Hydroelectric power generation regulation and control system and method based on big data analysis

Abstract

The invention provides a hydroelectric power generation regulation and control system and method based on big data analysis, and the system comprises an obtaining module which obtains the meteorological data of a hydropower station in a preset stage, and the historical storage runoff of the hydropower station in the preset stage; the runoff analysis module is used for analyzing to obtain a runoff influence coefficient, a historical storage runoff mean value and an actual storage runoff curve; the upstream water level analysis module obtains the actual upstream water level of the hydropower station in the preset stage; the water turbine combination module is used for analyzing to obtain a target water turbine combination; the downstream water level analysis module obtains a target downstream water level and an actual downstream water level of the hydropower station in a preset stage; the downstream water level adjusting module obtains the downstream water level adjusting amount of the hydropower station according to the actual downstream water level and the target downstream water level. The method has the effect that the actual downstream water level of the hydropower station is regulated and controlled according to the target downstream water level, so that the power generation efficiency of the hydropower station is maximized.

Description

Technical Field

[0001] The present application relates to the field of hydropower technology, and specifically to a hydropower generation control system and method based on big data analysis. Background Art

[0002] my country has abundant reserves of hydropower resources and exploitable hydropower resources. With the rapid development of my country's economy and science and technology, the development and utilization rate of hydropower energy in my country has gradually increased. The development and utilization of hydropower energy not only helps to alleviate the growing tension in my country's electricity resources, but also plays a vital role in flood control, irrigation and other people's livelihood aspects. Therefore, the power generation regulation of hydropower stations used for hydropower energy development is crucial to the effective development and utilization of hydropower energy. Reasonable power generation regulation of hydropower stations can also make important contributions to the national economy and social development.

[0003] The related technologies for power generation control of hydropower stations often fail to reasonably arrange the turbines of the hydropower stations according to their actual conditions, so that the hydropower stations can achieve the maximum power generation efficiency. They also fail to effectively control the downstream water level of the hydropower station in combination with the upstream water level of the hydropower station, and fail to maximize the power generation efficiency of the hydropower station. There is room for improvement. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a water conservancy power generation control system and method based on big data analysis, so as to improve the problem that the power generation control of the hydropower station in the related technology is often unable to reasonably arrange the turbines of the hydropower station according to the actual situation of the turbines of the hydropower station, so that the hydropower station can achieve the maximum power generation efficiency, and it is also impossible to effectively control the downstream water level of the hydropower station in combination with the upstream water level of the hydropower station, and it is impossible to maximize the power generation efficiency of the hydropower station.

[0005] In a first aspect of the present application, a hydropower generation control system based on big data analysis is provided, comprising:

[0006] Acquisition module: Acquisition of meteorological data of the hydropower station in a preset stage, and the historical inflow runoff of the hydropower station in the preset stage; the preset stage includes at least one stage cycle;

[0007] Recording the upstream water level of the hydropower station at the initial moment of the preset stage as the first upstream water level, and recording the downstream water level of the hydropower station at the initial moment of the preset stage as the first downstream water level;

[0008] Runoff analysis module: Analyze the meteorological data to obtain the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station; analyze the historical inflow runoff to obtain the historical inflow runoff mean of the hydropower station in each stage cycle;

[0009] The actual inflow runoff of the hydropower station is obtained according to the runoff influence coefficient and the historical inflow runoff; the actual inflow runoff curve of the hydropower station in a preset stage is obtained according to the actual inflow runoff of the hydropower station in each stage cycle;

[0010] Upstream water level analysis module: Analyze the actual inflow flow curve and the first upstream water level to obtain the actual upstream water level of the hydropower station at the preset stage;

[0011] Turbine combination module: obtain turbine data of the hydropower station; analyze the turbine data to obtain abnormal coefficients and configuration coefficients of each turbine in the hydropower station, and obtain the corresponding bearing coefficients of each turbine in the hydropower station according to the abnormal coefficients and configuration coefficients; combine the turbines in the hydropower station according to the bearing coefficients of each turbine in the hydropower station to obtain a turbine combination;

[0012] According to the bearing coefficients of each turbine in the turbine combination, a combined bearing coefficient corresponding to the turbine combination is obtained; according to the combined bearing coefficient, a combined water head corresponding to the turbine combination is obtained; and the combined water head is compared and analyzed with a preset water head threshold value to obtain an actual water head of the turbine combination;

[0013] Obtaining a turbine combination efficiency corresponding to the turbine combination according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination, and the number of turbines, and obtaining a target turbine combination according to the turbine combination efficiency;

[0014] Downstream water level analysis module: mark the actual water head of the target turbine combination as the target water head; obtain the target downstream water level of the hydropower station in the preset stage according to the actual upstream water level and the target water head; obtain the downstream water level increase of the hydropower station in each stage cycle of the preset stage according to the target turbine combination, and obtain the actual downstream water level of the hydropower station in each stage cycle of the preset stage according to the downstream water level increase and the first downstream water level;

[0015] Downstream water level adjustment module: compare and analyze the actual downstream water level and the target downstream water level to obtain the downstream water level adjustment amount of the hydropower station.

[0016] Preferably, the meteorological data is analyzed to obtain the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station; the historical inflow runoff is analyzed to obtain the mean value of the historical inflow runoff of the hydropower station in each stage cycle, specifically:

[0017] The meteorological data include rainfall and average temperature values ​​of the hydropower station within a preset period;

[0018] The rainfall is taken and marked to obtain the rainfall JY; the average temperature value is taken and marked to obtain the average temperature value WD;

[0019] The influence coefficient α of the meteorological data on the inflow of the hydropower station is calculated by the first calculation function α=a1×JY+a2×WD, where a1 and a2 are preset proportional factors;

[0020] Divide the preset stage into a preset number of stage cycles, and obtain the historical inflow runoff of each stage cycle; take values ​​and mark the historical inflow runoff to obtain the historical inflow runoff JL;

[0021] Through the second calculation function The historical mean value of the inflow flow of the hydropower station in each stage cycle is calculated, PJL, where n is the number of stages in the preset stage;

[0022] The actual inflow runoff of the hydropower station is obtained based on the runoff impact coefficient and the historical inflow runoff, which is:

[0023] The actual inflow flow SJL of the hydropower station in each stage cycle is calculated by the third calculation function SJL=α×PJL.

