumping. This is the current water level of the reservoir. This represents the actual pumping volume. The natural discharge rate per unit time. This refers to the actual pumping time. The water surface area of ​​the reservoir; Set safety constraints for the reservoir water level to ensure that the water level does not exceed the upper safety limit at the end of pumping; based on these safety constraints, substitute the water level change formula, and combine... Derive the maximum allowable pumping volume of the reservoir: ; in, This is the upper limit of the reservoir's pumping capacity. The upper limit of the safe water level of the reservoir, The water surface area of ​​the reservoir. The natural discharge rate per unit time. The planned completion time for the required energy storage capacity; Calculate the maximum allowable pumping speed of the reservoir. The formula is: ; in, This is the upper limit of the reservoir's pumping capacity. The planned completion time for the required energy storage capacity; Establish the formula for water level change at the pumped point: ; in, This represents the water level at the point where the pumping operation ends. This represents the current water level at the pumped-out location. This represents the natural replenishment per unit time, which varies with the pumping rate V. This refers to the actual pumping time. This represents the actual pumping volume. The area of ​​the water surface at the pumped-out point; Set ecological constraints on the water level at the pumping point so that the water level at the pumping point is at the end of the pumping process. Not lower than the ecological lower limit water level Based on ecological constraints, and substituting the water level change formula, the upper limit of the pumpable volume at the pumped location is derived. The formula is: ; in, This represents the natural replenishment per unit time, which varies with the pumping rate V. The planned completion time for the required energy storage capacity. This represents the current water level at the pumped-out location. This is the ecological lower limit water level. The area of ​​the water surface at the pumped-out point; Minimum allowable pumping speed at the pumped point The formula is: ; in, This represents the upper limit of the pumpable water volume at the pumped location. The planned completion time for the required energy storage capacity; The effective pumping rate range within the planned time period is determined as [ , When it appears > At that time, based on the correlation curve between pumping speed and natural replenishment per unit time, the pumping speed is increased to the adjusted value. ,reduce Reduce the upper limit of the pumpable water volume at the pumped point. This makes the recalculated based on Minimum allowable pumping speed ≤ Construct an effective interval; Within the effective range, multiple candidate pumping speeds are divided, and the total power consumption and energy storage time for each speed are calculated; those exceeding the energy storage time threshold are excluded. Among the candidate speeds, the speed with the lowest total power consumption and the shortest time to reach the energy storage standard is selected as the optimal pumping speed within the planned time.

[0008] In conjunction with the first aspect, in the fourth implementation of the first aspect of this application, the step of verifying the equipment load adaptability under the current pumping strategy by combining equipment operating data includes: Based on the equipment manual and operating experience, set safe operating thresholds for the equipment, including safe thresholds for motor winding temperature, guide bearing vibration, and motor power. The current motor winding temperature, guide bearing vibration value, and motor operating power are collected in real time and compared with the set safety threshold to determine the current operating status of the equipment. Adjust the equipment operating parameters based on the comparison results. When the current motor winding temperature exceeds the safety threshold, or the guide bearing vibration value exceeds the safety threshold, reduce the pumping volume and motor power within the determined pumping volume range until the equipment status parameters return to the safe range. When the motor operating power exceeds the safety threshold, reduce the power to the safety threshold and adjust the pumping volume accordingly. When all equipment status parameters are within the safe threshold range, maintain the current pumping volume and periodically recalibrate the equipment parameters.

[0009] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, the step of constructing a rainfall-water level coupled impact model based on meteorological forecast data and the current pumping process to analyze the impact of future rainfall on the reservoir water level and the water level at the pumping point includes: Set the model parameters, and input the predicted rainfall, rainfall duration, watershed runoff coefficient, and evaporation coefficient; The impact of rainfall on reservoir water level is analyzed. By combining the predicted rainfall, the runoff coefficient of the reservoir side basin, the basin area corresponding to the reservoir, the evaporation per unit time, the rainfall duration and the reservoir surface area, the increase in reservoir water level caused by rainfall is calculated. Combined with the reservoir water level when pumping ends without rainfall, the predicted reservoir water level at the end of pumping is obtained. The impact of rainfall on the water level at the pumping site is analyzed. The predicted rainfall, the runoff coefficient of the side basin at the pumping site, the corresponding basin area at the pumping site, the natural recharge per unit time, the rainfall duration, the evaporation per unit time, and the water surface area at the pumping site are comprehensively considered to calculate the increase in water level at the pumping site caused by rainfall. Combined with the water level at the pumping site when pumping ends without rainfall, the predicted water level at the pumping site at the end of pumping is obtained. Output the rainfall impact analysis results, generate curves showing the changes in the predicted reservoir water level and the predicted water level at the pumped location over time within a preset period, and mark whether the water level exceeds the safe upper limit or falls below the ecological lower limit.

[0010] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, the step of calculating the optimal pumping rate after incorporating rainfall factors based on the analysis results of the rainfall-water level coupling influence model includes: Based on the rainfall-water level coupling effect model, the predicted water level change curves of the reservoir and the pumped-out area within a preset time period are extracted, and the rainfall period output by the model is obtained simultaneously. The established pumping speed-power consumption per unit time correlation curve and the planned completion time of the required energy storage capacity in the power grid dispatch instructions are also retrieved. Combined with the total pumping volume required to complete the power storage, Construct a collaborative calculation model for rainfall, water level, pumping speed, energy consumption, and energy storage. When rainfall and pumping periods overlap, velocity constraints at both the reservoir and the pumping point are adjusted separately; the reservoir level increment during the overlapping period is considered on the reservoir side. Maximum allowable pumping volume of reservoirs during overlapping periods The formula is: ; in, The upper limit of the safe water level of the reservoir, This is the current water level of the reservoir. The water surface area of ​​the reservoir. This refers to the natural discharge volume of the reservoir per unit time. Duration of the overlapping period between rainfall and pumping; Maximum permissible pumping speed of reservoir during rainfall The calculation formula is: ; in, This represents the maximum allowable pumping volume of the reservoir during the overlapping period. Duration of the overlapping period between rainfall and pumping; The water level increase at the pumped location is considered during overlapping periods. Natural replenishment per unit time at the pumped-out point during overlapping periods Calculate the upper limit of the pumpable water volume at the point where water is pumped during the overlapping period. The formula is: ; in, The duration of the overlapping period between rainfall and pumping. This represents the current water level at the pumped-out location. This represents the increase in water level at the pumped-out points during the overlapping time period. This is the ecological lower limit water level at the pumped-out location. The surface area of ​​the water being pumped out; the minimum allowable pumping speed at the pumped-out location during rainfall. The calculation formula is: ; in, This represents the upper limit of the pumpable water volume at the point where water is pumped during the overlapping period. Forming overlapping time period speed ranges [ , Within the interval, multiple candidate pumping speeds are divided. The total power consumption and energy storage time for each candidate speed are calculated. Candidate speeds that exceed the energy storage time limit are eliminated. Among the remaining candidate speeds, the speed with the lowest total power consumption and the shortest energy storage time is selected as the optimal pumping speed.