[0024] Preferably, an analysis is performed based on the actual inflow runoff curve and the first upstream water level to obtain the actual upstream water level of the hydropower station at the preset stage, specifically:

[0025] Taking and marking the first upstream water level to obtain a first upstream water level YSW;

[0026] The actual upstream water level SSW of the hydropower station in each stage cycle is calculated by the fourth calculation function SSW=YSW+SJL×β, wherein β is the influence factor of the inflow flow on the upstream water level.

[0027] Preferably, turbine data of the hydropower station is obtained; the turbine data is analyzed to obtain the abnormal coefficient and configuration coefficient of each turbine in the hydropower station, and the corresponding bearing coefficient of each turbine in the hydropower station is obtained according to the abnormal coefficient and configuration coefficient, specifically:

[0028] The unit data includes the service life of each turbine in the hydropower station, the number of turbine blades, the specific speed of the turbine, and the number of historical abnormalities of each turbine, and the historical abnormal time corresponding to the historical abnormality number;

[0029] According to the historical abnormal time corresponding to the historical abnormal number of the turbine, the historical abnormal duration corresponding to the historical abnormal number of the turbine is obtained; the historical abnormal duration is valued and marked to obtain the historical abnormal duration YS; the historical abnormal number of times is valued and marked to obtain the historical abnormal number of times YC;

[0030] Through the fifth calculation function The abnormal coefficient JY of each turbine in the hydropower station is calculated, where b1 and b2 are preset proportional factors;

[0031] The service life of the turbine is taken and marked to obtain the service life of the turbine NX; the number of turbine blades is taken and marked to obtain the number of turbine blades YS; the specific speed of the turbine is taken and marked to obtain the specific speed of the turbine ZS;

[0032] The configuration coefficient PZ of each turbine in the hydropower station is calculated by the sixth calculation function PZ=c1×NX+c2×YS+c3×ZS, where c1, c2, and c3 are preset proportional factors;

[0033] The bearing coefficient CS of each turbine in the hydropower station is calculated by the seventh calculation function CS=d1×JY+d2×PZ, wherein d1 and d2 are preset proportional factors.

[0034] Preferably, the turbines in the hydropower station are combined according to the bearing coefficients of the turbines in the hydropower station to obtain a turbine combination, specifically:

[0035] Set the bearing coefficient difference range corresponding to the turbine combination and preset the bearing coefficient standard value;

[0036] Taking and marking the preset bearing coefficient standard value to obtain the preset bearing coefficient standard value CSB;

[0037] The bearing coefficient difference CCZ between the bearing coefficient CS of each turbine in the hydropower station and the preset bearing coefficient standard value is calculated by the eighth calculation function CCZ=CS-CSB;

[0038] According to the inclusion relationship between the bearing coefficient difference CCZ corresponding to each turbine in the hydropower station and the bearing coefficient difference interval corresponding to the turbine combination, the turbines in the hydropower station are divided into different turbine combinations.

[0039] Preferably, a combined bearing coefficient corresponding to the turbine combination is obtained according to the bearing coefficients of each turbine in the turbine combination; a combined water head corresponding to the turbine combination is obtained according to the combined bearing coefficient; and the combined water head is compared and analyzed with a preset water head threshold to obtain an actual water head of the turbine combination, specifically:

[0040] Through the ninth calculation function Calculate the combined bearing coefficient ZCS corresponding to the turbine combination;

[0041] The combined water head ZST corresponding to the turbine combination is calculated by the tenth calculation function ZST=ZCS×δ, where δ is a preset proportionality coefficient;

[0042] If the combined water head ZST corresponding to the turbine combination is greater than or equal to the preset water head threshold, the actual water head of the turbine is the preset water head threshold;

[0043] If the combined water head ZST corresponding to the turbine combination is less than the preset water head threshold, the actual water head of the turbine is the combined water head ZST.

[0044] Preferably, the turbine combination efficiency corresponding to the turbine combination is obtained according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination and the number of turbines, and the target turbine combination is obtained according to the turbine combination efficiency, specifically:

[0045] According to the configuration coefficient of the turbines in the turbine combination and the number of turbines in the turbine combination, the combined output coefficient, combined power generation flow and combined power generation efficiency corresponding to the turbine combination are obtained;

[0046] The combined output coefficient is taken and marked to obtain the combined output coefficient ZCL; the combined power generation flow is taken and marked to obtain the combined power generation flow ZFD; the combined power generation efficiency is taken and marked to obtain the combined power generation efficiency ZFX; the actual water head of the turbine combination is taken and marked to obtain the actual water head SST;

[0047] The turbine combination efficiency ZXL corresponding to the turbine combination is calculated by the eleventh calculation function ZXL=SST×ZCL×ZFD×ZFX;

[0048] The turbine combination with the largest turbine combination effect ZXL is marked as the target turbine combination.

[0049] Preferably, the target downstream water level of the hydropower station in the preset stage is obtained according to the actual upstream water level and the target water head; the downstream water level increase of the hydropower station in each stage cycle of the preset stage is obtained according to the target turbine combination, and the actual downstream water level of the hydropower station in each stage cycle of the preset stage is obtained according to the downstream water level increase and the first downstream water level, specifically:

[0050] The target water head is valued and marked to obtain the target water head MST;

[0051] The target downstream water level MXS corresponding to each stage cycle of the hydropower station in the preset stage is calculated by the twelfth calculation function MXS=SSW-MST;

[0052] Acquire a turbine combination flow corresponding to the turbine combination, and obtain a turbine combination flow SJL by taking a value and marking the turbine combination flow;

[0053] The downstream water level increase XSZ of the hydropower station in each stage cycle of the preset stage is calculated by the thirteenth calculation function XSZ=SJL×ε, where ε is the influence factor of the combined flow of the turbine on the downstream water level;

[0054] The first downstream water level of the hydropower station is obtained by taking and marking the first downstream water level XSW;

[0055] The actual downstream water level SXS of each stage cycle of the preset stage of the hydropower station is calculated by the fourteenth calculation function SXS=XSZ+XSW.

[0056] Preferably, the actual downstream water level and the target downstream water level are compared and analyzed to obtain the downstream water level adjustment amount of the downstream water level of the hydropower station, specifically:

[0057] Compare and analyze the actual downstream water level SXS with the target downstream water level MXS;

[0058] If the actual downstream water level SXS is greater than or equal to the target downstream water level MXS, the downstream discharge water level PSL of the hydropower station in the corresponding phase period is calculated by the fifteenth calculation function PSL=SXS-MXS;

[0059] If the actual downstream water level SXS is less than the target downstream water level MXS, the downstream diversion water level YSL of the hydropower station in the corresponding stage period is calculated by the sixteenth calculation function YSL=MXS-SXS.

[0060] In a second aspect of the present application, a method for regulating water conservancy and power generation based on big data analysis is provided. The method is applied to a water conservancy and power generation regulation system based on big data analysis. The method comprises the following steps:

[0061] Acquire meteorological data of the hydropower station in a preset stage, and the historical inflow runoff of the hydropower station in the preset stage; the preset stage includes at least one stage cycle;

[0062] Recording the upstream water level of the hydropower station at the initial moment of the preset stage as the first upstream water level, and recording the downstream water level of the hydropower station at the initial moment of the preset stage as the first downstream water level;

[0063] Analyze the meteorological data to obtain the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station; analyze the historical inflow runoff to obtain the mean value of the historical inflow runoff of the hydropower station in each stage cycle;

[0064] The actual inflow runoff of the hydropower station is obtained according to the runoff influence coefficient and the historical inflow runoff; the actual inflow runoff curve of the hydropower station in a preset stage is obtained according to the actual inflow runoff of the hydropower station in each stage cycle;

[0065] According to the actual inflow runoff curve and the first upstream water level, the actual upstream water level of the hydropower station in the preset stage is obtained by analysis;

[0066] Acquire turbine data of the hydropower station; analyze the turbine data to obtain abnormal coefficients and configuration coefficients of each turbine in the hydropower station, and obtain the corresponding bearing coefficients of each turbine in the hydropower station according to the abnormal coefficients and configuration coefficients; combine the turbines in the hydropower station according to the bearing coefficients of each turbine in the hydropower station to obtain a turbine combination;

[0067] According to the bearing coefficients of each turbine in the turbine combination, a combined bearing coefficient corresponding to the turbine combination is obtained; according to the combined bearing coefficient, a combined water head corresponding to the turbine combination is obtained; and the combined water head is compared and analyzed with a preset water head threshold value to obtain an actual water head of the turbine combination;

[0068] Obtaining a turbine combination efficiency corresponding to the turbine combination according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination, and the number of turbines, and obtaining a target turbine combination according to the turbine combination efficiency;

[0069] The actual water head of the target turbine combination is marked as the target water head; the target downstream water level of the hydropower station in the preset stage is obtained according to the actual upstream water level and the target water head; the downstream water level increase of the hydropower station in each stage cycle of the preset stage is obtained according to the target turbine combination, and the actual downstream water level of the hydropower station in each stage cycle of the preset stage is obtained according to the downstream water level increase and the first downstream water level;

[0070] The actual downstream water level and the target downstream water level are compared and analyzed to obtain the downstream water level adjustment amount of the hydropower station's downstream water level.

[0071] In summary, the beneficial effects of this application are:

[0072] 1. This application analyzes the meteorological data and historical inflow runoff of the hydropower station in a preset stage respectively, obtains the mean of the historical inflow runoff and the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station, and obtains the actual inflow runoff of the hydropower station based on the runoff influence coefficient and the mean of the historical inflow runoff, and obtains the actual upstream water level of the hydropower station based on the actual inflow runoff curve obtained from the actual inflow runoff and the first upstream water level. The analysis of meteorological data and historical inflow runoff makes it possible to obtain the actual upstream water level of the hydropower station more accurately.

[0073] 2. The present application obtains the abnormal coefficient and configuration coefficient of each turbine in the hydropower station by analyzing the turbine data of the hydropower station, and obtains the bearing coefficient of each turbine based on the abnormal coefficient and the configuration coefficient, combines the turbines based on the bearing coefficient to obtain a turbine combination, and obtains the combined head corresponding to the turbine combination based on the combined bearing coefficient corresponding to the turbine combination, obtains the actual head of the turbine combination based on the comparison analysis result of the combined head and the preset head threshold, and marks the actual head corresponding to the turbine combination with the maximum turbine combination efficiency as the target head, and finally obtains the target downstream water level of the hydropower station in the preset stage based on the actual upstream water level and the target head, and the power generation efficiency of the hydropower station can be maximized by adjusting the actual downstream water level of the hydropower station based on the target downstream water level. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present application, some of the drawings in the embodiments of the present application will be briefly described below. It should be understood that the following drawings only show some embodiments of the present application and therefore should not be considered as limiting the scope of the present application.

[0075] Figure 1 A schematic diagram of the structure of a hydropower generation control system based on big data analysis provided in an embodiment of the present application;

[0076] Figure 2 A schematic flow chart of a method for regulating and controlling hydropower generation based on big data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0077] Below is a combination of the embodiments and Figure 1 and Figure 2 The present application is further described in detail, but the implementation methods of the present application are not limited thereto.

[0078] Reference Figure 1As shown, it is a structural schematic diagram of a water conservancy power generation control system based on big data analysis provided by an embodiment of the present application. In the embodiment of the present application, the application scenario is to obtain the actual upstream water level of the hydropower station by analyzing the meteorological data and historical inflow runoff of the hydropower station, and to analyze the turbine data of the hydropower station to obtain the target head corresponding to the target turbine combination with the maximum turbine combination efficiency, and to obtain the target downstream water level of the hydropower station through the actual upstream water level and the target head, and to adjust the actual downstream water level of the hydropower station according to the target downstream water level to maximize the power generation efficiency of the hydropower station. The difference is that the power generation control of the hydropower station in the prior art often cannot reasonably arrange the turbines of the hydropower station according to the actual situation of the turbines of the hydropower station, so that the hydropower station can achieve the maximum power generation efficiency, and cannot be combined with the upstream water level of the hydropower station. Effectively regulating the downstream water level of a hydropower station cannot maximize the power generation efficiency of the hydropower station. The present application obtains a turbine combination by combining the turbines of the hydropower station according to the bearing coefficients of the turbines in the hydropower station, and marks the turbine combination with the highest turbine combination efficiency as the target turbine, and obtains the target head of the target turbine combination; and obtains the actual inflow runoff of the hydropower station through the meteorological data of the hydropower station and the historical inflow runoff of the hydropower station in a preset stage, and obtains the actual upstream water level of the hydropower station in the preset stage based on the actual inflow runoff curve obtained from the actual inflow runoff and the first upstream water level; obtains the target downstream water level of the hydropower station based on the actual upstream water level and the target head, and finally obtains the downstream water level adjustment amount of the downstream water level of the hydropower station based on the comparison and analysis results of the actual downstream water level and the target downstream water level.