[0011] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, adjusting the pumping strategy according to different impact situations includes: When the predicted water level of the reservoir does not exceed the upper limit of the reservoir's safety level and the predicted water level at the pumping point is not lower than the lower limit of the ecological level at the pumping point, the optimal pumping rate for the current period shall be maintained. When the predicted water level of the reservoir exceeds the upper limit of the reservoir's safety level, the pumping volume will be reduced to the preset upper limit threshold of the reservoir's first-level pumping volume. The pumping speed will be adjusted according to the planned completion time of the energy storage capacity, ensuring that the pumping speed is not lower than [the threshold value is missing from the original text]. ;Activate the reservoir's pre-discharge facilities to increase the natural discharge rate per unit time until the predicted reservoir water level drops below the reservoir's safe upper limit. When the predicted water level at the pumping site is lower than the ecological lower limit water level at the pumping site, pumping will stop when the water level reaches the ecological lower limit water level at the pumping site, and pumping will be switched to another pumping site.

[0012] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, the step of establishing a future energy storage state prediction model based on the current pumping rate and predicted rainfall, and dynamically adjusting the pumping strategy based on the prediction results, includes: Clarify the relationship between future energy storage capacity and the pumping speed to be adjusted, remaining pumping time, predicted water level difference and energy storage efficiency, and construct a prediction model for future energy storage status. A threshold is set for the deviation between future and required energy storage capacity, allowing the deviation to not exceed a certain percentage of the required energy storage capacity. The pumping speed is dynamically adjusted based on the forecast results. When the deviation is within the allowable range, the pumping speed to be adjusted is maintained consistent with the current pumping speed. When future energy storage is lower than required energy storage and the pumping speed to be adjusted has not reached the maximum allowable pumping speed and the corrected maximum speed for the time period, the pumping speed to be adjusted is increased within the specified range. When future energy storage is higher than required energy storage and the pumping speed to be adjusted is higher than the minimum allowable pumping speed and the corrected minimum speed for the time period, the pumping speed to be adjusted is decreased within the specified range. When the deviation cannot be brought to the target by adjusting the pumping speed due to reservoir water level constraints or equipment load constraints, an energy storage progress warning is generated and reported to the power grid dispatch center to coordinate subsequent operation plans. Iterative optimization is performed, with real-time pumping volume and updated rainfall data periodically re-collected, model parameters and future energy storage forecast results adjusted according to the new results to ensure that the final deviation between the actual energy storage capacity and the required energy storage capacity does not exceed the set limit percentage.

[0013] Secondly, the present invention provides a pumped storage equipment status analysis device based on big data.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention incorporates the safety of reservoir water level and the ecological water level at the pumping site into a unified analysis framework, creating a dual water level collaborative constraint system. By calculating the constraint parameters on the reservoir side and the pumping site side respectively, the dual constraint conflict is resolved, ensuring the safety of the reservoir and the ecology at the pumping site. 2. This invention takes the required energy storage capacity as the core, and combines the planned completion time, total pumping volume and dual water level balance relationship. It derives the effective speed range through a dual-constraint formula and selects the optimal pumping speed. 3. This invention is based on a rainfall-water level coupling influence model. After introducing rainfall factors, it calculates the optimal pumping speed during the overlapping period of rainfall and pumping, adjusts the strategy according to different scenarios, and dynamically responds to the impact of rainfall. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the steps of the pumped storage equipment status analysis method based on big data according to the present invention. Figure 2 This is a flowchart illustrating the process of determining the final pumping strategy based on both reservoir water level constraints and the water level constraints at the pumped-out point in the big data-based pumped-storage equipment status analysis method of this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example: Figures 1-2 As shown, the present invention provides a technical solution. like Figure 1 The schematic diagram illustrates the steps of a big data-based method for analyzing the status of pumped storage equipment. This invention provides a big data-based method for analyzing the status of pumped storage equipment, including: Step S100: Collect multi-source data from the pumped storage power station in real time, including equipment operation data, reservoir status data, data from the pumped-out area, data from the storage equipment, and weather forecast data, and perform preprocessing. Specifically, the equipment operation data collection includes the core status parameters of the reversible unit, including motor winding temperature, guide bearing vibration value, motor operating power, and pump efficiency curve; reservoir status data collection includes the current water level, upper limit of reservoir safety level, reservoir surface area, and natural discharge per unit time; pumped water data collection includes the current water level, lower limit of ecological water level, surface area, and natural replenishment per unit time; energy storage equipment data collection includes the current stored capacity and energy storage efficiency; and weather forecast data collection includes the rainfall level, rainfall period, and watershed runoff coefficient within the preset future time. The collected data were aligned along the time axis, outliers were removed using the 3σ criterion, missing data were filled in using linear interpolation, and power and water level parameters were converted into a standardized format to form a structured dataset.