[0079] A hydropower generation control system based on big data analysis, comprising:

[0080] Acquisition module: Acquisition of meteorological data of the hydropower station in a preset stage, and the historical inflow runoff of the hydropower station in the preset stage; the preset stage includes at least one stage cycle;

[0081] Recording the upstream water level of the hydropower station at the initial moment of the preset stage as the first upstream water level, and recording the downstream water level of the hydropower station at the initial moment of the preset stage as the first downstream water level;

[0082] Runoff analysis module: Analyze meteorological data to obtain the runoff influence coefficient of meteorological data on the inflow runoff of the hydropower station; analyze the historical inflow runoff to obtain the historical inflow runoff mean of the hydropower station in each stage cycle;

[0083] The actual inflow runoff of the hydropower station is obtained based on the runoff impact coefficient and the historical inflow runoff; the actual inflow runoff curve of the hydropower station in the preset stage is obtained based on the actual inflow runoff of the hydropower station in each stage cycle;

[0084] Upstream water level analysis module: Analyze the actual inflow flow curve and the first upstream water level to obtain the actual upstream water level of the hydropower station at the preset stage;

[0085] Turbine combination module: obtain the turbine data of the hydropower station; analyze the turbine data to obtain the abnormal coefficient and configuration coefficient of each turbine in the hydropower station, and obtain the corresponding bearing coefficient of each turbine in the hydropower station based on the abnormal coefficient and configuration coefficient; combine the turbines in the hydropower station according to the bearing coefficient of each turbine in the hydropower station to obtain a turbine combination;

[0086] The combined bearing coefficient corresponding to the turbine combination is obtained according to the bearing coefficient of each turbine in the turbine combination; the combined water head corresponding to the turbine combination is obtained according to the combined bearing coefficient; the combined water head is compared and analyzed with the preset water head threshold value to obtain the actual water head of the turbine combination;

[0087] The turbine combination efficiency corresponding to the turbine combination is obtained according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination and the number of turbines, and the target turbine combination is obtained according to the turbine combination efficiency;

[0088] Downstream water level analysis module: mark the actual water head of the target turbine combination as the target water head; obtain the target downstream water level of the hydropower station in the preset stage according to the actual upstream water level and the target water head; obtain the downstream water level increase of the hydropower station in each stage cycle of the preset stage according to the target turbine combination, and obtain the actual downstream water level of the hydropower station in each stage cycle of the preset stage according to the downstream water level increase and the first downstream water level;

[0089] Downstream water level adjustment module: compare and analyze the actual downstream water level and the target downstream water level to obtain the downstream water level adjustment amount of the hydropower station.

[0090] By analyzing the meteorological data, we can get the influence coefficient of meteorological data on the inflow of hydropower station; by analyzing the historical inflow, we can get the mean value of the historical inflow of hydropower station in each stage cycle, which is as follows:

[0091] Meteorological data include rainfall and average temperature values ​​for the hydropower station during the preset period;

[0092] The rainfall is taken and marked to obtain the rainfall JY; the average temperature value is taken and marked to obtain the average temperature value WD;

[0093] The influence coefficient α of the meteorological data on the inflow of the hydropower station is calculated by the first calculation function α=a1×JY+a2×WD, where a1 and a2 are preset proportional factors;

[0094] Divide the preset stage into a preset number of stage cycles, and obtain the historical inflow runoff of each stage cycle; take and mark the historical inflow runoff to obtain the historical inflow runoff JL;

[0095] Through the second calculation function The historical mean value of the inflow flow of the hydropower station in each stage cycle is calculated, PJL, where n is the number of stages in the preset stage;

[0096] The actual inflow of the hydropower station is obtained based on the runoff impact coefficient and the historical inflow runoff, which is:

[0097] The actual inflow flow SJL of the hydropower station in each stage cycle is calculated by the third calculation function SJL=α×PJL.

[0098] It should be noted that the preset stage refers to the time period when the hydropower station needs to generate electricity, which can be set according to the actual situation of the hydropower station.

[0099] In some embodiments, if the rainfall of the hydropower station in the preset stage is 30 mm, the rainfall JY is 30; if the average temperature of the hydropower station in the preset stage is 25°C, the average temperature WD is 25; at this time, if the preset proportional factors a1 and a2 are 0.025 and 0.02 respectively, the first calculation function α=a1×JY+a2×WD can be used to calculate that the runoff influence coefficient α of the meteorological data on the inflow runoff of the hydropower station is 1.25;

[0100] If the historical average inflow runoff of a hydropower station in a certain period is 50,000 cubic meters per second, the historical average inflow runoff PJL of the period is 5. If the runoff influence coefficient α is 1.25, the actual inflow runoff SJL can be calculated to be 62,500 cubic meters per second through the second calculation function SJL=α×PJL.

[0101] According to the actual inflow flow curve and the first upstream water level, the actual upstream water level of the hydropower station in the preset stage is obtained, which is:

[0102] The first upstream water level is obtained by taking and marking the first upstream water level YSW;

[0103] The actual upstream water level SSW of the hydropower station in each stage cycle is calculated by the fourth calculation function SSW=YSW+SJL×β, wherein β is the influence factor of the inflow flow on the upstream water level.

[0104] In some embodiments, if the first upstream water level of the hydropower station at the initial moment of the preset stage is 50m, the first upstream water level YSW takes a value of 50. If the actual inflow flow SJL in a certain stage period is 6.25, and the influence factor β of the inflow flow on the upstream water level is 4, then the fourth calculation function SSW=YSW+SJL×β can be used to calculate that the actual upstream water level SSW of the hydropower station in this stage period is 75m.

[0105] The turbine data of the hydropower station is obtained; the abnormal coefficient and configuration coefficient of each turbine in the hydropower station are obtained by analyzing the turbine data, and the corresponding bearing coefficient of each turbine in the hydropower station is obtained based on the abnormal coefficient and configuration coefficient, which is specifically:

[0106] The unit data includes the turbine service life, number of turbine blades, specific speed of turbines, and the number of historical abnormalities of each turbine in the hydropower station, as well as the historical abnormality time corresponding to the historical abnormality number;

[0107] According to the historical abnormal time corresponding to the historical abnormal number of the turbine, the historical abnormal duration corresponding to the historical abnormal number of the turbine is obtained; the historical abnormal duration is taken and marked to obtain the historical abnormal duration YS; the historical abnormal number is taken and marked to obtain the historical abnormal number YC;

[0108] Through the fifth calculation function The abnormal coefficient JY of each turbine in the hydropower station is calculated, where b1 and b2 are preset proportional factors;

[0109] The service life of the turbine is taken and marked to obtain the service life of the turbine NX; the number of turbine blades is taken and marked to obtain the number of turbine blades YS; the specific speed of the turbine is taken and marked to obtain the specific speed of the turbine ZS;

[0110] The configuration coefficient PZ of each turbine in the hydropower station is calculated by the sixth calculation function PZ=c1×NX+c2×YS+c3×ZS, where c1, c2, and c3 are preset proportional factors;

[0111] The bearing coefficient CS of each turbine in the hydropower station is calculated by the seventh calculation function CS=d1×JY+d2×PZ, wherein d1 and d2 are preset proportional factors.

[0112] In some embodiments, if the historical abnormality time corresponding to the historical abnormality number of a turbine is 20 days, the historical abnormality time YS is 20; if the historical abnormality number of the turbine is 5, the historical abnormality number YC is 5; if the preset proportional factors b1 and b2 are 100 and 1 respectively, the abnormality coefficient JY of the turbine can be calculated by the fifth calculation function to be 10;

[0113] If the service life of the turbine is 20 years, then the service life of the turbine NX is 20; if the number of turbine blades of the turbine is 20, then the number of turbine blades YS is 20; if the specific speed of the turbine is 40m-m 3 / s, the turbine specific speed ZS is 40; if the preset proportional factors c1, c2, c3 are 0.1, 0.1, 0.1 respectively, the configuration coefficient PZ of the turbine can be calculated to be 8 through the sixth calculation function PZ=c1×NX+c2×YS+c3×ZS;

[0114] If the abnormal coefficient JY of a turbine is 10, the configuration coefficient PZ is 8, and the preset proportional factors d1 and d2 are 0.2 and 1, then the bearing coefficient CS of the turbine can be calculated to be 10 through the seventh calculation function CS=d1×JY+d2×PZ.

[0115] The turbine combination is obtained by combining the turbines in the hydropower station according to the bearing coefficient of each turbine in the hydropower station, which is specifically:

[0116] Set the bearing coefficient difference range corresponding to the turbine combination and preset the bearing coefficient standard value;

[0117] The preset bearing coefficient standard value is obtained by taking and marking the preset bearing coefficient standard value CSB;

[0118] The bearing coefficient difference CCZ between the bearing coefficient CS of each turbine in the hydropower station and the preset bearing coefficient standard value is calculated by the eighth calculation function CCZ=CS-CSB;

[0119] According to the inclusion relationship between the bearing coefficient difference CCZ corresponding to each turbine in the hydropower station and the bearing coefficient difference interval corresponding to the turbine combination, the turbines in the hydropower station are divided into different turbine combinations.

[0120] It should be noted that each turbine combination corresponds to a bearing coefficient difference interval, and the turbine combination and the bearing coefficient difference interval correspond one to one. For example, the bearing coefficient difference interval of the first turbine combination is [0,3], then the turbines whose bearing coefficient CS and the bearing coefficient difference CCZ of the preset bearing coefficient standard value are between 0 and 3 and include 0 and 3 are all classified as the first turbine combination. For example, the bearing coefficient CS of a turbine is 8, and the preset bearing coefficient standard value CSB is 10. The bearing coefficient difference CCZ of the bearing coefficient CS of the turbine calculated by the eighth calculation function CCZ=CS-CSB and the preset bearing coefficient standard value CSB is 2, then the turbine is the turbine of the first turbine combination.

[0121] The combined bearing coefficient corresponding to the turbine combination is obtained according to the bearing coefficient of each turbine in the turbine combination; the combined water head corresponding to the turbine combination is obtained according to the combined bearing coefficient; the combined water head is compared and analyzed with the preset water head threshold to obtain the actual water head of the turbine combination, which is specifically:

[0122] Through the ninth calculation function Calculate the combined bearing coefficient ZCS corresponding to the turbine combination;

[0123] The combined water head ZST corresponding to the turbine combination is calculated by the tenth calculation function ZST=ZCS×δ, where δ is a preset proportionality coefficient;

[0124] If the combined water head ZST corresponding to the turbine combination is greater than or equal to the preset water head threshold, the actual water head of the turbine is the preset water head threshold;

[0125] If the combined water head ZST corresponding to the turbine combination is less than the preset water head threshold, the actual water head of the turbine is the combined water head ZST.

[0126] It should be noted that the head in the present application refers to the difference between the upstream water level and the downstream water level of the hydropower station; in addition, the preset proportional coefficient δ can be obtained according to the combination bearing coefficient-combined head comparison table. For example, the combined bearing coefficient ZCS corresponding to the turbine combination is 10. When the combined bearing coefficient ZCS is 10 obtained from the combination bearing coefficient-combined head comparison table, the preset proportional coefficient δ is 3. Then, through the tenth calculation function ZST=ZCS×δ, the combined head ZST corresponding to the turbine combination can be calculated to be 30m.

[0127] The turbine combination efficiency corresponding to the turbine combination is obtained based on the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination, and the number of turbines. The target turbine combination is obtained based on the turbine combination efficiency, which is specifically:

[0128] According to the configuration coefficient of the turbines in the turbine combination and the number of turbines in the turbine combination, the combined output coefficient, combined power generation flow and combined power generation efficiency corresponding to the turbine combination are obtained;

[0129] The combined output coefficient is taken and marked to obtain the combined output coefficient ZCL; the combined power generation flow is taken and marked to obtain the combined power generation flow ZFD; the combined power generation efficiency is taken and marked to obtain the combined power generation efficiency ZFX; the actual water head of the turbine combination is taken and marked to obtain the actual water head SST;

[0130] The turbine combination efficiency ZXL corresponding to the turbine combination is calculated by the eleventh calculation function ZXL=SST×ZCL×ZFD×ZFX;

[0131] The turbine combination with the largest turbine combination efficiency ZXL is marked as the target turbine combination.

[0132] It should be noted that after the turbine combination is determined, the corresponding combined output coefficient, combined power generation flow, and combined power generation efficiency of the turbine combination can be obtained. The combined output coefficient, combined power generation flow, and combined power generation efficiency of the turbine combination are the sum of the turbine output coefficients, the sum of the turbine power generation flow, and the sum of the turbine power generation efficiency of each turbine in the turbine combination, respectively.

[0133] In some embodiments, if the combined output coefficient of the turbine combination is 8, the combined output coefficient ZCL is 8; if the combined power generation flow of the turbine combination is 2m 3 / s, the combined power generation flow ZFD takes a value of 2; if the combined power generation efficiency of the turbine combination is 80%, the combined power generation efficiency ZFX takes a value of 0.8; if the actual water head of the turbine combination is 30m, the actual water head SST takes a value of 30; through the eleventh calculation function ZXL=SST×ZCL×ZFD×ZFX, the turbine combination efficiency ZXL corresponding to the turbine combination can be calculated to be 38.4.