[0018] In one specific embodiment, a pumped storage power station performed multi-source data acquisition and preprocessing from 9:00 to 10:00 on August 10, 2025. Among the acquired equipment operation data, the reversible unit's motor winding temperature was 79.2℃, the guide bearing vibration value was 0.038 mm / s, and the motor operating power was 252 MW. The pump efficiency curve corresponding to the current operating condition showed an efficiency of 89.5%. Reservoir status data included the current water level of 328.5 m, the upper safety limit water level of 335.0 m, and the water surface area of ​​1.92 × 10⁻⁶ m². 6 m², natural discharge rate per unit time 15.6 m³ / h; data collected at the pumping point: current water level 286.3 m, ecological lower limit water level 280.0 m, water surface area 1.35 × 10⁻⁶ m² ... 6 m², natural replenishment per unit time 10.2 m³ / h; energy storage equipment data: current energy storage capacity 385 MWh, energy storage efficiency 92.3%; meteorological forecast data: rainfall level for the next 6 hours is moderate rain, forecast value 32 mm, rainfall period 12:00-14:30, reservoir side watershed runoff coefficient 0.68, pumped watershed side watershed runoff coefficient 0.72; in the preprocessing stage, the data timestamps are unified to the minute level based on the power station's unified clock, using the 3σ criterion, mean 251.8 MW, standard deviation 3.2 MW, 265 MW abnormal power value collected at 9:15 is removed, linear interpolation is used to supplement the average rainfall intensity of 1.35 mm / h for the period of 13:30-13:45 based on 1.2 mm / h at 13:20 and 1.5 mm / h at 13:50, and the power, water level and other parameters are converted into a standardized format to finally form a structured dataset. Step S200: Determine the initial parameters of the pumping process based on the required power storage capacity; when there is no rainfall, analyze the balance relationship between the required power storage capacity, the required pumping volume, the reservoir water level, and the water level at the pumping point, and calculate the optimal pumping speed using constraint formulas. Specifically, the required energy storage difference is calculated based on the target energy storage capacity and the current energy storage capacity collected in real time by the energy storage equipment; the initial water level difference is calculated based on the current water level of the reservoir and the current water level at the pumping point. Based on the principle of energy conversion, combined with energy storage efficiency, gravitational acceleration, initial water level difference, and the planned pumping time set according to the grid dispatch requirements, the initial pumping volume is calculated. Set initial power parameters, match the initial operating power of the motor according to the initial pumping volume and pump efficiency curve, so that the initial operating power does not exceed the limit of the rated power of the motor, and record the pumping time and allowable power fluctuation range corresponding to the initial parameters.

[0019] The actual measured volume and the volume of regular columns were obtained from the reservoir side using drone mapping and sonar scanning. The ratio of the actual measured volume to the volume of regular columns was used as a correction factor for irregularity. The actual usable volume and the volume of the regular column at the pumped-out point are obtained from the measured data at the hydrological station. The ratio of the actual usable volume to the volume of the regular column is used as the irregularity correction coefficient. Collect the correlation curve of pumping speed and power consumption per unit time for reversible units, and collect the correlation curve of pumping speed and natural replenishment per unit time at the pumped point; obtain the planned completion time of the required energy storage capacity in the power grid dispatch instructions. The unit is hours. The formula for calculating the total pumping volume required to complete the required energy storage is: ; in, For the required total pumping volume, To store the required amount of electricity, It is the acceleration due to gravity. The initial water level difference. For energy storage efficiency; Analyze the pattern of water level changes in the reservoir during the pumping process, combined with Establish the logic for reservoir water level changes, using the following formula: ; in, This represents the reservoir water level at the end of pumping. This is the current water level of the reservoir. This represents the actual pumping volume. The natural discharge rate per unit time. This refers to the actual pumping time. The water surface area of ​​the reservoir; Set safety constraints for the reservoir water level to ensure that the water level does not exceed the upper safety limit at the end of pumping; based on these safety constraints, substitute the water level change formula, and combine... Derive the maximum allowable pumping volume of the reservoir: ; in, This is the upper limit of the reservoir's pumping capacity. The upper limit of the safe water level of the reservoir, The water surface area of ​​the reservoir. The natural discharge rate per unit time. The planned completion time for the required energy storage capacity; Calculate the maximum allowable pumping speed of the reservoir. The formula is: ; in, This is the upper limit of the reservoir's pumping capacity. The planned completion time for the required energy storage capacity; Establish the formula for water level change at the pumped point: ; in, This represents the water level at the point where the pumping operation ends. This represents the current water level at the pumped-out location. This represents the natural replenishment per unit time, which varies with the pumping rate V. This refers to the actual pumping time. This represents the actual pumping volume. The area of ​​the water surface at the pumped-out point; Set ecological constraints on the water level at the pumping point so that the water level at the pumping point is at the end of the pumping process. Not lower than the ecological lower limit water level Based on ecological constraints, and substituting the water level change formula, the upper limit of the pumpable volume at the pumped location is derived. The formula is: ; in, This represents the natural replenishment per unit time, which varies with the pumping rate V. The planned completion time for the required energy storage capacity. This represents the current water level at the pumped-out location. This is the ecological lower limit water level. The area of ​​the water surface at the pumped-out point; Minimum allowable pumping speed at the pumped point The formula is: ; in, This represents the upper limit of the pumpable water volume at the pumped location. The planned completion time for the required energy storage capacity; The effective pumping rate range within the planned time period is determined as [ , When it appears > At that time, based on the correlation curve between pumping speed and natural replenishment per unit time, the pumping speed is increased to the adjusted value. ,reduce Reduce the upper limit of the pumpable water volume at the pumped point. This makes the recalculated based on Minimum allowable pumping speed ≤ Construct an effective interval; Within the effective range, multiple candidate pumping speeds are divided, and the total power consumption and energy storage time for each speed are calculated; those exceeding the energy storage time threshold are excluded. Among the candidate speeds, the speed with the lowest total power consumption and the shortest time to reach the energy storage standard is selected as the optimal pumping speed within the planned time.