[0134] The target downstream water level of the hydropower station in the preset stage is obtained according to the actual upstream water level and the target water head; the downstream water level increase of the hydropower station in each stage cycle of the preset stage is obtained according to the target turbine combination; the actual downstream water level of the hydropower station in each stage cycle of the preset stage is obtained according to the downstream water level increase and the first downstream water level, specifically:

[0135] The target water head is valued and marked to obtain the target water head MST;

[0136] The target downstream water level MXS corresponding to each stage cycle of the hydropower station in the preset stage is calculated by the twelfth calculation function MXS=SSW-MST;

[0137] Obtain the turbine combination flow corresponding to the turbine combination, and obtain the turbine combination flow SJL by taking values ​​and marking the turbine combination flow;

[0138] The downstream water level increase XSZ of the hydropower station in each stage cycle of the preset stage is calculated by the thirteenth calculation function XSZ=SJL×ε, where ε is the influence factor of the combined flow of the turbine on the downstream water level;

[0139] The first downstream water level of the hydropower station is obtained by taking and marking the first downstream water level XSW;

[0140] The actual downstream water level SXS of each stage cycle of the preset stage of the hydropower station is calculated by the fourteenth calculation function SXS=XSZ+XSW.

[0141] In some embodiments, if the target water head is 30m, the target water head MST is 30; if the actual upstream water level SSW corresponding to the hydropower station in a certain stage cycle is 75m, the target downstream water level MXS corresponding to the hydropower station in the stage cycle can be calculated by the twelfth calculation function to be 45m;

[0142] If the turbine combination flow corresponding to the turbine combination is 15m / s, the turbine combination flow SJL takes a value of 15, and the influence factor ε of the turbine combination flow on the downstream water level is obtained based on the preset turbine combination flow-downstream water level comparison table. If the turbine combination flow is 15m / s, the influence factor ε is 2, and the actual downstream water level SXS of the hydropower station in one stage cycle can be calculated to be 30m through the fourteenth calculation function.

[0143] The actual downstream water level and the target downstream water level are compared and analyzed to obtain the downstream water level adjustment of the hydropower station, which is specifically:

[0144] Compare and analyze the actual downstream water level SXS with the target downstream water level MXS;

[0145] If the actual downstream water level SXS is greater than or equal to the target downstream water level MXS, the downstream discharge water level PSL of the hydropower station in the corresponding phase period is calculated by the fifteenth calculation function XL=SXS-MXS;

[0146] If the actual downstream water level SXS is less than the target downstream water level MXS, the downstream diversion water level YSL of the hydropower station in the corresponding stage period is calculated by the sixteenth calculation function YSL=MXS-SXS.

[0147] In some embodiments, if the actual downstream water level SXS of the hydropower station is 30 and the target downstream water level MXS is 25, then the actual downstream water level SXS is greater than the target downstream water level MXS, and the downstream drainage water level PSL of the hydropower station in the corresponding stage period is calculated by the fifteenth calculation function PSL=SXS-MXS to be 5m;

[0148] If the actual downstream water level SXS of the hydropower station is 30 and the target downstream water level MXS is 45, then the actual downstream water level SXS is less than the target downstream water level MXS. Then, the downstream water diversion level YSL of the hydropower station in the corresponding stage period is calculated to be 15m by the sixteenth calculation function YSL=MXS-SXS.

[0149] It should be noted that when the actual downstream water level SXS is greater than or equal to the target downstream water level MXS, the downstream water level of the hydropower station needs to be lowered according to the downstream drainage water level PSL; when the actual downstream water level SXS is less than the target downstream water level MXS, the downstream water level of the hydropower station needs to be increased by the downstream diversion water level YSL.

[0150] Reference Figure 2 FIG. 1 is a flow chart of a method for regulating and controlling water conservancy power generation based on big data analysis provided in an embodiment of the present application, the method comprising the following steps:

[0151] Obtaining meteorological data of the hydropower station in a preset stage and the historical inflow runoff of the hydropower station in the preset stage; the preset stage includes at least one stage cycle;

[0152] Recording the upstream water level of the hydropower station at the initial moment of the preset stage as the first upstream water level, and recording the downstream water level of the hydropower station at the initial moment of the preset stage as the first downstream water level;

[0153] Analyze the meteorological data to obtain the impact coefficient of meteorological data on the inflow of the hydropower station; analyze the historical inflow of the hydropower station to obtain the historical average inflow of the hydropower station in each stage cycle;

[0154] The actual inflow runoff of the hydropower station is obtained based on the runoff impact coefficient and the historical inflow runoff; the actual inflow runoff curve of the hydropower station in the preset stage is obtained based on the actual inflow runoff of the hydropower station in each stage cycle;

[0155] According to the actual inflow runoff curve and the first upstream water level, the actual upstream water level of the hydropower station in the preset stage is obtained by analysis;

[0156] Acquire turbine data of the hydropower station; analyze the turbine data to obtain abnormal coefficients and configuration coefficients of each turbine in the hydropower station, and obtain the corresponding bearing coefficients of each turbine in the hydropower station based on the abnormal coefficients and configuration coefficients; combine the turbines in the hydropower station based on the bearing coefficients of each turbine in the hydropower station to obtain a turbine combination;

[0157] The combined bearing coefficient corresponding to the turbine combination is obtained according to the bearing coefficient of each turbine in the turbine combination; the combined water head corresponding to the turbine combination is obtained according to the combined bearing coefficient; the combined water head is compared and analyzed with the preset water head threshold value to obtain the actual water head of the turbine combination;

[0158] The turbine combination efficiency corresponding to the turbine combination is obtained according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination and the number of turbines, and the target turbine combination is obtained according to the turbine combination efficiency;

[0159] The actual water head of the target turbine combination is marked as the target water head; the target downstream water level of the hydropower station in the preset stage is obtained according to the actual upstream water level and the target water head; the downstream water level increase of the hydropower station in each stage cycle of the preset stage is obtained according to the target turbine combination, and the actual downstream water level of the hydropower station in each stage cycle of the preset stage is obtained according to the downstream water level increase and the first downstream water level;

[0160] The actual downstream water level and the target downstream water level are compared and analyzed to obtain the downstream water level adjustment amount of the hydropower station's downstream water level.