[0020] In one specific embodiment, a pumped storage power station has a target storage capacity of 42 MWh. Combined with the current storage capacity of 24 MWh, the required storage capacity is calculated. =18MWh; The current water level of the reservoir is 315m, and the current water level at the pumping point is 268m. =47m. Assuming g=9.8N / kg, energy storage efficiency 90%, and planned pumping time... =5h, calculate the total pumping volume using the formula. ≈156m³. (Based on side survey of the reservoir) =0.9, =1.2×10 3 m 2 , =12m³ / h, =325m; measured at the pumping point =0.95, =5×10 3 m 2 , =266m, collect the pumping rate-power consumption curve of the unit and the replenishment curve of the pumped water point. Substitute into the formula to calculate the reservoir. =10860m³, =2172 m³ / h, calculated based on a pumping rate of 1950 m³ / h, the pumped water level is... =9560m³, =1912m³ / h. Within the effective range [1912, 2172]m³ / h, three candidate speeds of 1950m³ / h, 2050m³ / h, and 2150m³ / h were selected. The energy storage time to meet the standard was ≤5h for all three speeds, and the total power consumption was 160MWh, 168MWh, and 175MWh, respectively. Finally, 1950m³ / h was selected as the optimal pumping speed.

[0021] Step S300: Based on the equipment operation data, verify the equipment load adaptability under the current pumping strategy; Specifically, based on the equipment manual and operating experience, set safe operating thresholds for the equipment, including safe thresholds for motor winding temperature, guide bearing vibration, and motor power. The current motor winding temperature, guide bearing vibration value, and motor operating power are collected in real time and compared with the set safety threshold to determine the current operating status of the equipment. Adjust the equipment operating parameters based on the comparison results. When the current motor winding temperature exceeds the safety threshold, or the guide bearing vibration value exceeds the safety threshold, reduce the pumping volume and motor power within the determined pumping volume range until the equipment status parameters return to the safe range. When the motor operating power exceeds the safety threshold, reduce the power to the safety threshold and adjust the pumping volume accordingly. When all equipment status parameters are within the safe threshold range, maintain the current pumping volume and periodically recalibrate the equipment parameters.

[0022] In one specific embodiment, the equipment load adaptability under the current pumping strategy is verified. Based on the equipment manual and operating experience, the following safe operating thresholds are set: motor winding temperature safety threshold is 85℃, guide bearing vibration safety threshold is 0.05mm / s, and motor power safety threshold is 280MW. Real-time equipment operating data is collected: motor winding temperature is 82.3℃, guide bearing vibration is 0.04mm / s, and motor operating power is 260MW. After comparison with the safety thresholds, it is determined that the current equipment operating status is normal and the safety thresholds have not been exceeded. Therefore, the current pumping rate of 1.19×10⁻⁶ is maintained. 6 The pumping volume and motor operating power remain unchanged at 260MW, and the equipment parameters are automatically recalibrated every 30 minutes to ensure continuous safe operation. If the motor winding temperature exceeds 85℃ or the guide bearing vibration value exceeds 0.05mm / s during subsequent calibration, the system will automatically reduce the pumping volume and motor power within the determined pumping volume range until the equipment returns to a safe range. If the motor operating power exceeds 280MW, the power will be immediately reduced to 280MW, and the pumping volume will be adjusted simultaneously to ensure equipment safety and achieve the energy storage target.

[0023] Step S400: Based on meteorological forecast data and the current pumping process, construct a rainfall-water level coupled impact model to analyze the impact of future rainfall on the reservoir water level and the water level at the pumping point; based on the analysis results of the rainfall-water level coupled impact model, calculate the optimal pumping rate after incorporating rainfall factors; adjust the pumping strategy according to different impact scenarios. Specifically, set the model parameters and input the predicted rainfall, rainfall duration, watershed runoff coefficient, and evaporation coefficient; The impact of rainfall on reservoir water level is analyzed. By combining the predicted rainfall, the runoff coefficient of the reservoir side basin, the basin area corresponding to the reservoir, the evaporation per unit time, the rainfall duration and the reservoir surface area, the increase in reservoir water level caused by rainfall is calculated. Combined with the reservoir water level when pumping ends without rainfall, the predicted reservoir water level at the end of pumping is obtained. The impact of rainfall on the water level at the pumping site is analyzed. The predicted rainfall, the runoff coefficient of the side basin at the pumping site, the corresponding basin area at the pumping site, the natural recharge per unit time, the rainfall duration, the evaporation per unit time, and the water surface area at the pumping site are comprehensively considered to calculate the increase in water level at the pumping site caused by rainfall. Combined with the water level at the pumping site when pumping ends without rainfall, the predicted water level at the pumping site at the end of pumping is obtained. Output the rainfall impact analysis results, generate curves showing the changes in the predicted reservoir water level and the predicted water level at the pumped location over time within a preset period, and mark whether the water level exceeds the safe upper limit or falls below the ecological lower limit.

[0024] Based on the rainfall-water level coupling effect model, the predicted water level change curves of the reservoir and the pumped-out area within a preset time period are extracted, and the rainfall period output by the model is obtained simultaneously. The established pumping speed-power consumption per unit time correlation curve and the planned completion time of the required energy storage capacity in the power grid dispatch instructions are also retrieved. Combined with the total pumping volume required to complete the power storage, A collaborative calculation model for rainfall, water level, pumping rate, energy consumption, and energy storage was constructed. When rainfall and pumping periods overlapped, the velocity constraints of the reservoir and the pumped area were adjusted separately. The reservoir level increment during the overlapping period was considered. Maximum allowable pumping volume of reservoirs during overlapping periods The formula is: ; in, The upper limit of the safe water level of the reservoir, This is the current water level of the reservoir. The water surface area of ​​the reservoir. This refers to the natural discharge volume of the reservoir per unit time. Duration of the overlapping period between rainfall and pumping; Maximum permissible pumping speed of reservoir during rainfall The calculation formula is: ; in, This represents the maximum allowable pumping volume of the reservoir during the overlapping period. Duration of the overlapping period between rainfall and pumping; The water level increase at the pumped location is considered during overlapping periods. Natural replenishment per unit time at the pumped-out point during overlapping periods Calculate the upper limit of the pumpable water volume at the point where water is pumped during the overlapping period. The formula is: ; in, The duration of the overlapping period between rainfall and pumping. This represents the current water level at the pumped-out location. This represents the increase in water level at the pumped-out points during the overlapping time period. This is the ecological lower limit water level at the pumped-out location. The surface area of ​​the water being pumped out; the minimum allowable pumping speed at the pumped-out location during rainfall. The calculation formula is: ; in, This represents the upper limit of the pumpable water volume at the point where water is pumped during the overlapping period. Forming overlapping time period speed ranges [ , Within the interval, multiple candidate pumping speeds are divided. The total power consumption and energy storage time for each candidate speed are calculated. Candidate speeds that exceed the energy storage time limit are eliminated. Among the remaining candidate speeds, the speed with the lowest total power consumption and the shortest energy storage time is selected as the optimal pumping speed.

[0025] When the predicted water level of the reservoir does not exceed the upper limit of the reservoir's safety level and the predicted water level at the pumping point is not lower than the lower limit of the ecological level at the pumping point, the optimal pumping rate for the current period shall be maintained. When the predicted water level of the reservoir exceeds the upper limit of the reservoir's safety level, the pumping volume will be reduced to the preset upper limit threshold of the reservoir's first-level pumping volume. The pumping speed will be adjusted according to the planned completion time of the energy storage capacity, ensuring that the pumping speed is not lower than [the threshold value is missing from the original text]. ;Activate the reservoir's pre-discharge facilities to increase the natural discharge rate per unit time until the predicted reservoir water level drops below the reservoir's safe upper limit. When the predicted water level at the pumping site is lower than the ecological lower limit water level at the pumping site, pumping will stop when the water level reaches the ecological lower limit water level at the pumping site, and pumping will be switched to another pumping site.

[0026] In one specific embodiment, based on meteorological forecast data: predicted rainfall of 20 mm in the next 3 hours, rainfall duration of 1 hour, runoff coefficient of the reservoir side basin of 0.65, runoff coefficient of the pumped-out side basin of 0.7, evaporation rate of 0.1 mm / h, and combined with the current pumping progress: 0.5 hours of pumping has been completed, and the remaining planned pumping time is... =3h, =6×10 5 m³, current pumping speed 2×10 5 m³ / h; =325m =330m, corresponding to a drainage area of ​​30km² and a water surface area of ​​1.5×10⁻⁶. 5 m², irregularity correction factor =0.95, =12m³ / h; =282m =278m, corresponding to a drainage area of ​​20km² and a water surface area of ​​1×10⁻⁶. 5 m², irregularity correction factor =0.94, natural recharge per unit time 10 m³ / h, a rainfall-water level coupled influence model is constructed; after setting the parameters, the increase in reservoir water level caused by rainfall is calculated. ≈0.26m. Combined with the reservoir water level of 325.04m at the end of pumping without rainfall, the predicted end-of-pump water level is 325.3m, which is within the predicted range. ; Calculate the increase in water level at the pumped-out point due to rainfall. ≈0.27m, combined with the water level at the pumping point when pumping ended (281.4m) without rainfall, the predicted end-of-pump water level is 281.67m, which is higher than... The system outputs analysis results and generates water level change curves; the rainfall period extracted from the model output is 1.5h to 2.5h after the start of pumping, with an overlap duration of [missing information]. =1h, call the established correlation curve of pumping speed and power consumption per unit time to construct a collaborative calculation model; correct the speed constraint, reservoir side ≈6.801×10 5 m³, =6.801×10 5 m³ / h; =25m³ / h, ≈3.996×10 5 m³, ≈3.996×10 5 m³ / h, forming an effective speed range [3.996×10 m³ / h]. 5 m³ / h, 6.801×10 5 [m³ / h]; Divide the interval into 2.2×10 5 m³ / h, 2.4×10 5 Two candidate speeds (m³ / h) were selected, with total power consumption calculated to be 210 MWh and 228 MWh respectively, and battery storage times of 2.73 h and 2.5 h respectively. The speed with the lowest total power consumption, 2.2 × 10 m³ / h, was ultimately chosen. 5 The optimal pumping rate is m³ / h; since the predicted water levels at both the reservoir and the pumped-out area meet the standards, this rate is maintained until the end of the overlapping period.

[0027] Step S500: Based on the current pumping rate and predicted rainfall, establish a future energy storage state prediction model, and dynamically adjust the pumping strategy according to the prediction results. Specifically, clarify the correlation between future energy storage capacity and the pumping rate to be adjusted, remaining pumping time, predicted water level difference, and energy storage efficiency, and construct a future energy storage state prediction model; A threshold is set for the deviation between future and required energy storage capacity, allowing the deviation to not exceed a specified proportion of the required energy storage capacity. The pumping speed is dynamically adjusted based on the forecast results. When the deviation between future and required energy storage capacity is within the allowable range, the pumping speed to be adjusted is maintained consistent with the current pumping speed. When future energy storage capacity is lower than required energy storage capacity and the pumping speed to be adjusted has not reached the maximum allowable pumping speed and the corrected maximum speed for the time period, the pumping speed to be adjusted is increased within the specified range. When future energy storage capacity is higher than required energy storage capacity and the pumping speed to be adjusted is higher than the minimum allowable pumping speed and the corrected minimum speed for the time period, the pumping speed to be adjusted is decreased within the specified range. When the deviation cannot be brought to the target by adjusting the pumping speed due to reservoir water level constraints or equipment load constraints, an energy storage progress warning is generated and reported to the power grid dispatch center to coordinate subsequent operation plans. Iterative optimization is carried out by periodically re-collecting real-time pumping volume and updating rainfall data, correcting model parameters, and adjusting pumping strategies based on future energy storage prediction results, so that the final deviation between the actual energy storage and the required energy storage does not exceed the set limit ratio.