[0161] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A hydropower generation control system based on big data analysis, characterized in that: include: Acquisition module: Acquisition of meteorological data of the hydropower station in a preset stage, and the historical inflow runoff of the hydropower station in the preset stage; the preset stage includes at least one stage cycle; Recording the upstream water level of the hydropower station at the initial moment of the preset stage as the first upstream water level, and recording the downstream water level of the hydropower station at the initial moment of the preset stage as the first downstream water level; Runoff analysis module: Analyze the meteorological data to obtain the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station; analyze the historical inflow runoff to obtain the historical inflow runoff mean of the hydropower station in each stage cycle; The actual inflow runoff of the hydropower station is obtained according to the runoff influence coefficient and the historical inflow runoff; the actual inflow runoff curve of the hydropower station in a preset stage is obtained according to the actual inflow runoff of the hydropower station in each stage cycle; Upstream water level analysis module: Analyze the actual inflow flow curve and the first upstream water level to obtain the actual upstream water level of the hydropower station at the preset stage; Turbine combination module: obtain turbine data of the hydropower station; analyze the turbine data to obtain abnormal coefficients and configuration coefficients of each turbine in the hydropower station, and obtain the corresponding bearing coefficients of each turbine in the hydropower station according to the abnormal coefficients and configuration coefficients; combine the turbines in the hydropower station according to the bearing coefficients of each turbine in the hydropower station to obtain a turbine combination; According to the bearing coefficients of each turbine in the turbine combination, a combined bearing coefficient corresponding to the turbine combination is obtained; according to the combined bearing coefficient, a combined water head corresponding to the turbine combination is obtained; and the combined water head is compared and analyzed with a preset water head threshold value to obtain an actual water head of the turbine combination; Obtaining a turbine combination efficiency corresponding to the turbine combination according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination, and the number of turbines, and obtaining a target turbine combination according to the turbine combination efficiency; Downstream water level analysis module: mark the actual water head of the target turbine combination as the target water head; obtain the target downstream water level of the hydropower station in the preset stage according to the actual upstream water level and the target water head; obtain the downstream water level increase of the hydropower station in each stage cycle of the preset stage according to the target turbine combination, and obtain the actual downstream water level of the hydropower station in each stage cycle of the preset stage according to the downstream water level increase and the first downstream water level; Downstream water level adjustment module: compare and analyze the actual downstream water level and the target downstream water level to obtain the downstream water level adjustment amount of the hydropower station.

2. The hydropower generation control system based on big data analysis according to claim 1 is characterized in that: The meteorological data is analyzed to obtain the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station; the historical inflow runoff is analyzed to obtain the mean value of the historical inflow runoff of the hydropower station in each stage cycle, specifically: The meteorological data include the rainfall and average temperature value of the hydropower station within a preset period; The rainfall is taken and marked to obtain the rainfall JY; the average temperature value is taken and marked to obtain the average temperature value WD; The influence coefficient α of the meteorological data on the inflow of the hydropower station is calculated by the first calculation function α=a1×JY+a2×WD, where a1 and a2 are preset proportional factors; Divide the preset stage into a preset number of stage cycles, and obtain the historical inflow runoff of each stage cycle; take values ​​and mark the historical inflow runoff to obtain the historical inflow runoff JL; Through the second calculation function The historical mean value of the inflow flow of the hydropower station in each stage cycle is calculated, PJL, where n is the number of stages in the preset stage; The actual inflow runoff of the hydropower station is obtained based on the runoff impact coefficient and the historical inflow runoff, which is: The actual inflow flow SJL of the hydropower station in each stage cycle is calculated by the third calculation function SJL=α×PJL.

3. The hydropower generation control system based on big data analysis according to claim 2 is characterized in that: According to the actual inflow flow curve and the first upstream water level, the actual upstream water level of the hydropower station in the preset stage is obtained, which is: Taking and marking the first upstream water level to obtain a first upstream water level YSW; The actual upstream water level SSW of the hydropower station in each stage cycle is calculated by the fourth calculation function SSW=YSW+SJL×β, wherein β is the influence factor of the inflow flow on the upstream water level.

4. The hydropower generation control system based on big data analysis according to claim 3 is characterized in that: The turbine data of the hydropower station is obtained; the abnormal coefficient and configuration coefficient of each turbine in the hydropower station are obtained by analyzing the turbine data, and the corresponding bearing coefficient of each turbine in the hydropower station is obtained according to the abnormal coefficient and configuration coefficient, specifically: The unit data includes the service life of each turbine in the hydropower station, the number of turbine blades, the specific speed of the turbine, and the number of historical abnormalities of each turbine, and the historical abnormal time corresponding to the historical abnormality number; According to the historical abnormal time corresponding to the historical abnormal number of the turbine, the historical abnormal duration corresponding to the historical abnormal number of the turbine is obtained; the historical abnormal duration is valued and marked to obtain the historical abnormal duration YS; the historical abnormal number of times is valued and marked to obtain the historical abnormal number of times YC; Through the fifth calculation function The abnormal coefficient JY of each turbine in the hydropower station is calculated, where b1 and b2 are preset proportional factors; The service life of the turbine is taken and marked to obtain the service life of the turbine NX; the number of turbine blades is taken and marked to obtain the number of turbine blades YS; the specific speed of the turbine is taken and marked to obtain the specific speed of the turbine ZS; The configuration coefficient PZ of each turbine in the hydropower station is calculated by the sixth calculation function PZ=c1×NX+c2×YS+c3×ZS, where c1, c2, and c3 are preset proportional factors; The bearing coefficient CS of each turbine in the hydropower station is calculated by the seventh calculation function CS=d1×JY+d2×PZ, wherein d1 and d2 are preset proportional factors.

5. The hydropower generation control system based on big data analysis according to claim 4 is characterized in that: The turbine combination is obtained by combining the turbines in the hydropower station according to the bearing coefficient of each turbine in the hydropower station, which is specifically: Set the bearing coefficient difference range corresponding to the turbine combination and preset the bearing coefficient standard value; Taking and marking the preset bearing coefficient standard value to obtain the preset bearing coefficient standard value CSB; The bearing coefficient difference CCZ between the bearing coefficient CS of each turbine in the hydropower station and the preset bearing coefficient standard value is calculated by the eighth calculation function CCZ=CS-CSB; According to the inclusion relationship between the bearing coefficient difference CCZ corresponding to each turbine in the hydropower station and the bearing coefficient difference interval corresponding to the turbine combination, the turbines in the hydropower station are divided into different turbine combinations.

6. The hydropower generation control system based on big data analysis according to claim 5 is characterized in that: The combined bearing coefficient corresponding to the turbine combination is obtained according to the bearing coefficient of each turbine in the turbine combination; the combined water head corresponding to the turbine combination is obtained according to the combined bearing coefficient; the combined water head is compared and analyzed with the preset water head threshold to obtain the actual water head of the turbine combination, which is specifically: Through the ninth calculation function Calculate the combined bearing coefficient ZCS corresponding to the turbine combination; The combined water head ZST corresponding to the turbine combination is calculated by the tenth calculation function ZST=ZCS×δ, where δ is a preset proportionality coefficient; If the combined water head ZST corresponding to the turbine combination is greater than or equal to the preset water head threshold, the actual water head of the turbine is the preset water head threshold; If the combined water head ZST corresponding to the turbine combination is less than the preset water head threshold, the actual water head of the turbine is the combined water head ZST.

7. The hydropower generation control system based on big data analysis according to claim 6 is characterized in that: The turbine combination efficiency corresponding to the turbine combination is obtained according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination, and the number of turbines. The target turbine combination is obtained according to the turbine combination efficiency, which is specifically: According to the configuration coefficient of the turbines in the turbine combination and the number of turbines in the turbine combination, the combined output coefficient, combined power generation flow and combined power generation efficiency corresponding to the turbine combination are obtained; The combined output coefficient is taken and marked to obtain the combined output coefficient ZCL; the combined power generation flow is taken and marked to obtain the combined power generation flow ZFD; The combined power generation efficiency is valued and marked to obtain the combined power generation efficiency ZFX; the actual water head of the turbine combination is valued and marked to obtain the actual water head SST; The turbine combination efficiency ZXL corresponding to the turbine combination is calculated by the eleventh calculation function ZXL=SST×ZCL×ZFD×ZFX; The turbine combination with the largest turbine combination efficiency ZXL is marked as the target turbine combination.

8. The hydropower generation control system based on big data analysis according to claim 7 is characterized in that: The target downstream water level of the hydropower station in the preset stage is obtained according to the actual upstream water level and the target water head; the downstream water level increase of the hydropower station in each stage cycle of the preset stage is obtained according to the target turbine combination, and the actual downstream water level of the hydropower station in each stage cycle of the preset stage is obtained according to the downstream water level increase and the first downstream water level, specifically: The target water head is valued and marked to obtain the target water head MST; The target downstream water level MXS corresponding to each stage cycle of the hydropower station in the preset stage is calculated by the twelfth calculation function MXS=SSW-MST; Acquire a turbine combination flow corresponding to the turbine combination, and obtain a turbine combination flow SJL by taking a value and marking the turbine combination flow; The downstream water level increase XSZ of the hydropower station in each stage cycle of the preset stage is calculated by the thirteenth calculation function XSZ=SJL×ε, where ε is the influence factor of the combined flow of the turbine on the downstream water level; The first downstream water level of the hydropower station is obtained by taking and marking the first downstream water level XSW; The actual downstream water level SXS of each stage cycle of the preset stage of the hydropower station is calculated by the fourteenth calculation function SXS=XSZ+XSW.

9. The hydropower generation control system based on big data analysis according to claim 8 is characterized in that: The actual downstream water level and the target downstream water level are compared and analyzed to obtain the downstream water level adjustment of the hydropower station, which is specifically: Compare and analyze the actual downstream water level SXS with the target downstream water level MXS; If the actual downstream water level SXS is greater than or equal to the target downstream water level MXS, the downstream discharge water level PSL of the hydropower station in the corresponding phase period is calculated by the fifteenth calculation function PSL=SXS-MXS; If the actual downstream water level SXS is less than the target downstream water level MXS, the downstream diversion water level YSL of the hydropower station in the corresponding stage period is calculated by the sixteenth calculation function YSL=MXS-SXS.

10. A method for regulating water conservancy and power generation based on big data analysis, the method being used to implement a system for regulating water conservancy and power generation based on big data analysis as claimed in any one of claims 1 to 9, characterized in that: The method comprises the following steps: Acquire meteorological data of the hydropower station in a preset stage, and the historical inflow runoff of the hydropower station in the preset stage; the preset stage includes at least one stage cycle; Recording the upstream water level of the hydropower station at the initial moment of the preset stage as the first upstream water level, and recording the downstream water level of the hydropower station at the initial moment of the preset stage as the first downstream water level; Analyze the meteorological data to obtain the runoff influence coefficient of the meteorological data on the inflow runoff of the hydropower station; analyze the historical inflow runoff to obtain the mean value of the historical inflow runoff of the hydropower station in each stage cycle; The actual inflow runoff of the hydropower station is obtained according to the runoff influence coefficient and the historical inflow runoff; the actual inflow runoff curve of the hydropower station in a preset stage is obtained according to the actual inflow runoff of the hydropower station in each stage cycle; According to the actual inflow runoff curve and the first upstream water level, the actual upstream water level of the hydropower station in the preset stage is obtained by analysis; Acquire turbine data of the hydropower station; analyze the turbine data to obtain abnormal coefficients and configuration coefficients of each turbine in the hydropower station, and obtain the corresponding bearing coefficients of each turbine in the hydropower station according to the abnormal coefficients and configuration coefficients; combine the turbines in the hydropower station according to the bearing coefficients of each turbine in the hydropower station to obtain a turbine combination; According to the bearing coefficients of each turbine in the turbine combination, a combined bearing coefficient corresponding to the turbine combination is obtained; according to the combined bearing coefficient, a combined water head corresponding to the turbine combination is obtained; and the combined water head is compared and analyzed with a preset water head threshold value to obtain an actual water head of the turbine combination; Obtaining a turbine combination efficiency corresponding to the turbine combination according to the actual water head of the turbine combination, the configuration coefficient of the turbines in the turbine combination, and the number of turbines, and obtaining a target turbine combination according to the turbine combination efficiency; The actual water head of the target turbine combination is marked as the target water head; the target downstream water level of the hydropower station in the preset stage is obtained according to the actual upstream water level and the target water head; the downstream water level increase of the hydropower station in each stage cycle of the preset stage is obtained according to the target turbine combination, and the actual downstream water level of the hydropower station in each stage cycle of the preset stage is obtained according to the downstream water level increase and the first downstream water level; The actual downstream water level and the target downstream water level are compared and analyzed to obtain the downstream water level adjustment amount of the hydropower station's downstream water level.

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