[0028] In one specific embodiment, the current pumping rate of a pumped storage power station is 4.5 × 10⁻⁶. 5 With a pumping capacity of m³ / h, a predicted rainfall of 32 mm, a remaining pumping time of 5.5 h, a predicted water level difference of 42.2 m, and an energy storage efficiency of 92.3%, a future energy storage state prediction model is constructed. The deviation threshold between the future energy storage capacity and the required energy storage capacity of 250 MWh is set at ±5%, i.e., an allowable deviation of 12.5 MWh. The total pumping volume is 2.475 × 10⁻⁶ m³ / h. 6 Based on the calculated future energy storage capacity of m³, the value is 242.8 MWh, which deviates from 250 MWh by 7.2 MWh. This is within the acceptable range, and the capacity will be maintained at 4.5 × 10⁻⁶ m³ for now. 5 The pumping rate was measured at m³ / h. The real-time pumping volume was re-collected after 30 minutes: 2.25 × 10⁻⁶. 5 m³, corresponding to a stable pumping speed, updated predicted rainfall reduced to 28 mm, corrected predicted water level difference to 41.8 m, remaining pumping time 5 hours, corrected total pumping volume 2.25 × 10 m³. 6 m³ / h, with a future energy storage capacity of 218.7 MWh, a deviation of 31.3 MWh, exceeding the threshold, and the current speed has not reached the corrected maximum speed of 5 × 10 m³ / h. 5 m³ / h, in [4.5×10 5 5×10 5 The capacity in the m³ / h range is increased to 4.725 × 10⁻⁶ m³ / h in a 5% gradient. 5 m³ / h, the recalculated total pumping capacity is 2.3625 × 10⁻⁶ m³ / h. 6 m³, with a future energy storage capacity of 229.1 MWh, the deviation still exceeds the threshold, and needs to be further increased to 4.95 × 10 m³. 5The calculated future energy storage capacity is 239.5 MWh, with a deviation of 10.5 MWh, which meets the requirements. Therefore, this speed is maintained. Subsequent iterations and optimizations are performed every 30 minutes. The final actual energy storage capacity is 248.2 MWh, which deviates from the required energy storage capacity of 250 MWh by 1.8 MWh, which is below the 5% threshold.

[0029] like Figure 2 The flowchart of the procedure for determining the final pumping volume based on the combined constraints of reservoir water level and pumped-out water level in the big data-based pumped-storage equipment status analysis method is shown. This invention provides a big data-based pumped-storage equipment status analysis method, including: Parallel computation of two key constraint parameters to calculate the upper limit of reservoir pumping capacity based on the reservoir's safe upper limit water level. To ensure that the reservoir water level does not exceed the limit when pumping ends; to calculate the lower limit of pumping volume at the pumped location based on the ecological lower limit water level at the pumped location. To ensure that the downstream ecological water level does not fall below the safety threshold during the pumping process; Perform core logic judgments and comparisons and The size relationship, when ≤ This indicates that ecological and safety constraints can be satisfied simultaneously, and a reasonable pumping volume operating range can be determined accordingly. , [, and set the final pumping volume within this range.] To implement the pumping strategy; when > When this occurs, it indicates a conflict between the two factors, making a solution impossible. In this case, an early warning is triggered, and ecological security is prioritized, forcibly setting the pumping rate to [a certain value]. The conflict situation and adjustment plan will be reported to the dispatch center for manual coordination, and a pumping strategy with ecological protection as the core will be implemented.

[0030] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, 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 equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for status analysis of pumped storage equipment based on big data, characterized in that, include: Real-time collection of multi-source data from pumped storage power stations, including equipment operation data, reservoir status data, data from the pumped-out areas, data from the storage equipment, and weather forecast data, followed by preprocessing. Determine the initial parameters for the pumping process based on the required energy storage capacity. When there is no rainfall, analyze the balance between the required power storage, the required pumping volume, the reservoir water level, and the water level at the pumping point, and calculate the optimal pumping rate using constraint formulas. By combining equipment operation data, the equipment load adaptability under the current pumping strategy is verified; based on meteorological forecast data and the current pumping process, a rainfall-water level coupling influence model is constructed to analyze the impact of future rainfall on the reservoir water level and the water level at the pumping point; based on the analysis results of the rainfall-water level coupling influence model, the optimal pumping speed after incorporating rainfall factors is calculated; and the pumping strategy is adjusted according to different influence situations. Based on the current pumping rate and predicted rainfall, a model for predicting the future energy storage status is established, and the pumping strategy is dynamically adjusted according to the prediction results.

2. The method for analyzing the status of pumped storage equipment based on big data according to claim 1, characterized in that, The real-time acquisition of multi-source data from the pumped storage power station, including equipment operation data, reservoir status data, data from the pumped-out area, data from the storage equipment, and weather forecast data, is preprocessed, including: The equipment operation data collection includes core status parameters of the reversible unit, such as motor winding temperature, guide bearing vibration value, motor operating power, and pump efficiency curve; reservoir status data collection includes the current water level, upper limit of reservoir safety level, reservoir surface area, and natural discharge per unit time; pumped water data collection includes the current water level, lower limit of ecological water level, surface area, and natural replenishment per unit time; energy storage equipment data collection includes the current stored capacity and energy storage efficiency; and weather forecast data collection includes the rainfall level, rainfall period, and watershed runoff coefficient within the preset future time. The collected data were aligned along the time axis, outliers were removed using the 3σ criterion, missing data were filled in using linear interpolation, and power and water level parameters were converted into a standardized format to form a structured dataset.

3. The method for analyzing the status of pumped storage equipment based on big data according to claim 1, characterized in that, The determination of initial parameters for the pumping process based on the required energy storage includes: The required energy storage difference is calculated based on the target energy storage capacity and the current energy storage capacity collected in real time by the energy storage equipment; the initial water level difference is calculated based on the current water level of the reservoir and the current water level at the pumping point; and the initial pumping volume is calculated based on the energy conversion principle, combined with energy storage efficiency, gravitational acceleration, initial water level difference, and the planned pumping duration set according to the grid dispatch requirements. Set initial power parameters, match the initial operating power of the motor according to the initial pumping volume and pump efficiency curve, so that the initial operating power does not exceed the limit of the rated power of the motor, and record the pumping time and allowable power fluctuation range corresponding to the initial parameters.

4. The method for analyzing the status of pumped storage equipment based on big data according to claim 1, characterized in that, When there is no rainfall, the balance between the required power storage, the required pumping volume, and the reservoir water level and the water level at the pumping point is analyzed. Using constraint formulas, the optimal pumping rate is calculated, including: The actual measured volume and the volume of regular columns were obtained from the reservoir side using drone mapping and sonar scanning. The ratio of the actual measured volume to the volume of regular columns was used as a correction factor for irregularity. The actual usable volume and the volume of the regular column at the pumped-out point are obtained from the measured data at the hydrological station. The ratio of the actual usable volume to the volume of the regular column is used as the irregularity correction coefficient. Collect the correlation curve of pumping speed and power consumption per unit time for reversible units, and collect the correlation curve of pumping speed and natural replenishment per unit time at the pumped point; obtain the planned completion time of the required energy storage capacity in the power grid dispatch instructions. The unit is hours. The formula for calculating the total pumping volume required to complete the required energy storage is: ； in, For the required total pumping volume, To store the required amount of electricity, It is the acceleration due to gravity. The initial water level difference. For energy storage efficiency; Analyze the pattern of water level changes in the reservoir during the pumping process, combined with Establish the logic for reservoir water level changes, using the following formula: ； in, This represents the reservoir water level at the end of pumping. This is the current water level of the reservoir. This represents the actual pumping volume. The natural discharge rate per unit time. This refers to the actual pumping time. The water surface area of ​​the reservoir; Set safety constraints for the reservoir water level to ensure that the water level does not exceed the upper safety limit at the end of pumping; based on these safety constraints, substitute the water level change formula, and combine... Derive the maximum allowable pumping volume of the reservoir: ； in, This is the upper limit of the reservoir's pumping capacity. The upper limit of the safe water level of the reservoir, The water surface area of ​​the reservoir. The natural discharge rate per unit time. The planned completion time for the required energy storage capacity; Calculate the maximum allowable pumping speed of the reservoir. The formula is: ； in, This is the upper limit of the reservoir's pumping capacity. The planned completion time for the required energy storage capacity; Establish the formula for water level change at the pumped point: ； in, This represents the water level at the point where the pumping operation ends. This represents the current water level at the pumped-out location. This represents the natural replenishment per unit time, which varies with the pumping rate V. This refers to the actual pumping time. This represents the actual pumping volume. The area of ​​the water surface at the pumped-out point; Set ecological constraints on the water level at the pumping point so that the water level at the pumping point is at the end of the pumping process. Not lower than the ecological lower limit water level Based on ecological constraints, and substituting the water level change formula, the upper limit of the pumpable volume at the pumped location is derived. The formula is: ； in, This represents the natural replenishment per unit time, which varies with the pumping rate V. The planned completion time for the required energy storage capacity. This represents the current water level at the pumped-out location. This is the ecological lower limit water level. The area of ​​the water surface at the pumped-out point; Minimum allowable pumping speed at the pumped point The formula is: ； in, This represents the upper limit of the pumpable water volume at the pumped location. The planned completion time for the required energy storage capacity; The effective pumping rate range within the planned time period is determined as [ , When it appears > At that time, based on the correlation curve between pumping speed and natural replenishment per unit time, the pumping speed is increased to the adjusted value. ,reduce Reduce the upper limit of the pumpable water volume at the pumped point. This makes the recalculated based on Minimum allowable pumping speed ≤ Construct an effective interval; Within the effective range, multiple candidate pumping speeds are divided, and the total power consumption and energy storage time for each speed are calculated; those exceeding the energy storage time threshold are excluded. Among the candidate speeds, the speed with the lowest total power consumption and the shortest time to reach the energy storage standard is selected as the optimal pumping speed within the planned time.

5. The method for analyzing the status of pumped storage equipment based on big data according to claim 1, characterized in that, The process of verifying the equipment load adaptability under the current pumping strategy by combining equipment operation data includes: Based on the equipment manual and operating experience, set safe operating thresholds for the equipment, including safe thresholds for motor winding temperature, guide bearing vibration, and motor power. The current motor winding temperature, guide bearing vibration value, and motor operating power are collected in real time and compared with the set safety threshold to determine the current operating status of the equipment. Adjust the equipment operating parameters based on the comparison results. When the current motor winding temperature exceeds the safety threshold, or the guide bearing vibration value exceeds the safety threshold, reduce the pumping volume and motor power within the determined pumping volume range until the equipment status parameters return to the safe range. When the motor operating power exceeds the safety threshold, reduce the power to the safety threshold and adjust the pumping volume accordingly. When all equipment status parameters are within the safe threshold range, maintain the current pumping volume and periodically recalibrate the equipment parameters.

6. The method for analyzing the status of pumped storage equipment based on big data according to claim 1, characterized in that, Based on meteorological forecast data and the current pumping process, a rainfall-water level coupled impact model is constructed to analyze the impact of future rainfall on the reservoir water level and the water level at the pumping site, including: Set the model parameters, and input the predicted rainfall, rainfall duration, watershed runoff coefficient, and evaporation coefficient; The impact of rainfall on reservoir water level is analyzed. By combining the predicted rainfall, the runoff coefficient of the reservoir side basin, the basin area corresponding to the reservoir, the evaporation per unit time, the rainfall duration and the reservoir surface area, the increase in reservoir water level caused by rainfall is calculated. Combined with the reservoir water level when pumping ends without rainfall, the predicted reservoir water level at the end of pumping is obtained. The impact of rainfall on the water level at the pumping site is analyzed. The predicted rainfall, the runoff coefficient of the side basin at the pumping site, the corresponding basin area at the pumping site, the natural recharge per unit time, the rainfall duration, the evaporation per unit time, and the water surface area at the pumping site are comprehensively considered to calculate the increase in water level at the pumping site caused by rainfall. Combined with the water level at the pumping site when pumping ends without rainfall, the predicted water level at the pumping site at the end of pumping is obtained. Output the rainfall impact analysis results, generate curves showing the changes in the predicted reservoir water level and the predicted water level at the pumped location over time within a preset period, and mark whether the water level exceeds the safe upper limit or falls below the ecological lower limit.

7. The method for status analysis of pumped storage equipment based on big data according to claim 1, characterized in that, The analysis results based on the rainfall-water level coupling influence model are used to calculate the optimal pumping rate after incorporating rainfall factors, including: Based on the rainfall-water level coupling effect model, the predicted water level change curves of the reservoir and the pumped-out area within a preset time period are extracted, and the rainfall period output by the model is obtained simultaneously. The established pumping speed-power consumption per unit time correlation curve and the planned completion time of the required energy storage capacity in the power grid dispatch instructions are also retrieved. Combined with the total pumping volume required to complete the power storage, Construct a collaborative calculation model for rainfall, water level, pumping speed, energy consumption, and energy storage. When rainfall and pumping periods overlap, velocity constraints at both the reservoir and the pumping point are adjusted separately; the reservoir level increment during the overlapping period is considered on the reservoir side. Maximum allowable pumping volume of reservoirs during overlapping periods The formula is: ； in, The upper limit of the safe water level of the reservoir, This is the current water level of the reservoir. The water surface area of ​​the reservoir. This refers to the natural discharge volume of the reservoir per unit time. Duration of the overlapping period between rainfall and pumping; Maximum permissible pumping speed of reservoir during rainfall The calculation formula is: ； in, This represents the maximum allowable pumping volume of the reservoir during the overlapping period. Duration of the overlapping period between rainfall and pumping; The water level increase at the pumped location is considered during overlapping periods. Natural replenishment per unit time at the pumped-out point during overlapping periods Calculate the upper limit of the pumpable water volume at the point where water is pumped during the overlapping period. The formula is: ； in, The duration of the overlapping period between rainfall and pumping. This represents the current water level at the pumped-out location. This represents the increase in water level at the pumped-out points during the overlapping time period. This is the ecological lower limit water level at the pumped-out location. The area of ​​the water surface at the pumped-out point; Minimum allowable pumping speed at the pumped-out point during rainfall The calculation formula is: ； in, This represents the upper limit of the pumpable water volume at the point where water is pumped during the overlapping period. Forming overlapping time period speed ranges [ , Within the interval, multiple candidate pumping speeds are divided. The total power consumption and energy storage time for each candidate speed are calculated. Candidate speeds that exceed the energy storage time limit are eliminated. Among the remaining candidate speeds, the speed with the lowest total power consumption and the shortest energy storage time is selected as the optimal pumping speed.

8. The method for analyzing the status of pumped storage equipment based on big data according to claim 1, characterized in that, The adjustment of the pumping strategy based on different impact scenarios includes: When the predicted water level of the reservoir does not exceed the upper limit of the reservoir's safety level and the predicted water level at the pumping point is not lower than the lower limit of the ecological level at the pumping point, the optimal pumping rate for the current period shall be maintained. When the predicted water level of the reservoir exceeds the upper limit of the reservoir's safety level, the pumping volume will be reduced to the preset upper limit threshold of the reservoir's first-level pumping volume. The pumping speed will be adjusted according to the planned completion time of the energy storage capacity, ensuring that the pumping speed is not lower than [the threshold value is missing from the original text]. ;Activate the reservoir's pre-discharge facilities to increase the natural discharge rate per unit time until the predicted reservoir water level drops below the reservoir's safe upper limit. When the predicted water level at the pumping site is lower than the ecological lower limit water level at the pumping site, pumping will stop when the water level reaches the ecological lower limit water level at the pumping site, and pumping will be switched to another pumping site.

9. The method for status analysis of pumped storage equipment based on big data according to claim 1, characterized in that, The process of establishing a future energy storage state prediction model based on the current pumping rate and predicted rainfall, and dynamically adjusting the pumping strategy based on the prediction results, includes: Clarify the relationship between future energy storage capacity and the pumping speed to be adjusted, remaining pumping time, predicted water level difference, and energy storage efficiency, and construct a prediction model for future energy storage status. A threshold is set for the deviation between future and required energy storage capacity, allowing the deviation to not exceed a specified proportion of the required energy storage capacity. The pumping speed is dynamically adjusted based on the forecast results. When the deviation between future and required energy storage capacity is within the allowable range, the pumping speed to be adjusted is maintained consistent with the current pumping speed. When future energy storage capacity is lower than required energy storage capacity and the pumping speed to be adjusted has not reached the maximum allowable pumping speed and the corrected maximum speed for the time period, the pumping speed to be adjusted is increased within the specified range. When future energy storage capacity is higher than required energy storage capacity and the pumping speed to be adjusted is higher than the minimum allowable pumping speed and the corrected minimum speed for the time period, the pumping speed to be adjusted is decreased within the specified range. When the deviation cannot be brought to the target by adjusting the pumping speed due to reservoir water level constraints or equipment load constraints, an energy storage progress warning is generated and reported to the power grid dispatch center to coordinate subsequent operation plans. Iterative optimization is carried out by periodically re-collecting real-time pumping volume and updating rainfall data, correcting model parameters, and adjusting pumping strategies based on future energy storage prediction results, so that the final deviation between the actual energy storage and the required energy storage does not exceed the set limit ratio.

10. A pumped storage equipment status analysis device based on big data, characterized in that, The device uses the big data-based pumped storage equipment status analysis method according to any one of claims 1-9.

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